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Definition of hypothesis

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The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 14 May. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

Britannica English: Translation of hypothesis for Arabic Speakers

Britannica.com: Encyclopedia article about hypothesis

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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[ hahy- poth - uh -sis , hi- ]

• a proposition, or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation working hypothesis or accepted as highly probable in the light of established facts.
• a proposition assumed as a premise in an argument.
• the antecedent of a conditional proposition.
• a mere assumption or guess.

/ haɪˈpɒθɪsɪs /

• a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory
• an assumption used in an argument without its being endorsed; a supposition
• an unproved theory; a conjecture

/ hī-pŏth ′ ĭ-sĭs /

, Plural hypotheses hī-pŏth ′ ĭ-sēz′

• A statement that explains or makes generalizations about a set of facts or principles, usually forming a basis for possible experiments to confirm its viability.
• plur. hypotheses (heye- poth -uh-seez) In science, a statement of a possible explanation for some natural phenomenon. A hypothesis is tested by drawing conclusions from it; if observation and experimentation show a conclusion to be false, the hypothesis must be false. ( See scientific method and theory .)

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Derived forms.

• hyˈpothesist , noun

Other Words From

• hy·pothe·sist noun
• counter·hy·pothe·sis noun plural counterhypotheses
• subhy·pothe·sis noun plural subhypotheses

Word History and Origins

Origin of hypothesis 1

Synonym Study

Example sentences.

Though researchers have struggled to understand exactly what contributes to this gender difference, Dr. Rohan has one hypothesis.

The leading hypothesis for the ultimate source of the Ebola virus, and where it retreats in between outbreaks, lies in bats.

In 1996, John Paul II called the Big Bang theory “more than a hypothesis.”

To be clear: There have been no double-blind or controlled studies that conclusively confirm this hair-loss hypothesis.

The bacteria-driven-ritual hypothesis ignores the huge diversity of reasons that could push someone to perform a religious ritual.

And remember it is by our hypothesis the best possible form and arrangement of that lesson.

Taken in connection with what we know of the nebulæ, the proof of Laplace's nebular hypothesis may fairly be regarded as complete.

What has become of the letter from M. de St. Mars, said to have been discovered some years ago, confirming this last hypothesis?

To admit that there had really been any communication between the dead man and the living one is also an hypothesis.

"I consider it highly probable," asserted Aunt Maria, forgetting her Scandinavian hypothesis.

Related Words

• explanation
• interpretation
• proposition
• supposition

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Definition of hypothesis noun from the Oxford Advanced Learner's Dictionary

• to formulate/confirm a hypothesis
• a hypothesis about the function of dreams
• There is little evidence to support these hypotheses.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• Her hypothesis concerns the role of electromagnetic radiation.
• Her study is based on the hypothesis that language simplification is possible.
• It is possible to make a hypothesis on the basis of this graph.
• None of the hypotheses can be rejected at this stage.
• Scientists have proposed a bold hypothesis.
• She used this data to test her hypothesis
• The hypothesis predicts that children will perform better on task A than on task B.
• The results confirmed his hypothesis on the use of modal verbs.
• These observations appear to support our working hypothesis.
• a speculative hypothesis concerning the nature of matter
• an interesting hypothesis about the development of language
• Advances in genetics seem to confirm these hypotheses.
• His hypothesis about what dreams mean provoked a lot of debate.
• Research supports the hypothesis that language skills are centred in the left side of the brain.
• The survey will be used to test the hypothesis that people who work outside the home are fitter and happier.
• This economic model is really a working hypothesis.
• speculative
• concern something
• be based on something
• predict something
• on a/​the hypothesis
• hypothesis about
• hypothesis concerning

Questions about grammar and vocabulary?

Find the answers with Practical English Usage online, your indispensable guide to problems in English.

• It would be pointless to engage in hypothesis before we have the facts.

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The Language of Thought Hypothesis

The language of thought hypothesis (LOTH) proposes that thinking occurs in a mental language. Often called Mentalese , the mental language resembles spoken language in several key respects: it contains words that can combine into sentences; the words and sentences are meaningful; and each sentence’s meaning depends in a systematic way upon the meanings of its component words and the way those words are combined. For example, there is a Mentalese word whale that denotes whales, and there is a Mentalese word mammal that denotes mammals. These words can combine into a Mentalese sentence whales are mammals , which means that whales are mammals. To believe that whales are mammals is to bear an appropriate psychological relation to this sentence. During a prototypical deductive inference, I might transform the Mentalese sentence whales are mammals and the Mentalese sentence Moby Dick is a whale into the Mentalese sentence Moby Dick is a mammal . As I execute the inference, I enter into a succession of mental states that instantiate those sentences.

LOTH emerged gradually through the writings of Augustine, Boethius, Thomas Aquinas, John Duns Scotus, and many others. William of Ockham offered the first systematic treatment in his Summa Logicae (c. 1323), which meticulously analyzed the meaning and structure of Mentalese expressions. LOTH was quite popular during the late medieval era, but it slipped from view in the sixteenth and seventeenth centuries. From that point through the mid-twentieth century, it played little serious role within theorizing about the mind.

In the 1970s, LOTH underwent a dramatic revival. The watershed was publication of Jerry Fodor’s The Language of Thought (1975). Fodor argued abductively: our current best scientific theories of psychological activity postulate Mentalese; we therefore have good reason to accept that Mentalese exists. Fodor’s analysis exerted tremendous impact. LOTH once again became a focus of discussion, some supportive and some critical. Debates over the existence and nature of Mentalese continue to figure prominently within philosophy and cognitive science. These debates have pivotal importance for our understanding of how the mind works.

1.1 The Representational Theory of Thought

1.2 compositional semantics, 1.3 logical structure, 2. scope of loth, 3. mental computation, 4.1 argument from cognitive science practice, 4.2 argument from the productivity of thought, 4.3 argument from the systematicity of thought, 4.4 argument from the systematicity of thinking, 5. the connectionist challenge, 6.1 learning a language, 6.2 understanding a language, 7. naturalizing the mind, 8. individuation of mentalese expressions, other internet resources, related entries, 1. mental language.

What does it mean to posit a mental language? Or to say that thinking occurs in this language? Just how “language-like” is Mentalese supposed to be? To address these questions, we will isolate some core commitments that are widely shared among LOT theorists.

Folk psychology routinely explains and predicts behavior by citing mental states, including beliefs, desires, intentions, fears, hopes, and so on. To explain why Mary walked to the refrigerator, we might note that she believed there was orange juice in the refrigerator and wanted to drink orange juice. Mental states such as belief and desire are called propositional attitudes . They can be specified using locutions of the form

X believes that p .

X desires that p .

X intends that p .

X fears that p .

By replacing “ p ” with a sentence, we specify the content of X ’s mental state. Propositional attitudes have intentionality or aboutness : they are about a subject matter. For that reason, they are often called intentional states .

The term “propositional attitude” originates with Russell (1918–1919 [1985]) and reflects his own preferred analysis: that propositional attitudes are relations to propositions . A proposition is an abstract entity that determines a truth-condition . To illustrate, suppose John believes that Paris is north of London. Then John’s belief is a relation to the proposition that Paris is north of London , and this proposition is true iff Paris is north of London. Beyond the thesis that propositions determine truth-conditions, there is little agreement about what propositions are like. The literature offers many options, mainly derived from theories of Frege (1892 [1997]), Russell (1918–1919 [1985]), and Wittgenstein (1921 [1922]).

Fodor (1981: 177–203; 1987: 16–26) proposes a theory of propositional attitudes that assigns a central role to mental representations . A mental representation is a mental item with semantic properties (such as a denotation, or a meaning, or a truth-condition, etc.). To believe that p , or hope that p , or intend that p , is to bear an appropriate relation to a mental representation whose meaning is that p . For example, there is a relation belief* between thinkers and mental representations, where the following biconditional is true no matter what English sentence one substitutes for “ p ”:

X believes that p iff there is a mental representation S such that X believes* S and S means that p .

More generally:

• (1) Each propositional attitude A corresponds to a unique psychological relation A* , where the following biconditional is true no matter what sentence one substitutes for “ p ”: X As that p iff there is a mental representation S such that X bears A * to S and S means that p .

On this analysis, mental representations are the most direct objects of propositional attitudes. A propositional attitude inherits its semantic properties, including its truth-condition, from the mental representation that is its object.

Proponents of (1) typically invoke functionalism to analyze A *. Each psychological relation A * is associated with a distinctive functional role : a role that S plays within your mental activity just in case you bear A * to S . When specifying what it is to believe* S , for example, we might mention how S serves as a basis for inferential reasoning, how it interacts with desires to produce actions, and so on. Precise functional roles are to be discovered by scientific psychology. Following Schiffer (1981), it is common to use the term “belief-box” as a placeholder for the functional role corresponding to belief*: to believe* S is to place S in your belief box. Similarly for “desire-box”, etc.

(1) is compatible with the view that propositional attitudes are relations to propositions. One might analyze the locution “ S means that p ” as involving a relation between S and a proposition expressed by S . It would then follow that someone who believes* S stands in a psychologically important relation to the proposition expressed by S . Fodor (1987: 17) adopts this approach. He combines a commitment to mental representations with a commitment to propositions. In contrast, Field (2001: 30–82) declines to postulate propositions when analyzing “ S means that p ”. He posits mental representations with semantic properties, but he does not posit propositions expressed by the mental representations.

The distinction between types and tokens is crucial for understanding (1). A mental representation is a repeatable type that can be instantiated on different occasions. In the current literature, it is generally assumed that a mental representation’s tokens are neurological. For present purposes, the key point is that mental representations are instantiated by mental events . Here we construe the category of events broadly so as to include both occurrences (e.g., I form an intention to drink orange juice) and enduring states (e.g., my longstanding belief that Abraham Lincoln was president of the United States). When mental event e instantiates representation S , we say that S is tokened and that e is a tokening of S . For example, if I believe that whales are mammals, then my belief (a mental event) is a tokening of a mental representation whose meaning is that whales are mammals.

According to Fodor (1987: 17), thinking consists in chains of mental events that instantiate mental representations:

• (2) Thought processes are causal sequences of tokenings of mental representations.

A paradigm example is deductive inference: I transition from believing* the premises to believing* the conclusion. The first mental event (my belief* in the premises) causes the second (my belief* in the conclusion).

(1) and (2) fit together naturally as a package that one might call the representational theory of thought (RTT). RTT postulates mental representations that serve as the objects of propositional attitudes and that constitute the domain of thought processes. [ 1 ]

RTT as stated requires qualification. There is a clear sense in which you believe that there are no elephants on Jupiter. However, you probably never considered the question until now. It is not plausible that your belief box previously contained a mental representation with the meaning that there are no elephants on Jupiter. Fodor (1987: 20–26) responds to this sort of example by restricting (1) to core cases . Core cases are those where the propositional attitude figures as a causally efficacious episode in a mental process. Your tacit belief that there are no elephants on Jupiter does not figure in your reasoning or decision-making, although it can come to do so if the question becomes salient and you consciously judge that there are no elephants on Jupiter. So long as the belief remains tacit, (1) need not apply. In general, Fodor says, an intentional mental state that is causally efficacious must involve explicit tokening of an appropriate mental representation. In a slogan: “No Intentional Causation without Explicit Representation” (Fodor 1987: 25). Thus, we should not construe (1) as an attempt at faithfully analyzing informal discourse about propositional attitudes. Fodor does not seek to replicate folk psychological categories. He aims to identify mental states that resemble the propositional attitudes adduced within folk psychology, that play roughly similar roles in mental activity, and that can support systematic theorizing.

Dennett’s (1977 [1981]) review of The Language of Thought raises a widely cited objection to RTT:

In a recent conversation with the designer of a chess-playing program I heard the following criticism of a rival program: “it thinks it should get its queen out early”. This ascribes a propositional attitude to the program in a very useful and predictive way, for as the designer went on to say, one can usefully count on chasing that queen around the board. But for all the many levels of explicit representation to be found in that program, nowhere is anything roughly synonymous with “I should get my queen out early” explicitly tokened. The level of analysis to which the designer’s remark belongs describes features of the program that are, in an entirely innocent way, emergent properties of the computational processes that have “engineering reality”. I see no reason to believe that the relation between belief-talk and psychological talk will be any more direct.

In Dennett’s example, the chess-playing machine does not explicitly represent that it should get the queen out early, yet in some sense it acts upon a belief that it should do so. Analogous examples arise for human cognition. For example, we often follow rules of deductive inference without explicitly representing the rules.

To assess Dennett’s objection, we must distinguish sharply between mental representations and rules governing the manipulation of mental representations (Fodor 1987: 25). RTT does not require that every such rule be explicitly represented. Some rules may be explicitly represented—we can imagine a reasoning system that explicitly represents deductive inference rules to which it conforms. But the rules need not be explicitly represented. They may merely be implicit in the system’s operations. Only when consultation of a rule figures as a causally efficacious episode in mental activity does RTT require that the rule be explicitly represented. Dennett’s chess machine explicitly represents chess board configurations and perhaps some rules for manipulating chess pieces. It never consults any rule akin to Get the Queen out early . For that reason, we should not expect that the machine explicitly represents this rule even if the rule is in some sense built into the machine’s programming. Similarly, typical thinkers do not consult inference rules when engaging in deductive inference. So RTT does not demand that a typical thinker explicitly represent inference rules, even if she conforms to them and in some sense tacitly believes that she should conform to them.

Natural language is compositional : complex linguistic expressions are built from simpler linguistic expressions, and the meaning of a complex expression is a function of the meanings of its constituents together with the way those constituents are combined. Compositional semantics describes in a systematic way how semantic properties of a complex expression depend upon semantic properties of its constituents and the way those constituents are combined. For example, the truth-condition of a conjunction is determined as follows: the conjunction is true iff both conjuncts are true.

Historical and contemporary LOT theorists universally agree that Mentalese is compositional:

Compositionality of mental representations (COMP) : Mental representations have a compositional semantics: complex representations are composed of simple constituents, and the meaning of a complex representation depends upon the meanings of its constituents together with the constituency structure into which those constituents are arranged.

Clearly, mental language and natural language must differ in many important respects. For example, Mentalese surely does not have a phonology. It may not have a morphology either. Nevertheless, COMP articulates a fundamental point of similarity. Just like natural language, Mentalese contains complex symbols amenable to semantic analysis.

What is it for one representation to be a “constituent” of another? According to Fodor (2008: 108), “constituent structure is a species of the part/whole relation”. Not all parts of a linguistic expression are constituents: “John ran” is a constituent of “John ran and Mary jumped”, but “ran and Mary” is not a constituent because it is not semantically interpretable. The important point for our purposes is that all constituents are parts. When a complex representation is tokened, so are its parts. For example,

intending that \(P \amp Q\) requires having a sentence in your intention box… one of whose parts is a token of the very same type that’s in the intention box when you intend that \(P\), and another of whose parts is a token of the very same type that’s in the intention box when you intend that \(Q\). (Fodor 1987: 139)

More generally: mental event \(e\) instantiates a complex mental representation only if \(e\) instantiates all of the representation’s constituent parts. In that sense, \(e\) itself has internal complexity.

The complexity of mental events figures crucially here, as highlighted by Fodor in the following passage (1987: 136):

Practically everybody thinks that the objects of intentional states are in some way complex… [For example], what you believe when you believe that \(P \amp Q\) is… something composite, whose elements are—as it might be—the proposition that P and the proposition that Q . But the (putative) complexity of the intentional object of a mental state does not, of course, entail the complexity of the mental state itself… LOT claims that mental states —and not just their propositional objects— typically have constituent structure .

Many philosophers, including Frege and Russell, regard propositions as structured entities. These philosophers apply a part/whole model to propositions but not necessarily to mental events during which thinkers entertain propositions. LOTH as developed by Fodor applies the part/whole model to the mental events themselves:

what’s at issue here is the complexity of mental events and not merely the complexity of the propositions that are their intentional objects. (Fodor 1987: 142)

On this approach, a key element of LOTH is the thesis that mental events have semantically relevant complexity.

Contemporary proponents of LOTH endorse RTT+COMP. Historical proponents also believed something in the vicinity (Normore 1990, 2009; Panaccio 1999 [2017]), although of course they did not use modern terminology to formulate their views. We may regard RTT+COMP as a minimalist formulation of LOTH, bearing in mind that many philosophers have used the phrase “language of thought hypothesis” to denote one of the stronger theses discussed below. As befits a minimalist formulation, RTT+COMP leaves unresolved numerous questions about the nature, structure, and psychological role of Mentalese expressions.

In practice, LOT theorists usually adopt a more specific view of the compositional semantics for Mentalese. They claim that Mentalese expressions have logical form (Fodor 2008: 21). More specifically, they claim that Mentalese contains analogues to the familiar logical connectives ( and , or , not , if-then , some , all , the ). Iterative application of logical connectives generates complex expressions from simpler expressions. The meaning of a logically complex expression depends upon the meanings of its parts and upon its logical structure. Thus, LOT theorists usually endorse a doctrine along the following lines:

Logically structured mental representations (LOGIC) : Some mental representations have logical structure. The compositional semantics for these mental representations resembles the compositional semantics for logically structured natural language expressions.

Medieval LOT theorists used syllogistic and propositional logic to analyze the semantics of Mentalese (King 2005; Normore 1990). Contemporary proponents instead use the predicate calculus , which was discovered by Frege (1879 [1967]) and whose semantics was first systematically articulated by Tarski (1933 [1983]). The view is that Mentalese contains primitive words—including predicates, singular terms, and logical connectives—and that these words combine to form complex sentences governed by something like the semantics of the predicate calculus.

The notion of a Mentalese word corresponds roughly to the intuitive notion of a concept . In fact, Fodor (1998: 70) construes a concept as a Mentalese word together with its denotation. For example, a thinker has the concept of a cat only if she has in her repertoire a Mentalese word that denotes cats.

Logical structure is just one possible paradigm for the structure of mental representations. Human society employs a wide range of non-sentential representations, including pictures, maps, diagrams, and graphs. Non-sentential representations typically contain parts arranged into a compositionally significant structure. In many cases, it is not obvious that the resulting complex representations have logical structure. For example, maps do not seem to contain logical connectives (Fodor 1991: 295; Millikan 1993: 302; Pylyshyn 2003: 424–5). Nor is it evident that they contain predicates (Camp 2018; Rescorla 2009c), although some philosophers contend that they do (Blumson 2012; Casati & Varzi 1999; Kulvicki 2015).

Theorists often posit mental representations that conform to COMP but that lack logical structure. The British empiricists postulated ideas , which they characterized in broadly imagistic terms. They emphasized that simple ideas can combine to form complex ideas. They held that the representational import of a complex idea depends upon the representational import of its parts and the way those parts are combined. So they accepted COMP or something close to it (depending on what exactly “constituency” amounts to). [ 2 ] They did not say in much detail how compounding of ideas was supposed to work, but imagistic structure seems to be the paradigm in at least some passages. LOGIC plays no significant role in their writings. [ 3 ] Partly inspired by the British empiricists, Prinz (2002) and Barsalou (1999) analyze cognition in terms of image-like representations derived from perception. Armstrong (1973) and Braddon-Mitchell and Jackson (2007) propose that propositional attitudes are relations not to mental sentences but to mental maps analogous in important respects to ordinary concrete maps.

One problem facing imagistic and cartographic theories of thought is that propositional attitudes are often logically complex (e.g., John believes that if Plácido Domingo does not sing then either Gustavo Dudamel will conduct or the concert will be cancelled ). Images and maps do not seem to support logical operations: the negation of a map is not a map; the disjunction of two maps is not a map; similarly for other logical operations; and similarly for images. Given that images and maps do not support logical operations, theories that analyze thought in exclusively imagistic or cartographic terms will struggle to explain logically complex propositional attitudes. [ 4 ]

There is room here for a pluralist position that allows mental representations of different kinds: some with logical structure, some more analogous to pictures, or maps, or diagrams, and so on. The pluralist position is widespread within cognitive science, which posits a range of formats for mental representation (Block 1983; Camp 2009; Johnson-Laird 2004: 187; Kosslyn 1980; Mandelbaum et al. 2022; McDermott 2001: 69; Pinker 2005: 7; Sloman 1978: 144–76). Fodor himself (1975: 184–195) suggests a view on which imagistic mental representations co-exist alongside, and interact with, logically structured Mentalese expressions.

Given the prominent role played by logical structure within historical and contemporary discussion of Mentalese, one might take LOGIC to be definitive of LOTH. One might insist that mental representations comprise a mental language only if they have logical structure. We need not evaluate the merits of this terminological choice.

RTT concerns propositional attitudes and the mental processes in which they figure, such as deductive inference, reasoning, decision-making, and planning. It does not address perception, motor control, imagination, dreaming, pattern recognition, linguistic processing, or any other mental activity distinct from high-level cognition. Hence the emphasis upon a language of thought : a system of mental representations that underlie thinking, as opposed to perceiving, imagining, etc. Nevertheless, talk about a mental language generalizes naturally from high-level cognition to other mental phenomena.

Perception is a good example. The perceptual system transforms proximal sensory stimulations (e.g., retinal stimulations) into perceptual estimates of environmental conditions (e.g., estimates of shapes, sizes, colors, locations, etc.). Helmholtz (1867 [1925]) proposed that the transition from proximal sensory input to perceptual estimates features an unconscious inference , similar in key respects to high-level conscious inference yet inaccessible to consciousness. Helmholtz’s proposal is foundational to contemporary perceptual psychology , which constructs detailed mathematical models of unconscious perceptual inference (Knill & Richards 1996; Rescorla 2015). Fodor (1975: 44–55) argues that this scientific research program presupposes mental representations. The representations participate in unconscious inferences or inference-like transitions executed by the perceptual system. [ 5 ]

Navigation is another good example. Tolman (1948) hypothesized that rats navigate using cognitive maps : mental representations that represent the layout of the spatial environment. The cognitive map hypothesis, advanced during the heyday of behaviorism, initially encountered great scorn. It remained a fringe position well into the 1970s, long after the demise of behaviorism. Eventually, mounting behavioral and neurophysiological evidence won it many converts (Gallistel 1990; Gallistel & Matzel 2013; Jacobs & Menzel 2014; O’Keefe & Nadel 1978; Weiner et al. 2011). Although a few researchers remain skeptical (Mackintosh 20002), there is now a broad consensus that mammals (and possibly even some insects) navigate using mental representations of spatial layout. Rescorla (2017b) summarizes the case for cognitive maps and reviews some of their core properties.

To what extent should we expect perceptual representations and cognitive maps to resemble the mental representations that figure in high-level human thought? It is generally agreed that all these mental representations have compositional structure. For example, the perceptual system can bind together a representation of shape and a representation of size to form a complex representation that an object has a certain shape and size; the representational import of the complex representation depends in a systematic way upon the representational import of the component representations. On the other hand, it is not clear that perceptual representations have logical structure (Block 2023: 182–190; Burge 2022: 190–201), including even predicative structure (Burge 2010: 540–544; Burge 2022: 44–45: Fodor 2008: 169–195). Nor is it evident that cognitive maps contain logical connectives or predicates (Rescorla 2009a, 2009b). Perceptual processing and non-human navigation certainly do not seem to instantiate mental processes that would exploit putative logical structure. In particular, they do not seem to instantiate deductive inference.

These observations provide ammunition for pluralism about representational format. Pluralists can posit one system of compositionally structured mental representations for perception, another for navigation, another for high-level cognition, and so on. Different representational systems potentially feature different compositional mechanisms. As indicated in section 1.3 , pluralism figures prominently in contemporary cognitive science. Pluralists face some pressing questions. Which compositional mechanisms figure in which psychological domains? Which representational formats support which mental operations? How do different representational formats interface with each other? Further research bridging philosophy and cognitive science is needed to address such questions.

Modern proponents of LOTH typically endorse the computational theory of mind (CTM), which claims that the mind is a computational system. Some authors use the phrase “language of thought hypothesis” so that it definitionally includes CTM as one component.

In a seminal contribution, Turing (1936) introduced what is now called the Turing machine : an abstract model of an idealized computing device. A Turing machine contains a central processor, governed by precise mechanical rules, that manipulates symbols inscribed along a linear array of memory locations. Impressed by the enormous power of the Turing machine formalism, many researchers seek to construct computational models of core mental processes, including reasoning, decision-making, and problem solving. This enterprise bifurcates into two main branches. The first branch is artificial intelligence (AI), which aims to build “thinking machines”. Here the goal is primarily an engineering one—to build a system that instantiates or at least simulates thought—without any pretense at capturing how the human mind works. The second branch, computational psychology , aims to construct computational models of human mental activity. AI and computational psychology both emerged in the 1960s as crucial elements in the new interdisciplinary initiative cognitive science , which studies the mind by drawing upon psychology, computer science (especially AI), linguistics, philosophy, economics (especially game theory and behavioral economics), anthropology, and neuroscience.

From the 1960s to the early 1980s, computational models offered within psychology were mainly Turing-style models. These models embody a viewpoint known as the classical computational theory of mind (CCTM). According to CCTM, the mind is a computational system similar in important respects to a Turing machine, and certain core mental processes are computations similar in important respects to computations executed by a Turing machine.

CCTM fits together nicely with RTT+COMP. Turing-style computation operates over symbols, so any Turing-style mental computations must operate over mental symbols. The essence of RTT+COMP is postulation of mental symbols. Fodor (1975, 1981) advocates RTT+COMP+CCTM. He holds that certain core mental processes are Turing-style computations over Mentalese expressions.

One can endorse RTT+COMP without endorsing CCTM. By positing a system of compositionally structured mental representations, one does not commit oneself to saying that operations over the representations are computational . Historical LOT theorists could not even formulate CCTM, for the simple reason that the Turing formalism had not been discovered. In the modern era, Harman (1973) and Sellars (1975) endorse something like RTT+COMP but not CCTM. Horgan and Tienson (1996) endorse RTT+COMP+CTM but not C CTM, i.e., classical CTM. They favor a version of CTM grounded in connectionism , an alternative computational framework that differs quite significantly from Turing’s approach. Thus, proponents of RTT+COMP need not accept that mental activity instantiates Turing-style computation.

Fodor (1981) combines RTT+COMP+CCTM with a view that one might call the formal-syntactic conception of computation (FSC). According to FSC, computation manipulates symbols in virtue of their formal syntactic properties but not their semantic properties.

FSC draws inspiration from modern logic, which emphasizes the formalization of deductive reasoning. To formalize, we specify a formal language whose component linguistic expressions are individuated non-semantically (e.g., by their geometric shapes). We describe the expressions as pieces of formal syntax, without considering what if anything the expressions mean. We then specify inference rules in syntactic, non-semantic terms. Well-chosen inference rules will carry true premises to true conclusions. By combining formalization with Turing-style computation, we can build a physical machine that manipulates symbols based solely on the formal syntax of the symbols. If we program the machine to implement appropriate inference rules, then its syntactic manipulations will transform true premises into true conclusions.

CCTM+FSC says that the mind is a formal syntactic computing system: mental activity consists in computation over symbols with formal syntactic properties; computational transitions are sensitive to the symbols’ formal syntactic properties but not their semantic properties. The key term “sensitive” is rather imprecise, allowing some latitude as to the precise import of CCTM+FSC. Intuitively, the picture is that a mental symbol’s formal syntax rather than its semantics determines how mental computation manipulates it. The mind is a “syntactic engine”.

Fodor (1987: 18–20) argues that CCTM+FSC helps illuminate a crucial feature of cognition: semantic coherence . For the most part, our thinking does not move randomly from thought to thought. Rather, thoughts are causally connected in a way that respects their semantics. For example, deductive inference carries true beliefs to true beliefs. More generally, thinking tends to respect epistemic properties such as warrant and degree of confirmation. In some sense, then, our thinking tends to cohere with semantic relations among thoughts. How is semantic coherence achieved? How does our thinking manage to track semantic properties? CCTM+FSC gives one possible answer. It shows how a physical system operating in accord with physical laws can execute computations that coherently track semantic properties. By treating the mind as a syntax-driven machine, we explain how mental activity achieves semantic coherence. We thereby answer the question: How is rationality mechanically possible ?

Fodor’s argument convinced many researchers that CCTM+FSC decisively advances our understanding of the mind’s relation to the physical world. But not everyone agrees that CCTM+FSC adequately integrates semantics into the causal order. A common worry is that the formal syntactic picture veers dangerously close to epiphenomenalism (Block 1990; Kazez 1994). Pre-theoretically, semantic properties of mental states seem highly relevant to mental and behavioral outcomes. For example, if I form an intention to walk to the grocery store, then the fact that my intention concerns the grocery store rather than the post office helps explain why I walk to the grocery store rather than the post office. Burge (2010) and Peacocke (1994) argue that cognitive science theorizing likewise assigns causal and explanatory importance to semantic properties. The worry is that CCTM+FSC cannot accommodate the causal and explanatory importance of semantic properties because it depicts them as causally irrelevant: formal syntax, not semantics, drives mental computation forward. Semantics looks epiphenomenal, with syntax doing all the work (Stich 1983).

Fodor (1990, 1994) expends considerable energy trying to allay epiphenomenalist worries. Advancing a detailed theory of the relation between Mentalese syntax and Mentalese semantics, he insists that FSC can honor the causal and explanatory relevance of semantic properties. Fodor’s treatment is widely regarded as problematic (Arjo 1996; Aydede 1997b, 1998; Aydede & Robbins 2001; Perry 1998; Prinz 2011; Wakefield 2002), although Rupert (2008) and Schneider (2005) espouse somewhat similar positions.

Partly in response to epiphenomenalist worries, some authors recommend that we replace FSC with an alternative semantic conception of computation (Block 1990; Burge 2010: 95–101; Figdor 2009; O’Brien & Opie 2006; Peacocke 1994, 1999; Rescorla 2012a). Semantic computationalists claim that computational transitions are sometimes sensitive to semantic properties, perhaps in addition to syntactic properties. More specifically, semantic computationalists insist that mental computation is sometimes sensitive to semantics. Thus, they reject any suggestion that the mind is a “syntactic engine” or that mental computation is sensitive only to formal syntax. [ 6 ] To illustrate, consider Mentalese conjunction. This mental symbol expresses the truth-table for conjunction. According to semantic computationalists, the symbol’s meaning is relevant (both causally and explanatorily) to mechanical operations over it. That the symbol expresses the truth-table for conjunction rather than, say, disjunction influences the course of computation. We should therefore reject any suggestion that mental computation is sensitive to the symbol’s syntactic properties rather than its semantic properties. The claim is not that mental computation explicitly represents semantic properties of mental symbols. All parties agree that, in general, it does not. There is no homunculus inside your head interpreting your mental language. The claim is rather that semantic properties influence how mental computation proceeds. (Compare: the momentum of a baseball thrown at a window causally influences whether the window breaks, even though the window does not explicitly represent the baseball’s momentum.)

Proponents of the semantic conception differ as to how exactly they gloss the core claim that some computations are “sensitive” to semantic properties. They also differ in their stance towards CCTM. Block (1990) and Rescorla (2014a) focus upon CCTM. They argue that a symbol’s semantic properties can impact mechanical operations executed by a Turing-style computational system. In contrast, O’Brien and Opie (2006) favor connectionism over CCTM.

Theorists who reject FSC must reject Fodor’s explanation of semantic coherence. What alternative explanation might they offer? So far, the question has received relatively little attention. Rescorla (2017a) argues that semantic computationalists can explain semantic coherence and simultaneously avoid epiphenomenalist worries by invoking neural implementation of semantically-sensitive mental computations.

Fodor’s exposition sometimes suggests that CTM, CCTM, or CCTM+FSC is definitive of LOTH (1981: 26). Yet not everyone who endorses RTT+COMP endorses CTM, CCTM, or FSC. One can postulate a mental language without agreeing that mental activity is computational, and one can postulate mental computations over a mental language without agreeing that the computations are sensitive only to syntactic properties. For most purposes, it is not important whether we regard CTM, CCTM, or CCTM+FSC as definitive of LOTH. More important is that we track the distinctions among the doctrines.

4. Arguments for LOTH

The literature offers many arguments for LOTH. This section introduces four influential arguments, each of which supports LOTH abductively by citing its explanatory benefits. Section 5 discusses some prominent objections to the four arguments.

Fodor (1975) defends RTT+COMP+CCTM by appealing to scientific practice: our best cognitive science postulates Turing-style mental computations over Mentalese expressions; therefore, we should accept that mental computation operates over Mentalese expressions. Fodor develops his argument by examining detailed case studies, including perception, decision-making, and linguistic comprehension. He argues that, in each case, computation over mental representations plays a central explanatory role. Fodor’s argument was widely heralded as a compelling analysis of then-current cognitive science. The argument from cognitive science practice has subsequently been developed and updated both by Fodor and by other authors, such as Quilty-Dunn, Porot, and Mandelbaum (forthcoming).

When evaluating cognitive science support for LOTH, it is crucial to specify what version of LOTH one has in mind. Specifically, establishing that certain mental processes operate over mental representations is not enough to establish RTT. For example, one might accept that mental representations figure in perception and animal navigation but not in high-level human cognition. Gallistel and King (2009) defend COMP+CCTM+FSC through a number of (mainly non-human) empirical case studies, but they do not endorse RTT. They focus on relatively low-level phenomena, such as animal navigation, without discussing human decision-making, deductive inference, problem solving, or other high-level cognitive phenomena.

During your lifetime, you will only entertain a finite number of thoughts. In principle, though, there are infinitely many thoughts you might entertain. Consider:

Mary gave the test tube to John’s daughter.

Mary gave the test tube to John’s daughter’s daughter.

Mary gave the test tube to John’s daughter’s daughter’s daughter.

The moral usually drawn is that you have the competence to entertain a potential infinity of thoughts, even though your performance is bounded by biological limits upon memory, attention, processing capacity, and so on. In a slogan: thought is productive .

RTT+COMP straightforwardly explains productivity. We postulate a finite base of primitive Mentalese symbols, along with operations for combining simple expressions into complex expressions. Iterative application of the compounding operations generates an infinite array of mental sentences, each in principle within your cognitive repertoire. By tokening a mental sentence, you entertain the thought expressed by it. This explanation leverages the recursive nature of compositional mechanisms to generate infinitely many expressions from a finite base. It thereby illuminates how finite creatures such as ourselves are able to entertain a potential infinity of thoughts.

Fodor and Pylyshyn (1988) argue that, since RTT+COMP provides a satisfying explanation for productivity, we have good reason to accept RTT+COMP. A potential worry about this argument is that it rests upon an infinitary competence never manifested within actual performance. One might dismiss the supposed infinitary competence as an idealization that, while perhaps convenient for certain purposes, does not stand in need of explanation.

There are systematic interrelations among the thoughts a thinker can entertain. For example, if you can entertain the thought that John loves Mary, then you can also entertain the thought that Mary loves John. Systematicity looks like a crucial property of human thought and so demands a principled explanation.

RTT+COMP gives a compelling explanation. According to RTT+COMP, your ability to entertain the thought that p hinges upon your ability to bear appropriate psychological relations to a Mentalese sentence S whose meaning is that p . If you are able to think that John loves Mary, then your internal system of mental representations includes a mental sentence John loves Mary , composed of mental words John , loves , and Mary combined in the right way. If you have the capacity to stand in psychological relation A * to John loves Mary , then you also have the capacity to stand in relation A* to a distinct mental sentence Mary loves John . The constituent words John , loves , and Mary make the same semantic contribution to both mental sentences ( John denotes John, loves denotes the loving relation, and Mary denotes Mary), but the words are arranged in different constituency structures so that the sentences have different meanings. Whereas John loves Mary means that John loves Mary, Mary loves John means that Mary loves John. By standing in relation A * to the sentence Mary loves John , you entertain the thought that Mary loves John. Thus, an ability to think that John loves Mary entails an ability to think that John loves Mary. By comparison, an ability to think that John loves Mary does not entail an ability to think that whales are mammals or an ability to think that \(56 + 138 = 194\).

Fodor (1987: 148–153) supports RTT+COMP by citing its ability to explain systematicity. In contrast with the productivity argument, the systematicity argument does not depend upon infinitary idealizations that outstrip finite performance. Note that neither argument provides any direct support for CTM. Neither argument even mentions computation.

There are systematic interrelations among which inferences a thinker can draw. For example, if you can infer p from p and q , then you can also infer m from m and n . The systematicity of thinking requires explanation. Why is it that thinkers who can infer p from p and q can also infer m from m and n ?

RTT+COMP+CCTM gives a compelling explanation. During an inference from p and q to p , you transit from believing* mental sentence \(S_1 \amp S_2\) (which means that p and q ) to believing* mental sentence \(S_{1}\) (which means that p ). According to CCTM, the transition involves symbol manipulation. A mechanical operation detaches the conjunct \(S_{1}\) from the conjunction \(S_1 \amp S_2\). The same mechanical operation is applicable to a conjunction \(S_{3} \amp S_{4}\) (which means that m and n ), corresponding to the inference from m and n to n . An ability to execute the first inference entails an ability to execute the second, because drawing the inference in either case corresponds to executing a single uniform mechanical operation. More generally, logical inference deploys mechanical operations over structured symbols, and the mechanical operation corresponding to a given inference pattern (e.g., conjunction introduction, disjunction elimination, etc.) is applicable to any premises with the right logical structure. The uniform applicability of a single mechanical operation across diverse symbols explains inferential systematicity. Fodor and Pylyshyn (1988) conclude that inferential systematicity provides reason to accept RTT+COMP+CCTM.

Fodor and Pylyshyn (1988) endorse an additional thesis about the mechanical operations corresponding to logical transitions. In keeping with FSC, they claim that the operations are sensitive to formal syntactic properties but not semantic properties. For example, conjunction elimination responds to Mentalese conjunction as a piece of pure formal syntax, much as a computer manipulates items in a formal language without considering what those items mean.

Semantic computationalists reject FSC. They claim that mental computation is sometimes sensitive to semantic properties. Semantic computationalists can agree that drawing an inference involves executing a mechanical operation over structured symbols, and they can agree that the same mechanical operation uniformly applies to any premises with appropriate logical structure. So they can still explain inferential systematicity. However, they can also say that the postulated mechanical operation is sensitive to semantic properties. For example, they can say that conjunction elimination is sensitive to the meaning of Mentalese conjunction.

In assessing the debate between FSC and semantic computationalism, one must distinguish between logical versus non-logical symbols. For present purposes, it is common ground that the meanings of non-logical symbols do not inform logical inference. The inference from \(S_1 \amp S_2\) to \(S_{1}\) features the same mechanical operation as the inference from \(S_{3} \amp S_{4}\) to \(S_{4}\), and this mechanical operation is not sensitive to the meanings of the conjuncts \(S_{1}\), \(S_{2}\), \(S_{3}\), or \(S_{4}\). It does not follow that the mechanical operation is insensitive to the meaning of Mentalese conjunction. The meaning of conjunction might influence how the logical inference proceeds, even though the meanings of the conjuncts do not.

In the 1960s and 1970s, cognitive scientists almost universally modeled mental activity as rule-governed symbol manipulation. In the 1980s, connectionism gained currency as an alternative computational framework. Connectionists employ computational models, called neural networks , that differ quite significantly from Turing-style models. There is no central processor. There are no memory locations for symbols to be inscribed. Instead, there is a network of nodes bearing weighted connections to one another. During computation, waves of activation spread through the network. A node’s activation level depends upon the weighted activations of the nodes to which it is connected. Nodes function somewhat analogously to neurons, and connections between nodes function somewhat analogously to synapses. One should receive the neurophysiological analogy cautiously, as there are numerous important differences between neural networks and actual neural configurations in the brain (Bechtel & Abramson 2002: 341–343; Bermúdez 2010: 237–239; Clark 2014: 87–89; Harnish 2002: 359–362).

Connectionists raise many objections to the classical computational paradigm (Rumelhart, McClelland, & the PDP Research Group 1986; Horgan & Tienson 1996; McLaughlin & Warfield 1994; Bechtel & Abrahamsen 2002), such as that classical systems are not biologically realistic or that they are unable to model certain psychological tasks. Classicists in turn launch various arguments against connectionism. The most famous arguments showcase productivity, systematicity of thought, and systematicity of thinking. Fodor and Pylyshyn (1988) argue that these phenomena support classical CTM over connectionist CTM.

Fodor and Pylyshyn’s argument hinges on the distinction between eliminative connectionism and implementationist connectionism (cf. Pinker & Prince 1988). Eliminative connectionists advance neural networks as a replacement for the Turing-style formalism. They deny that mental computation consists in rule-governed symbol manipulation. Implementationist connectionists allow that, in some cases, mental computation may instantiate rule-governed symbol manipulation. They advance neural networks not to replace classical computations but rather to model how classical computations are implemented in the brain. The hope is that, because neural network computation more closely resembles actual brain activity, it can illuminate the physical realization of rule-governed symbol manipulation.

Building on Aydede’s (2015) discussion, we may reconstruct Fodor and Pylyshyn’s argument like so:

• Representational mental states and processes exist. An explanatorily adequate account of cognition should acknowledge these states and processes.
• The representational states and processes that figure in high-level cognition have certain fundamental properties: thought is productive and systematic ; inferential thinking is systematic . The states and processes have these properties as a matter of nomic necessity : it is a psychological law that they have the properties.
• A theory of mental computation is explanatorily adequate only if it explains the nomic necessity of systematicity and productivity.
• The only way to explain the nomic necessity of systematicity and productivity is to postulate that high-level cognition instantiates computation over mental symbols with a compositional semantics. Specifically, we must accept RTT+COMP.
• Either a connectionist theory endorses RTT+COMP or it does not.
• If it does, then it is a version of implementationist connectionism.
• If it does not, then it is a version of eliminative connectionism. As per (iv), it does not explain productivity and systematicity. As per (iii), it is not explanatorily adequate.
• Conclusion : Eliminative connectionist theories are not explanatorily adequate.

The argument does not say that neural networks are unable to model systematicity. One can certainly build a neural network that is systematic. For example, one might build a neural network that can represent that John loves Mary only if it can represent that Mary loves John. The problem is that one might just as well build a neural network that can represent that John loves Mary but cannot represent that Mary loves John. Hence, nothing about the connectionist framework per se guarantees systematicity. For that reason, the framework does not explain the nomic necessity of systematicity. It does not explain why all the minds we find are systematic. In contrast, the classical framework mandates systematicity, and so it explains the nomic necessity of systematicity. The only apparent recourse for connectionists is to adopt the classical explanation, thereby becoming implementationist rather than eliminative connectionists.

Fodor and Pylyshyn’s argument has spawned a massive literature, including too many rebuttals to survey here. The most popular responses fall into five categories:

• Deny (i) . Some connectionists deny that cognitive science should posit representational mental states. They believe that mature scientific theorizing about the mind will delineate connectionist models specified in non-representational terms (P.S. Churchland 1986; P.S. Churchland & Sejnowski 1989; P.M. Churchland 1990; P.M. Churchland & P.S. Churchland 1990; Ramsey 2007). If so, then Fodor and Pylyshyn’s argument falters at its first step. There is no need to explain why representational mental states are systematic and productive if one rejects all talk about representational mental states.
• Accept (viii) . Some authors, such as Marcus (2001), feel that neural networks are best deployed to illuminate the implementation of Turing-style models, rather than as replacements for Turing-style models.
• Deny (ii). Some authors claim that Fodor and Pylyshyn greatly exaggerate the extent to which thought is productive (Rumelhart & McClelland 1986) or systematic (Dennett 1991; Johnson 2004). Horgan and Tienson (1996: 91–94) question the systematicity of thinking. They contend that we deviate from norms of deductive inference more than one would expect if we were following the rigid mechanical rules postulated by CCTM.
• Deny (iv) . Braddon-Mitchell and Fitzpatrick (1990) offer an evolutionary explanation for the systematicity of thought, bypassing any appeal to structured mental representations. In a similar vein, Horgan and Tienson (1996: 90) seek to explain systematicity by emphasizing how our survival depends upon our ability to keep track of objects in the environment and their ever-changing properties. Clark (1991) argues that systematicity follows from the holistic nature of thought ascription.
• Deny (vi) . Chalmers (1990, 1993), Smolensky (1991), and van Gelder (1991) claim that one can reject Turing-style models while still postulating mental representations with compositionally and computationally relevant internal structure.

We focus here on (vi).

As discussed in section 1.2 , Fodor elucidates constituency structure in terms of part/whole relations. A complex representation’s constituents are literal parts of it. One consequence is that, whenever the first representation is tokened, so are its constituents. Fodor takes this consequence to be definitive of classical computation. As Fodor and McLaughlin (1990: 186) put it:

for a pair of expression types E1, E2, the first is a Classical constituent of the second only if the first is tokened whenever the second is tokened.

Thus, structured representations have a concatenative structure: each token of a structured representation involves a concatenation of tokens of the constituent representations. Connectionists who deny (vi) espouse a non-concatenative conception of constituency structure, according to which structure is encoded by a suitable distributed representation . Developments of the non-concatenative conception are usually quite technical (Elman 1989; Hinton 1990; Pollack 1990; Smolensky 1990, 1991, 1995; Touretzky 1990). Most models use vector or tensor algebra to define operations over connectionist representations, which are codified by activity vectors across nodes in a neural network. The representations are said to have implicit constituency structure: the constituents are not literal parts of the complex representation, but they can be extracted from the complex representation through suitable computational operations over it.

Fodor and McLaughlin (1990) grant that distributed representations may have constituency structure “in an extended sense”. But they insist that distributed representations are ill-suited to explain systematicity. They focus especially on the systematicity of thinking, the classical explanation for which postulates mechanical operations that respond to constituency structure. Fodor and McLaughlin argue that the non-concatenative conception cannot replicate the classical explanation and offers no satisfactory substitute for it. Chalmers (1993) and Niklasson and van Gelder (1994) disagree. They contend that a neural network can execute structure-sensitive computations over representations that have non-concatenative constituency structure. They conclude that connectionists can explain productivity and systematicity without retreating to implementationist connectionism.

Aydede (1995, 1997a) agrees that there is a legitimate notion of non-concatenative constituency structure, but he questions whether the resulting models are non-classical. He denies that we should regard concatenative structure as integral to LOTH. According to Aydede, concatenative structure is just one possible physical realization of constituency structure. Non-concatenative structure is another possible realization. We can accept RTT+COMP without glossing constituency structure in concatenative terms. On this view, a neural network whose operations are sensitive to non-concatenative constituency structure may still count as broadly classical and in particular as manipulating Mentalese expressions.

The debate between classical and connectionist CTM is still active, although not as active as during the 1990s. Recent anti-connectionist arguments tend to have a more empirical flavor. For example, Gallistel and King (2009) defend CCTM by canvassing a range of non-human empirical case studies. According to Gallistel and King, the case studies manifest a kind of productivity that CCTM can easily explain but eliminative connectionism cannot.

6. Regress Objections to LOTH

LOTH has elicited too many objections to cover in a single encyclopedia entry. We will discuss two objections, both alleging that LOTH generates a vicious regress. The first objection emphasizes language learning . The second emphasizes language understanding .

Like many cognitive scientists, Fodor holds that children learn a natural language via hypothesis formation and testing . Children formulate, test, and confirm hypotheses about the denotations of words. For example, a child learning English will confirm the hypothesis that “cat” denotes cats. According to Fodor, denotations are represented in Mentalese. To formulate the hypothesis that “cat” denotes cats, the child uses a Mentalese word cat that denotes cats. It may seem that a regress is now in the offing, sparked by the question: How does the child learn Mentalese? Suppose we extend the hypothesis formation and testing model (henceforth HF) to Mentalese. Then we must posit a meta-language to express hypotheses about denotations of Mentalese words, a meta-meta-language to express hypotheses about denotations of meta-language words, and so on ad infinitum (Atherton and Schwartz 1974: 163).

Fodor responds to the threatened regress by denying we should apply HF to Mentalese (1975: 65). Children do not test hypotheses about the denotations of Mentalese words. They do not learn Mentalese at all. The mental language is innate .

The doctrine that some concepts are innate was a focal point in the clash between rationalism versus empiricism. Rationalists defended the innateness of certain fundamental ideas, such as god and cause , while empiricists held that all ideas derive from sensory experience. A major theme in the 1960s cognitive science revolution was revival of a nativist picture, inspired by the rationalists, on which many key elements of cognition are innate. Most famously, Chomsky (1965) explained language acquisition by positing innate knowledge about possible human languages. Fodor’s innateness thesis was widely perceived as going way beyond all precedent, verging on the preposterous (P.S. Churchland 1986; Putnam 1988). How could we have an innate ability to represent all the denotations we mentally represent? For example, how could we innately possess a Mentalese word carburetor that represents carburetors?

In evaluating these issues, it is vital to distinguish between learning a concept versus acquiring a concept. When Fodor says that a concept is innate, he does not mean to deny that we acquire the concept or even that certain kinds of experience are needed to acquire it. Fodor fully grants that we cannot mentally represent carburetors at birth and that we come to represent them only by undergoing appropriate experiences. He agrees that most concepts are acquired . He denies that they are learned . In effect, he uses “innate” as a synonym for “unlearned” (1975: 96). One might reasonably challenge Fodor’s usage. One might resist classifying a concept as innate simply because it is unlearned. However, that is how Fodor uses the word “innate”. Properly understood, then, Fodor’s position is not as far-fetched as it may sound. [ 7 ]

Fodor gives a simple but striking argument that concepts are unlearned. The argument begins from the premise that HF is the only potentially viable model of concept learning. Fodor then argues that HF is not a viable model of concept learning, from which he concludes that concepts are unlearned. He offers various formulations and refinements of the argument over his career. Here is a relatively recent rendition (2008: 139):

Now, according to HF, the process by which one learns C must include the inductive evaluation of some such hypothesis as “The C things are the ones that are green or triangular”. But the inductive evaluation of that hypothesis itself requires ( inter alia ) bringing the property green or triangular before the mind as such… Quite generally, you can’t represent anything as such and such unless you already have the concept such and such . All that being so, it follows, on pain of circularity, that “concept learning” as HF understands it can’t be a way of acquiring concept C … Conclusion: If concept learning is as HF understands it, there can be no such thing . This conclusion is entirely general; it doesn’t matter whether the target concept is primitive (like green ) or complex (like green or triangular ).

Fodor’s argument does not presuppose RTT, COMP, or CTM. To the extent that the argument works, it applies to any view on which people have concepts.

If concepts are not learned, then how are they acquired? Fodor offers some preliminary remarks (2008: 144–168), but by his own admission the remarks are sketchy and leave numerous questions unanswered (2008: 144–145). Prinz (2011) critiques Fodor’s positive treatment of concept acquisition.

The most common rejoinder to Fodor’s innateness argument is to deny that HF is the only viable model of concept learning. The rejoinder acknowledges that concepts are not learned through hypothesis testing but insists they are learned through other means . Three examples:

• Margolis (1998) proposes an acquisition model that differs from HF but that allegedly yields concept learning. Fodor (2008: 140–144) retorts that Margolis’s model does not yield genuine concept learning. Margolis and Laurence (2011) insist that it does.
• Carey (2009) maintains that children can “bootstrap” their way to new concepts using induction, analogical reasoning, and other techniques. She develops her view in great detail, supporting it partly through her groundbreaking experimental work with young children. Fodor (2010) and Rey (2014) object that Carey’s bootstrapping theory is circular: it surreptitiously presupposes that children already possess the very concepts whose acquisition it purports to explain. Beck (2017) and Carey (2014) respond to the circularity objection.
• Shea (2016) argues that connectionist modeling can explain concept acquisition in non-HF terms and that the resulting models instantiate genuine learning.

A lot depends here upon what counts as “learning” and what does not, a question that seems difficult to adjudicate. A closely connected question is whether concept acquisition is a rational process or a mere causal process. To the extent that acquiring some concept is a rational achievement, we will want to say that one learned the concept. To the extent that acquiring the concept is a mere causal process (more like catching a cold than confirming a hypothesis), we will feel less inclined to say that genuine learning took place (Fodor 1981: 275).

These issues lie at the frontier of psychological and philosophical research. The key point for present purposes is that there are two options for halting the regress of language learning: we can say that thinkers acquire concepts but do not learn them; or we can say that thinkers learn concepts through some means other than hypothesis testing. Of course, it is not enough just to note that the two options exist. Ultimately, one must develop one’s favored option into a compelling theory. But there is no reason to think that doing so would reinitiate the regress. In any event, explaining concept acquisition is an important task facing any theorist who accepts that we have concepts, whether or not the theorist accepts LOTH. Thus, the learning regress objection is best regarded not as posing a challenge specific to LOTH but rather as highlighting a more widely shared theoretical obligation: the obligation to explain how we acquire concepts.

For further discussion, see the entry on innateness. See also the exchange between Cowie (1999) and Fodor (2001).

What is it to understand a natural language word? On a popular picture, understanding a word requires that you mentally represent the word’s denotation. For example, understanding the word “cat” requires representing that it denotes cats. LOT theorists will say that you use Mentalese words to represent denotations. The question now arises what it is to understand a Mentalese word. If understanding the Mentalese word requires representing that it has a certain denotation, then we face an infinite regress of meta-languages (Blackburn 1984: 43–44).

The standard response is to deny that ordinary thinkers represent Mentalese words as having denotations (Bach 1987; Fodor 1975: 66–79). Mentalese is not an instrument of communication. Thinking is not “talking to oneself” in Mentalese. A typical thinker does not represent, perceive, interpret, or reflect upon Mentalese expressions. Mentalese serves as a medium within which her thought occurs, not an object of interpretation. We should not say that she “understands” Mentalese in the same way that she understands a natural language.

There is perhaps another sense in which the thinker “understands” Mentalese: her mental activity coheres with the meanings of Mentalese words. For example, her deductive reasoning coheres with the truth-tables expressed by Mentalese logical connectives. More generally, her mental activity is semantically coherent. To say that the thinker “understands” Mentalese in this sense is not to say that she represents Mentalese denotations. Nor is there any evident reason to suspect that explaining semantic coherence will ultimately require us to posit mental representation of Mentalese denotations. So there is no regress of understanding.

For further criticism of this regress argument, see the discussions of Knowles (1998) and Laurence and Margolis (1997). [ 8 ]

Naturalism is a movement that seeks to ground philosophical theorizing in the scientific enterprise. As so often in philosophy, different authors use the term “naturalism” in different ways. Usage within philosophy of mind typically connotes an effort to depict mental states and processes as denizens of the physical world, with no irreducibly mental entities or properties allowed. In the modern era, philosophers have often recruited LOTH to advance naturalism. Indeed, LOTH’s supposed contribution to naturalism is frequently cited as a significant consideration in its favor. One example is Fodor’s use of CCTM+FSC to explain semantic coherence. The other main example turns upon the problem of intentionality .

How does intentionality arise? How do mental states come to be about anything, or to have semantic properties? Brentano (1874 [1973: 97]) maintained that intentionality is a hallmark of the mental as opposed to the physical: “The reference to something as an object is a distinguishing characteristic of all mental phenomena. No physical phenomenon exhibits anything similar”. In response, contemporary naturalists seek to naturalize intentionality . They want to explain in naturalistically acceptable terms what makes it the case that mental states have semantic properties. In effect, the goal is to reduce the intentional to the non-intentional. Beginning in the 1980s, philosophers have offered various proposals about how to naturalize intentionality. Most proposals emphasize causal or nomic links between mind and world (Aydede & Güzeldere 2005; Dretske 1981; Fodor 1987, 1990; Stalnaker 1984), sometimes also invoking teleological factors (Millikan 1984, 1993; Neander 2017l; Papineau 1987; Dretske 1988) or historical lineages of mental states (Devitt 1995; Field 2001). Another approach, functional role semantics , emphasizes the functional role of a mental state: the cluster of causal or inferential relations that the state bears to other mental states. The idea is that meaning emerges at least partly through these causal and inferential relations. Some functional role theories cite causal relations to the external world (Block 1987; Loar 1982), and others do not (Cummins 1989).

Even the best developed attempts at naturalizing intentionality, such as Fodor’s (1990) version of the nomic strategy, face serious problems that no one knows how to solve (M. Greenberg 2014; Loewer 1997). Partly for that reason, the flurry of naturalizing attempts abated in the 2000s. Burge (2010: 298) reckons that the naturalizing project is not promising and that current proposals are “hopeless”. He agrees that we should try to illuminate representationality by limning its connections to the physical, the causal, the biological, and the teleological. But he insists that illumination need not yield a reduction of the intentional to the non-intentional.

LOTH is neutral as to the naturalization of intentionality. An LOT theorist might attempt to reduce the intentional to the non-intentional. Alternatively, she might dismiss the reductive project as impossible or pointless. Assuming she chooses the reductive route, LOTH provides guidance regarding how she might proceed. According to RTT,

X A ’s that p iff there is a mental representation S such that X bears A * to S and S means that p .

The task of elucidating “ X A ’s that p ” in naturalistically acceptable terms factors into two sub-tasks (Field 2001: 33):

• Explain in naturalistically acceptable terms what it is to bear psychological relation A * to mental representation S .
• Explain in naturalistically acceptable terms what it is for mental representation S to mean that p .

As we have seen, functionalism helps with (a). Moreover, COMP provides a blueprint for tackling (b). We can first delineate a compositional semantics describing how S ’s meaning depends upon semantic properties of its component words and upon the compositional import of the constituency structure into which those words are arranged. We can then explain in naturalistically acceptable terms why the component words have the semantic properties that they have and why the constituency structure has the compositional import that it has.

How much does LOTH advance the naturalization of intentionality? Our compositional semantics for Mentalese may illuminate how the semantic properties of a complex expression depend upon the semantic properties of primitive expressions, but it says nothing about how primitive expressions get their semantic properties in the first place. Brentano’s challenge ( How could intentionality arise from purely physical entities and processes? ) remains unanswered. To meet the challenge, we must invoke naturalizing strategies that go well beyond LOTH itself, such as the causal or nomic strategies mentioned above. Those naturalizing strategies are not specifically linked to LOTH and can usually be tailored to semantic properties of neural states rather than semantic properties of Mentalese expressions. Thus, it is debatable how much LOTH ultimately helps us naturalize intentionality. Naturalizing strategies orthogonal to LOTH seem to do the heavy lifting.

How are Mentalese expressions individuated? Since Mentalese expressions are types, answering this question requires us to consider the type/token relation for Mentalese. We want to fill in the schema

e and e * are tokens of the same Mentalese type iff R ( e , e *).

What should we substitute for R ( e , e *)? The literature typically focuses on primitive symbol types, and we will follow suit here.

It is almost universally agreed among contemporary LOT theorists that Mentalese tokens are neurophysiological entities of some sort. One might therefore hope to individuate Mentalese types by citing neural properties of the tokens. Drawing R ( e , e *) from the language of neuroscience induces a theory along the following lines:

Neural individuation : e and e * are tokens of the same primitive Mentalese type iff e and e * are tokens of the same neural type.

This schema leaves open how neural types are individuated. We may bypass that question here, because neural individuation of Mentalese types finds no proponents in the contemporary literature. The main reason is that it conflicts with multiple realizability : the doctrine that a single mental state type can be realized by physical systems that are wildly heterogeneous when described in physical, biological, or neuroscientific terms. Putnam (1967) introduced multiple realizability as evidence against the mind/brain identity theory , which asserts that mental state types are brain state types. Fodor (1975: 13–25) further developed the multiple realizability argument, presenting it as foundational to LOTH. Although the multiple realizability argument has subsequently been challenged (Polger 2004), LOT theorists widely agree that we should not individuate Mentalese types in neural terms.

The most popular strategy is to individuate Mentalese types functionally:

Functional individuation : e and e * are tokens of the same primitive Mentalese type iff e and e * have the same functional role.

Field (2001: 56–67), Fodor (1994: 105–109), and Stich (1983: 149–151) pursue functional individuation. They specify functional roles using a Turing-style computationalism formalism, so that “functional role” becomes something like “computational role”, i.e., role within mental computation.

Functional roles theories divide into two categories: molecular and holist . Molecular theories isolate privileged canonical relations that a symbol bears to other symbols. Canonical relations individuate the symbol, but non-canonical relations do not. For example, one might individuate Mentalese conjunction solely through the introduction and elimination rules governing conjunction while ignoring any other computational rules. If we say that a symbol’s “canonical functional role” is constituted by its canonical relations to other symbols, then we can offer the following theory:

Molecular functional individuation : e and e * are tokens of the same primitive Mentalese type iff e and e * have the same canonical functional role.

One problem facing molecular individuation is that, aside from logical connectives and a few other special cases, it is difficult to draw any principled demarcation between canonical and non-canonical relations (Schneider 2011: 106). Which relations are canonical for SOFA? [ 9 ] Citing the demarcation problem, Schneider espouses a holist approach that individuates mental symbols through total functional role , i.e., every single aspect of the role that a symbol plays within mental activity:

Holist functional individuation : e and e * are tokens of the same primitive Mentalese type iff e and e * have the same total functional role.

Holist individuation is very fine-grained: the slightest difference in total functional role entails that different types are tokened. Since different thinkers will always differ somewhat in their mental computations, it now looks like two thinkers will never share the same mental language. This consequence is worrisome, for two reasons emphasized by Aydede (1998). First, it violates the plausible publicity constraint that propositional attitudes are in principle shareable. Second, it apparently precludes interpersonal psychological explanations that cite Mentalese expressions. Schneider (2011: 111–158) addresses both concerns, arguing that they are misdirected.

A crucial consideration when individuating mental symbols is what role to assign to semantic properties. Here we may usefully compare Mentalese with natural language. It is widely agreed that natural language words do not have their denotations essentially. The English word “cat” denotes cats, but it could just as well have denoted dogs, or the number 27, or anything else, or nothing at all, if our linguistic conventions had been different. Virtually all contemporary LOT theorists hold that a Mentalese word likewise does not have its denotation essentially. The Mentalese word cat denotes cats, but it could have had a different denotation had it born different causal relations to the external world or had it occupied a different role in mental activity. In that sense, cat is a piece of formal syntax. Fodor’s early view (1981: 225–253) was that a Mentalese word could have had a different denotation but not an arbitrarily different denotation: cat could not have denoted just anything—it could not have denoted the number 27—but it could have denoted some other animal species had the thinker suitably interacted with that species rather than with cats. Fodor eventually (1994, 2008) embraces the stronger thesis that a Mentalese word bears an arbitrary relation to its denotation: cat could have had any arbitrarily different denotation. Most contemporary theorists agree (Egan 1992: 446; Field 2001: 58; Harnad 1994: 386; Haugeland 1985: 91: 117–123; Pylyshyn 1984: 50).

The historical literature on LOTH suggests an alternative semantically permeated view: Mentalese words are individuated partly through their denotations. The Mentalese word cat is not a piece of formal syntax subject to reinterpretation. It could not have denoted another species, or the number 27, or anything else. It denotes cats by its inherent nature . From a semantically permeated viewpoint, a Mentalese word has its denotation essentially. Thus, there is a profound difference between natural language and mental language. Mental words, unlike natural language words, bring with them one fixed semantic interpretation. The semantically permeated approach is present in Ockham, among other medieval LOT theorists (Normore 2003, 2009). In light of the problems facing neural and functional individuation, Aydede (2005) recommends that we consider taking semantics into account when individuating Mentalese expressions. Rescorla (2012b) concurs, defending a semantically permeated approach as applied to at least some mental representations. He proposes that certain mental computations operate over mental symbols with essential semantic properties, and he argues that the proposal fits well with many sectors of cognitive science. [ 10 ]

A recurring complaint about the semantically permeated approach is that inherently meaningful mental representations seem like highly suspect entities (Putnam 1988: 21). How could a mental word have one fixed denotation by its inherent nature ? What magic ensures the necessary connection between the word and the denotation? These worries diminish in force if one keeps firmly in mind that Mentalese words are types. Types are abstract entities corresponding to a scheme for classifying, or type-identifying , tokens. To ascribe a type to a token is to type-identify the token as belonging to some category. Semantically permeated types correspond to a classificatory scheme that takes semantics into account when categorizing tokens. As Burge emphasizes (2007: 302), there is nothing magical about semantically-based classification. On the contrary, both folk psychology and cognitive science routinely classify mental events based at least partly upon their semantic properties.

A simplistic implementation of the semantically permeated approach individuates symbol tokens solely through their denotations:

Denotational individuation : e and e * are tokens of the same primitive Mentalese type iff e and e * have the same denotation.

As Aydede (2000) and Schneider (2011) emphasize, denotational individuation is unsatisfying. Co-referring words may play significantly different roles in mental activity. Frege’s (1892 [1997]) famous Hesperus-Phosphorus example illustrates: one can believe that Hesperus is Hesperus without believing that Hesperus is Phosphorus. As Frege put it, one can think about the same denotation “in different ways”, or “under different modes of presentation”. Different modes of presentation have different roles within mental activity, implicating different psychological explanations. Thus, a semantically permeated individuative scheme adequate for psychological explanation must be finer-grained than denotational individuation allows. It must take mode of presentation into account. But what it is to think about a denotation “under the same mode of presentation”? How are “modes of presentation” individuated? Ultimately, semantically permeated theorists must grapple with these questions. Rescorla (2020) offers some suggestions about how to proceed. [ 11 ]

Chalmers (2012) complains that semantically permeated individuation sacrifices significant virtues that made LOTH attractive in the first place. LOTH promised to advance naturalism by grounding cognitive science in non-representational computational models. Representationally-specified computational models seem like a significant retrenchment from these naturalistic ambitions. For example, semantically permeated theorists cannot accept the FSC explanation of semantic coherence, because they do not postulate formal syntactic types manipulated during mental computation.

How compelling one finds naturalistic worries about semantically permeated individuation will depend on how impressive one finds the naturalistic contributions made by formal mental syntax. We saw earlier that FSC arguably engenders a worrisome epiphenomenalism. Moreover, the semantically permeated approach in no way precludes a naturalistic reduction of intentionality. It merely precludes invoking formal syntactic Mentalese types while executing such a reduction. For example, proponents of the semantically permeated approach can still pursue the causal or nomic naturalizing strategies discussed in section 7 . Nothing about either strategy presupposes formal syntactic Mentalese types. Thus, it is not clear that replacing a formal syntactic individuative scheme with a semantically permeated scheme significantly impedes the naturalistic endeavor.

No one has yet provided an individuative scheme for Mentalese that commands widespread assent. The topic demands continued investigation, because LOTH remains highly schematic until its proponents clarify sameness and difference of Mentalese types.

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How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Aydede, Murat, “The Language of Thought Hypothesis,” Stanford Encyclopedia of Philosophy (Spring 2019 Edition), Edward N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2019/entries/language-thought/ >. [This was the previous entry on the language of thought hypothesis in the Stanford Encyclopedia of Philosophy — see the version history .]
• Bibliography on the language of thought , in PhilPapers.org.
• Bibliography on the philosophy of artificial intelligence , curated by Eric Dietrich, in PhilPapers.org.

artificial intelligence | belief | Church-Turing Thesis | cognitive science | computation: in physical systems | concepts | connectionism | consciousness: representational theories of | folk psychology: as a theory | functionalism | intentionality | mental content: causal theories of | mental imagery | mental representation | mind: computational theory of | naturalism | physicalism | propositional attitude reports | qualia | reasoning: automated | Turing, Alan | Turing machines

Acknowledgments

I owe a profound debt to the Murat Aydede, author of the previous entry on the same topic. His exposition hugely influenced my work on the entry, figuring indispensably as a springboard, a reference, and a standard of excellence. Some of my formulations in the introduction and in sections 1.1, 2, 3, 4.3, 5, 6.1, and 7 closely track formulations from the previous entry. Section 5’s discussion of connectionism is directly based on the previous entry’s treatment. I also thank Calvin Normore, Melanie Schoenberg, and the Stanford Encyclopedia editors for helpful comments.

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What is a Hypothesis?

A hypothesis is an explanation for a phenomenon that can be tested in some way that ideally either proves or disproves it. For the duration of testing, the hypothesis is taken to be true, and the goal of the researcher is to rigorously test its terms. The concept is a very important part of the scientific method , and it also holds true in other disciplines as well. For example, some historians have put forward the hypothesis that the Salem Witch Trials were brought about by the consumption of grains contaminated with ergot, resulting in a mass hysteria .

When someone formulates a hypothesis, he or she does so with the intention of testing it, and he or she should not know the outcome of potential tests before the hypothesis is made. When formulating a hypothesis, the ideals of the scientific method are often kept in mind, so it is designed to be testable in a way that could be replicated by other people. It is also kept clear and simple, and the hypothesis relies on known information and reasoning.

A hypothesis does not have to be right or wrong, but the person formulating it does have to be prepared to test the theory to its limits. If someone hypothesizes that exposure to X causes Y in lab rats, for example, he or she must see if exposure to other things also causes Y. When scientists publish results which support a hypothesis, they often detail the steps they took to try to disprove it as well as the steps that confirmed it, to make the case that much stronger.

In some instances, a hypothesis turns out to be wrong, and this is considered perfectly acceptable, because it still furthers the cause of science. In the example above, for instance, by showing that exposure to X does not cause Y, a scientist can illustrate that further research on Y is needed. In this example, the fact that the hypothesis is wrong does not necessarily mean that substance X is safe, because substance X could still cause something else.

It is also possible for a hypothesis to turn out to be inconclusive after testing. This can be because a scientist lacks the necessary tools for the testing, suggesting that advanced scientific techniques could be used in the future to test the idea. It can also be the result of not having enough information, or a hypothesis that is simply poorly formed and hard to test.

Ever since she began contributing to the site several years ago, Mary has embraced the exciting challenge of being a LanguageHumanities researcher and writer. Mary has a liberal arts degree from Goddard College and spends her free time reading, cooking, and exploring the great outdoors.

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• By: Gajus Some hypotheses can be expressed as a mathematical formula.
• By: lculig Hypotheses are often tested in laboratory conditions.
• By: nandyphotos A hypothesis that turns out to be wrong is still considered positive, as it furthers the cause of science.

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A statement of the expected relationship between things being studied, which is intended to explain certain facts or observations. An idea to be tested.

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Hypothesis Definition (Science)

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A hypothesis is an explanation that is proposed for a phenomenon. Formulating a hypothesis is a step of the scientific method .

Alternate Spellings: plural: hypotheses

Examples: Upon observing that a lake appears blue under a blue sky, you might propose the hypothesis that the lake is blue because it is reflecting the sky. One alternate hypothesis would be that the lake is blue because water is blue.

Hypothesis Versus Theory

Although in common usage the terms hypothesis and theory are used interchangeably, the two words mean something different from each other in science. Like a hypothesis, a theory is testable and may be used to make predictions. However, a theory has been tested using the scientific method many times. Testing a hypothesis may, over time, lead to the formulation of a theory.

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym:   proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

• It should never be written as a question.
• You should be able to test it in the real world to see if it’s right or wrong.
• It needs to be clear and exact.
• It should list the factors that will be used to figure out the relationship.
• It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
• It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
• It should be written in as simple a way as possible so that everyone can understand it.
• It must explain what happened to make an answer necessary.
• It should be testable in a fair amount of time.
• It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

• Patterns of similarity between the phenomenon under investigation and existing hypotheses.
• Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
• The formulations are derived from accepted scientific theories and proposed by researchers.
• In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
• Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

• Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
• Students who experience test anxiety before an English examination will get lower scores than students who do not experience test anxiety.
• Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

• Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
• Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
• Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

• The crop yield will go up a lot if the amount of fertilizer is increased.
• Patients who have surgery and are exposed to more stress will need more time to get better.
• Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

• The test scores of two groups of students are very different from each other.
• There is a link between gender and being happy at work.
• There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

• Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
• The results of a memory recall test will not reveal any significant gap in performance between children and adults.
• There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

• There is a link between how many hours you spend studying and how well you do in school.
• Drinking sugary drinks is bad for your health as a whole.
• There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

• A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
• It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
• The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
• A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

• The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
• Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
• Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
• Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
• Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
• The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
• The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

• Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
• Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
• Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
• Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
• Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
• Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
• Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
• Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

Choose the best answer. 

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Further Reading

• RNA-DNA World Hypothesis
• BYJU’S. (2023). Hypothesis. Retrieved 01 Septermber 2023, from https://byjus.com/physics/hypothesis/#sources-of-hypothesis
• Collegedunia. (2023). Hypothesis. Retrieved 1 September 2023, from https://collegedunia.com/exams/hypothesis-science-articleid-7026#d
• Hussain, D. J. (2022). Hypothesis. Retrieved 01 September 2023, from https://mmhapu.ac.in/doc/eContent/Management/JamesHusain/Research%20Hypothesis%20-Meaning,%20Nature%20&%20Importance-Characteristics%20of%20Good%20%20Hypothesis%20Sem2.pdf
• Media, D. (2023). Hypothesis in the Scientific Method. Retrieved 01 September 2023, from https://www.verywellmind.com/what-is-a-hypothesis-2795239#toc-hypotheses-examples
• Rashid, M. H. A. (Apr 20, 2022). Research Methodology. Retrieved 01 September 2023, from https://limbd.org/hypothesis-definitions-functions-characteristics-types-errors-the-process-of-testing-a-hypothesis-hypotheses-in-qualitative-research/#:~:text=Functions%20of%20a%20Hypothesis%3A&text=Specifically%2C%20a%20hypothesis%20serves%20the,providing%20focus%20to%20the%20study.

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Last updated on September 8th, 2023

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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How to Define ‘Antisemitism’ Is a Subject of Bitter Debate.

Activists, university officials and political leaders are deeply divided over what, precisely, constitutes antisemitism.

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By Vimal Patel

• May 8, 2024

Many donors, politicians and Jewish students have pressured their colleges to confront antisemitism more forcefully. But one challenge can make the whole exercise feel like quicksilver.

There’s no consensus about what, precisely, constitutes antisemitism.

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Supporters of the alliance’s definition say that it helps press colleges to stop tolerating behavior against Jews that would be unacceptable if it were directed at racial minority groups or L.G.B.T.Q. students.

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Debates over how to define antisemitism have been a flashpoint on several of the university task forces that have been created in response to student protests over the Israel-Hamas war.

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A similar committee at Columbia University has avoided settling on a definition of antisemitism — a decision that has also led to criticism .

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Vimal Patel writes about higher education with a focus on speech and campus culture. More about Vimal Patel