Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Deductive Reasoning

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction from inductive reasoning. It uses a general principle or law to predict specific results. From these general principles, a scientist can predict specific results that remain valid as long as the general principles are correct.For example, a researcher can make specific predictions from the hypothesis "butterflies are attracted...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Generalization, Discrimination, and Extinction

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...

Types of Hypothesis Testing

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A folk taxonomy of magic.

Cognition·2026

Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026

Same author

Resolving Feynman's restaurant problem reveals optimal solutions and human strategies.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Considering Psychological Mechanisms Can Change the Interpretation of Bayesian Models.

Topics in cognitive science·2026

Same author

Aha! moments correspond to metacognitive prediction errors.

Cognition·2026

Same author

Whither symbols in the era of advanced neural networks?

Trends in cognitive sciences·2026

Same journal

Pronoun Resolution in Turkish: The Interplay of Referential Form, Word Order, and Implicit Causality.

Cognitive science·2026

Same journal

What's in a Color?: Language, Synesthesia, and Categorical Perception.

Cognitive science·2026

Same journal

Reasoning Beyond Explicit Rules: Adults' and Children's Use of Closure Principles in Novel Cases.

Cognitive science·2026

Same journal

Intermediary Object States Are Activated by Sentences Describing Completed Events.

Cognitive science·2026

Same journal

Large Language Models Estimate Fine-Grained Human Color-Concept Associations.

Cognitive science·2026

Same journal

Computational Models of Causal Reasoning: Bayesian Accounts of Normative Violations.

Cognitive science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Using category structures to test iterated learning as a method for identifying inductive biases.

Thomas L Griffiths¹, Brian R Christian, Michael L Kalish

¹Department of Psychology, University of California, BerkeleyDepartment of English, University of WashingtonInstitute of Cognitive Science, University of Louisiana at Lafayette.

Cognitive Science

|June 4, 2011

Summary

This summary is machine-generated.

This study introduces iterated learning, a novel method to uncover inductive biases in cognitive science. By using participant responses to generate new stimuli, it reveals underlying assumptions guiding human inductive inferences.

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Related Experiment Videos

Last Updated: Jun 1, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Area of Science:

Cognitive Science
Psychology
Artificial Intelligence

Background:

Inductive problems are central to cognitive science, requiring evaluation of hypotheses based on data.
Effective problem-solving hinges on inductive biases, which are assumptions guiding hypothesis selection when data is ambiguous.

Purpose of the Study:

To introduce and validate a novel experimental method for identifying human inductive biases.
To demonstrate how iterated learning can reveal the prior probability distributions that characterize these biases.

Main Methods:

The study employs an "iterated learning" procedure where participant responses on one trial generate stimuli for subsequent trials.
A formal analysis, assuming Bayesian agents, predicts this method's efficacy in uncovering inductive biases.
Experiments utilized well-studied category structures to test the method's applicability.

Main Results:

Iterated learning successfully revealed the inductive biases of human learners across experimental conditions.
The method demonstrated its capability to identify the prior probability distributions guiding inductive inferences.
Results align with theoretical predictions of Bayesian learning models.

Conclusions:

Iterated learning provides a powerful tool for investigating the inductive biases that underpin human cognition.
This method offers a new avenue for understanding how humans learn and make inferences in complex environments.
The findings contribute to a deeper understanding of cognitive processes in decision-making and hypothesis evaluation.