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Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Purposive Learning01:22

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...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Cognitive Learning01:21

Cognitive Learning

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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.
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Avoidance Learning and Learned Helplessness01:14

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Related Experiment Video

Updated: Jun 1, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Language evolution by iterated learning with bayesian agents.

Thomas L Griffiths1, Michael L Kalish

  • 1University of California, BerkeleyUniversity of Louisiana, Lafayette.

Cognitive Science
|June 4, 2011
PubMed
Summary
This summary is machine-generated.

Iterated learning, where language is learned from others, shapes language evolution. Bayesian inference models show that this process can mirror learners' innate biases or resemble statistical algorithms like Gibbs sampling and Expectation-Maximization (EM).

Related Experiment Videos

Last Updated: Jun 1, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Bayesian Inference

Background:

  • Language transmission occurs through iterated learning, with individuals learning from others who have also learned the language.
  • Understanding the computational principles underlying language acquisition and evolution is crucial for explaining linguistic universals.

Purpose of the Study:

  • To analyze the consequences of iterated learning for Bayesian inference-based learning algorithms.
  • To formally connect inductive biases in language acquisition with emergent language structures.

Main Methods:

  • Modeling iterated learning using Bayesian inference principles, assuming learners compute posterior distributions over languages.
  • Analyzing two learning strategies: sampling from the posterior distribution and selecting the maximum posterior probability language.
  • Identifying connections to Markov chain Monte Carlo (MCMC) algorithms, specifically Gibbs sampling and Expectation-Maximization (EM).

Main Results:

  • When learners sample languages from the posterior, iterated learning converges to a distribution determined solely by the learner's prior (inductive biases).
  • This sampling process is equivalent to Gibbs sampling, a type of MCMC algorithm.
  • When learners select the maximum posterior probability language, iterated learning is influenced by both prior biases and transmission fidelity, resembling a variant of the EM algorithm.

Conclusions:

  • Iterated learning acts as a powerful filter, shaping language to reflect the cognitive constraints (priors) of its learners.
  • This provides a formal link between language acquisition constraints and the resulting linguistic universals observed across languages.
  • The study suggests that iterated learning naturally leads to languages that mirror the cognitive architecture of the populations that speak them.