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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...
Observational Learning01:12

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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...
Cognitive Learning01:21

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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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Impact of Schemas01:30

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Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Steps in the Modeling Process01:14

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Association Areas of the Cortex01:21

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Related Experiment Video

Updated: Jun 28, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

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

Published on: February 8, 2019

Observed attention allocation processes in category learning.

Toshihiko Matsuka1, James E Corter

  • 1Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ, USA.

Quarterly Journal of Experimental Psychology (2006)
|October 22, 2008
PubMed
Summary
This summary is machine-generated.

Human category learners may not always optimize attention due to information costs. However, they can learn to allocate attention optimally based on cost-benefit analysis, especially with redundant cues.

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Machine Learning

Background:

  • Category learning involves allocating attention to relevant stimulus dimensions.
  • Optimal attention allocation is often assumed in computational models.
  • Human attentional strategies may deviate from theoretical optima.

Purpose of the Study:

  • Investigate how category learners allocate attention across stimulus dimensions.
  • Examine factors influencing attention allocation, such as information costs.
  • Test predictions of computational models against human learning data.

Main Methods:

  • Two empirical studies using a computer interface with an 'information-board' display.
  • Collected detailed data on participants' attention allocation and information search patterns.
  • Manipulated feature diagnosticity and cue redundancy across stimulus dimensions.

Main Results:

  • Experiment 1: Overall viewing times supported optimal attention, but detailed analysis revealed instance- or category-specific allocation.
  • Experiment 2: Learners allocated attention cost-beneficially with redundant cues, focusing on one dimension.
  • Findings challenge models assuming fixed attention weighting.

Conclusions:

  • Human attention allocation in category learning is complex and context-dependent.
  • Learners adapt strategies based on perceived costs and benefits of information.
  • Computational models need refinement to account for dynamic, cost-sensitive attention.