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Related Concept Videos

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...
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The Nativist Approach01:21

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The nativist approach to infant cognitive development proposes that infants are born with inherent knowledge structures that allow them to interpret the world almost immediately. This perspective contrasts with earlier developmental theories, such as those proposed by Jean Piaget, which emphasized a more gradual acquisition of cognitive abilities through interaction with the environment. One key concept in this approach is object permanence — the understanding that objects continue to exist...
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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 Learning

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Related Experiment Video

Updated: May 13, 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

Expectancy learning from probabilistic input by infants.

Alexa R Romberg1, Jenny R Saffran

  • 1Department of Psychology and Waisman Center, University of Wisconsin - Madison Madison, WI, USA.

Frontiers in Psychology
|February 27, 2013
PubMed
Summary
This summary is machine-generated.

Infants learn sequential patterns from both predictable and unpredictable environmental input. Even with variable experiences, 12-month-olds update expectations and predict likely outcomes in real-time.

Keywords:
eye-trackinginfantpredictionsequence learningstatistical learning

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Last Updated: May 13, 2026

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08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

  • Developmental Psychology
  • Cognitive Science
  • Infant Learning

Background:

  • Infants possess a remarkable ability to extract environmental regularities, crucial for development.
  • Existing research highlights infants' capacity for recognizing deterministic sequential patterns.
  • Limited understanding exists regarding how infants build and update temporal structure representations, especially for unpredictable patterns.

Purpose of the Study:

  • To investigate the development of infants' expectations for simple temporal structures over time.
  • To examine how infants update their representations based on new environmental information.
  • To explore real-time learning processes in 12-month-old infants.

Main Methods:

  • Measured anticipatory eye movements in 12-month-old infants towards targets in two locations.
  • Exposed infants to either deterministic (consistent location) or probabilistic (variable location) initial trial sequences.
  • Subsequently, all infants experienced identical trial sequences to observe adaptation and learning.

Main Results:

  • Infants in both deterministic and probabilistic conditions learned to predict the most likely target location by the experiment's end.
  • Local context significantly influenced predictions, with infants adjusting to previous trial outcomes.
  • Infants with prior probabilistic experience demonstrated greater flexibility in adapting predictions.

Conclusions:

  • Twelve-month-old infants effectively learn from both deterministic and probabilistic sequential information.
  • Infants dynamically update their expectations based on recent environmental input, demonstrating real-time learning.
  • Prior exposure to variability enhances infants' adaptive prediction capabilities.