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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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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...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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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...
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Associative Learning01:27

Associative Learning

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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.
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Observational Learning01:12

Observational Learning

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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...
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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Related Experiment Video

Updated: Apr 28, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

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Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Martin A Rohrmeier1, Ian Cross2

  • 1Cluster Languages of Emotion, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany; Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom.

Consciousness and Cognition
|June 7, 2014
PubMed
Summary
This summary is machine-generated.

This study shows that statistical chunk learning enables online learning, even with incorrect structures. This suggests continuous statistical learning underlies implicit learning and impacts study designs.

Keywords:
Artificial grammar learningCompetitive chunkingComputational modellingImplicit learningIncidental learningN-gram modelOnline learningSimple recurrent networkStatistical learningUnsupervised learning

Related Experiment Videos

Last Updated: Apr 28, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

5.8K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psycholinguistics

Background:

  • Humans exhibit rapid learning of complex structures across domains.
  • Above-chance performance in untrained groups of artificial grammar learning studies suggests unsupervised learning.
  • The extent of unsupervised learning in untrained, testing situations requires further investigation.

Purpose of the Study:

  • To model the plausibility of unsupervised online-learning effects.
  • To investigate the role of statistical chunk learning in implicit learning paradigms.
  • To evaluate the impact of online learning on study design and testing methodologies.

Main Methods:

  • Modeling unsupervised online-learning effects using n-gram, chunking, and simple recurrent network models.
  • Applying a novel evaluation framework alternating grammaticality judgments and stimulus learning.
  • Analyzing the performance of different models under unsupervised conditions.

Main Results:

  • N-gram and chunking models demonstrated a strong online learning effect.
  • Simple recurrent network models showed a weaker online learning effect.
  • Online learning was plausible when ungrammatical sequences contained a high proportion of grammatical chunks.

Conclusions:

  • Online learning is a plausible effect of statistical chunk learning.
  • Continuous statistical learning likely underlies implicit learning paradigms.
  • Findings have implications for designing and testing learning studies.