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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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Sensitivity, Specificity, and Predicted Value01:13

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Related Experiment Video

Updated: May 24, 2026

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

Predictive value and reward in implicit classification learning.

Judith M Lam1, Tobias Wächter, Christoph Globas

  • 1Clinical Neurorehabilitation, Department of Neurology, University of Zürich and Rehabilitation Initiative and Technology Center of Zürich, Zürich, Switzerland.

Human Brain Mapping
|March 16, 2012
PubMed
Summary
This summary is machine-generated.

Emotional context significantly impacts learning. This study shows that both the content learned and feedback received influence implicit learning and brain processing, with positive feedback enhancing memory and brain activity.

Related Experiment Videos

Last Updated: May 24, 2026

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Learning Science

Background:

  • Learning efficacy is influenced by emotional context, shaped by learned content and feedback.
  • Implicit learning, a type of unconscious learning, is particularly sensitive to these contextual factors.
  • Understanding how the brain processes content and feedback in implicit learning is crucial for educational and therapeutic applications.

Purpose of the Study:

  • To investigate the influence of content and feedback on implicit learning efficacy.
  • To explore the neural processing of these factors using functional magnetic resonance imaging (fMRI).
  • To determine how different types of feedback (positive vs. negative) and content predictiveness affect learning and brain activation patterns.

Main Methods:

  • A probabilistic classification task involving 150 trials where participants predicted outcomes (sun/rain) based on card combinations.
  • Feedback was provided using smileys (positive) or frowneys (negative).
  • Functional magnetic resonance imaging (fMRI) was used on a subset of participants to monitor brain activity during the task.

Main Results:

  • Card combinations predicting 'sun' were remembered better than those predicting 'rain'.
  • Positive feedback (smileys) enhanced learning more effectively than negative feedback (frowneys).
  • Positive feedback engaged brain regions including the nucleus accumbens, sensorimotor cortex, and posterior cingulum.
  • Higher predictive value of card combinations correlated with increased activation in the lateral cerebellum.

Conclusions:

  • Both the emotional context of learned content and the type of feedback significantly influence implicit classification learning.
  • Positive feedback during learning is processed in the sensorimotor cortex, potentially involving dopaminergic pathways, similar to motor skill acquisition.
  • Cerebellar activation suggests the formation of internal predictive models during learning, especially for highly predictive cues.