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Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Determination of Expected Frequency01:08

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Associative Learning01:27

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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 bonus...

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Related Experiment Video

Updated: Jun 10, 2026

An Operant Intra-/Extra-dimensional Set-shift Task for Mice
08:35

An Operant Intra-/Extra-dimensional Set-shift Task for Mice

Published on: January 22, 2016

Dissociable dynamic effects of expectation during statistical learning.

Hannah H McDermott1,2,3, Federico de Martino3, Caspar M Schwiedrzik4,5,6

  • 1Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.

Elife
|March 3, 2026
PubMed
Summary
This summary is machine-generated.

Brain predictions optimize behavior. Expectation suppression (ES) shows neural activity changes, with within-trial effects supporting opposing process theory (OPT) and across-trial effects suggesting distinct learning stages.

Keywords:
EEGMVPAexpectation suppressionhumanneurosciencepredictionpredictive processing

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Last Updated: Jun 10, 2026

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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:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Neuroscience

Background:

  • The brain uses predictions from statistical regularities to optimize behavior.
  • Predictive processing explains expectation suppression (ES), but its neural mechanisms remain debated.
  • Sharpening, dampening, and opposing process theories (OPT) offer different explanations for ES dynamics.

Purpose of the Study:

  • To investigate the temporal dynamics of expectation effects on neural representations.
  • To test the opposing process theory (OPT) regarding expectation suppression.
  • To differentiate within-trial and across-trial effects of expectation on neural decoding.

Main Methods:

  • Utilized a statistical learning task with predictive image sequences.
  • Employed multivariate electroencephalography (EEG) to decode stimulus information.
  • Analyzed neural decoding accuracy at both single-trial and across-trial levels over time.

Main Results:

  • Within-trial analysis revealed early latency increases and later latency decreases in decoding accuracy, supporting OPT.
  • Across-trial analysis showed initial decreases followed by later increases in decoding accuracy.
  • Dissociable temporal dynamics were observed for within-trial and across-trial expectation effects.

Conclusions:

  • Within-trial neural dynamics align with the opposing process theory (OPT).
  • Across-trial dynamics suggest distinct stages of associative learning influence sharpening and dampening.
  • Hierarchical learning mechanisms may underlie the observed dissociable expectation effects.