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Statistical Significance

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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Related Experiment Video

Updated: Aug 7, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

A Statistical Foundation for Derived Attention.

Samuel Paskewitz¹, Matt Jones²

¹Department of Psychiatry, Children's Hospital, Anschutz Medical Campus, University of Colorado Denver.

Journal of Mathematical Psychology

|March 13, 2023

Summary

This summary is machine-generated.

A new Bayesian model of derived attention explains how organisms focus on important cues. This model advances understanding of learning and attention, predicting inattention after backward blocking.

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Area of Science:

Cognitive Psychology
Computational Neuroscience
Machine Learning

Background:

Derived attention theory posits organisms attend to cues with strong associations.
Previous models, like Rescorla-Wagner, explain learned predictiveness and inattention to blocked cues.

Purpose of the Study:

Introduce a novel Bayesian derived attention model.
Provide a normative, statistical explanation for derived attention.
Explain a wider array of attentional phenomena.

Main Methods:

Combine Bayesian linear regression with approximate Bayesian learning.
Simultaneously estimate cue-outcome associations and prior variance.
Model assumes cue-outcome associations share the same prior variance, representing inherent cue importance.

Main Results:

The Bayesian model explains learned predictiveness, inattention to blocked cues, and value-based salience.
It also accounts for retrospective revaluation.
Novel prediction: inattention following backward blocking.

Conclusions:

The Bayesian derived attention model offers a more comprehensive explanation of attentional phenomena.
It provides statistical insights into how uncertainty and predictiveness influence attention.
Further development may clarify the interplay between uncertainty and attention.