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Altering spatial priority maps via statistical learning of target selection and distractor filtering.

Oscar Ferrante¹, Alessia Patacca¹, Valeria Di Caro¹

¹Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Italy.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior

|November 4, 2017

Summary

Statistical learning (SL) of target and distractor locations influences attention. Findings show these processes share neuronal mechanisms, demonstrating cross-talk in attentional priority maps.

Keywords:

Attentional capture Distractor filtering Priority maps Probability cueing Target selection

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Area of Science:

Cognitive Neuroscience
Visual Attention
Statistical Learning

Background:

Cognitive systems learn environmental regularities via statistical learning (SL).
SL guides attention, biasing target selection and distractor filtering based on spatial probabilities.
Mechanisms underlying SL for attention and potential shared neural resources remain unclear.

Purpose of the Study:

To investigate whether statistical learning of target selection and distractor filtering share neuronal machinery.
To examine the direct and indirect effects of spatial probability distributions on attention.

Main Methods:

Visual search experiments manipulating spatial probabilities of targets and distractors.
Measuring performance to assess direct effects on selection/filtering and indirect 'transfer' effects.
Analyzing correlations between direct and indirect effects at the individual participant level.

Main Results:

Statistical learning of target and distractor locations implicitly biases attention.
Significant indirect effects observed: target probabilities affected distractor filtering, and distractor probabilities affected target selection.
Strong correlations between direct and indirect effects suggest shared neuronal mechanisms.

Conclusions:

Statistical learning for target selection and distractor filtering involve partially shared neuronal machinery.
Evidence of cross-talk between these processes supports the role of attentional priority maps.
Findings advance understanding of how the brain learns and adapts attentional control.