Statistical learning of target and distractor spatial probability shape a common attentional priority computation.

Area of Science:

• Cognitive Neuroscience
• Neuroplasticity
• Attentional Control

Background:

• Dedicated neurocognitive mechanisms are proposed for suppressing irrelevant distractors.
• Experience-dependent attentional learning may induce plastic changes in attentional circuitry.
• Previous research suggested statistical learning (SL) affects a shared neural substrate for spatial priority.

Purpose of the Study:

• To investigate if dedicated mechanisms support selective spatial priority encoding under conflicting target and distractor manipulations.
• To determine if concurrent target- and distractor-related SL influences spatial priority independently or via a shared mechanism.

Main Methods:

• Three experiments involving human participants discriminating target direction while ignoring salient distractors.
• Concurrent manipulation of target and distractor spatial probability distributions at specific locations.
• Analysis of selection and suppression biases resulting from conflicting SL contingencies.

Main Results:

• Target-related SL selection bias was only marginally reduced by adverse distractor contingency.
• Distractor-related SL suppression bias was erased or reversed by adverse target contingency.
• Conflicting SL manipulations resulted in the adjustment of a unique spatial priority computation.

Conclusions:

• Results suggest SL, even with conflicting inputs, adjusts a single spatial priority map.
• This indicates reliance on shared neural substrates for attentional filtering and priority allocation.
• Attentional learning appears to modify a common spatial priority map rather than separate target/distractor maps.