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Related Experiment Video

Updated: Oct 15, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

Active inference, selective attention, and the cocktail party problem.

Emma Holmes¹, Thomas Parr², Timothy D Griffiths³

¹Department of Speech Hearing and Phonetic Sciences, UCL, London, WC1N 1PF, UK; Wellcome Centre for Human Neuroimaging, UCL, London, WC1N 3AR, UK.

Neuroscience and Biobehavioral Reviews

|October 23, 2021

Summary

This summary is machine-generated.

This study introduces a generative model for active inference in attention. It reveals that sensory precision differences explain selective attention in the cocktail party paradigm, impacting reaction times and event-related potentials.

Keywords:

Active inference Cocktail party listening Preparatory attention Selective attention Spatial attention Temporal attention

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Last Updated: Oct 15, 2025

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Published on: July 5, 2015

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Auditory Perception

Background:

The 'cocktail party' paradigm is a classic challenge for understanding auditory selective attention.
Active inference provides a framework for modeling brain function, including attention.
Previous models have not fully explained the interplay between preparatory and selective attention.

Purpose of the Study:

To introduce a novel generative model for active inference of attention.
To test competing hypotheses on how human listeners direct attention in a dichotic listening task.
To elucidate the neural mechanisms underlying attentional modulation of auditory processing.

Main Methods:

Developed a generative model based on active inference principles.
Simulated auditory processing in a 'cocktail party' paradigm with spatial cues.
Manipulated sensory precision at attended and unattended locations.
Analyzed simulated reaction times and event-related potentials (ERPs).

Main Results:

Assigning lower sensory precision to unattended stimuli explains the report of competing sentence words.
Temporal changes in sensory precision were not required to explain faster reaction times with longer cue-target intervals.
Temporal changes in sensory precision were necessary to account for ramping effects on ERPs, including contingent negative variation (CNV).

Conclusions:

The model successfully explains attentional selection in a complex auditory environment.
Different neural processes likely underlie reaction time improvements and ERP ramping during attentional preparation.
Sensory precision modulation is a key mechanism in active inference models of attention.