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Related Experiment Video

Updated: May 10, 2026

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Attention as reward-driven optimization of sensory processing.

Matthew Chalk¹, Iain Murray, Peggy Seriès

¹Group for Neural Theory, LNC, DEC, Ecole Supérieure, Paris 75005, France matthewjchalk@gmail.com.

Neural Computation

|June 20, 2013

Summary

This summary is machine-generated.

Attention optimizes visual processing by learning a probabilistic model of sensory input and rewards. This internal model explains how neural responses adapt to changing environments and task demands, consistent with efficient coding principles.

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Published on: January 23, 2017

Area of Science:

Neuroscience
Computational Neuroscience
Visual Perception

Background:

Attention significantly alters visual neuron responses, affecting receptive fields and firing rates.
The efficient coding hypothesis posits that sensory processing optimizes for input statistics.

Purpose of the Study:

To extend the efficient coding hypothesis by incorporating task demands and reward statistics.
To develop a normative framework explaining attention-dependent neural changes.

Main Methods:

Proposed a computational model integrating sensory input statistics and reward probabilities.
Modeled attention as an optimization process of an internal probabilistic model.

Main Results:

The model successfully replicated several attention-dependent changes in mid-level visual cortex neuron responses.
Demonstrated consistency with existing divisive normalization models of attention.

Conclusions:

Attention-dependent neural changes reflect the brain's optimization of an internal model for sensory and behavioral demands.
The proposed framework offers a normative explanation for attention mechanisms in visual processing.