Elsevier

Vision Research

Volume 116, Part B, November 2015, Pages 194-209
Vision Research

Selective disinhibition: A unified neural mechanism for predictive and post hoc attentional selection

https://doi.org/10.1016/j.visres.2014.12.010Get rights and content
Under an Elsevier user license
open archive

Highlights

  • Selective disinhibition can implement sensory and decisional biasing by attention.

  • Simulates the effects of both predictive and post hoc attentional cues.

  • Produces multiplicative input gain, lower noise correlations and lower Fano factor.

  • Yields stable network dynamics that are robust to noise and initial conditions.

  • Reproduces major behavioral hallmarks of attention.

Abstract

The natural world presents us with a rich and ever-changing sensory landscape containing diverse stimuli that constantly compete for representation in the brain. When the brain selects a stimulus as the highest priority for attention, it differentially enhances the representation of the selected, “target” stimulus and suppresses the processing of other, distracting stimuli. A stimulus may be selected for attention while it is still present in the visual scene (predictive selection) or after it has vanished (post hoc selection). We present a biologically inspired computational model that accounts for the prioritized processing of information about targets that are selected for attention either predictively or post hoc. Central to the model is the neurobiological mechanism of “selective disinhibition” – the selective suppression of inhibition of the representation of the target stimulus. We demonstrate that this mechanism explains major neurophysiological hallmarks of selective attention, including multiplicative neural gain, increased inter-trial reliability (decreased variability), and reduced noise correlations. The same mechanism also reproduces key behavioral hallmarks associated with target-distracter interactions. Selective disinhibition exhibits several distinguishing and advantageous features over alternative mechanisms for implementing target selection, and is capable of explaining the effects of selective attention over a broad range of real-world conditions, involving both predictive and post hoc biasing of sensory competition and decisions.

Keywords

Post hoc selection
Attention mechanisms
Computational models
Dynamical systems
Noise correlations

Cited by (0)

1

Present address: Centre for Neuroscience, Indian Institute of Science, Bangalore 560012, India.