ArticleJournal of the Royal Society, Interface2019
Excitatory versus inhibitory feedback in Bayesian formulations of scene construction.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Recurrent connections facilitate occluded object recognition by explaining-away.Nature communications · 2026Article
- Support for the Time-Varying Drift Rate Model of Perceptual Discrimination in Dynamic and Static Noise Using Bayesian Model-Fitting Methodology.Entropy (Basel, Switzerland) · 2024Article
- Continuous action with a neurobiologically inspired computational approach reveals the dynamics of selection history.PLoS computational biology · 2023Article
- The effects of distractors on brightness perception based on a spiking network.Scientific reports · 2023Article
- Scene memory and spatial inhibition in visual search : A neural dynamic process model and new experimental evidence.Attention, perception & psychophysics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects-as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.
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Registered trials
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