Bayesian Brain – inferenceLab

Bayesian accounts of covert selective attention: a tutorial review

I am very happy to announce my new tutorial review paper. Vincent, B. T. (2015) Bayesian accounts of covert selective attention: a tutorial review, Attention, Perception, & Psychophysics, 77(4), 1013-1032. If you do not have an institutional subscription to Attention, Perception, & Psychophysics, Springer allow me to self-archive my author-accepted manuscript (legal). Get the preprints here: [manuscript pdf], [supplementary pdf]. The final publication …

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How do we use the past to predict the future in oculomotor search?

When we conduct visual search for an item of interest we can not only ask ‘what looks like my target’ but we can also ask ‘where do I expect the target to be?’ In a forthcoming paper in the journal Vision Research I asked: how do we use our past experience to construct predictions of where a …

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Combining prior beliefs and sensory evidence

If we want to try to locate a target of interest given a brief glimpse of a visual scene, then we can use at least two sources of information. Firstly, we can use any visual cues which give away the target’s location. However, in many cases the visual cues are insufficient to work out precisely …

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Parallel processing of uncertain sensory information account for search asymmetry effects

The yes/no detection task is a classic method used to probe the inner workings of how humans process information. In this paper I was interested in one quite specific experimental phenomenon of visual information processing: that of search asymmetries. If you search for an item A amongst distracters B, then you will have some level …

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Optimal feature integration in visual search

One of the broad aims of my work is to apply the approach of Bayesian Decision Theory to attentional phenomena. In this particular paper, published back in 2009, I examined one specific aspect of the approach: the decision rule. Described very succinctly Bayesian Decision Theory consists of two main steps. Firstly, we make an inference about …

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