Decision Making & Discounting

Recently, I've started to change focus from perceptual decision making to more 'high level' decision making. This work focusses upon time- and probability-discounting. I've started by building sophisticated Bayesian methods which we can apply to the study of how people trade off the immediacy or the certainty of obtaining a reward. 

  • discounting-visualizer is an interactive R/Shiny app which helps you explore discount functions. Use it to see how parameters change the discount function, and for creating publication quality graphics.
  • delay-discounting-analysis is a Bayesian analysis toolbox for delay discounting data. This toolbox aims to evolve into everything you need to analyse discounting experiment data, and is described in the paper below.
  • Vincent, B. T. (2016). Hierarchical bayesian estimation and hypothesis testing for delay discounting tasks. Behavior Research Methods. 48(4), 1608-1620.  [project page | pdf | code | cited by]
  • More tools and empirical papers are in the works.

Perceptual Decision Making & Attention

To what extent can we understand attention as a by-product of doing Bayesian inference about the state of the world? From this point of view, attention is not a 'thing' but a by-product of our perceptual decision making processes. See my two review papers for an introduction to this approach:

  • Vincent, B. T. (2015) A tutorial on Bayesian models of perception, Journal of Mathematical Psychology, 66:103–114. [blog post | code | pdf | cited by]
  • Vincent, B. T. (2015) Bayesian accounts of covert selective attention: a tutorial review, Attention, Perception, & Psychophysics, 77(4), 1013-1032. [blog post | code | pdf | cited by]
  • Vincent, B. T., Baddeley, R. J., Troscianko, T., & Gilchrist, I. D. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 15. [blog post | pdf | cited by]

Sampling the world

How do we sample information from the world with eye movements? This is a complex but popular area of study, with many questions and competing viewpoints.

One of the most important contributions was to see that, when we frame eye guidance in a probabilistic manner, we find that behavioural biases or patterns are highly informative of where people look, more so that what they are actually looking at.

  • Tatler, B. W., & Vincent, B. T. (2009). The prominence of behavioural biases in eye guidance. Visual Cognition, 17(6-7), 1029-1054. [blog post | pdf | cited by]
  • Vincent, B. T., Baddeley, R., Correani, A., Troscianko, T., & Leonards, U. (2009). Do we look at lights? Using mixture modelling to distinguish between low- and high-level factors in natural image viewing. Visual Cognition, 17(6-7), 856-879. [blog post | pdf | cited by]

Metabolic costs in the brain

The brain is an information processing device, but one which is power hungry. How do neural circuits (in the visual system) balance information processing and metabolic cost? Our computational modelling work has lead to the view that while neuron firing rates are economised in the primary visual cortex, synaptic activity costs are economised in the retina.

  • receptive field code - Matlab code for calculating receptive fields under metabolic constraints
  • Vincent, B. T., Baddeley, R. J., Troscianko, T., & Gilchrist, I. D. (2005). Is the early visual system optimised to be energy efficient? Network, 16(2-3), 175-190. [blog post | code | pdf | cited by]
  • Vincent, B. T., & Baddeley, R. J. (2003). Synaptic energy efficiency in retinal processing. Vision Research, 43(11), 1285-1292. [blog post | code | pdf | cited by]