Decision Making & Discounting

I am currently focussing on 'high-level' decision making. More specifically I am interested in behaviour in delayed and risky choice (DARC) scenarios. I provide 2 state of the art methods papers (with code) to first collect data from DARC experiments, and then to analyse this data. Modelling/theory work is underway, and novel empirical studies are starting to emerge from the lab. Here are my initial contributions:

  • darc-toolbox-matlab is a cutting edge toolbox for running delayed and risky choice experiments. These experiments use Bayesian Adaptive Design to maximise measurement precision for a given number of experimental trials. [code | Google Group forum]. It is outlined in the paper below:
  • Vincent, B. T., & Rainforth, T. (2017, October 20). The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design. Retrieved from
  • Skrynka, J., & Vincent, B. T. (2017, July 27). Subjective hunger, not blood glucose, influences domain general time preference. Retrieved from
  • 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]
  • 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. See examples with exponential discounting, hyperbolic discounting, and the magnitude effect

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]
  • Vincent, B. T. (2011). Search asymmetries: parallel processing of uncertain sensory information. Vision Research, 51(15), 1741-1750. [blog post | pdf]
  • Baddeley, R., Vincent, B. T., & Attewell, D. (2011). Information theory and perception: The role of constraints, and what do we maximize information about?. In Terzis, G., & Arp, R. (Eds.), In Information in Living Systems: Philosophical and Scientific Perspectives MIT Press. [pdf]
  • Vincent, B. T. (2011). Covert visual search: Prior beliefs are optimally combined with sensory evidence. Journal of Vision, 11(13), 25. [blog post | pdf]
  • 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]

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]