The current focus of the lab is inter-temporal choice behaviour. We have been building advanced methods to advance the study of inter-temporal choice through efficient adaptive experiments, and through Bayesian hierarchical data analysis for robust research conclusions. Please see the Publications page for our latest work. But our recent highlights are:
- Vincent, B. T. (preprint). Still no link between temporal discounting and body composition. Retrieved from psyarxiv.com/xdg6y [data and code on OSF]
- Xu, P., Gonzalez-Vallejo, C., Vincent, B. T. (2020) Waiting in inter-temporal choice tasks affects discounting and subjective time perception, Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0000771 [data & code]
- Veillard, M. L., & Vincent, B. T. (2020). Temporal discounting does not influence Body Mass Index. Physiology & Behavior. 221, 1-12. [pdf | preprint | data & code]
- Vincent, B. T., & Stewart, N. (2020). The case of muddled units in temporal discounting. Cognition. 198, 1-11. [pdf | code]
- Skrynka, J., & Vincent, B. T. (2019). Hunger increases delay discounting of food and non-food rewards. Psychonomic Bulletin & Review, 26(5), 1729–1737. [pdf, pre-print, materials on OSF]
Advanced methods development
- 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 psyarxiv.com/yehjb.
- Vincent, B. T. (2016). Hierarchical bayesian estimation and hypothesis testing for delay discounting tasks. Behavior Research Methods. 48(4), 1608-1620. [pdf | code]
- Check out our useful interactive visualisation of exponential and hyperbolic discounting. The R/Shiny code is available in this GitHub repository.
Previous research topics
Perceptual decision making
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 with overt attention
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]
The energy efficient 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]