Supervisor: Dr Nikos Gekas
Co-supervisor:

School: Psychology

Description:

A growing body of work suggests that the brain could be using a probabilistic strategy to infer the true state of the environment from noisy and incomplete data. This is referred to as the ‘Bayesian coding hypothesis’. In that framework, the role of goal-oriented attention might be to reshape the observers’ internal model in order to improve their predictions of the received reward at the potential cost of learning a worse internal model of the received sensory inputs. What kind of internal model would an observer learn if presented with two different distributions for stimulus statistics and reward? Moreover, what are the timescales of integration of incoming information relating to reward (loss function) and stimulus statistics (expectation)?

This project aims to identify important differences between attention and expectations in behaviour and, in turn, potential differences in their neural mechanisms.

Required skills:

• Demonstrable programming ability in Matlab or Python. Background in experimental psychology or willingness to learn if from a quantitative background.

Desirable skills:

• Previous experience with programming and running psychophysical experiments (Psychtoolbox or PsychoPy). Previous experience with Bayesian models of perception.

Background readings:

1. Summerfield, C., & Egner, T. (2009). Expectation (and attention) in visual cognition. Trends in cognitive sciences, 13(9), 403-409.
2. Mamassian, P., Landy, M., & Maloney, L. T. (2002). Bayesian modelling of visual perception. Probabilistic models of the brain, 13-36.
3. Gekas, N., Seitz, A. R., & Seriès, P. (2015). Expectations developed over multiple timescales facilitate visual search performance. Journal of vision, 15(9), 10-10.
4. Gekas, N., Chalk, M., Seitz, A. R., & Seriès, P. (2013). Complexity and specificity of experimentally-induced expectations in motion perception. Journal of Vision, 13(4), 8-8.

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