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Category: Hypothesis

Description: Bayesian and active inference theories of the brain propose that we update our beliefs in a probabilistic way by integrating all available sources of information, weighted by their (perceived) reliability. If these theories are correct, actions (including eye movements) should be sensitive to the probabilistic context of the stimuli they are interacting with – that is, actions should be deployed to maximise chances of success by anticipating more likely outcomes [1]. Previous work has shown that this is the case for motor actions [2] but this is still to be established in relation to eye movements during real-world tasks, such as ball interception. This study will follow up our previous work that has tested whether eye movements are adjusted to maximise the accuracy of predictions in a probabilistic manner when the likelihood of an event changes [3,4]. This work will address central theoretical questions about whether the purpose of actions is to minimise prediction error, as suggested in influential neurocomputational theories of the brain [1,2]. We will test these questions using an interceptive task that consists of oncoming balls, projected from two horizontally aligned locations, that have to be intercepted. The probability of the projection location will be varied such that in some conditions the balls will have a 50/50 split across the two locations, while in other conditions it will be biased in 70/30 and 90/10 weightings. 1. Friston, K., Adams, R. A., Perrinet, L., & Breakspear, M. (2012). Perceptions as hypotheses: saccades as experiments. Frontiers in Psychology, 3, 151. 2. Körding, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10(7), 319-326. 3. Arthur, T., & Harris, D. J. (2021). Predictive eye movements are adjusted in a Bayes-optimal fashion in response to unexpectedly changing environmental probabilities. Cortex, 145, 212-225. 4. Arthur, T., Harris, D., Buckingham, G., Brosnan, M., Wilson, M., Williams, G., & Vine, S. (2021). An examination of active inference in autistic adults using immersive virtual reality. Scientific Reports, 11(1), 1-14 5. Lawson, R.P., Bisby, J., Nord, C., Burgess, N., Rees, G. (2021) The computational, pharmacological, and physiological determinants of sensory learning under uncertainty. Current Biology, 31(1) 163-172.