Projects per year
Among all their sensations, agents need to distinguish between those caused by themselves and those caused by external causes. The ability to infer agency is particularly challenging under conditions of uncertainty. Within the predictive processing framework, this should happen through active control of prediction error that closes the action-perception loop. Here we use a novel, temporally-sensitive, behavioural proxy for prediction error to show that it is minimised most quickly when volatility is high and when participants report agency, regardless of the accuracy of the judgement. We demonstrate broad effects of uncertainty on accuracy of agency judgements, movement, policy selection, and hypothesis switching. Measuring autism traits, we find differences in policy selection, sensitivity to uncertainty and hypothesis switching despite no difference in overall accuracy.
- Action-perception loop
- Autism traits
- Prediction error
1/01/20 → 31/12/24
Egan, G., Rosa, M., Lowery, A., Stuart, G., Arabzadeh, E., Skafidas, E., Ibbotson, M., Petrou, S., Paxinos, G., Mattingley, J., Garrido, M., Sah, P., Robinson, P. A., Martin, P., Grunert, U., Tanaka, K., Mitra, P., Johnson, G., Diamond, M., Margrie, T., Leopold, D., Movshon, J., Markram, H., Victor, J., Hill, S. & Jirsa, V.
Australian National University , ETH Zurich, Australian Research Council (ARC), Karolinska Institute, QIMR Berghofer Medical Research Institute, Ecole Polytechnique Federale de Lausanne , Monash University, University of Melbourne, University of New South Wales, University of Queensland , University of Sydney, Monash University – Internal University Contribution, National Institutes of Health (United States), Cornell University, New York University, MRC National Institute for Medical Research, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Duke University, Cold Spring Harbor Laboratory, RIKEN
25/06/14 → 31/12/21