1. Goal-directed decision-making and planning. We study how humans and other animals make decisions and plan, especially in complex and ecologically valid situations full of action choices. To this aim, we perform experiments on human and animal decision-making and build computational models within the framework of probabilistic (Bayesian) inference. We developed novel theories of goal-directed choice and planning that emphasize the roles of prediction and internal models (aka model-based control and active inference) and the interplay of decision and action dynamics during situated decisions (aka embodied choice). Key publications:
- Pezzulo G., Donnarumma F., Maisto D., Stoianov I. (2019) Planning at decision time and in the background during spatial navigation. Current Opinion in Behavioral Science, 29, 69-76
- Pezzulo, G., Rigoli, F. Friston, K. (2018) Hierarchical Active Inference: a Theory of Motivated Control. Trends in Cognitive Sciences 22(4), 294-306
- Pezzulo, G., Cisek, P. (2016) Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition. Trends in Cognitive Sciences, 20(6), 414-424
- Pezzulo, G., van der Meer, M., Lansink, C., Pennartz, C. (2014) Internally generated sequences in learning and executing goal-directed behavior. Trends in Cognitive Sciences, 18(12), 647-657
- Verschure, P., Pennartz, C., Pezzulo, G. (2014) The why, what, where, when and how of goal directed choice: neuronal and computational principles. Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 369:20130483.
2. Social interaction and joint action. We study how human co-actors coordinate their actions in space and time to achieve joint goals, such as building something together or performing team sports. To this aim, we perform experiments on human-human joint action and build computational models within the framework of probabilistic (Bayesian) inference. We developed novel theories of joint action that emphasize mutual prediction and the exchange of coordination signals (aka sensorimotor communication). Key publications:
- Pezzulo G., Iodice, P., Donnarumma, F., Dindo, H., Knoblich, G. (2017). Avoiding accidents at the champagne reception: A study of joint lifting and balancing. Psychological Science, in press
- Candidi, M., Curioni, A., Donnarumma, F., Sacheli, L. M., Pezzulo, G. (2015) Interactional leader-follower sensorimotor communication strategies during repetitive joint actions. Journal of the Royal Society Interface, 12(110), 20150644
- Pezzulo, G., Donnarumma, F., and Dindo, H. (2013) Human sensorimotor communication: A theory of signaling in online social interactions. PLoS ONE, 8(11):e79876.
3. The grounding of higher cognition in sensorimotor prediction. We study the ways human higher cognitive abilities, and especially future oriented abilities such as prospection and imagination, stem from (and are grounded in) sensorimotor skills. In keeping with embodied theories of cognition, we start from the hypothesis that the architecture of motor prediction and control of our earlier evolutionary ancestors was gradually improved to afford prospective functions and cognitive control – and the latter (higher cognitive) abilities retain essential elements of sensorimotor, predictive control. To test this hypothesis, we perform human experiments and build computational models, within the framework of probabilistic (Bayesian) inference. Key publications:
- Pezzulo, G., Kemere, C., and van der Meer, M. (2017) Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Annals of the New York Academy of Sciences, 1396, 144-165.
- Pezzulo, G., Rigoli, F., Friston, K. (2015) Active inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134:17–35.
- Lepora, N. F., Pezzulo, G. (2015) Embodied choice: how action influences perceptual decision making. PLoS Computational Biology, 11(4):e1004110, 2015.
4. Probabilistic models of brain and cognition. We develop novel computational models of brain and cognition, mostly within the framework of probabilistic generative models and active inference (but sometimes also dynamical systems or connectionist – or combinations of these approaches). We proposed novel computational approaches to a variety of cognitive abilities, including perception, decision, planning and learning. In turn, these models offer empirical prediction that we test experimentally. Key publications:
- Donnarumma, F., Costantini, M., Ambrosini, E., Friston, K., Pezzulo, G. (2017) Action perception as hypothesis testing. Cortex in press
- Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., Pezzulo, G. (2016) Active inference and learning. Neuroscience & Biobehavioral Reviews, 68: 862-879.
- Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G. (2016) Active inference: A process theory. Neural Computation, 29(1): 1-49.
5. Interoception and embodied predictive coding. We study the ways interoceptive streams are used to steer homeostatic / allostatic regulation and how they influence cognitive, motivational and emotional processes such as perception, action selection and emotion recognition. We also study how maladaptive interoceptive processing may produce psychopathological conditions. Key publications:
- Iodice P. Porciello G., Bufalari I., Barca L., Pezzulo G. (2019) An interoceptive illusion of effort induced by false heart-rate feedback. Proceedings of the National Academy of Sciences 116 (28) 13897-13902
- Pezzulo G., Iodice, P., Barca, L., Chausse, P., Monceau, S., Mermillod, M. (2018) Increased heart rate after exercise facilitates the processing of fearful but not disgusted faces. Scientific Reports, 8, 398