Aistis Stankevicius MRes

Aistis Stankevicius

Research Interests

I am interested in using a combination of experimental (behavioural paradigms, functional magnetic resonance imaging) and theoretical (reinforcement learning and Bayesian models) approaches to address the problem of formalisation and quantification of brain function abnormalities caused by major depressive disorder (MDD) and other psychiatric conditions. I investigate computational models based on current understanding of neurophysiological mechanisms underpinning normal patterns of expected reward processing, and disturbed patterns associated with psychiatric disorders. The aim of my project is to develop a better understanding of reward processing mechanisms (e.g. related to anhedonia in patients with MDD) in both healthy people and psychiatric patients, which will furnish important perspectives and conceptual constraints to guide future research in attempts to understand the neurobiological origins of psychiatric disorders, and could improve speed and effectiveness of therapeutic interventions.

  Benefits of social vs. non-social feedback on learning and generosity. Results from the Tipping Game
Colombo, M, Stankevicius, A & Seriés, P 2014, 'Benefits of social vs. non-social feedback on learning and generosity. Results from the Tipping Game' Frontiers in Psychology, vol 5, 1154. DOI: 10.3389/fpsyg.2014.01154
Although much work has recently been directed at understanding social decision-making, relatively little is known about how different types of feedback impact adaptive changes in social behavior. To address this issue quantitatively, we designed a novel associative learning task called the “Tipping Game,” in which participants had to learn a social norm of tipping in restaurants. Participants were found to make more generous decisions from feedback in the form of facial expressions, in comparison to feedback in the form of symbols such as ticks and crosses. Furthermore, more participants displayed learning in the condition where they received social feedback than participants in the non-social condition. Modeling results showed that the pattern of performance displayed by participants receiving social feedback could be explained by a lower sensitivity to economic costs.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Colombo, Matteo, Stankevicius, Aistis & Seriés, Peggy.
Number of pages: 9
Publication Date: Oct 2014
Publication Information
Category: Article
Journal: Frontiers in Psychology
Volume: 5
ISSN: 1664-1078
Original Language: English
DOIs: 10.3389/fpsyg.2014.01154
  Optimism as a Prior Belief about the Probability of Future Reward
Stankevicius, A, Huys, QJM, Kalra, A, Seriès, P & Loewenstein, Y (ed.) 2014, 'Optimism as a Prior Belief about the Probability of Future Reward' PLoS Computational Biology, vol 10, no. 5, pp. e1003605. DOI: 10.1371/journal.pcbi.1003605
Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Stankevicius, Aistis, Huys, Quentin J. M., Kalra, Aditi & Seriès, Peggy.
Pages: e1003605
Publication Date: 22 May 2014
Publication Information
Category: Article
Journal: PLoS Computational Biology
Volume: 10
Issue number: 5
ISSN: 1553-734X
Original Language: English
DOIs: 10.1371/journal.pcbi.1003605