Professor Konstantinos Tsetsos
Research Professor in Psychology, Psychology
Research Professor in Psychology, Trinity Inst. of Neurosciences (TCIN)
Biography
I am a cognitive scientist using neurophysiological and computational methods to understand how people make decisions. I trained in Computer Science (BSc, Athens) and Cognitive Science (MSc, Edinburgh; PhD, UCL), followed by a postdoc at the University of Oxford, a Wellcome Trust Career Development Fellowship at Birkbeck, and a Marie Curie Fellowship at UKE in Hamburg. I led an ERC Starting Grant first at UKE and then at the University of Bristol, where I was Senior Lecturer and then Associate Professor in the School of Psychological Science. Since 2025, I am Research Professor at Trinity College Dublin, where I lead an ERC Consolidator Grant project focusing on the computational and neural mechanisms underpinning preference variability.
Publications and Further Research Outputs
Peer-Reviewed Publications
Douglas G. Lee, Konstantinos Tsetsos, Giovanni Pezzulo, Nitzan Shahar, Marius Usher, Variability and Accessibility of Information Guide Gaze Dynamics in Decision Making, Decision, 2025
Tohidi-Moghaddam, Maryam, Tsetsos, Konstantinos, The timescale and direction of influence of a third inferior alternative in human value-learning, Communications Psychology, 3, (1), 2025
Russell J. Boag, Reilly J. Innes, Niek Stevenson, Giwon Bahg, Jerome R. Busemeyer, Gregory E. Cox, Chris Donkin, Michael J. Frank, Guy E. Hawkins, Andrew Heathcote, Craig Hedge, Veronika Lerche, Simon D. Lilburn, Gordon D. Logan, Dora Matzke, Steven Mileti", Adam F. Osth, Thomas J. Palmeri, Per B. Sederberg, Henrik Singmann, Tom Stafford, Mark Steyvers, Luke Strickland, Jennifer S. Trueblood, Konstantinos Tsetsos, Brandon M. Turner, Marius Usher, Leendert van Maanen, Don van Ravenzwaaij, Joachim Vandekerckhove, Andreas Voss, Emily R. Weichart, Gabriel Weindel, Corey N. White, Nathan J. Evans, Scott D. Brown, Birte U. Forstmann, An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling, Advances in Methods and Practices in Psychological Science, 2025
Perspectives on the Mechanistic Underpinnings of Choice Biases in, Springer Nature Switzerland, 2025, pp73-95 , [Tsetsos, Konstantinos, Cao, Yinan]
Tsetsos, K., Unlocking a new dimension in the speed"accuracy trade-off, Trends in Cognitive Sciences, 27, (6), 2023, p510-511
M Siems, TH Donner, K Tsetsos, FV 1 GABA induced changes of functional connectivity dissociate amplitude- and phase-coupling during task but not rest, Clinical Neurophysiology, 2023
Pirrone, A., Tsetsos, K., Toward an Atlas of Canonical Cognitive Mechanisms, Cognitive Science, 47, (2), 2023
Cao, Y., Tsetsos, K., Clarifying the role of an unavailable distractor in human multiattribute choice, eLife, 11, 2022
Pirrone, A., Tsetsos, K., On multiple sources of value sensitivity, Proceedings of the National Academy of Sciences of the United States of America, 119, (36), 2022
Tickle, H., Tsetsos, K., Speekenbrink, M., Summerfield, C., Human Optional Stopping in a Heteroscedastic World, Psychological Review, 130, (1), 2021, p1-22
Dumbalska, T., Li, V., Tsetsos, K., Summerfield, C., Erratum: A map of decoy influence in human multialternative choice (Proceedings of the National Academy of Sciences of the United States of America (2020) 117 (25169"25178) DOI: 10.1073/pnas.2005058117), Proceedings of the National Academy of Sciences of the United States of America, 118, (17), 2021
Talluri, B.C., Urai, A.E., Bronfman, Z.Z., Brezis, N., Tsetsos, K., Usher, M., Donner, T.H., Choices change the temporal weighting of decision evidence, Journal of Neurophysiology, 125, (4), 2021, p1468-1481
Pfeffer, T., Ponce-Alvarez, A., Tsetsos, K., Meindertsma, T., Gahnström, C.J., van den Brink, R.L., Nolte, G., Engel, A.K., Deco, G., Donner, T.H., Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability, Science Advances, 7, (29), 2021
Dumbalska, T., Li, V., Tsetsos, K., Summerfield, C., A map of decoy influence in human multialternative choice, Proceedings of the National Academy of Sciences of the United States of America, 117, (40), 2020, p25169-25178
Luyckx, F., Spitzer, B., Blangero, A., Tsetsos, K., Summerfield, C., Selective Integration during Sequential Sampling in Posterior Neural Signals, Cerebral Cortex, 30, (8), 2020, p4454-4464
de Gee, J.W., Tsetsos, K., Schwabe, L., Urai, A.E., McCormick, D., McGinley, M.J., Donner, T.H., Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains, eLife, 9, 2020, p1-25
Usher, M., Tsetsos, K., Glickman, M., Chater, N., Selective Integration: An Attentional Theory of Choice Biases and Adaptive Choice, Current Directions in Psychological Science, 28, (6), 2019, p552-559
Urai, A.E., De Gee, J.W., Tsetsos, K., Donner, T.H., Choice history biases subsequent evidence accumulation, eLife, 8, 2019
Turner, B.M., Schley, D.R., Muller, C., Tsetsos, K., Competing theories of multialternative, multiattribute preferential choice, Psychological Review, 125, (3), 2018, p329-362
Moran, R., Tsetsos, K., The standard Bayesian model is normatively invalid for biological brains, Behavioral and Brain Sciences, 41, 2018
Talluri, B.C., Urai, A.E., Tsetsos, K., Usher, M., Donner, T.H., Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence, Current Biology, 28, (19), 2018, p3128-3135.e8
Glickman, M., Tsetsos, K., Usher, M., Attentional Selection Mediates Framing and Risk-Bias Effects, Psychological Science, 29, (12), 2018, p2010-2019
Tsetsos, K., The Dynamics of Selective Integration during Rapid Experiential Decisions, CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, 2017, p1230-1235
Kunar, M.A., Watson, D.G., Tsetsos, K., Chater, N., The influence of attention on value integration, Attention, Perception, and Psychophysics, 79, (6), 2017, p1615-1627
Tickle, H., Speekenbrink, M., Tsetsos, K., Michael, E., Summerfield, C., Near-optimal integration of magnitude in the human parietal cortex, Journal of Cognitive Neuroscience, 28, (4), 2016, p589-603
Tsetsos, K., Moran, R., Moreland, J.C., Chater, N., Usher, M., Summerfield, C., Reply to Davis-Stober et al.: Violations of rationality in a psychophysical task are not aggregation artifacts, Proceedings of the National Academy of Sciences of the United States of America, 113, (33), 2016, pE4764-E4766
Tsetsos, K., Moran, R., Moreland, J., Chater, N., Usher, M., Summerfield, C., Economic irrationality is optimal during noisy decision making, Proceedings of the National Academy of Sciences of the United States of America, 113, (11), 2016, p3102-3107
Tsetsos K, Pfeffer T, Jentgens P, Donner TH., Action Planning and the Timescale of Evidence Accumulation., PloS one, 10, (6), 2015, pe0129473
Tsetsos, K., Chater, N., Usher, M., Examining the mechanisms underlying contextual preference reversal: Comment on Trueblood, Brown, and Heathcote (2014), Psychological Review, 122, (4), 2015, p838-847
Tsetsos, K., Pfeffer, T., Jentgens, P., Donner, T.H., Erratum: Action planning and the timescale of evidence accumulation (PLoS ONE 10:6 (e0129473) DOI: 10.1371/journal.pone.0129473), PLoS ONE, 10, (7), 2015
Summerfield, C., Tsetsos, K., Do humans make good decisions?, Trends in Cognitive Sciences, 19, (1), 2015, p27-34
Bronfman, Z.Z., Brezis, N., Moran, R., Tsetsos, K., Donner, T., Usher, M., Decisions reduce sensitivity to subsequent information, Proceedings of the Royal Society B: Biological Sciences, 282, (1810), 2015
Cheadle, S., Wyart, V., Tsetsos, K., Myers, N., deGardelle, V., HerceCastañón, S., Summerfield, C., Adaptive gain control during human perceptual choice, Neuron, 81, (6), 2014, p1429-1441
Tsetsos, K., Wyart, V., Shorkey, S.P., Summerfield, C., Neural mechanisms of economic commitment in the human medial prefrontal cortex, eLife, 3, 2014
Ossmy, O., Moran, R., Pfeffer, T., Tsetsos, K., Usher, M., Donner, T.H., The timescale of perceptual evidence integration can be adapted to the environment, Current Biology, 23, (11), 2013, p981-986
Usher, M., Tsetsos, K., Yu, E.C., Lagnado, D.A., Dynamics of decision-making: From evidence accumulation to preference and belief, Frontiers in Psychology, 4, (OCT), 2013
Tsetsos, K., Gao, J., McClelland, J.L., Usher, M., Using time-varying evidence to test models of decision dynamics: Bounded diffusion vs. The leaky competing accumulator model, Frontiers in Neuroscience, (JUN), 2012, p1-17
Olivola, C.Y., Sussman, A.B., Tsetsos, K., Kang, O.E., Todorov, A., Republicans Prefer Republican-Looking Leaders: Political Facial Stereotypes Predict Candidate Electoral Success Among Right-Leaning Voters, Social Psychological and Personality Science, 3, (5), 2012, p605-613
Summerfield, C., Tsetsos, K., Building bridges between perceptual and economic decision-making: Neural and computational mechanisms, Frontiers in Neuroscience, (MAY), 2012
Tsetsos, K., Chater, N., Usher, M., Salience driven value integration explains decision biases and preference reversal, Proceedings of the National Academy of Sciences of the United States of America, 109, (24), 2012, p9659-9664
Tsetsos, K., Usher, M., McClelland, J.L., Testing multi-alternative decision models with non-stationary evidence, Frontiers in Neuroscience, (MAY), 2011
Tsetsos, K., Usher, M., Chater, N., Preference Reversal in Multiattribute Choice, Psychological Review, 117, (4), 2010, p1275-1291
Usher, M., Tsetsos, K., Chater, N., Postscript: Contrasting Predictions for Preference Reversal, Psychological Review, 117, (4), 2010, p1291-1293
Research Expertise
Projects
- Title
- The Dynamics of Attribute Weighting in Multiattribute Choice
- Summary
- How much flavour do we give up in pursuit of a healthier choice, and how many extra minutes of commute do we sacrifice in fostering a more sustainable future? Daily decisions involve navigating multiattribute tradeoffs by assigning weights to different attributes. Understanding the mechanisms underlying attribute weighting is key to explaining and predicting choice behaviour. However, these mechanisms are poorly understood. Past research has examined how people adjust attribute weights in response to shifts in goals or in the context. Beyond induced shifts, it is unknown whether attribute weights maintain stability in stationary contexts, from one decision to the next. My central hypothesis is that, akin to other representations in the brain (e.g., memories), attribute weights are in constant flux, being controlled by inherently noisy and dynamical processes. This project aims to unravel these processes. Attribute weights are inherently subjective and not directly observable. Reading them out from single choices is impossible, as choices are perturbed by noise and influenced by normatively irrelevant factors. To overcome this limitation, I will use multiattribute choice tasks with magnetoencephalography and, focusing on decision-relevant neural signals, I will separate the influence of attribute weights from the distorting influence of irrelevant factors. This will enable me to read-out attribute weights at each decision, and to subsequently chart their temporal dynamics across successive multiattribute decisions. Understanding the dynamics of attribute weighting will offer mechanistic insights on how preferences change, informing century-old debates on the nature of preferences. These insights will be valuable to clinicians seeking to understand pathological preference variability or persistence (e.g., in addiction); and to policy makers probing whether specific consumer tradeoffs (e.g., between monetary and environmental attributes) are stable or changeable.
- Funding Agency
- European Research Council
- Date From
- 01.11.2025
- Title
- Information Sampling in Multiattribute Choice (INFOSAMPLE)
- Summary
- Do we prefer a small flat with a short commute or a large house with a long commute? Many real-life decisions require combining information across different attributes. It has been shown that during such multiattribute decisions people serially attend to (or sample) a subset of the available information. The way this process takes place largely influences the final choice: for example, if the "commute" attribute is considered for longer, then the small flat will tend to appear better. Up to date, information sampling has been studied within the social sciences using eye-tracking techniques. However, in the context of choice tasks, the way eye fixations influence the upcoming choice is not precisely known and, thus, this line of research has not yielded any definitive mechanistic conclusions. Recently, theorists have proposed different mechanisms of information sampling but these proposals were not constrained by relevant data. I propose to fill this gap in a data-driven fashion by harnessing tools from sensory neuroscience. Using magnetoencephalography (MEG) we will simultaneously track the locus of attention and the tendency to choose one alternative over the other, during the entire time-course of a single multiattribute decision. This approach will enable us to unravel the computational and neural mechanisms that guide attention towards different aspects of a multiattribute choice problem. This project will yield a neurophysiologically detailed theory of multiattribute choice"from the level of neurotransmitters, to large-scale brain networks, to behaviour" that will ultimately shed light on century-long questions, such as why humans reverse their preferences irrationally, when irrelevant alternatives are added to the choice-set. The emerging framework will be useful to policy makers and practitioners, interested in a descriptively enriched model of choice; and to clinicians aiming to understand how information sampling goes awry in neuropsychiatric disorders.
- Funding Agency
- European Research Council
- Date From
- 01-DEC-2018
- Date To
- 01-MAY-2024
- Title
- Cortical Dynamics of Decision Irrationality
- Summary
- Imagine that you choose a large house with a long commute over a small flat with a short train ride. Will the appearance of an inferior, thus irrelevant, alternative"a small flat with a longer train ride"make you revise your initial choice? Contrary to what theories in economics prescribe people typically reverse their original choice in the presence of an inferior option. Contextual preference reversals (CPRs) of this form are hallmarks of human irrationality. Such deviations from rationality have been described in numerous behavioural studies over the past decades. But what are the mechanisms that produce CPRs? And why do CPRs differ across individuals? I aim to answer these big open questions. Specifically, I plan to elucidate the brain mechanisms that mediate CPRs, from the level of neurotransmitters, to large-scale brain networks, to behaviour. To this end, I will combine, for the first time, three novel approaches: a model-based approach I developed for quantifying irrational decision-making in a physiologically tractable fashion; delineating the neural interactions underlying decisions with magnetoenecephalography (MEG); and pharmacological manipulation of these interactions. I will then use the MEG data to guide the development of a biophysically constrained mechanistic model of irrational decision-making. This model will be used to generate novel predictions about CPRs in the healthy brain as well as in several neurophysiological disorders. In sum, my research will tackle long-standing problems from the behavioural sciences with techniques from the life sciences, thus providing a deeper understanding of the limits of human behaviour. I will be thoroughly trained in advanced neurophysiological techniques in preparation for a career in the nascent field of "computational psychiatry". Finally, this project will bring together leading scientists from the behavioural, neural, and physical sciences, establishing a new multi-disciplinary EU research network.
- Funding Agency
- European Commission
- Date From
- 01-APR-2016
- Date To
- 01-AUG-2018
- Title
- Career Development Fellowship
- Funding Agency
- Wellcome Trust
- Date From
- 01-MAR-2015
- Date To
- 01-MAR-2016