Cognitive Psychology seminars

Traditional work in the study of human reward-based learning involves designing an experimental task---often inspired by Reinforcement Learning (RL) theory---and fits a small set of computational models---often inspired by RL algorithms---to that dataset. For example, researchers often model human behavior on bandit tasks using variants of Q-learning. While this approach has been highly productive, leading to landmark discoveries such as the dopamine reward prediction error hypothesis, it also has limitations. This talk focuses on the lack of generalizability of such models: Even if they closely fit behavior on the original task, models derived from the one-task-one-model paradigm usually predict behavior on other tasks quite poorly. I argue that this lack of generalizability is a fundamental problem for the cognitive sciences: we intuitively expect our models to be robust to superficial task differences, such as variations in the number of choice options, reward probabilities, or the exact kind of non-stationarity. I will propose potential solutions to this problem along two dimensions: the behavioral dataset and the computational model. Regarding computational models, I will introduce work in which we moved beyond the limitations of hand-crafted one-off models by employing flexible, data-driven methods. These methods allowed us to compare classes of models instead of individual model instances, allowing us to cover the space of possible models more exhaustively, and innovate cognitive mechanisms very efficiently. For the behavioral dataset, we move from using single learning tasks to a comprehensive task space that encompasses most existing paradigms in the literature, while closing the gaps between them in a near-continuous fashion. Our results suggest that more general models in conjunction with broader datasets can pave the road toward increasingly general models of human reward-based learning and decision making, and a persistent departure from many aspects of RL theory. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-20. Recording duration: 00:51:19.

Reward expectations – one’s prediction about the likelihood of future outcomes - play a central role in shaping the satisfaction derived from those outcomes. Most existing research treats expectations as static, assuming they remain fixed in time. However, real-life expectations are often dynamic, fluctuating as new information becomes available. For example, during a soccer game, your expectations of seeing your team winning will likely rise and fall as the game unfolds. In the main part of this talk, I will present a series of studies demonstrating that human expectations can be tracked at sub-second timescales. Using slot machines as a case study, we leverage the continuous deceleration of the reels to elicit moment-by-moment fluctuations in rewardexpectations. To capture these dynamics, we take complementary approaches: we use the high temporal resolution of electroencephalography (EEG) to track neural signatures of evolving expectations, and we develop a novel behavioral paradigm (“Slot or Not”) designed to measure changes in expectations via betting behavior. Across four studies, we show that expectations fluctuate continuously and can be tracked both behaviorally and neurally. Extending these findings, a subsequent intracranial study shows that the human orbitofrontal cortex (OFC) encodes the moment-by-moment changes of reward expectations. In the second part of this talk, I will return to the relationship between expectations andsatisfaction. If expectations shape satisfaction, and if they are best conceptualized as dynamic trajectories rather than static quantities, a key question arises: does the trajectory leading up to an outcome influence how that outcome is evaluated? I will outline a new research direction aimed at formalizing this relationship using computational modeling. This is ongoing work, and I welcome feedback on how best to formalize these ideas. Finally, I will discuss potential extensions of this framework to psychopathology, asking whether alterations in dynamic expectations may characterize conditions such as Major Depressive Disorder and Gambling disorder. Together, this work introduces a new framework for studying expectations as dynamic processes, offering a richer understanding Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-04-29. Recording duration: 00:42:25.

The eyes send a continuous stream of about two million nerve fibers to the brain, but only a fraction of this information is stored as visual memories. This talk will detail three neurocomputational models that attempt an understanding how the visual system makes on-the-fly decisions about how to encode that information. First, the STST family of models (Bowman & Wyble 2007; Wyble, Potter, Bowman & Nieuwenstein 2011) proposes mechanisms for temporal segmentation of continuous input. The conclusion of this work is that the visual system has mechanisms for rapidly creating brief episodes of attention that highlight important moments in time, and also separates each episode from temporally adjacent neighbors to benefit learning. Next, the RAGNAROC model (Wyble et al. 2019) describes a decision process for determining the spatial focus (or foci) of attention in a spatiotopic field and the neural mechanisms that provide enhancement of targets and suppression of highly distracting information. This work highlights the importance of integrating behavioral and electrophysiological data to provide empirical constraints on a neurally plausible model of spatial attention. The model also highlights how a neural circuit can make decisions in a continuous space, rather than among discrete alternatives. Finally, the binding pool (Swan & Wyble 2014; Hedayati, O’Donnell, Wyble in Prep) provides a mechanism for selectively encoding specific attributes (i.e. color, shape, category) of a visual object to be stored in a consolidated memory representation. The binding pool is akin to a holographic memory system that layers representations of select latent representations corresponding to different attributes of a given object. Moreover, it can bind features into distinct objects by linking them to token placeholders. Future work looks toward combining these models into a coherent framework for understanding the full measure of on-the-fly attentional mechanisms and how they improve learning.

To organize various experiences in a coherent mental representation, we need to properly estimate the duration and temporal order of different events. Yet, our perception of time is noisy and vulnerable to various illusions. Studying these illusions can elucidate the mechanism by which the brain perceives time. In this talk, I will review a few studies on how the brain perceives duration of events and the temporal order between self-generated motion and sensory feedback. Combined with computational models at different levels, these experiments illustrated that the brain incorporates the prior knowledge of the statistical distribution of the duration of stimuli and the decay of memory when estimating duration of an individual event, and adjusts its perception of temporal order to changes in the statistics of the environment.

Humans continuously need to learn to make good choices – be it using a new video-conferencing set up, figuring out what questions to ask to successfully secure a reliable babysitter, or just selecting which location in a house is least likely to be interrupted by toddlers during work calls. However, the goals we seek to attain – such as using zoom successfully – are often vaguely defined and previously unexperienced, and in that sense cannot be known by us as being rewarding. We hypothesized that learning to make good choices in such situations nevertheless leverages reinforcement learning processes, and that executive functions in general, and working memory in particular, play a crucial role in defining the reward function for arbitrary outcomes in such a way that they become reinforcing. I will show results from a novel behavioral protocol, as well as preliminary computational and imaging evidence supporting our hypothesis.