MathPsych virtual talks

Bayesian evidence synthesis (BES) is a method for evaluating the empirical support for a common theory across a set of heterogeneous studies. Evidence is aggregated by multiplying Bayes factors from the individual studies, yielding the product Bayes factor (PBF). The PBF numerator aggregates evidence for the joint predicted hypothesis, which states that the study-specific predicted hypothesis holds in each study. This hypothesis reflects the belief that the theory holds across different contexts, treating each study-specific predicted hypothesis as an instance of the theory. However, since Bayes factors are relative measures of evidence, interpreting support for the joint predicted hypothesis via the PBF requires a clear understanding of the alternative against which it is compared. This joint alternative hypothesis arises “automatically” in the PBF denominator when multiplying the individual Bayes factors and states that the study-specific alternative hypothesis holds in each study. However, due to its implicit, bottom-up construction, this joint alternative is at risk of lacking a clear substantive or theoretical interpretation, which makes it unclear how results from BES can be understood. In this project, we investigate how different constellations of study-specific alternative hypotheses give rise to distinct joint alternative hypotheses. We focus on three common types of alternative hypotheses at the study-level: the null, the complement, and the unconstrained alternative. We develop principled recommendations for constructing meaningful joint alternative hypotheses that support clear interpretation of BES and are useful in the context of theory-testing.

When presented simultaneously, the percepts of multiple ambiguous visual stimuli (e.g., a lattice of Necker cubes) tend to align. This phenomenon, perceptual coupling, offers unique opportunities to investigate the subtleties of visual perception, such as perceptual grouping and ambiguity resolution. However, these systems portray several unintuitive and unresolved dynamics regarding inter-stimulus linkages and top-down influences. Previous research indicates a dependence of coupling strength on the degree of ambiguity and more frequent reversals when multiple stimuli are presented simultaneously opposed to individually. Further, the interplay between exogenous and endogenous triggers of reversals (e.g. through visual attention) has so far seen little progress in terms of precise, testable models. We propose a new mathematical model describing perceptual coupling based on a network of bistable stimuli following cusp catastrophe dynamics. By combining our own novel empirical results with current theoretical insights, we incorporate the role of attention, inter-stimulus connectivity and top-down influences into one testable explanatory model. Given the cusp dynamics at its core, our model elegantly captures the bistable nature of stimulus interpretations while allowing for a precise simulation of the temporal dynamics of perceptual coupling. With this model we aim to describe key phenomena of perceptual coupling in an intuitive manner.

Standard computational models represent emotions as static state points in a feature space. We argue that this representational assumption is structurally insufficient: emotional experiences are better modeled as trajectory objects on a latent affective manifold rather than as isolated points. We introduce a formally defined multiview framework in which a smooth latent affective manifold A generates both high-dimensional observational data (e.g., physiological measurements) and coarse-grained verbal labels via distinct projection maps. Emotions are defined as temporally ordered trajectory objects on A, characterized by intrinsic geometric properties of their paths rather than by their endpoints alone. Under mild smoothness assumptions, intrinsic geometry of A can be approximated from multiview observations using diffusion maps. In a synthetic multiview simulation, we demonstrate that trajectory families with identical endpoint labels achieve strong separation (AUC ≈ 0.90) via intrinsic path features, while endpoint-only representations perform at chance. This illustrates that trajectory-level modeling possesses strictly greater representational capacity than static point-based approaches under multiview observation constraints. The proposed framework provides a computationally tractable foundation for modeling affective dynamics and establishes a basis for future extensions involving metric recovery and manifold-based dynamical analysis.

Understanding human learning requires disentangling the contributions of multiple interacting cognitive systems. Rather than arising from a single mechanism, the learning process reflects the joint operation of working memory (WM) and reinforcement learning (RL), which support rapid, updating, and gradual value-based learning, respectively. Previous investigations of this interaction have largely relied on discrete alternative-choice paradigms, where response spaces are inherently categorical. While these designs have yielded important insights, they constrain decision-making to predefined options and may obscure how WM and RL operate in more naturalistic, continuous environments. In the present study, we examine WM–RL dynamics using a continuous version of the RLWM task, eliminating discrete response targets and allowing actions to vary along an unconstrained dimension. Behavioral data were collected from 85 participants, followed by a surprise test phase. Consistent with prior findings, results indicate that under high WM load, RL plays a greater role in shaping learning by gradually strengthening stimulus–response associations that are later recalled more reliably. Although WM facilitates rapid early learning, these quickly acquired associations are more susceptible to forgetting, whereas associations learned more gradually under greater reliance on RL show enhanced retention. These findings demonstrate that the canonical interaction between WM and RL generalizes beyond discrete choice paradigms to continuous response spaces, underscoring the robustness of this dual-system account of learning. Future work should explore how reward value further influences learning asymmetries in continuous domains.

We tested whether the drift diffusion model (DDM) can account for response times (RTs) in risky choice—an application where the model’s fit to data has received limited scrutiny. We compiled 14 publicly available datasets (N(participants) = 1,388, N(choices) = 199,157). We fitted a hierarchical integrative DDM with drift rates governed by cumulative prospect theory (CPT-DDM). A core DDM prediction is a negative relationship between choice strength and RT. Datasets varied considerably in the degree to which they showed this pattern, and model fit varied accordingly. Parameter estimates from the DDM-CPT model showed high concordance with estimates from a pure CPT model ignoring response times (concordance correlations: 0.73–0.87). This indicates that the two models infer similar underlying preferences, even when the CPT-DDM provides a poor account of the RT data. The general fit of the CPT-DDM was rather poor in experiments using accept-reject choices and 2AFC tasks with simple lotteries. On the other hand, in experiments where participants chose between two lotteries with more than one non-zero outcome, the CPT-DDM provides a satisfying fit to observed data. We conclude that while the CPT-DDM can capture choice–RT relationships in some settings, task features matter critically. A more comprehensive model integrating information acquisition and decision dynamics is needed to fully account for RTs in risky choice.

Numerosity perception is suggested to operate through an innate mechanism called the Approximate Number System (ANS), which allows the perception of numerosity without the need for computational or language-based information. In this study we investigated numerosity perception in individuals with intellectual disability (ID) by using a numerosity perception task to study how people convert non-symbolic stimuli such as a group of dots into symbolic ones such as Arabic numbers. We showed six different levels of dots with increasing difficulty (5 to 70) to two groups: people with ID and the control group. In the first condition of the experiment, participants received no feedback, while in the second condition they received feedback on the accuracy of their judgments. In accordance with the findings in the literature and our hypothesis, an underestimation bias was observed for the control group, which was reduced by providing feedback. On the other hand, for the individuals with ID a different pattern of results was observed suggesting a response bias to respond greater than the criterion value. Providing feedback reduced this response bias, which indicated that feedback was efficient for this group also. The systematic inconsistent responding for people with ID may either imply a lack of a mental number line representation, or the usage of a different mechanism for numerosity perception, which can be calibrated by providing feedback.

People tend to choose the options they look at more. Prior work explains this effect by assuming that visual attention increases preference for the attended option. However, it remains unclear why looking at an option increases its preference and when this relationship is weakened. Across three studies using an experimental paradigm that combines eye-tracking and a think-aloud protocol, we provide evidence that visual attention shapes preference by activating attributes associated with the attended option. Critically, this mechanism can be modulated by contextual factors such as stimulus desirability and behavioral goals, and when these factors are incongruent, the influence of visual attention on choice is weakened. To account for these patterns, we propose a new computational model of gaze-driven attribute activation. Our model subsumes existing theories as special cases, and uniquely predicts which attributes are brought to mind by gaze and how they shape downstream decisions. Together, our work offers a mechanistic explanation of a fundamental driver of choice as well as its boundary conditions.

Quitting involves being forced to stop despite wanting to continue. In laboratory tasks, quitting is often conflated with stopping decisions. They are modeled as optimal threshold-setting that balances expected reward and cost on the task. In real-world contexts, these types of decisions are known to be dependent on psychological factors besides utilitarian ones. In this work, an observational study was conducted where endurance runners self-reported incidences of thoughts of stopping a run as and when they experienced them. The runners were instructed to report the time for each stopping thought while engaged in a running session and could decide to stop running whenever they wanted to. The endurance experience for each runner was controlled by a constant running pace measured from their subjective endurance levels. Psychological variables were measured using scales for personality traits (NEO-FFI), procrastination (Pure Procrastination Scale) and impulsiveness (Barrett Scale). Participants who quit their runs ahead of when they had wanted to stop displayed a distinctive cascading pattern of stopping thoughts, represented by two dimensions on a t-SNE space. An exponential rate model estimated each runner’s probability of quitting based on two parameters, rate of stopping thoughts and the growth rate of the decision variable. The rate parameter showed negative associations with procrastination and anxiety factors and positive association with the conscientiousness factor. A single t-SNE dimension loaded positively onto the rate parameter suggesting the influence of stopping thoughts on a subject’s quitting propensity. These results suggest that stopping thoughts bias the decision process towards quitting and need to be incorporated into models of stopping and quitting, for greater phenomenological discriminability.