Attention & Cognitive Control

The stop signal task is a popular tool for studying response inhibition. Participants perform a response time task (go task) and, occasionally, the go stimulus is followed by a stop signal after a variable delay, indicating that subjects should withhold their response (stop task). One issue in modeling performance in the task is the issue of potential trigger failures: if a response is given after the stop signal, this may be due either to an unsuccessful inhibition (stop signal processing terminates "too late") or by a failure to trigger processing of the stop signal at all. Up to now, testing for the occurrence of trigger failures and estimating its probability has only been studied in fully-parametrized race models. Here we first suggest a statistical test for the existence of trigger failures. Second, we propose a more general (latent variable) modeling approach using concepts from the statistical theory of copulas that permits estimation of trigger failure probabilities.

This is an in-person presentation on July 19, 2026 (09:00 ~ 09:20 EDT).

Attentional control is our ability to maintain and implement a goal and goal-relevant information in the face of distraction. Recent research has highlighted the difficulty of reliably estimating individual differences of attentional control using experimental tasks. One suggestion to increase reliability is to improve on the design of the tasks measuring attentional control such as the Stroop task, the flanker task or the Antisaccade task. Here, we revisit the classic Stroop task, but increase task difficulty to improve its psychometric properties. Specifically, we manipulated the intensity of the to-be-identified colors across multiple levels of difficulty utilizing a four alternative forced choice paradigm. The initial findings from the experiment reveal that reduced color intensity impairs task performance, manifested through slower response times and decreased accuracy. Moreover, as the intensity of the colors decreased, the magnitude of Stroop congruency effects slightly increased, affecting both reaction times and accuracy effects. These results suggest that the difficulty induced by low-intensity stimuli may heighten the demands on control processes, exacerbating the conflict experienced during incongruent trials. The enhanced congruency effects observed in low-intensity conditions imply that this paradigm could serve as a valuable tool for measuring individual differences in attentional control. The four alternative forced choice paradigm additionally allows us to separate perceptual difficulties resulting from low-intensity items and the effect of conflict in the task. We discuss empirical results and modeling approaches.

This is an in-person presentation on July 19, 2026 (09:20 ~ 09:40 EDT).

Theoretical accounts of cognitive resource allocation typically address either externally driven performance (e.g., workload, multitasking) or internally generated thought (e.g., mind wandering) but rarely both within a single formal framework. We propose a particle-based stochastic process model in which cognitive resources are formalised as the number and distribution of representational particles tracking competing internal and external states, such that allocation is determined by how these particles are propagated and updated over time. The model instantiates a sequential Monte Carlo architecture in which particles evolve in response to task demands and endogenous fluctuations. External task performance (e.g., choices, response times) and internal indicators (e.g., thought reports) are treated as noisy observations generated by this shared particle system. Resource allocation varies stochastically and continuously, without assuming fixed trade-offs or discrete switching. We outline the structure of the model and present illustrative simulations demonstrating how distinct internal and external demand regimes give rise to characteristic latent dynamics and observable performance patterns. The framework provides a unified quantitative account of internal-external trade-offs grounded in a process-level, sampling-based account of cognition.

This is an in-person presentation on July 19, 2026 (09:40 ~ 10:00 EDT).

Everyday cognitive control requires a balance between stability and flexibility. Although these abilities are often treated as conflicting, it remains unclear whether they must be governed by a single trade-off mechanism. To test for this possibility, we model reaction time data from a well-balanced task-switching paradigm using a dynamical systems account in which task sets correspond to attractors in a working memory landscape. In this framework, we formalize stability as the local curvature around an attractor (which determines how strongly the state is restored after perturbation) and flexibility as the barrier height between attractors (which determines switching difficulty). This separation yields a mechanistic decomposition in which stability and flexibility are controlled by distinct geometric properties. We have implemented this shared 3D dynamical landscape model and fit it to task-switching reaction time data using parallel tempering. A unique landscape was estimated for each task condition, representing the item-wise and list-wise statistical properties of each task block. As a next step, we treat the alignment between these task-defined landscapes and each participant’s effective low-dimensional dynamics as an individual-specific parameter. Differences in this alignment should yield distinct effective 2D geometries—producing systematic changes in apparent local curvature and barrier height—while the underlying attractor structure should remain fixed. We will further examine whether the stability–flexibility relationship in our model is participant-dependent. For example, some participants may exhibit a negative coupling (trade-off), whereas others may show positive coupling (co-improvement) or near-independence, depending on how the induced 2D curvature and barrier height co-vary.

This is an in-person presentation on July 19, 2026 (10:00 ~ 10:20 EDT).