We are looking for a PhD candidate to join our research project entitled “Next-Generation Cognitive Testing: A Theory-Driven Approach to Multi-Scale Dynamics in Cognition”, funded by an ERC Consolidator grant awarded to Dora Matzke. The project starts September 1st 2026.

Few aspects of human performance are as salient and consequential as its variability. Fluctuations are evident in essentially all areas of cognition. For example, attention fluctuates with alertness modulated by circadian timing, while cognitive abilities change over longer time scales due to skill acquisition and forgetting. Given the critical role of cognition in nearly all aspects of human functioning, including safety-critical situations, these fluctuations have profound implications. Yet, the causes and temporal scales of cognitive fluctuations remain poorly understood. The project will develop a unified theoretical framework, for the first time synthetizing the dynamics of the diverse factors underlying fluctuations in cognitive abilities across multiple temporal scales. This interdisciplinary approach will create new opportunities for the emerging field of ecological momentary assessment (EMA) in cognitive testing, enhancing reliability and predictive power, and advancing understanding of cognitive dynamics in real-world contexts. The project includes two synergistic strands: (1) Theoretical work to create a dynamic quantitative modeling framework, linking accounts of short-term variability to longer-term adaptations in performance; (2) Empirical work using EMA to investigate how and why performance on a wide range of cognitive tasks fluctuates across time scales from sub-seconds to several weeks.

This PhD project has three main objectives:

(1) Develop novel adaptive evidence-accumulation models for a range of cognitive tasks (e.g., stop-signal, Stroop), providing theory-driven accounts of trial-to-trial fluctuations in performance.

(2) Implement these models within a Bayesian hierarchical framework to link task-level dynamics to longer-term fluctuations driven by biological factors and learning.

(3) Design and coordinate a large-scale data collection using a measurement-burst EMA approach to test the models and address key questions about the nature and time scales of cognitive variability.