Neurocognitive modeling meets deep learning: Understanding differences in cognition across the human life span

• Applicant: Dr. Mischa von Krause
• Subject Area: General, Cognitive and Mathematical Psychology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 550639909

In an aging society, understanding age differences in cognition is becoming increasingly important. Mathematical process models of cognition offer an interesting approach to studying cognitive parameters across the human lifespan. By utilizing both response times and accuracy data obtained from cognitive tasks, models such as the diffusion decision model enable researchers to better understand what drives age differences in cognition, such as the well-established fact that older adults tend to show slower response times than younger adults. Perhaps rather surprisingly, model-driven analyses have often shown that these age differences in response times can mostly be explained by differences in speed-accuracy settings and the speed of non-decisional processes (encoding, motor response execution). Contrarily, the studies often found few age differences in the speed of evidence accumulation or mental speed. However, as can be expected for a relatively new field of studies, there are still open questions. In the proposed research programme, three important gaps in the literature will be addressed, thus helping us better understand when, how, and why age differences in cognitive parameters occur. First, the generalizability of findings across researcher degrees of freedom and demographics is still widely understudied. To address this gap, it is planned to conduct a multiverse analysis, comparing different model architectures, estimation procedures, and data cleaning methods; it is also planned to conduct a big data analysis comparing age effects across a wide range of demographics. Second, previous studies have mostly ignored possible dynamics in parameters within experimental blocks. In this research proposal, we plan to develop and apply a novel model of age differences in within-block dynamics based on very recent advances in deep learning based dynamic modeling. Third, findings on how age differences in cognitive parameters are linked to neurophysiological measures are unclear and sometimes puzzling. To address this gap, we plan to conduct both a meta-analysis quantitative summarizing previous findings, and to develop a new joint model of neurophysiology and cognition. Said model will then by validated and applied to a large dataset of twelve cognitive tasks. In sum, the planned endeavors across the three work packages should greatly enhance our knowledge of age differences in neurocognitive parameters: By expanding our knowledge on the generalizability of previous findings, by scrutinizing within-block dynamics, and by better understanding the neurophysiological basis of patterns in cognitive parameters across age groups. All three of the gaps will be addressed by utilizing state-of-the-art deep learning methods. These methods have only recently become available and, by greatly enlarging the scope of neurocognitive models that can be developed and applied, promise an important step forward in understand age differences in cognition.

• DFG Programme: Research Grants