Biology subjects

van Maanen, L.

Publications and source records attributed to van Maanen, L..

4 recordsLinked to original sources

bioRxiv2025

Trial-Level Sequence Modeling Reveals Hidden Dynamics of Dual-Task Interference

Theories of dual-task interference assume that the same cognitive operations underlie multitasking regardless of stimulus timing, yet this core assumption has remained untested due to methodological limitations of behavioral averaging. Here, we combine hidden multivariate pattern (HMP) analysis with deep spatiotemporal sequence modeling of single-trial EEG to uncover the neural dynamics of multitasking in the psychological refractory period (PRP) paradigm. Using a deep spatiotemporal sequence model trained on Long stimulus-onset asynchrony (SOA) trials, we identify Encoding, Central, and Response operations and show that these same operations occur in the Short SOA condition, demonstrating shared cognitive processes across interference conditions. Additionally, trial-level decoding reveals multiple distinct sequences of cognitive operations across both tasks during interference, varying both within and across individuals. These sequences predict behavioral differences in reaction time and accuracy, revealing how interference timing within the cognitive operation sequence influences performance. In other words, we found trial-by-trial variability related to individual strategies directly affecting accuracy and reaction time (RT). Our findings challenge static bottleneck accounts and establish trial-level sequence modeling as a powerful tool to investigate the hidden dynamics of multitasking.

neuroscience↗

bioRxiv2025

Decision-making components and times revealed by the single-trial electro-encephalogram

Decision-making stems from a sequence of information processing steps between the onset of the stimulus and the response. Despite extensive research, uncertainty remains about the actual cognitive sequence involved that leads to the reaction time. Using the hidden multivariate pattern method we modeled the single-trial electroencephalogram of participants performing a decision task as a sequence of an unknown number of events estimated as trial-recurrent, time-varying, stable topographies. We provide evidence for five events occurring during participants decision making and capturing encoding, attention orientation, decision and motor execution times. This interpretation is supported by the observation that a targeted manipulation of stimulus intensity yields Pierons law in the interval between encoding and attention orientation, and Fechners law in the interval between attention orientation and decision commitment. The final, decision-related, event is represented in the brain as a ramping signal in parietal areas whose timing, amplitude and build-up predict the participants decision accuracy.

neuroscience↗

bioRxiv2024

Trial-by-trial detection of cognitive events in neural time-series

Measuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with the by-trial inter-event intervals following probability distributions. By estimating event-specific multivariate patterns, and between-event time interval distributions, the method allows to recover the by-trial location of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using three previously published datasets. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the intervals between each event. Traditional exploration of tasks cognitive structures and electrophysiological analyses can thus be enhanced by HMP estimates.

neuroscience↗

bioRxiv2020

Investigating the origin and consequences of endogenous default options in repeated economic choices.

Classical value-based decision theories state that economic choices are solely based on the value of available options. Experimental evidence suggests, however, that individuals choices are biased towards default options, prompted by the framing of decisions. Although the effects of default options created by exogenous framing - such as how choice options are displayed - are well-documented, little is known about the potential effects and properties of endogenous framing, that is, originating from an individuals internal state. In this study, we investigated the existence and properties of endogenous default options in a task involving choices between risky lotteries. By manipulating and examining the effects of three experimental features - time pressure, time spent on task and relative choice proportion towards a specific option -, we reveal and dissociate two features of endogenous default options which bias individuals choices: a natural tendency to prefer certain types of options (natural default), and the tendency to implicitly learn a default option from past choices (learned default). Additional analyses suggest that while the natural default may bias the standard choice process towards an option category, the learned default effects may be attributable to a second independent choice process. Overall, these investigations provide a first experimental evidence of how individuals build and apply diverse endogenous default options in economic decision-making and how this biases their choices.

neuroscience↗