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Nalborczyk, L.

Publications and source records attributed to Nalborczyk, L..

2 recordsLinked to original sources

Uncovering the representational geometry of durations

Is there a geometry of time in the human mind? A canonical measure of time in psychology is duration, a time interval quantifiable as a magnitude. Durations have been proposed to be arranged along a mental timeline: a unidimensional, linear, and spatialised representation of time. Here, we asked whether such a mental timeline is sufficient to account for the experience of duration. To address this, we tested the same participants in two experiments: a behavioural similarity judgment task, in which participants rated the similarity of duration pairs, and an electroencephalography (EEG) experiment in which they detected oddball durations in a sequence. Behavioural and EEG data were used to construct representational dissimilarity matrices, whose geometry was compared against theoretical models of duration organisation. Our results reveal that most variance in behavioural similarity judgements is explained by three latent dimensions, interpretable as: magnitude (monotonic ordering of durations), contextual encoding (distance to the geometric mean of the duration set), and a periodic component. These three dimensions are jointly consistent with a latent generalised helical model, which provided excellent fit to the behavioural data. Individual helical model parameters further correlated with endogenous neural oscillations measured during rest, suggesting that an individual's duration space is partially constrained by intrinsic dynamics. The neural geometry was also found to be dynamic, unfolding in two successive stages: a strong logarithmic encoding of durations peaking around 150 ms after duration offset, followed by a spring-like geometry starting around 300 ms after offset. Together, these findings describe multidimensional psychological and neural geometries of duration space, and characterise their relationship.

neuroscience↗

Precise temporal localisation of M/EEG effects with Bayesian generalised additive multilevel models

Time-resolved electrophysiological measurements such as those obtained through magneto- and electroencephalography (M/EEG) offer a unique window onto the neural activity underlying cognitive processes. Researchers are often interested in determining whether and when these signals differ across experimental conditions or participant groups. The conventional approach involves mass univariate statistical testing across time and space followed by corrections for multiple comparisons or some form of cluster-based inference. While effective for controlling error rates at the cluster-level, clusterbased inference comes with a significant limitation: by shifting the focus of inference from individual time points to clusters, it prevents drawing conclusions about the precise onset or offset of observed effects. Here, we present a model-based alternative for analysing M/EEG timeseries, such as event-related potentials or time-resolved decoding accuracy. Our approach leverages Bayesian generalised additive multilevel models, providing posterior odds that an effect exceeds zero (or chance) at each time point, while naturally accounting for temporal dependencies and between-subject variability. Using both simulated and empirical M/EEG datasets, we show that this approach substantially outperforms conventional methods in estimating the onset and offset of neural effects, yielding more precise and reliable estimates. We provide an open-source R package implementing the method and describe how it can be integrated into M/EEG analysis pipelines using MNE-Python.

neuroscience↗