Biology subjects

Geuzebroek, A. C.

Publications and source records attributed to Geuzebroek, A. C..

4 recordsLinked to original sources

bioRxiv2025

A Movement-Independent Signature of Urgency During Human Perceptual Decision Making

How does the brain adjust its decision processes to ensure timely decision completion? Computational modelling and electrophysiological investigations have pointed to dynamic urgency processes that serve to progressively reduce the quantity of evidence required to reach choice commitment as time elapses. To date, such urgency dynamics have been observed exclusively in neural signals that accumulate evidence for a specific motor plan. Across three complementary experiments, we show that a classic ERP component, the Contingent Negative Variation (CNV), also traces dynamic urgency but exhibits unique properties not observed in effector-selective signals. Firstly, it provides a representation of urgency alone, growing only as a function of time and not evidence strength. Secondly, when choice reports must be withheld until a response cue, the CNV peaks and decays long before response execution, mirroring the early termination dynamics of a motor-independent evidence accumulation signal. These properties suggest that the brain may use urgency signals not only to expedite motor planning but also to hasten cognitive deliberation. These data demonstrate that urgency processes operate in a variety of perceptual choice scenarios and that they can be monitored in a model-independent manner via non-invasive brain signals. Significance StatementComputational models suggest that, when decisions are time-constrained the brain progressively lowers the amount of evidence it requires to reach choice commitment, thus increasingly sacrificing accuracy for timely decision completion. To date, neurophysiological investigations have identified signatures of these urgency effects exclusively in areas of the brain that plan the decision-reporting actions. Here, we characterise a human electroencephalogram signature of urgency that exhibits several novel properties: it traces the urgency component of the decision and terminates upon choice commitment even when the decision-reporting action is deferred until later. These observations suggest that urgency can serve to hasten the deliberation process and not just the movements that a decision entails.

neuroscience↗

bioRxiv2025

Centroparietal signals track multiple rounds of evidence accumulation in an intermittent evidence task

Making decisions often requires the integration of multiple pieces of information. An extensive body of research has investigated the neural architecture underpinning evidence accumulation in perceptual tasks where information is continuously present, but less is known about how this neural architecture operates in situations affording only intermittent glimpses of an evidence source. In two electroencephalography (EEG) experiments, participants judged the direction of up to two pulses of motion evidence separated by gaps of varying duration. Our behavioural analysis found that participants used both pulses but underutilised the second, and showed no systematic decrease in accuracy as a function of gap duration. At the neural level, motor beta lateralisation tracked cumulative evidence across pulses, maintaining a sustained representation of the decision variable through the gap and until response. In contrast, the centroparietal positivity (CPP), a previously-characterised signature of evidence accumulation, built up transiently to a peak that scaled with each pulses contribution to the decision variable (i.e. the absolute belief update it produced), falling back to baseline in between pulses. These patterns were recapitulated in a model where pulse-information transiently integrated at the CPP level is fed to and maintained at a bounded motor level. In this model, the evidence in the second pulse is only integrated to the extent that the evidence in the first pulse falls short of the bound, or not integrated at all if a bound has already been hit.

neuroscience↗

bioRxiv2023

Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis

Central to our understanding of how visual evoked potentials (VEPs) contribute to visual processing is the question of where their anatomical sources are. Three well-established measures of low-level visual cortical activity are widely used: the first component ("C1") of the transient and multifocal VEP, and the steady-state VEP (SSVEP). Although primary visual cortex (V1) activity has often been implicated in the generation of all three signals, their dominant sources remain uncertain due to the limited resolution and methodological heterogeneity of source modelling. Here, we provide the first characterisation of all three signals in one analytic framework centred on the cruciform model, which describes how scalp topographies of V1 activity vary with stimulus location due to the retinotopy and unique folding pattern of V1. We measured the transient C1, multifocal C1, and SSVEPs driven by an 18.75Hz and 7.5Hz flicker, and regressed them against forward-models of areas V1, V2 and V3 generated from the Benson-2014 retinotopy atlas. The topographic variations of all four VEP signals across the visual field were better captured by V1 models, explaining between 2-6 times more variance than V2/V3. Models with all three visual areas improved fit further, but complementary analyses of temporal dynamics across all three signals indicated that the bulk of extrastriate contributions occur considerably later than V1. Overall, our data support the use of peak C1 amplitude and SSVEPs to probe V1 activity, although the SSVEP contains stronger extrastriate contributions. Moreover, we provide elaborated heuristics to distinguish visual areas in VEP data based on signal lateralization as well as polarity inversion.

neuroscience↗

bioRxiv2022

Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process

Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1-second coherence-targets within continuous random dot motion, and how they are adjusted across contexts with Weak, Strong, or randomly Mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well-captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the Strong-target context. We thus tested two alternative models in which decision bound dynamics were constrained directly by Beta measurements, respectively featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the importance of neurally-informed modelling.

neuroscience↗