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Poster A50 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
A Discrete Motor-Independent Signature of Urgency During Human Perceptual Decision Making
Harvey McCone1 (), Ciara A. Devine1, Jessica Dully1, Emmet McNickle1, David P. McGovern2, Cian Judd1, Anna C. Geuzebroek3, John S. Butler4, Simon P. Kelly3, Redmond G. O’Connell1; 1School of Psychology & Trinity College Institute of Neuroscience, Trinity College Dublin, 2School of Psychology, Dublin City University, 3School of Electrical and Electronic Engineering, University College Dublin, 4School of Mathematics & Statistics, Technological University Dublin
When faced with a strict deadline, how does the brain adjust its decision processes to account for the passage of time? 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 the Contingent Negative Variation (CNV) represents a discrete, motor-independent signature of urgency, aligning closely with model predicted bound adjustments and exhibiting additional properties not observed in previously identified urgency signatures. Firstly, it provides a discrete representation of urgency as it grows 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, instead mirroring the time course of a motor-independent evidence accumulation signal (Centro-Parietal Positivity (CPP)). Our data demonstrate that urgency processes can be monitored in a model-independent manner via non-invasive brain signals, and that these signals can be used to inform computational models, leading to improved fits to behaviour.
Keywords: decision-making urgency drift-diffusion modelling EEG