Publication detail

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O'Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (non-decision time; NDT), including VET, are tracked by the fastest response times. Evidence that VET is tracked by N200 latencies was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times, a model-independent estimate of NDT. Fitting a novel neuro-cognitive model of decision making also yielded a slope of 1 between N200 latency and model-estimated NDT in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.

@article{nunez_etal:2019:potential,
    title   = {{T}he latency of a visual evoked potential tracks the onset of decision making},
    author  = {Nunez, Michael D. and Gosai, Aishwarya and Vandekerckhove, Joachim and Srinivasan, Ramesh},
    year    = {2019},
    journal = {NeuroImage},
    volume  = {197},
    pages   = {93--108}
}