Abstract
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 subjects) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (including VET) are tracked by the fastest response times. A one-to-one relationship between N200 latencies and VET was found by directly fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times. A one-to-one relationship was also found between single-trial N200 latencies and response times. Fitting a novel neuro-cognitive model of decision-making also showed a 1-to-1 relationship between N200 latency and non-decision time, 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 temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.
Significance Statement Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. In this study, we report evidence that visual evoked potentials (EPs) around 200 ms after stimulus presentation track the time of visual figure-ground segregation before the onset of evidence accumulation during decision making. These EP latencies vary across individuals, are modulated by external visual noise, and have a one-to-one relationship with response time. Hierarchical Bayesian model-fitting was also used to relate these EPs to a specific cognitive parameter that tracks time related to visual encoding in a decision-making model of response time. This work adds to the growing literature that suggests that EEG signals can track the component cognitive processes of decision making.
