
단일 의사결정 과정에서 신경 신호는 확률적 확산 과정을 나타내며, 이는 선택과 반응 시간의 변동성을 설명한다.


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Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons ( [Shadlen and Newsome, 1996][1] ; [Shadlen and Kiani, 2013][2] ). Neurons in the parietal and prefrontal cortex ( [Kim and Shadlen, 1999][3] ; [Romo et al., 2004][4] ; [Hernández et al., 2002][5]; [Ding and Gold, 2012][6] ) are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound ( [Roitman and Shadlen, 2002][7] ). Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time ( [Gold and Shadlen, 2007][8] ). Here, we elucidate this drift-diffusion signal on individual decisions. We recorded simultaneously from hundreds of neurons in the lateral intraparietal cortex (LIP) of monkeys while they made decisions about the direction of random dot motion. We show that a single scalar quantity, derived from the weighted sum of the population activity, represents a combination of deterministic drift and stochastic diffusion. Moreover, we provide direct support for the hypothesis that this drift-diffusion signal approximates the quantity responsible for the variability in choice and reaction times. The population-derived signals rely on a small subset of neurons with response fields that overlap the choice targets. These neurons represent the integral of noisy evidence. Another subset of direction-selective neurons with response fields that overlap the motion stimulus appear to represent the integrand. This parsimonious architecture would escape detection by state-space analyses, absent a clear hypothesis.

### Competing Interest Statement

The authors have declared no competing interest.

 [1]: #ref-60
 [2]: #ref-59
 [3]: #ref-32
 [4]: #ref-53
 [5]: #ref-26
 [6]: #ref-9
 [7]: #ref-52
 [8]: #ref-23

단일 의사결정의 신경 계산 과정에 대한 직접 관찰