The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Abstract

SummaryLateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, decision variables (DVs) are represented as partially potentiated action plans, where ensembles increase their average responses for stronger evidence supporting their preferred actions. As another consequence, DV representation and readout are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Here, we challenge those core principles using a novel face-discrimination task, where LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on task context, necessitating distinct readouts. We show similar manifolds in lateral and medial prefrontal cortices, suggesting a ubiquitous representational geometry across decision-making circuits.

A representational similarity analysis of cognitive control during color-word Stroop

“…With respect to target coding, however, one speculative interpretation is that, during the relatively late phase of response selection and execution, control networks may lose modular structure as the circuitry collectively converges on a behavioral choice. This interpretation accords with the fact that “choice axes” are encoded within multiple key nodes of frontoparietal decision circuitry (e.g., in macaque LIP: Roitman & Shadlen, 2002; in macaque caudal DLPFC: Mante, Sussillo, Shenoy, & Newsome, 2013; in human DACC and pre-SMA: Minxha, Adolphs, Fusi, Mamelak, & Rutishauser, 2020; see also Okazawa, Hatch, Mancoo, Machens, & Kiani, 2021). This account could be addressed using the enriched experimental design described above, identifying when and where choice coding is emphasized over the course of a trial.…”

Resolving the neural mechanism of core object recognition in space and time: A computational approach

“…Multiple experiments showed that the proposed model can replicate what was expected from humans or animals while making decisions about an object. Firstly, we showed that the speed and the accuracy of the model in an abject recognition task with noisy stimuli followed a tangent hyperbolic chronometric and a sigmoid psychometric function, respectively in keeping with various studies of perceptual decision-making (57,58) (Heidari Gorji et al, 2018, Okazawa et al, 2020, Okazawa et al, 2021. We demonstrated that the decision bound in the decision-making stage of the model truly adjusted the speed and the accuracy of a decision, in a way that has been observed in behavioral studies (Hanks et al, 2014).…”

A representational similarity analysis of cognitive control during color-word Stroop

“…With respect to target coding, however, one speculative interpretation is that, during the relatively late phase of response selection and execution, control networks may lose modular structure as the circuitry collectively converges on a behavioral choice. This interpretation accords with the fact that "choice axes" are encoded within multiple key nodes of frontoparietal decision circuitry, for example: in macaque LIP (Roitman and Shadlen, 2002), in macaque caudal DLPFC (Mante et al, 2013), and in human dACC and pre-SMA (Minxha et al, 2020; see also Okazawa et al, 2021). This account could be addressed using the enriched experimental design described above, identifying when and where choice coding is emphasized over the course of a trial.…”