2021
DOI: 10.1016/j.neuron.2020.12.004
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Extracting the dynamics of behavior in sensory decision-making experiments

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Cited by 84 publications
(125 citation statements)
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“…To address this possibility, we fit the data with PsyTrack, a psychophysical model with continuous latent states [58,59]. The PsyTrack model describes sensory decision-making using an identical Bernoulli GLM, but with dynamic weights that drift according to a Gaussian random walk (see Methods sec.…”
Section: Data Provide Evidence For Discrete Not Continuous Statesmentioning
confidence: 99%
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“…To address this possibility, we fit the data with PsyTrack, a psychophysical model with continuous latent states [58,59]. The PsyTrack model describes sensory decision-making using an identical Bernoulli GLM, but with dynamic weights that drift according to a Gaussian random walk (see Methods sec.…”
Section: Data Provide Evidence For Discrete Not Continuous Statesmentioning
confidence: 99%
“…In order to perform model comparison with the PsyTrack model of [58,59], we utilized the code provided at https://github.com/nicholas-roy/psytrack.…”
Section: Comparison With Psytrack Model Of Roy Et Almentioning
confidence: 99%
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“…Single latent variable HMMs can be implemented in artificial neural networks and therefore at least in principle by the brain, so it is unclear why mice do not perform this optimal strategy (Tran et al 2016). An ethological explanation can be proposed from our observation that using the optimal strategy offers only marginal increases in expected reward over another simple computational algorithm (i.e., the drift diffusion model derived from the logistic regression) (Roy et al 2021). Moreover, given a tendency for exploration or stochasticity, the HMM requires more trials in which the agent switches between ports to achieve equal reward.…”
Section: Stickiness Captures the Deviation Of Mouse Behavior From Optimalitymentioning
confidence: 99%
“…Presumably, the continuous experiential information accrued by the self is reflected in and used by the brain to execute adaptive behavior to support ongoing decision-making. Yet such information is often treated as task-irrelevant and ignored or factored out (Roy et al, 2021), to an at best incomplete, or at worst inaccurate picture of involved neural mechanisms. Indeed, the seemingly widespread distribution of similar decision-making information across the brain (Allen et al, 2017; Steinmetz et al, 2019) may in part be due to a lack of accounting for the unique constellation of internal, experiential, temporal, and contextual information encountered by an individual that drives decision-making, referred to here as “subjective experience”.…”
Section: Introductionmentioning
confidence: 99%