The Interaction Between Elapsed Time and Decision Accuracy Differs Between Humans and Rats

Shevinsky, Carly A; Reinagel, Pamela

doi:10.3389/fnins.2019.01211

Cited by 16 publications

(41 citation statements)

References 49 publications

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“…Finally, we validated our interpretation of the GLM-HMM’s latent states when we compared the response times and violation rates (quantities not used to fit our model) associated with engaged and disengaged states. We found that the most extreme response times were typically associated with the disengaged states, which is consistent with previous findings linking accuracy and response times [32, 53, 61]. Taken together, these results shed substantial new light on the factors governing sensory decision-making in rodents, and provide a powerful set of tools for identifying hidden states in behavioral data.…”

Section: Introductionsupporting

confidence: 90%

“…Previous literature has revealed that humans and monkeys commit more errors on long duration trials than short duration trials [32, 53, 61]. We, thus, looked at the distributions of response times for the engaged state, compared to for the disengaged states (the biased left and biased right states), for mice performing the IBL task.…”

Section: Resultsmentioning

confidence: 99%

“…Another exciting future direction will be to explore how the GLM-HMM framework contributes to the wider discussion on the observed differences in lapse rates across species [60, 61]. While the lapse rates of rodents performing perceptual decision-making tasks are often as high as 10-20% [35, 48, 49], lapse rates for non-human primates and humans performing these tasks are often much lower [60, 61]. Could these differences be due to different species using different strategies during these tasks?…”

Section: Discussionmentioning

confidence: 99%

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Mice alternate between discrete strategies during perceptual decision-making

Ashwood

Roy

Stone

et al. 2020

Preprint

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Classical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. Here we provide analyses showing that this common view is incorrect. We use a latent variable modeling framework to show that decision-making behavior in mice reflects an interplay between different strategies that alternate on a timescale of tens to hundreds of trials. This model provides a powerful alternate explanation for "lapses" commonly observed during psychophysical experiments. Formally, our approach consists of a Hidden Markov Model (HMM) with states corresponding to different decision-making strategies, each parameterized by a distinct Bernoulli generalized linear model (GLM). We fit the resulting model (GLM-HMM) to choice data from two large cohorts of mice in different perceptual decision-making tasks. For both datasets, we found that mouse decision-making was far better described by a GLM-HMM with 3 or 4 states than by a traditional psychophysical model with lapses. The identified states were highly consistent across animals, consisting of a single "engaged" state, in which the strategy relied heavily on the sensory stimulus, and multiple biased or disengaged states in which accuracy was low. These states persisted for many trials, suggesting that lapses were not independent, but reflected state dynamics in which animals were relatively engaged or disengaged for extended periods of time. We found that for most animals, response times and violation rates were positively correlated with disengagement, providing independent correlates of the identified changes in strategy. The GLM-HMM framework thus provides a powerful lens for the analysis of decision-making, and suggests that standard measures of psychophysical performance mask the presence of slow but dramatic alternations in strategy across trials.

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Section: Introductionsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Mice alternate between discrete strategies during perceptual decision-making

Ashwood

Roy

Stone

et al. 2020

Preprint

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“…We noticed with surprise that some rats in a previous study (10) had longer mean response times to easier random dot motion stimuli (Figure 1 E,F), in spite of having unremarkable psychometric curves with minimal lapse (Figure 1A,B, performance at high coherence near 100%). Other rats in the same cohort with comparable psychometric curves (Figure 1 The inverted chronometric response functions were attributable mostly to the correct trials (Figure 2 A,B green; c.f.…”

Section: Resultsmentioning

confidence: 75%

“…No new experiments are reported in this paper; the data are from a previous publication (10) and the previously unpublished supplemental video is from (2). Those studies were both performed in full accordance with ethical guidelines for animal research, as approved and overseen by the UCSD IACUC.…”

Section: Methodsmentioning

confidence: 99%

Some rats take more time to make easier perceptual decisions

Reinagel

2021

Preprint

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When subjects control the duration of sampling a sensory stimulus before making a decision, they generally take more time to make more difficult sensory discriminations. This has been found to be true of many rats performing visual tasks. But two rats performing visual motion discrimination were found to have inverted chronometric response functions: their average response time paradoxically increased with stimulus strength. We hypothesize that corrective decision reversals may underlie this unexpected observation.

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Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

Asadpour,

Tan,

Lenfesty

et al. 2024

Comput Brain Behav

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Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting.

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The Interaction Between Elapsed Time and Decision Accuracy Differs Between Humans and Rats

Cited by 16 publications

References 49 publications

Mice alternate between discrete strategies during perceptual decision-making

Mice alternate between discrete strategies during perceptual decision-making

Some rats take more time to make easier perceptual decisions

Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

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