Lapses in perceptual decisions reflect exploration

Pisupati, Sashank; Chartarifsky-Lynn, Lital; Khanal, Anup; Churchland, Anne K.

doi:10.1101/613828

Cited by 22 publications

(31 citation statements)

References 62 publications

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“…Inspired by this possibility, we hypothesized that the slow drift reflects a separate decision process, independent of sensory evidence, that leads the animal to be more or less likely to make a saccade (i.e., an increase or decrease in both hit rate and false alarm rate). In previous work, similar behavioral processes have been described as urgency in a drift diffusion model [15,34], exploration in a multisensory discrimination task [76], and impulsivity in a response inhibition framework [6]. For our purposes, we term this process "impulsivity," which reflects the animal's tendency to make a decision without incorporating sensory evidence ( Fig.…”

Section: Resultsmentioning

confidence: 98%

“…Perceptual decisions have often been characterized with models that accumulate (noisy) sensory evidence, such as variants of the drift-diffusion model [50,11,79], and there are neural signatures of this accumulation process [31,47,29,36,10,35,39]. Decisions can be better characterized by extending these models to include additional factors, such as an urgency signal [15,34,68], duration of fixation [51], the number of alternative choices [14], the reward or choice history of previous trials [1,102], or a model term describing errors (i.e., 'lapses') that occur independently of sensory evidence [105,76]. Less is known about the neural signatures of these cognitive factors [75].…”

Section: Introductionmentioning

confidence: 99%

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Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

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An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Why do lapses occur? Explanations for presence of lapse errors range from inattention, to motor error, to incomplete knowledge of the task, and "uncertainty-guided exploration" [11,28,36,47]. However, a common thread to these explanations is that lapses arise independently, at a roughly constant rate across trials.…”

Section: Introductionmentioning

confidence: 99%

“…That discrete states underpin mouse choice behavior calls for new normative models to explain why mice may develop these states to begin with. While the disengaged and engaged states may correspond to exploratory and exploitative behavior respectively [12,13,36]; the existence of this seemingly suboptimal behavior may instead be the result of an optimal learning strategy as in [33]. We leave the development and testing of normative theories for the behavior that we describe here to a future work.…”

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confidence: 99%

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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“…Many results rely on the accurate and stable decision-making of experimental subjects, whereas other conclusions derive from quantifying specific deficits in choice behavior. For example, causal manipulations of 5 neural activity that result in impaired task accuracy can be traced back to specific disruptions in the sensory processing of task stimuli (Licata et al, 2017;Pisupati et al, 2019). Conversely, manipulations which were found to selectively suppress the impact of task-irrelevant covariates lead to improved accuracy (Akrami et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Extracting the Dynamics of Behavior in Decision-Making Experiments

Roy

Bak

Akrami

et al. 2020

Preprint

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Understanding how animals update their decision-making behavior over time is an important problem in neuroscience. Decision-making strategies evolve over the course of learning, and continue to vary even in well-trained animals. However, the standard suite of behavioral analysis tools is ill-equipped to capture the dynamics of these strategies. Here, we present a flexible method for characterizing time-varying behavior during decision-making experiments. We show that it successfully captures trial-to-trial changes in an animal's sensitivity to not only task-relevant stimuli, but also task-irrelevant covariates such as choice, reward, and stimulus history. We use this method to derive insights from training data collected in mice, rats, and human subjects performing auditory discrimination and visual detection tasks. With this approach, we uncover the detailed evolution of an animal's strategy during learning, including adaptation to time-varying task statistics, suppression of sub-optimal strategies, and shared behavioral dynamics between subjects within an experimental population. 45 et al., 2009). Other work from Kattner et al. (2017) extended the standard psychometric curve to allow its parameters to vary continuously across trials. Bak et al. (2016) described a model for defining smoothly evolving weights that could track changing sensitivities to specific behavioral covariates. While the model could track behavioral dynamics in theory, the optimization procedure strongly constrained both the complexity of the model and the size of the data to which it could 50 65 unprecedented insight into the development of behavioral strategies. ResultsOur primary contribution is a method for characterizing the evolution of animal decision-making behavior on a trial-to-trial basis. Our approach consists of a dynamic Bernoulli generalized linear model (GLM), defined by a set of smoothly evolving psychophysical weights. These weights 70 characterize the animal's decision-making strategy at each trial in terms of a linear combination of available task variables. The larger the magnitude of a particular weight, the more the animal's decision relies on the corresponding task variable. Learning to perform a new task therefore involves driving the weights on "relevant" variables (e.g., sensory stimuli) to large values, while driving weights on irrelevant variables (e.g., bias, choice history) toward zero. However, classical 75 modeling approaches require that weights remain constant over long blocks of trials, which precludes tracking of trial-to-trial behavioral changes that arise during learning and in non-stationary task environments. Below, we describe our modeling approach in more detail. Dynamic Psychophysical Model for Decision-Making TasksAlthough our method is applicable to any binary decision-making task, for concreteness we intro-80 duce our method in the context of the task used by the International Brain Lab (IBL) (illustrated in Figure 1A) (IBL et al., 2020). In this visual detection task, a mouse is positioned in f...

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Lapses in perceptual decisions reflect exploration

Cited by 22 publications

References 62 publications

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Mice alternate between discrete strategies during perceptual decision-making

Extracting the Dynamics of Behavior in Decision-Making Experiments

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