<h2>Summary</h2> Humans, even as infants, use cognitive strategies, such as exploration and hypothesis testing, to learn about causal interactions in the environment. In animal learning studies, however, it is challenging to disentangle higher-order behavioral strategies from errors arising from imperfect task knowledge or inherent biases. Here, we trained head-fixed mice on a wheel-based auditory two-choice task and exploited the intra- and inter-animal variability to understand the drivers of errors during learning. During learning, performance errors are dominated by a choice bias, which, despite appearing maladaptive, reflects a dynamic strategy. Early in learning, mice develop an internal model of the task contingencies such that violating their expectation of reward on correct trials (by using short blocks of non-rewarded "probe" trials) leads to an abrupt shift in strategy. During the probe block, mice behave more accurately with less bias, thereby using their learned stimulus-action knowledge to test whether the outcome contingencies have changed. Despite having this knowledge, mice continued to exhibit a strong choice bias during reinforced trials. This choice bias operates on a timescale of tens to hundreds of trials with a dynamic structure, shifting between left, right, and unbiased epochs. Biased epochs also coincided with faster motor kinematics. Although bias decreased across learning, expert mice continued to exhibit short bouts of biased choices interspersed with longer bouts of unbiased choices and higher performance. These findings collectively suggest that during learning, rodents actively probe their environment in a structured manner to refine their decision-making and maintain long-term flexibility. 

Performance errors during rodent learning reflect a dynamic choice strategy

The flow of neural activity across the neocortex during active sensory discrimination is constrained by task-specific cognitive demands, movements, and internal states. During behavior, the brain appears to sample from a broad repertoire of activation motifs. Understanding how these patterns of local and global activity are selected in relation to both spontaneous and task-dependent behavior requires in-depth study of densely sampled activity at single neuron resolution across large regions of cortex. In a significant advance toward this goal, we developed procedures to record mesoscale 2-photon Ca 2+ imaging data from two novel in vivo preparations that, between them, allow simultaneous access to nearly all of the mouse dorsal and lateral neocortex. As a proof of principle, we aligned neural activity with both behavioral primitives and high-level motifs to reveal the existence of large populations of neurons that coordinated their activity across cortical areas with spontaneous changes in movement and/or arousal. The methods we detail here facilitate the identification and exploration of widespread, spatially heterogeneous neural ensembles whose activity is related to diverse aspects of behavior. 

Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice

Animal behaviour is shaped to a large degree by internal cognitive states, but it is unknown whether these states are similar across species. To address this question, we developed a virtual reality setup in which mice and macaques engage in the same naturalistic visual foraging task. We exploited the richness of a wide range of facial features extracted from video recordings during the task, to train a Markov-Switching Linear Regression (MSLR). By doing so, we identified, on a single-trial basis, a set of internal states that reliably predicted when the animals were going to react to the presented stimuli. Even though the model was trained purely on reaction times, it could also predict task outcome, supporting the behavioural relevance of the inferred states. The identified states were comparable between mice and monkeys. Furthermore, each state corresponded to a characteristic pattern of facial features, highlighting the importance of facial expressions as manifestations of internal cognitive states across species.

Thoughtful faces: inferring internal states across species using facial features

Brain activity controls adaptive behavior but also drives unintentional incidental movements. Such movements could thus potentially be used to read out internal cognitive variables also neurally computed. Establishing this, however, would require ruling out that incidental movements reflect cognition only because they are coupled with task-related responses through the biomechanics of the body. We addressed this issue in a foraging task for mice where multiple decision variables are simultaneously encoded even if, at any given time, only one of them is used. We found that characteristic features of the face simultaneously encode not only the currently used decision variables, but also independent and unexpressed ones, and we show that these features partially originate from neural activity in the secondary motor cortex. Our results suggest that the face reflects ongoing computations above and beyond those related to task demands, demonstrating the ability of noninvasive monitoring to expose otherwise latent cognitive states. 

pdf/facial-expressions-in-mice-reveal-latent-cognitive-variables-3j7mqkihfu.pdf

Facial expressions in mice reveal latent cognitive variables and their neural correlates

Goal: Poor arousal management may lead to reduced cognitive performance.Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music.Methods: We employ a Bayesian filtering approach within an expectationmaximization framework to track the hidden states during the n-back task in the presence of calming and exciting music.We decode the arousal and performance states from the skin conductance and behavioral signals, respectively.We derive an arousal-performance model based on the Yerkes-Dodson law.We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation.Results: The quantified arousal and performance are presented.The existence of Yerkes-Dodson law can be interpreted from the arousal-performance relationship.Findings display higher matrices of performance within the exciting music.Conclusions: The performance-based arousal decoder has a better agreement with the Yerkes-Dodson law.Our study can be implemented in designing non-invasive closed-loop systems. 

Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music

During sensory-guided behavior, an animal’s decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal’s arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.

/pdf/decision-making-dynamics-are-predicted-by-arousal-and-2kj4xbpn.pdf

Decision-making dynamics are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance. 

Decision-making dynamics are predicted by arousal and uninstructed movements.

Transitions between discrete performance states in auditory and visual tasks are predicted by arousal and uninstructed movements

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Decision-making dynamics are predicted by arousal and uninstructed movements

Decision-making dynamics are predicted by arousal and uninstructed movements.

Transitions between discrete performance states in auditory and visual tasks are predicted by arousal and uninstructed movements