Decision-making dynamics are predicted by arousal and uninstructed movements

Neural circuit underlying individual differences in visual escape habituation

Emotions like fear help organisms respond to threats. Repeated predator exposure leads to adaptive responses with unclear neural mechanisms behind individual variability. We identify two escape behaviors in mice-persistent escape (T1) and rapid habituation (T2)-linked to unique arousal states under repetitive looming stimuli. Combining multichannel recording, circuit mapping, optogenetics, and behavioral analyses, we find parallel pathways from the superior colliculus (SC) to the basolateral amygdala (BLA) via the ventral tegmental area (VTA) for T1 and via the mediodorsal thalamus (MD) for T2. T1 involves heightened arousal, while T2 features rapid habituation. The MD integrates SC and insular cortex inputs to modulate arousal and defensive behaviors. This work reveals neural circuits underpinning adaptive threat responses and individual variability.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.

Deciphering neuronal variability across states reveals dynamic sensory encoding

Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.

Behavioral modulations can alter the visual tuning of neurons in the mouse thalamocortical pathway

Behavioral influences shape processing in the retina and the dorsal lateral geniculate nucleus (dLGN), although their precise effects on visual tuning remain debated. Using 2-photon functional Ca2+ imaging, we characterize the dynamics of dLGN axon activity in the primary visual cortex of awake behaving mice, examining the effects of visual stimulation, pupil size, stillness, locomotion, and anesthesia. In awake recordings, nasal visual motion triggers pupil dilation and, occasionally, locomotion, increasing responsiveness and leading to an overrepresentation of boutons tuned to nasal motion. These effects are pronounced during quiet wakefulness, weaker during locomotion, and absent under anesthesia. Accounting for dynamic changes in responsiveness reduces tuning biases, revealing an overall preservation of retinal representations of visual motion in the visual thalamocortical pathway. Thus, stimulus-driven behavioral modulations can alter tuning and bias classification of early visual neurons, underscoring the importance of considering such influences in sensory processing experiments.

Decoding a Cognitive Performance State From Behavioral Data in the Presence of Auditory Stimuli

Cognitive performance state is an unobserved state that refers to the overall performance of cognitive functions. Deriving an informative observation vector as well as the adaptive model and decoder would be essential in decoding the hidden performance. We decode the performance from behavioral observation data using the Bayesian state-space approach. Forming an observation from the paired binary response with the associated continuous reaction time may lead to an overestimation of the performance, especially when an incorrect response is accompanied by a fast reaction time. We apply the marked point process (MPP) framework such that the performance decoder takes the correct/incorrect responses and the reaction time associated with correct responses as an observation. We compare the MPP-based performance with two other decoders in which the pairs of binary and continuous signals are taken as the observation; one decoder considers an autoregressive (AR) model for the performance state, and the other one employs an autoregressive-autoregressive conditional heteroskedasticity (AR-ARCH) model which incorporates the time-varying and adaptive innovation term within the model. To evaluate decoders, we use the simulated data and the n-back experimental data in the presence of multiple music sessions. The Bayesian state-space approach is a promising way to decode the performance state. With respect to individual perspective, the estimated MPP-based and ARCH-based performance states outperform the AR-based estimation. Based on the aggregated data analysis, the ARCH-based performance decoder outperforms the other decoders. Performance decoders can be employed in educational settings and smart workplaces to monitor one's performance and contribute to developing a feedback controller in closed-loop architecture to improve cognitive performance.

Internal states emerge early during learning of a perceptual decision-making task

Recent work has shown that during perceptual decision-making tasks, animals frequently alternate between different internal states or strategies. However, the question of how or when these emerge during learning remains an important open problem. Does an animal alternate between multiple strategies from the very start of training, or only after extensive exposure to a task? Here we address this question by developing a dynamic latent state model, which we applied to training data from mice learning to perform a visual decision-making task. Remarkably, we found that mice exhibited distinct "engaged" and "biased" states even during early training, with multiple states apparent from the second training session onward. Moreover, our model revealed that the gradual improvement in task performance over the course of training arose from a combination of two factors: (1) increased sensitivity to stimuli across all states; and (2) increased proportion of time spent in a higher-accuracy "engaged" state relative to biased or disengaged states. These findings highlight the power of our approach for characterizing the temporal evolution of multiple strategies across learning.

From innate to instructed: A new look at perceptual decision-making

Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.

Different state-dependence of population codes across cortex

During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an 'optimal' state and two 'sub-optimal' states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.

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

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 expectation-maximization framework to track the hidden states during the [Formula: see text]-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.