Can observers maintain more than 1 attentional set and search for 2 features in parallel? Previous studies that relied on attentional capture by irrelevant distractors to answer this question focused on features from the same dimension and specifically, on color. They showed that 2 separate color templates can guide attention selectively and simultaneously. Here, the authors investigated attentional guidance by 2 features from different dimensions. In three spatial-cueing experiments, they compared contingent capture during single-set versus dual-set search. The results showed that attention was guided less efficiently by 2 features than by just 1. This impairment varied considerably across target-feature dimensions (color, size, shape and orientation). Confronted with previous studies, our findings suggest avenues for future research to determine whether impaired attentional guidance by multiple templates occurs only in cross-dimensional disjunctive search or also in within-dimension search. The present findings also showed that although performance improved when the target feature repeated on successive trials, a relevant-feature cue did not capture attention to a larger extent when its feature matched that of the previous target. These findings suggest that selection history cannot account for contingent capture and affects processes subsequent to target selection. (PsycINFO Database Record

Recent neuroscience studies in awake and behaving animals demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from this video data. In this work we introduce a new semi-supervised framework that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this method, the Partitioned Subspace Variational Autoencoder (PS-VAE), on head-fixed mouse behavioral videos. In a close up video of a mouse face, where we track pupil location and size, our method extracts unsupervised outputs that correspond to the eyelid and whisker pad positions, with no additional user annotations required. We use this resulting interpretable behavioral representation to construct saccade and whisking detectors, and quantify the accuracy with which these signals can be decoded from neural activity in visual cortex. In a two-camera mouse video we show how our method separates movements of experimental equipment from animal behavior, and extracts unsupervised features like chest position, again with no additional user annotation needed. This allows us to construct paw and body movement detectors, and decode individual features of behavior from widefield calcium imaging data. Our results demonstrate how the interpretable partitioning of behavioral videos provided by the PS-VAE can facilitate downstream behavioral and neural analyses.

Cognitive alterations are frequently observed and studied in persons who present with psychiatric and neurological disorders. Recently an increased number of investigations on the self and attendant processes have interested psychologists, psychiatrists, and neuropsychologists, particularly when a person has suffered brain damage that upsets their daily function in economic, social, personal, behavioural, and emotional ways. At the same time, many theories have been developed to explain the observable changes in ego‐identity of persons with brain injury as well as diverse rehabilitation programs centred on the re‐establishment of the ego‐identity changes. The present article focuses on: (1) the elucidation of the construct of self in current personality theoretical formulations; (2) the major negative effects of brain impairments on the cognitive, neurobehavioural, emotional, and personality functions of persons with brain injuries; (3) the conceptual underpinnings of Kurt Goldstein's views on the neuropsychological rehabilitation endeavour; (4) the illustration, using several clinical examples, that it is possible, following intensive neuropsychological rehabilitative interventions, to restore the shattered self of some brain‐injured persons; and (5) the growing number of holistic programs of rehabilitation for brain‐injured persons all over the world. In conclusion, the sequelae of brain injury not only imply alterations in neurological or cognitive process, but also involve changes in personality aspects. Nevertheless the number of holistic programs has increased, and a particular model of the holistic approach to the neuropsychological rehabilitation of persons with brain injury has been implemented in diverse countries such as Australia, Belgium, Denmark, England, Finland, France, Germany, Holland, Israel, Italy, South Africa, and the United States. Although each one of these programs has been modified to resolve the particular necessities and resources of the different countries, all of them implement Kurt Goldstein's ideas concerning the rehabilitation of persons with brain injuries.

Contextual effects require integration of top-down predictions and bottom-up visual information. Given the widely assumed link between integration and consciousness, we asked whether contextual effects require consciousness. In two experiments (total N = 60), an ambiguous stimulus (which could be read as either B or 13) was presented alongside masked numbers (12 and 14) or letters (A and C). Context biased stimulus classification when it was consciously and unconsciously perceived. However, unconsciously perceived contexts evoked smaller effects. This finding was replicated and generalized into another language in a further experiment ( N = 46) using a different set of stimuli, strengthening the claim that symbolic contextual effects can occur without awareness. Moreover, four experiments (total N = 160) suggested that these unconscious effects might be limited to the categorical level (numbers context vs. letters context) and do not extend to the lexical level (words context vs. nonwords context). Taken together, our results suggest that although consciousness may not be necessary for effects that require simple integration or none at all, it is nevertheless required for integration over larger semantic windows.

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

Pose estimation algorithms are shedding new light on animal behavior and intelligence. Most existing models are only trained with labeled frames (supervised learning). Although effective in many cases, the fully supervised approach requires extensive image labeling, struggles to generalize to new videos, and produces noisy outputs that hinder downstream analyses. We address each of these limitations with a semi-supervised approach that leverages the spatiotemporal statistics of unlabeled videos in two different ways. First, we introduce unsupervised training objectives that penalize the network whenever its predictions violate smoothness of physical motion, multiple-view geometry, or depart from a low-dimensional subspace of plausible body configurations. Second, we design a new network architecture that predicts pose for a given frame using temporal context from surrounding unlabeled frames. These context frames help resolve brief occlusions or ambiguities between nearby and similar-looking body parts. The resulting pose estimation networks achieve better performance with fewer labels, generalize better to unseen videos, and provide smoother and more reliable pose trajectories for downstream analysis; for example, these improved pose trajectories exhibit stronger correlations with neural activity. We also propose a Bayesian post-processing approach based on deep ensembling and Kalman smoothing that further improves tracking accuracy and robustness. We release a deep learning package that adheres to industry best practices, supporting easy model development and accelerated training and prediction. Our package is accompanied by a cloud application that allows users to annotate data, train networks, and predict new videos at scale, directly from the browser.

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