3 research outputs found
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pFC Neurons Reflect Categorical Decisions about Ambiguous Stimuli
We examined whether pFC neuron activity reflects categorical decisions in monkeys categorizing ambiguous stimuli. A morphing system was used to systematically vary stimulus shape and precisely define category boundaries. Ambiguous stimuli were centered on a category boundary, that is, they were a mix of 50% of two prototypes and therefore had no category information, so monkeys guessed at their category membership. We found that the monkey's trial-by-trial decision about the category membership of an ambiguous image was reflected in pFC activity. Activity to the same ambiguous image differed significantly, depending on which category the monkey had assigned it to. This effect only occurred when that scheme was behaviorally relevant. These indicate that pFC activity reflects categorical decisions.National Institute of Mental Health (U.S.) (Grant 2R01MH065252-06
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Sensory processing and categorization in cortical and deep neural networks
Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results
Mécanismes cognitifs dans la catégorisation d'objets visuels
La catégorisation est un processus fondamental de la reconnaissance d'objets. Pour comprendre ses mécanismes sous-jacents, cette thèse interroge le rôle du niveau de catégorisation, de l'attention, de la mémoire, et de la relation entre les catégories d'objets, dans la catégorisation de scènes naturelles. Les résultats de la première étude indiquent que les performances de catégorisation sont influencées par les caractéristiques diagnostiques de la tâche. Une seconde étude montre que des objets naturels peuvent être catégorisés en quasi-absence d'attention. Les résultats de la troisième étude indiquent que les scènes sont encodées en mémoire à long-terme sans instruction explicite et catégorisées automatiquement. La dernière étude explore les interactions entre deux représentations d'objets actives simultanément. Plus le degré de relation entre deux objets est élevé, plus le traitement du second objet est affecté. Pour expliquer ces résultats un modèle, inspiré de la physiologie, est proposé qui postule que le niveau d'interaction entre des catégories d'objet actives simultanément dépend du niveau de chevauchement entre les patterns d'activité du cortex inféro-temporal produits par chacun des objets. Les résultats de cette thèse sont compatibles avec l'idée que les caractéristiques visuelles des objets sont traitées automatiquement (étude 3) en quasi-absence d'attention (étude 2) et représentées dans la voie visuelle ventrale de façon distribuée et continue. Les performances de catégorisation dépendraient de la similarité des catégories cibles et distracteurs (étude 1) ou de la similarité entre les représentations actives de deux objets (étude 4).Categorization is a fundamental process of object recognition. To determine its underlying mechanisms, a series of experiments examined the roles of stimulus properties, categorization level, attention, memory, and category-relatedness in natural scene categorization tasks. The results of the first study suggest that categorization performance is driven by characteristics that are diagnostic for the task. A second study shows that visual objects embedded in complex natural scenes can be categorized in the near-absence of attention. The results of a third study suggest that long-term encoding of complex scenes happens without any explicit instruction, and information about object categories is processed automatically. The final study explores the interaction between two concurrently active category representations by presenting two objects in a rapid sequence. The greater the degree of relatedness between two objects, the more affected the processing of the second object is. To explain these results a physiologically inspired model is proposed, which posits that the extent of interaction between concurrently active objects depends on the extent of overlap between the activity patterns in the infero-temporal cortex elicited by the two objects. The results of this thesis support the idea that visual object characteristics are processed automatically (study 3) in the near-absence of attention (study 2) and represented in the ventral stream in a distributed and continuous manner. Categorization performance would depend on the similarity between and within the target and the distractor categories (study 1) or on the similarity between two active object representations (study 4)