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Instead, our data show that during decision making under risk, value estimates based on learned and described information evoke differential patterns of activity within value-sensitive regions.

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Heuristics are methods for problem solving based on learned experiences and logical guesswork rather pre-established mathematical formulas.

Based on the results of feature selection, we further achieved the Bayesian network construction at both syndrome and symptom levels to explain the relationships among all the nodes and the probability inference could be computed based on learned network structure and conditional probability tables.

Our approach is based on learned probabilistic forward models and information theoretic policy search.

The results indicated that higher-order cognition of Topology was supported by neural networks in: (1) the parietal cortex which is involved in activating possible responses based on learned stimulus-response associations, and (2) the prefrontal cortex that is recruited when there is a need to generate and evaluate hypotheses, and select between competing responses.

Actually, a limitation of the Kamphuis et al. study was that because of a lack of behavioral controls the authors could not exclude that the experimental animals might not have resorted to a workaround strategy based on learned associations between portraits and targets.

It also mimics the human mind's categorization of things, in which objects are quickly and accurately (based on learned visual cues) divided into immediate, distant, and various degrees in between.

In this paper, a new method based on learned bone-structure edge detectors and a coarse-to-fine deformable surface model is proposed to segment and identify vertebrae in 3D CT thoracic images.

Our study subjects successfully formed identity relations between elements based on the common shared feature "shape", and also based on learned associative pairings of specific items.

This pattern of specificity suggests that NCM is specialized for discrimination and memory processes that could serve species and individual recognition, based on learned vocal communication signals.

I introduce a general active, indirect elicitation framework for automated environment design that learns relevant characteristics of participants based on observations of their behavior and optimizes designs based on learned models.

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