Representations of task context play a crucial role in shaping human behavior. While the nature of these representations remains poorly understood, existing theories share a number of basic assumptions. One of these is that task representations are discrete, independent, and non-overlapping. We present here an alternative view, according to which task representations are instead viewed as graded, distributed patterns occupying a shared, continuous representational space. In recent work, we have implemented this view in a computational model of routine sequential action. In the present article, we focus specifically on this model's implications for understanding task representation, considering the implications of the account for two influential concepts: (1) cognitive underspecification, the idea that task representations may be imprecise or vague, especially in contexts where errors occur, and (2) information-sharing, the idea that closely related operations rely on common sets of internal representations.
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http://dx.doi.org/10.1007/s00426-002-0103-8 | DOI Listing |
J Neurosci
January 2025
Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern, Switzerland.
The hippocampus supports a multiplicity of functions, with the dorsal region contributing to spatial representations and memory, and the ventral hippocampus (vH) being primarily involved in emotional processing. While spatial encoding has been extensively investigated, how the vH activity is tuned to emotional states, e.g.
View Article and Find Full Text PDFPhysiol Meas
January 2025
Nanchang University, 1st Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330031, CHINA.
Background And Objective: In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones.
View Article and Find Full Text PDFComput Biol Med
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address:
Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany.
The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects.
View Article and Find Full Text PDFPLoS Biol
January 2025
Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, Heraklion, Crete, Greece.
Goal-directed behavior requires the effective suppression of distractions to focus on the task at hand. Although experimental evidence suggests that brain areas in the prefrontal and parietal lobe contribute to the selection of task-relevant and the suppression of task-irrelevant stimuli, how conspicuous distractors are encoded and effectively ignored remains poorly understood. We recorded neuronal responses from 2 regions in the prefrontal and parietal cortex of macaques, the frontal eye fields (FEFs) and the lateral intraparietal (LIP) area, during a visual search task, in the presence and absence of a salient distractor.
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