1,558 results match your criteria learn representational

Neural representations of the amount and the delay time of reward in intertemporal decision making.

Hum Brain Mapp 2021 May 2. Epub 2021 May 2.

Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.

Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). Read More

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Representation, Interaction, Interpretation. Making sense of the context in clinical reasoning.

Med Educ 2021 May 1. Epub 2021 May 1.

Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Background: All thinking occurs in some sort of context, rendering the relation between context and clinical reasoning a matter of significant interest. Context however has a notoriously vague and contested meaning and there is a profound disagreement between different research traditions studying clinical reasoning in how context is understood. Empirical evidence examining the impact (or not) of context on clinical reasoning cannot be interpreted without reference to the meaning ascribed to context. Read More

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Explaining risky choices with judgments: Framing, the zero effect, and the contextual relativity of gist.

J Exp Psychol Learn Mem Cogn 2021 Apr 29. Epub 2021 Apr 29.

Department of Human Development.

Contemporary theories of decision-making are compared with respect to their predictions about the judgments that are hypothesized to underlie risky choice framing effects. Specifically, we compare predictions of psychophysical models, such as prospect theory, to the cognitive representational approach of fuzzy-trace theory in which the presence or absence of zero is key to framing effects. Three experiments implemented a high-power design in which many framing problems were administered to participants, who rated the attractiveness of either the certain or risky options. Read More

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Fictionalism of Anticipation.

Biosemiotics 2021 Apr 15:1-17. Epub 2021 Apr 15.

Institute of Applied Mathematics, Vilnius University, Vilnius, Lithuania.

A promising recent approach for understanding complex phenomena is recognition of anticipatory behavior of living organisms and social organizations. The anticipatory, predictive action permits learning, novelty seeking, rich experiential existence. I argue that the established frameworks of anticipation, adaptation or learning imply overly passive roles of anticipatory agents, and that a standpoint reflects the core of anticipatory behavior better than representational or future references. Read More

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#MentalHealthArt: How Instagram artists promote mental health awareness online.

Public Health 2021 Apr 14;194:67-74. Epub 2021 Apr 14.

Department of Psychology, Bowling Green State University, Bowling Green, OH, 43403, USA. Electronic address:

Objectives: Instagram artwork about mental illness was examined to learn how artists promote awareness about mental health and mental illness.

Study Design: Mixed methods predictive and descriptive analyses were conducted on a public dataset of artwork posts from Instagram.

Methods: One thousand art images were classified by media (painting, drawing, collage, photograph, digital art, printmaking, sculpture, jewelry, or other) and style (representational, nonrepresentational, and functional). Read More

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Quantifying the separability of data classes in neural networks.

Neural Netw 2021 Apr 5;139:278-293. Epub 2021 Apr 5.

Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg (FAU), Germany; Cognitive Neuroscience Center, University of Groningen, The Netherlands. Electronic address:

We introduce the Generalized Discrimination Value (GDV) that measures, in a non-invasive manner, how well different data classes separate in each given layer of an artificial neural network. It turns out that, at the end of the training period, the GDV in each given layer L attains a highly reproducible value, irrespective of the initialization of the network's connection weights. In the case of multi-layer perceptrons trained with error backpropagation, we find that classification of highly complex data sets requires a temporal reduction of class separability, marked by a characteristic 'energy barrier' in the initial part of the GDV(L) curve. Read More

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Sleep strengthens integration of spatial memory systems.

Learn Mem 2021 05 15;28(5):162-170. Epub 2021 Apr 15.

Institute for Medical Psychology and Behavioral Neurobiology, University Tübingen, 72076 Tübingen, Germany.

Spatial memory comprises different representational systems that are sensitive to different environmental cues, like proximal landmarks or local boundaries. Here we examined how sleep affects the formation of a spatial representation integrating landmark-referenced and boundary-referenced representations. To this end, participants ( = 42) were familiarized with an environment featuring both a proximal landmark and a local boundary. Read More

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Symbolic categorization of novel multisensory stimuli in the human brain.

Neuroimage 2021 Apr 2;235:118016. Epub 2021 Apr 2.

Centre for Mind/Brain Sciences, University of Trento, Italy.

When primates (both human and non-human) learn to categorize simple visual or acoustic stimuli by means of non-verbal matching tasks, two types of changes occur in their brain: early sensory cortices increase the precision with which they encode sensory information, and parietal and lateral prefrontal cortices develop a categorical response to the stimuli. Contrary to non-human animals, however, our species mostly constructs categories using linguistic labels. Moreover, we naturally tend to define categories by means of multiple sensory features of the stimuli. Read More

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Transcranial Magnetic Stimulation as a Tool to Investigate Motor Cortex Excitability in Sport.

Brain Sci 2021 Mar 28;11(4). Epub 2021 Mar 28.

Deparment of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy.

Transcranial magnetic stimulation, since its introduction in 1985, has brought important innovations to the study of cortical excitability as it is a non-invasive method and, therefore, can be used both in healthy and sick subjects. Since the introduction of this cortical stimulation technique, it has been possible to deepen the neurophysiological aspects of motor activation and control. In this narrative review, we want to provide a brief overview regarding TMS as a tool to investigate changes in cortex excitability in athletes and highlight how this tool can be used to investigate the acute and chronic responses of the motor cortex in sport science. Read More

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Prefrontal contributions to action control in rodents.

Int Rev Neurobiol 2021 19;158:373-393. Epub 2020 Dec 19.

Optophysiology, University of Freiburg, Faculty of Biology, BrainLinks-BrainTools, Bernstein Center, Intelligent Machine-Brain Interfacing Technology (IMBIT), Freiburg, Germany. Electronic address:

The rodent medial prefrontal cortex (mPFC) is typically considered to be involved in cognitive aspects of action control, e.g., decision making, rule learning and application, working memory and generally guiding adaptive behavior (Euston, Gruber, & McNaughton, 2012). Read More

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December 2020

Which Task Characteristics Do Students Rely on When They Evaluate Their Abilities to Solve Linear Function Tasks? - A Task-Specific Assessment of Self-Efficacy.

Front Psychol 2021 12;12:596901. Epub 2021 Mar 12.

University of Education Freiburg, Freiburg im Breisgau, Germany.

Self-efficacy is an important predictor of learning and achievement. By definition, self-efficacy requires a task-specific assessment, in which students are asked to evaluate whether they can solve concrete tasks. An underlying assumption in previous research into such assessments was that self-efficacy is a one-dimensional construct. Read More

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Multidimensional Face Representation in a Deep Convolutional Neural Network Reveals the Mechanism Underlying AI Racism.

Front Comput Neurosci 2021 10;15:620281. Epub 2021 Mar 10.

Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.

The increasingly popular application of AI runs the risk of amplifying social bias, such as classifying non-white faces as animals. Recent research has largely attributed this bias to the training data implemented. However, the underlying mechanism is poorly understood; therefore, strategies to rectify the bias are unresolved. Read More

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Unity and diversity of neural representation in executive functions.

J Exp Psychol Gen 2021 Mar 25. Epub 2021 Mar 25.

Key Laboratory of Cognition and Personality of Ministry of Education.

Although the unity and diversity model of executive functions (EFs) has been replicated, there are some studies questioning the validity of the EFs construct. This debate can be partially resolved by directly combining the brain activity pattern in different executive control processes. Previous univariate activation studies have suggested that the neural substrates of different EFs (e. Read More

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The Role of Understanding on Architectural Beauty: Evidence From the Impact of Semantic Description on the Aesthetic Evaluation of Architecture.

Psychol Rep 2021 Mar 23:332941211002135. Epub 2021 Mar 23.

Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, P. R. China.

There is evidence that greater aesthetic experience can be linked to artworks when their corresponding meanings can be successfully inferred and understood. Modern cultural-expo architecture can be considered a form of artistic creation and design, and the corresponding design philosophy may be derived from representational objects or abstract social meanings. The present study investigates whether cultural-expo architecture with an easy-to-understand architectural appearance design is perceived as more beautiful and how architectural photographs and different types of descriptions of architectural appearance designs interact and produce higher aesthetic evaluations. Read More

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Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks.

Front Comput Neurosci 2021 4;15:543872. Epub 2021 Mar 4.

Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA-Institute Brain Structure-Function Relationship (JBI-1 / INM-10), Research Centre Jülich, Jülich, Germany.

Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified by the researcher. This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Read More

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Max-Margin Deep Diverse Latent Dirichlet Allocation With Continual Learning.

IEEE Trans Cybern 2021 Mar 17;PP. Epub 2021 Mar 17.

Deep probabilistic aspect models are widely utilized in document analysis to extract the semantic information and obtain descriptive topics. However, there are two problems that may affect their applications. One is that common words shared among all documents with low representational meaning may reduce the representation ability of learned topics. Read More

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Health promotion innovations scale up: combining insights from framing and actor-network to foster reflexivity.

Health Promot Int 2021 Mar 16. Epub 2021 Mar 16.

Chaire de recherche du Canada Approches communautaires et inégalités de santé (CACIS), Université de Montréal, Qc, Canada.

There are numerous hurdles down the road for successfully scaling up health promotion innovations into formal programmes. The challenges of the scaling-up process have mainly been conceived in terms of available resources and technical or management problems. However, aiming for greater impact and sustainability involves addressing new contexts and often adding actors whose perspectives may challenge established orientations. Read More

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Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition.

Front Comput Neurosci 2021 22;15:625804. Epub 2021 Feb 22.

Department of Psychology, Tsinghua University, Beijing, China.

Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Read More

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February 2021

Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis.

J Neural Eng 2021 Mar 25;18(4). Epub 2021 Mar 25.

Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.

. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e. Read More

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Brain-behavior relationships in the perceptual decision-making process through cognitive processing stages.

Neuropsychologia 2021 May 5;155:107821. Epub 2021 Mar 5.

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. Electronic address:

Perceptual decision making - the process of detecting and categorizing information - has been studied extensively over the last two decades. In this study, we aim to bridge the gap between neural and behavioral representations of the perceptual decision-making process. The neural characterization of decision-making was investigated by evaluating the duration and neural signature of the information processing stages. Read More

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Volitional learning promotes theta phase coding in the human hippocampus.

Proc Natl Acad Sci U S A 2021 Mar;118(10)

Laboratory of Synthetic Perceptive, Emotive and Cognitive Systems, Institute for Bioengineering of Catalonia, Barcelona Institute of Science and Technology, 08028 Barcelona, Spain;

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4-12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3-8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Read More

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Building an adaptive interface via unsupervised tracking of latent manifolds.

Neural Netw 2021 May 20;137:174-187. Epub 2021 Jan 20.

Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA; Shirley Ryan Ability Lab, Chicago, IL, 60611, USA. Electronic address:

In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Read More

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Contrastive Similarity Matching for Supervised Learning.

Neural Comput 2021 Feb 22:1-29. Epub 2021 Feb 22.

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, U.S.A.

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. Read More

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February 2021

Implementation-Independent Representation for Deep Convolutional Neural Networks and Humans in Processing Faces.

Front Comput Neurosci 2020 26;14:601314. Epub 2021 Jan 26.

Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.

Deep convolutional neural networks (DCNN) nowadays can match human performance in challenging complex tasks, but it remains unknown whether DCNNs achieve human-like performance through human-like processes. Here we applied a reverse-correlation method to make explicit representations of DCNNs and humans when performing face gender classification. We found that humans and a typical DCNN, VGG-Face, used similar critical information for this task, which mainly resided at low spatial frequencies. Read More

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January 2021

Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks.

Neural Netw 2021 May 22;137:63-74. Epub 2021 Jan 22.

University of Alberta, Department of Computing Science & Department of Psychology, 116 St. and 85 Ave., Edmonton, Alberta, Canada. Electronic address:

As deep neural net architectures minimize loss, they accumulate information in a hierarchy of learned representations that ultimately serve the network's final goal. Different architectures tackle this problem in slightly different ways, but all create intermediate representational spaces built to inform their final prediction. Here we show that very different neural networks trained on two very different tasks build knowledge representations that display similar underlying patterns. Read More

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Mood and Risk-Taking as Momentum for Creativity.

Tsutomu Harada

Front Psychol 2020 21;11:610562. Epub 2021 Jan 21.

Graduate School of Business Administration, Kobe University, Kobe, Japan.

This study examined the effects of mood and risk-taking on divergent and convergent thinking using a Q-learning computation model. The results revealed that while mood was not significantly related to divergent or convergent thinking (as creative thinking types), risk-taking exerted positive effects on divergent thinking in the face of negative rewards. The results were consistent with the representational change theory in insight problem solving. Read More

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January 2021

Semantic Knowledge of Famous People and Places Is Represented in Hippocampus and Distinct Cortical Networks.

J Neurosci 2021 Mar 5;41(12):2762-2779. Epub 2021 Feb 5.

Center for Learning & Memory.

Studies have found that anterior temporal lobe (ATL) is critical for detailed knowledge of object categories, suggesting that it has an important role in semantic memory. However, in addition to information about entities, such as people and objects, semantic memory also encompasses information about places. We tested predictions stemming from the PMAT model, which proposes there are distinct systems that support different kinds of semantic knowledge: an anterior temporal (AT) network, which represents information about entities; and a posterior medial (PM) network, which represents information about places. Read More

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Cross-Representational Signaling and Cohesion Support Inferential Comprehension of Text-Picture Documents.

Front Psychol 2020 18;11:592509. Epub 2021 Jan 18.

LaRAC, Univ. Grenoble Alpes, Grenoble, France.

Learning from a text-picture multimedia document is particularly effective if learners can link information within the text and across the verbal and the pictorial representations. The ability to create a mental model successfully and include those implicit links is related to the ability to generate inferences. Text processing research has found that text cohesion facilitates the generation of inferences, and thus text comprehension for learners with poor prior knowledge or reading abilities, but is detrimental for learners with good prior knowledge or reading abilities. Read More

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January 2021

Learning Diverse Models for End-to-End Ensemble Tracking.

IEEE Trans Image Process 2021 26;30:2220-2231. Epub 2021 Jan 26.

In visual tracking, how to effectively model the target appearance using limited prior information remains an open problem. In this paper, we leverage an ensemble of diverse models to learn manifold representations for robust object tracking. The proposed ensemble framework includes a shared backbone network for efficient feature extraction and multiple head networks for independent predictions. Read More

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January 2021

Learning Efficient Binarized Object Detectors with Information Compression.

IEEE Trans Pattern Anal Mach Intell 2021 Jan 11;PP. Epub 2021 Jan 11.

In this paper, we propose a binarized detection learning method (BiDet) for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. Specifically, we generalize the information bottleneck (IB) principle to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized. Read More

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January 2021