Article
Chemistry, Analytical
Mircea Hulea, George Iulian Uleru, Constantin Florin Caruntu
Summary: The control of anthropomorphic hands should be carried out by artificial units with high biological plausibility, such as adaptive spiking neural networks. These networks can enable robots to learn motions independently through mechanisms like Hebbian learning. This bioinspired concept allows robots to stop their movements based on specific angles without the need for external stimuli.
Article
Computer Science, Artificial Intelligence
Samvel Mkhitaryan, Philippe Giabbanelli, Maciej K. Wozniak, Gonzalo Napoles, Nanne De Vries, Rik Crutzen
Summary: FCMpy is an open-source Python module that provides tools for building and analyzing Fuzzy Cognitive Maps (FCMs) in end-to-end projects. It allows users to derive fuzzy causal weights, implement machine learning algorithms, and conduct scenario analysis. The module aims to facilitate the development and testing of FCM models for researchers from various fields.
PEERJ COMPUTER SCIENCE
(2022)
Article
Neurosciences
Xiaokai Xia, Mingqian Guo, Ling Wang
Summary: It has been found that manipulating the ratio of congruent to incongruent trials in conflict tasks can affect the size of conflict effects through control learning and irrelevant stimulus-response learning mechanisms. While previous studies have identified the brain regions associated with control-learning-modulated conflict effects, less is known about the neural substrates underlying conflict effects modulated by irrelevant S-R learning. In this fMRI study, participants performed a Simon task with dynamically changing ratios of congruent to incongruent trials, and the learning models quantitatively learned the probability of irrelevant S-R associations. The behavioral and fMRI results showed that large unsigned prediction errors associated with slow responses and transiently increased activity in the fronto-parietal and cingulo-opercular network, suggesting that learning of irrelevant S-R associations modulates reactive control and provides a new way to modulate cognitive control compared to the control learning account.
Article
Computer Science, Artificial Intelligence
Yeonggyu Yun, Hye-Young Jung
Summary: The study examines the effects of policy reforms on public medical insurance on households using fuzzy cognitive map. A hybrid approach is adopted to construct maps for low-income households and general households separately. Results show that government subsidy increases have the largest impacts on households, demonstrating the flexibility and extensibility of FCM.
Article
Computer Science, Artificial Intelligence
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
Summary: This paper investigates the application of Hebbian learning strategies in training Convolutional Neural Networks (CNNs). Experimental comparisons are made between Hebbian learning and other methods, demonstrating the effectiveness of Hebbian learning in training feature extraction layers and reducing training time.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Ying-Hsun Lai, Tung-Cheng Wu, Chin-Feng Lai, Laurence Tianruo Yang, Xiaokang Zhou
Summary: This article proposes a new expected advantage learning method to moderate the maximum value of expectation-based deep reinforcement learning for industrial applications. By replacing the sigmoid function with the tanh function as the softmax activation value, the proposed method successfully reduces the issue of numerical overfitting in cognitive computing.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Neurosciences
M. Ganesh Kumar, Cheston Tan, Camilo Libedinsky, Shih-Cheng Yen, Andrew Y. Y. Tan
Summary: The study shows that classic agents can learn to navigate to a single reward location and adapt to reward location displacement, but are unable to learn multiple cue-reward location tasks. By improving the agent's architecture and learning methods, this limitation can be overcome.
Article
Computer Science, Information Systems
Adolfo Perrusquia, Wen Yu, Xiaoou Li
Summary: This paper discusses a human behavior learning approach for nonlinear systems control, utilizing neural reinforcement learning algorithms and other cognitive models to accelerate the learning process. By introducing persistent exciting signals and experience replay methods, the learning accuracy is improved and the sensitivity problem of human actions is addressed. The stability and convergence of neural network based reinforcement learning is also discussed, with simulation results confirming the effectiveness of the approach in two benchmark control problems.
INFORMATION SCIENCES
(2021)
Article
Physics, Multidisciplinary
Ajay Deep Kachhvah, Sarika Jalan
Summary: This letter investigates the effects of adaptive development of pure two- and three-simplicial complexes on the nature of the transition to desynchrony of oscillator ensembles. The adaptation in the pure simplicial coupling is based on the Hebbian learning rule, where the coupling weight increases if the oscillators forming it are in phase and decreases if they are out of phase. The coupling weights in these pure simplicial complexes give rise to first-order routes to desynchronization, which are entirely characterized by respective Hebbian learning parameters.
NEW JOURNAL OF PHYSICS
(2022)
Correction
Automation & Control Systems
Bart Kosko
Summary: This passage points out that two prime symbols are missing in an equation, which are needed to indicate the derivative in the correct equation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Joshua Bensemann, Michael Witbrock
Summary: Recent attempts have been made to use Artificial Intelligence systems to model aspects of consciousness, with Deep Neural Networks given additional functionality to emulate phenological aspects of consciousness by self-generating information representing multi-modal inputs. These added functions were found to improve model accuracy after training and reduce the amount of training needed to reach highest accuracy scores.
Article
Clinical Neurology
Matthew Weightman, Neeraj Lalji, Chin-Hsuan Sophie Lin, Joseph M. Galea, Ned Jenkinson, R. Chris Miall
Summary: Brief bursts of anodal stimulation to the cerebellum during a visuomotor adaptation task were found to enhance motor adaptation significantly better than standard TDCS. Short duration, event related, anodal TDCS targeting the cerebellum enhances motor adaptation compared to the standard model.
Article
Biochemical Research Methods
Jakub Fil, Neil Dalchau, Dominique Chu
Summary: Hebbian theory explains how neurons in the brain adapt to stimuli for learning. It is an unsupervised learning method that does not require feedback, making it suitable for autonomous learning in systems. This paper explores the design of molecular systems to exhibit protointelligent behaviors and proposes a chemical reaction network (CRN) that can autonomously learn across multiple input channels.
ACS SYNTHETIC BIOLOGY
(2022)
Article
Psychology, Social
William T. L. Cox, Xizhou Xie, Patricia G. Devine
Summary: This study examined how people learn when their stereotypes are confirmed, disconfirmed, or left untested. The findings showed that stereotype-confirming evidence had more influence than stereotype-disconfirming evidence, and untested stereotypes could lead to memory misrepresentation. The results pose challenges for efforts to reduce stereotyping.
JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoyang Wang, Xiufen Ye
Summary: This paper proposes a reinforcement learning framework that achieves sample-efficient self-stabilized online learning control by imitating cognitive mechanisms of the brain. Multiple actors were applied, working competitively or cooperatively, to maintain system stability during the learning process.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Neurosciences
Vesal Rasoulzadeh, Muhammet Ikbal Sahan, Jean-Philippe van Dijck, Elger Abrahamse, Anna Marzecova, Tom Verguts, Wim Fias
Summary: Recent studies have shown that spatial attention is involved in marking the serial position of items in verbal working memory, and accessing these items may involve horizontal shifts of spatial attention. Electroencephalography recordings demonstrate that retrieving serial information from verbal working memory involves spatial attention processes that share similarities with those operating on visuospatial working memory and external space.
Article
Neurosciences
Pieter Verbeke, Kate Ergo, Esther De Loof, Tom Verguts
Summary: This study tested a computational model proposing that midfrontal theta oscillations implement hierarchical learning through synchrony binding. Results suggest that the brain uses theta power and synchronization to flexibly switch between task rule modules.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Neurosciences
Cristian B. Calderon, Esther De Loof, Kate Ergo, Anna Snoeck, Carsten N. Boehler, Tom Verguts
Summary: Behavioral evidence suggests that reward prediction errors play a key role in episodic memory acquisition. In a novel task where RPEs were manipulated, fMRI results confirmed that signed RPEs are encoded in the ventral striatum and mediate their effects on episodic memory accuracy. Connectivity between processing areas and the hippocampus and ventral striatum increased with RPE value, supporting their central role in episodic memory formation.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Neurosciences
Ziwen Peng, Chuanyong Xu, Ning Ma, Qiong Yang, Ping Ren, Rongzhen Wen, Lili Jin, Jierong Chen, Zhen Wei, Tom Verguts, Qi Chen
Summary: The study found that patients with OCD and their UFDRs exhibit reduced white matter connectivity in the goal-directed network, which may explain symptom onset and impaired cognitive flexibility. These alterations could be an endophenotype of OCD.
BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Pieter Verbeke, Tom Verguts
Summary: Research has found that finding the optimal balance between shared and separated neural representations is a crucial challenge. The multiplicative adaptive modulation network outperformed others in terms of task accuracy and developed hidden units that optimally share representations between tasks.
Article
Biochemical Research Methods
Cristian Buc Calderon, Tom Verguts, Michael J. Frank
Summary: Human cognition is characterized by the ability to adaptively produce precise spatiotemporal sequences given a certain context. Existing neural network models fail to account for these properties due to the limitation of storing sequence and timing information in the same neural network weights. The ACDC model introduced in this study modularly stores sequence and timing information in distinct loci of the network, enabling a wide range of temporal properties and flexible learning and performance abilities.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Psychology, Multidisciplinary
Mehdi Senoussi, Pieter Verbeke, Tom Verguts
Summary: The limitation in working memory storage capacity is a result of time-based binding, which is essential for flexible cognition. This theory is supported by simulations and empirical evidence, distinguishing it from other resource theories.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Psychology, Biological
Mehdi Senoussi, Pieter Verbeke, Kobe Desender, Esther De Loof, Durk Talsma, Tom Verguts
Summary: This study reveals that the peak frequency of theta oscillations shifts adaptively in response to task demands, supporting flexible task implementation. The frequency shift is associated with behavioral performance.
NATURE HUMAN BEHAVIOUR
(2022)
Article
Multidisciplinary Sciences
Kobe Desender, Luc Vermeylen, Tom Verguts
Summary: The authors find that current measures of metacognition are confounded with response caution and propose an alternative dynamic measure. They show a relationship between response caution and the popular measure of metacognition, M-ratio. Additionally, they demonstrate that using a dynamic measure, v-ratio, can avoid the impact of the speed-accuracy tradeoff in metacognition assessment.
NATURE COMMUNICATIONS
(2022)
Article
Neurosciences
Meng Liu, Wenshan Dong, Yiling Wu, Pieter Verbeke, Tom Verguts, Qi Chen
Summary: Considerable evidence suggests that the dorsolateral prefrontal cortex (DLPFC) plays a crucial role in hierarchical learning. This study investigated the causal relationship between frontal theta oscillations and hierarchical learning. The findings show that theta stimulation can modulate the low-level learning rate and that environmental features may influence the effect of theta stimulation.
Article
Biochemical Research Methods
Ziwen Peng, Luning He, Rongzhen Wen, Tom Verguts, Carol A. Seger, Qi Chen
Summary: This study investigates the cognitive and computational mechanisms underlying the influence of Pavlovian cues on instrumental behavior in OCD patients and healthy controls. The results indicate a weaker Pavlovian influence on instrumental behavior in OCD patients, especially when negative cues associated with punishment are present. This study provides deeper insight into our understanding of deficits in OCD from the perspective of Pavlovian influences on instrumental behavior and may have implications for OCD treatment modalities focused on reducing compulsive behaviors.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Psychology, Mathematical
Maud Beeckmans, Pieter Huycke, Tom Verguts, Pieter Verbeke
Summary: The standard approach of carrying out a goodness-of-recovery study to determine the amount of data needed for useful parameter estimations from a computational model may not always be optimal. This paper proposes a novel approach using a generalized concept of statistical power and a Python-based toolbox to determine the required data size for parameter estimates. Simulations indicate that a high number of trials per person is necessary for high-powered studies in a specific computational model.
BEHAVIOR RESEARCH METHODS
(2023)
Article
Neuroimaging
Ziwen Peng, Tingxin He, Ping Ren, Lili Jin, Qiong Yang, Chuanyong Xu, Rongzhen Wen, Jierong Chen, Zhen Wei, Tom Verguts, Qi Chen
Summary: This study found an imbalance in striatal connectivity, specifically increased connectivity in the caudate, in OCD patients. The imbalance in connectivity was only observed in the OCD patient group and was negatively associated with task-switch performance. These findings suggest that the abnormal increase in caudate activity may serve as a clinical characteristic for OCD.
NEUROIMAGE-CLINICAL
(2022)
Article
Neurosciences
Meng Liu, Wenshan Dong, Shaozheng Qin, Tom Verguts, Qi Chen
Summary: This study investigates the neural basis of human perception and learning, revealing that the brain employs hierarchical learning and encodes low-level and high-level learning signals separately.
Article
Psychology, Experimental
Danesh Shahnazian, Mehdi Senoussi, Ruth M. Krebs, Tom Verguts, Clay B. Holroyd
Summary: In this study, a data-driven approach was used to reanalyze a task involving coffee and tea making, revealing the involvement of the inferior-temporal gyrus and lateral prefrontal cortex in maintaining temporal and contextual information for hierarchically organized action sequences. Additionally, temporal information appears to be more strongly encoded in areas over the left hemisphere.
TOPICS IN COGNITIVE SCIENCE
(2022)
