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neurocomputational model of compulsivity: deviating from an uncertain goal-directed system

BRAIN 2024年第6期147卷 2230-2244页

作者： Kim, Taekwan Lee, Sang Wan Lho, Silvia Kyungjin Moon, Sun-Young Kim, Minah Kwon, Jun Soo Seoul Natl Univ Coll Nat Sci Dept Brain & Cognit Sci Seoul 08826 South Korea Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Ctr Neurosci Inspired Artificial Intelligence Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Kim Jaechul Grad Sch AI Daejeon 34141 South Korea Seoul Natl Univ Hosp Dept Neuropsychiat 101 Daehak Ro Seoul 03080 South Korea Seoul Natl Univ Dept Psychiat Coll Med Seoul 03080 South Korea

Despite a theory that an imbalance in goal-directed versus habitual systems serve as building blocks of compulsions, research has yet to delineate how this occurs during arbitration between the two systems in obsessive-compulsive disorder. Inspired by a brain model in which the inferior frontal cortex selectively gates the putamen to guide goal-directed or habitual actions, this study aimed to examine whether disruptions in the arbitration process via the fronto-striatal circuit would underlie imbalanced decision-making and compulsions in *** patients with obsessive-compulsive disorder [mean (standard deviation) age = 26.93 (6.23) years, 12 females (40%)] and 30 healthy controls [mean (standard deviation) age = 24.97 (4.72) years, 17 females (57%)] underwent functional MRI scans while performing the two-step Markov decision task, which was designed to dissociate goal-directed behaviour from habitual behaviour. We employed a neurocomputational model to account for an uncertainty-based arbitration process, in which a prefrontal arbitrator (i.e. inferior frontal gyrus) allocates behavioural control to a more reliable strategy by selectively gating the putamen. We analysed group differences in the neural estimates of uncertainty of each strategy. We also compared the psychophysiological interaction effects of system preference (goal-directed versus habitual) on fronto-striatal coupling between groups. We examined the correlation between compulsivity score and the neural activity and connectivity involved in the arbitration *** computational model captured the subjects' preferences between the strategies. Compared with healthy controls, patients had a stronger preference for the habitual system (t = -2.88, P = 0.006), which was attributed to a more uncertain goal-directed system (t = 2.72, P = 0.009). Before the allocation of controls, patients exhibited hypoactivity in the inferior frontal gyrus compared with healthy controls when this region tracked

关键词： obsessive-compulsive disorder compulsivity arbitration goal-directed behaviour neurocomputational model

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Exploring speed-accuracy tradeoff in reaching movements: a neurocomputational model

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13377-13403页

作者： Parziale, Antonio Senatore, Rosa Marcelli, Angelo Univ Salerno DIEM Via Giovanni Paolo II 132 I-84084 Fisciano SA Italy

The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human-machine interface. This tradeoff is formalized by Fitts' law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required accuracy in reaching a target or farther is the target, the slower has to be the movement. A variety of computational models of neuromusculoskeletal systems have been proposed to pinpoint the neurobiological mechanisms that are involved in human movement. We introduce a neurocomputational model of spinal cord to unveil how the tradeoff between speed and accuracy elicits from the interaction between neural and musculoskeletal systems. model simulations showed that the speed-accuracy tradeoff is not an intrinsic property of the neuromuscular system, but it is a behavioral trait that emerges from the strategy adopted by the central nervous system for executing faster movements. In particular, results suggest that the velocity of a previous learned movement is regulated by the monosynaptic connection between cortical cells and alpha motoneurons.

关键词： Human movement Speed-accuracy tradeoff Fitts' law neurocomputational model

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Construction of a Hierarchical Organization in Semantic Memory: A model Based on Neural Masses and Gamma-Band Synchronization

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COGNITIVE COMPUTATION 2024年第1期16卷 326-347页

作者： Ursino, Mauro Pirazzini, Gabriele Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon I-47521 Cesena Italy

Semantic memory is characterized by a hierarchical organization of concepts based on shared properties. However, this aspect is insufficiently dealt with in recent neurocomputational models. Moreover, in many cognitive problems that exploit semantic memory, gamma-band synchronization can be relevant in favoring information processing and feature binding. In this work, we propose an attractor network model of semantic memory. Each computational unit, coding for a different feature, is described with a neural mass circuit oscillating in the gamma range. The model is trained with an original nonsymmetric Hebb rule based on a presynaptic gating mechanism. After training, the network creates a taxonomy of categories, distinguishes between subordinate and superordinate concepts, and discriminates between salient and marginal features. Examples are provided concerning a fourteen-animal taxonomy, including several subcategories. A sensitivity analysis reveals the robustness of the network but also points out conditions leading to confusion among categories, similar to the one observed in dreaming and some neurological disorders. Finally, the analysis emphasizes the role of fast GABAergic interneurons and inhibitory-excitatory balance to allow the correct synchronization of features. The model represents an original attempt to deal with a hierarchical organization of objects in semantic memory and correlated patterns, still exploiting gamma-band synchronization to favor neural processing. The same ideas, introduced in a more sophisticated multilayer network, can deepen our knowledge of semantic memory organization in the brain. Finally, they can open new perspectives in quantitatively analyzing neurological disorders connected with distorted semantics.

关键词： neurocomputational model Gamma rhythm Hebb rule Phase synchronization Category formation Feature integration

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Confidence bias prescribes the neurocomputational mechanism of decision-making

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CELL REPORTS 2025年第5期44卷 115563页

作者： Sun, Fanru Ni, Yinmei Lu, Weiwen Su, Jie Wang, Sidong Wan, Xiaohong Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res Beijing Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Peking Univ Sch Psychol & Cognit Sci Beijing Peoples R China Peking Univ Beijing Key Lab Behav & Mental Hlth Beijing Peoples R China

A couple of decision-making models with different ingredients have successfully interpreted choices, confidence, and related neural activities. However, empirical and theoretical evidence is currently lacking to distinguish these models clearly. Here, we investigated the decision-congruent confidence bias, where confidence favors evidence from the chosen option, yet the choice and its correctness or optimality are determined equally by alternative evidence strengths across both perceptual and value-based decision-making tasks. This confidence bias is manifested by confidence-coding neural activities, particularly in the dorsal anterior cingulate cortex. Further analyses on an array of neurocomputational models show that only the decision-making model equipped with mutual inhibition and an urgency signal can produce such a selective bias across almost all parameter regimes. These findings suggest that mutual inhibition and an urgency signal are two indispensable features embedded in the decision-making process, while confidence bias might be its consequence.

关键词： decision-making neurocomputational model confidence bias urgency signal mutual inhibition fMRI

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Rapid Learning of Complex Sequences With Time Constraints: A Dynamic Neural Field model

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2021年第4期13卷 853-864页

作者： Ferreira, Flora Wojtak, Weronika Sousa, Emanuel Louro, Luis Bicho, Estela Erlhagen, Wolfram Univ Minho Ctr Math P-4800058 Guimaraes Portugal Univ Minho Ctr Algoritmi P-4800058 Guimaraes Portugal

Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This article presents a neurocomputational model based on the theoretical framework of dynamic neural fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimensions. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions.

关键词： Sociology Statistics Timing Robots Color Adaptation models Computational modeling Dynamic field theory human-robot interactions interval timing neurocomputational model sequence learning

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A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 849323页

作者： Veronneau-Veilleux, Florence Robaey, Philippe Ursino, Mauro Nekka, Fahima Univ Montreal Fac Pharm Montreal PQ Canada Univ Ottawa Childrens Hosp Eastern Ontario Ottawa ON Canada Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marconi Bologna Italy Univ Montreal Ctr Rech Math Montreal PQ Canada McGill Univ Ctr Appl Math Biosci & Med Montreal PQ Canada

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features

关键词： attention deficit hyperactivity disorder tonic and phasic dopamine neurocomputational model basal ganglia reinforcement learning

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Prediction of Learning Improvement in Mathematics through a Video Game using neurocomputational models 11

Prediction of Learning Improvement in Mathematics through a ...

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Torres-Molina, Richard Riofrio-Valdivieso, Andres Bustamante-Orellana, Carlos Ortega-Zamorano, Francisco Yachay Tech Univ Sch Math Sci & Informat Technol Urcuqui Ecuador Univ Malaga Dept Comp Sci & Languages Malaga Spain

ISBN: (纸本)9789897583506

Learning math is important for the academic life of students: the development of mathematical skills is influenced by different characteristics of students such as geographical position, economic level, parents' education, achievement level, teacher objectives, social level, use of information and communication technologies by teachers, learner motivation, gender, age, preferences for playing video games, and the school year of the students. In this work, these previously mentioned characteristics were considered as the attributes (inputs) of a multilayer neural network that uses a backpropagation algorithm to predict the percentage of improvement in mathematics through a 2D mathematical video game that was developed to allow the children to practice addition and subtraction operations. After applying the neural model, using the twelve attributes mentioned before and the backpropagation algorithm, there was a network of one layer with ten neurons and another network of two layers with 5 neurons in the first layer and 20 neurons in the second layer. Both architectures produced a mean squared error smaller than 0.0069 in the prediction of the student's percentage of improvement in mathematics, being the best configurations found in this study for the neural model. These results lead to the conclusion that we are able to predict the percentage of improvement in math that the students could achieve after playing the game, and therefore, claiming if the video game is recommendable or not for certain students.

关键词： neurocomputational model Mathematics Learning Video Game

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Towards Endowing Intelligent Cars with the Ability to Learn the Routines of Multiple Drivers: A Dynamic Neural Field model 1

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Wojtak, Weronika Ferreira, Flora Guimaraes, Pedro Barbosa, Paulo Monteiro, Sergio Erlhagen, Wolfram Bicho, Estela Univ Minho Res Ctr Math P-4800058 Guimaraes Portugal Univ Minho Res Ctr Algoritmi P-4800058 Guimaraes Portugal

ISBN: (数字)9783030869731

ISBN: (纸本)9783030869731;9783030869724

Driving a car is often a routine activity that includes visiting the same locations at about the same time on a certain day of the week. Here, we present a learning system based on Dynamic Neural Fields that allows an intelligent vehicle to acquire sequential and temporal information about daily driving routines. Learning is fast, implicit (no need to specify destinations in advance), continuous, and can be adapted to different temporal scales. The learned information can be recalled to predict the driver's destination intention, when to arrive at a specific location and when to leave there. Importantly, the system allows to learn and recall multiple routines corresponding to different drivers and different days. This personalized information can be used for planning the next trip for every user of a shared vehicle.

关键词： neurocomputational model Learning driver routines Space and time prediction Dynamic neural field

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A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

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NEUROBIOLOGY OF LEARNING AND MEMORY 2022年第0期193卷 000-000页

作者： Kaushik, Pramod Naude, Jeremie Raju, Surampudi Bapi Alexandre, Frederic Int Inst Informat Technol Hyderabad India Univ Montpellier CNRS Inst Genom Fonct Inst Natl Sante & Rech Med Montpellier France Inria Bordeaux Sud Ouest Talence France Univ Bordeaux LaBRI Bordeaux INP CNRSUMR 5800 Talence France Univ Bordeaux Inst Neurodegenerat Dis CNRS UMR 5293 Bordeaux France

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

关键词： Classical Conditioning Reward Prediction Error Dissociation VTA VTA GABA neurocomputational model

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The Presence of Background Noise Extends the Competitor Space in Native and Non-Native Spoken-Word Recognition: Insights from Computational modeling

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COGNITIVE SCIENCE 2022年第2期46卷 e13110页

作者： Karaminis, Themis Hintz, Florian Scharenborg, Odette Edge Hill Univ Dept Psychol St Helens Rd Ormskirk L39 4QP England Max Planck Inst Psycholinguist Dept Psychol Language Nijmegen Netherlands Delft Univ Technol Multimedia Comp Grp Delft Netherlands

Oral communication often takes place in noisy environments, which challenge spoken-word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken-word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non-native) using computational modeling. We developed ListenIN (Listen-In-Noise), a neural-network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non-native spoken-word comprehension. We also examined the model's activation states during online spoken-word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non-native listening. Taken together, our results support accounts positing a "many-additional-competitors scenario" for the effects of noise on spoken-word recognition.

关键词： Spoken-word recognition Phonological competition Competitor space Noise Non-native listening Computational modeling neurocomputational model Deep neural networks

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