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Takacs A, Toth‐Faber E, Schubert L, Tarnok Z, Ghorbani F, Trelenberg M, Nemeth D, Münchau A, Beste C. Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome. Hum Brain Mapp 2024; 45:e26719. [PMID: 38826009 PMCID: PMC11144952 DOI: 10.1002/hbm.26719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.
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Affiliation(s)
- Adam Takacs
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTechnische Universität DresdenDresdenGermany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität DresdenDresdenGermany
| | - Eszter Toth‐Faber
- Institute of PsychologyELTE Eötvös Loránd UniversityBudapestHungary
- Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Lina Schubert
- Institute of Systems Motor ScienceUniversity of LübeckLübeckGermany
| | - Zsanett Tarnok
- Vadaskert Child and Adolescent Psychiatry Hospital and Outpatient ClinicBudapestHungary
| | - Foroogh Ghorbani
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTechnische Universität DresdenDresdenGermany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität DresdenDresdenGermany
| | - Madita Trelenberg
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTechnische Universität DresdenDresdenGermany
| | - Dezso Nemeth
- INSERMUniversité Claude Bernard Lyon 1, CNRS, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292BronFrance
- NAP Research Group, Institute of Psychology, Eötvös Loránd University and Institute of Cognitive Neuroscience and Psychology, HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Department of Education and Psychology, Faculty of Social SciencesUniversity of Atlántico MedioLas Palmas de Gran CanariaSpain
| | | | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTechnische Universität DresdenDresdenGermany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität DresdenDresdenGermany
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Kóbor A, Janacsek K, Hermann P, Zavecz Z, Varga V, Csépe V, Vidnyánszky Z, Kovács G, Nemeth D. Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli. J Cogn Neurosci 2024; 36:1239-1264. [PMID: 38683699 DOI: 10.1162/jocn_a_02173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.
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Affiliation(s)
- Andrea Kóbor
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | - Karolina Janacsek
- Centre of Thinking and Learning, Institute for Lifecourse Development, School of Human Sciences, University of Greenwich, United Kingdom
- ELTE Eötvös Loránd University, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | | | - Vera Varga
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
- University of Pannonia, Hungary
| | - Valéria Csépe
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
- University of Pannonia, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | | | - Dezso Nemeth
- INSERM, CRNL U1028 UMR5292, France
- ELTE Eötvös Loránd University & HUN-REN Research Centre for Natural Sciences, Hungary
- University of Atlántico Medio, Spain
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Kumar S, Dasgupta I, Daw ND, Cohen JD, Griffiths TL. Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. PLoS Comput Biol 2023; 19:e1011316. [PMID: 37624841 PMCID: PMC10497163 DOI: 10.1371/journal.pcbi.1011316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 09/12/2023] [Accepted: 06/29/2023] [Indexed: 08/27/2023] Open
Abstract
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building "task metamers" that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.
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Affiliation(s)
- Sreejan Kumar
- Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | | | - Nathaniel D. Daw
- Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- Department of Psychology, Princeton University, Princeton, New Jersey, United States of America
| | - Jonathan. D. Cohen
- Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- Department of Psychology, Princeton University, Princeton, New Jersey, United States of America
| | - Thomas L. Griffiths
- Department of Psychology, Princeton University, Princeton, New Jersey, United States of America
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
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Takacs A, Beste C. A neurophysiological perspective on the integration between incidental learning and cognitive control. Commun Biol 2023; 6:329. [PMID: 36973381 PMCID: PMC10042851 DOI: 10.1038/s42003-023-04692-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 03/10/2023] [Indexed: 03/29/2023] Open
Abstract
AbstractAdaptive behaviour requires interaction between neurocognitive systems. Yet, the possibility of concurrent cognitive control and incidental sequence learning remains contentious. We designed an experimental procedure of cognitive conflict monitoring that follows a pre-defined sequence unknown to participants, in which either statistical or rule-based regularities were manipulated. We show that participants learnt the statistical differences in the sequence when stimulus conflict was high. Neurophysiological (EEG) analyses confirmed but also specified the behavioural results: the nature of conflict, the type of sequence learning, and the stage of information processing jointly determine whether cognitive conflict and sequence learning support or compete with each other. Especially statistical learning has the potential to modulate conflict monitoring. Cognitive conflict and incidental sequence learning can engage in cooperative fashion when behavioural adaptation is challenging. Three replication and follow-up experiments provide insights into the generalizability of these results and suggest that the interaction of learning and cognitive control is dependent on the multifactorial aspects of adapting to a dynamic environment. The study indicates that connecting the fields of cognitive control and incidental learning is advantageous to achieve a synergistic view of adaptive behaviour.
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Kóbor A, Tóth-Fáber E, Kardos Z, Takács Á, Éltető N, Janacsek K, Csépe V, Nemeth D. Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context. Sci Rep 2023; 13:1127. [PMID: 36670165 PMCID: PMC9859780 DOI: 10.1038/s41598-023-27642-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
Predictions supporting risky decisions could become unreliable when outcome probabilities temporarily change, making adaptation more challenging. Therefore, this study investigated whether sensitivity to the temporal structure in outcome probabilities can develop and remain persistent in a changing decision environment. In a variant of the Balloon Analogue Risk Task with 90 balloons, outcomes (rewards or balloon bursts) were predictable in the task's first and final 30 balloons and unpredictable in the middle 30 balloons. The temporal regularity underlying the predictable outcomes differed across three experimental conditions. In the deterministic condition, a repeating three-element sequence dictated the maximum number of pumps before a balloon burst. In the probabilistic condition, a single probabilistic regularity ensured that burst probability increased as a function of pumps. In the hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. In every condition, the regularity was absent in the middle 30 balloons. Participants were not informed about the presence or absence of the regularity. Sensitivity to both the deterministic and hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate this sensitivity. In conclusion, humans can adapt their risky choices in a changing decision environment by exploiting the statistical structure that controls how the environment changes.
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Affiliation(s)
- Andrea Kóbor
- Brain Imaging Centre, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary.
| | - Eszter Tóth-Fáber
- Doctoral School of Psychology, ELTE Eötvös Loránd University, Izabella utca 46, 1064, Budapest, Hungary.,Institute of Psychology, ELTE Eötvös Loránd University, Izabella utca 46, 1064, Budapest, Hungary.,Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Zsófia Kardos
- Brain Imaging Centre, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary.,Department of Cognitive Science, Budapest University of Technology and Economics, Egry József utca 1, 1111, Budapest, Hungary
| | - Ádám Takács
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Noémi Éltető
- Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8, 72076, Tübingen, Germany
| | - Karolina Janacsek
- Institute of Psychology, ELTE Eötvös Loránd University, Izabella utca 46, 1064, Budapest, Hungary.,Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary.,Centre of Thinking and Learning, Institute for Lifecourse Development, School of Human Sciences, Faculty of Education, Health and Human Sciences, University of Greenwich, Old Royal Naval College, Park Row, 150 Dreadnought, SE10 9LS, London, UK
| | - Valéria Csépe
- Brain Imaging Centre, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary.,Faculty of Modern Philology and Social Sciences, University of Pannonia, Egyetem utca 10, 8200, Veszprém, Hungary
| | - Dezso Nemeth
- Institute of Psychology, ELTE Eötvös Loránd University, Izabella utca 46, 1064, Budapest, Hungary. .,Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary. .,Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Bâtiment 462 - Neurocampus 95 Boulevard Pinel, F-69500, Bron, France.
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Marković D, Reiter AMF, Kiebel SJ. Revealing human sensitivity to a latent temporal structure of changes. Front Behav Neurosci 2022; 16:962494. [PMID: 36325156 PMCID: PMC9621332 DOI: 10.3389/fnbeh.2022.962494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022] Open
Abstract
Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.
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Affiliation(s)
- Dimitrije Marković
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- *Correspondence: Dimitrije Marković
| | - Andrea M. F. Reiter
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Department of Child and Adolescence Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, University Hospital Würzburg, Würzburg, Germany
- German Center of Prevention Research on Mental Health, Julius-Maximilians Universität Würzburg, Würzburg, Germany
| | - Stefan J. Kiebel
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Centre for Tactile Internet with Human-in-the-Loop (CeTI), Technische Universität Dresden, Dresden, Germany
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