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Quinton JC, Gautheron F, Smeding A. Embodied sequential sampling models and dynamic neural fields for decision-making: Why hesitate between two when a continuum is the answer. Neural Netw 2024; 179:106526. [PMID: 39053301 DOI: 10.1016/j.neunet.2024.106526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 06/02/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
As two alternative options in a forced choice task are separated by design, two classes of computational models of decision-making have thrived independently in the literature for nearly five decades. While sequential sampling models (SSM) focus on response times and keypresses in binary decisions in experimental paradigms, dynamic neural fields (DNF) focus on continuous sensorimotor dimensions and tasks found in perception and robotics. Recent attempts have been made to address limitations in their application to other domains, but strong similarities and compatibility between prominent models from both classes were hardly considered. This article is an attempt at bridging the gap between these classes of models, and simultaneously between disciplines and paradigms relying on binary or continuous responses. A unifying formulation of representative SSM and DNF equations is proposed, varying the number of units which interact and compete to reach a decision. The embodiment of decisions is also considered by coupling cognitive and sensorimotor processes, enabling the model to generate decision trajectories at trial level. The resulting mechanistic model is therefore able to target different paradigms (forced choices or continuous response scales) and measures (final responses or dynamics). The validity of the model is assessed statistically by fitting empirical distributions obtained from human participants in moral decision-making mouse-tracking tasks, for which both dichotomous and nuanced responses are meaningful. Comparing equations at the theoretical level, and model parametrizations at the empirical level, the implications for psychological decision-making processes, as well as the fundamental assumptions and limitations of models and paradigms are discussed.
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Affiliation(s)
| | - Flora Gautheron
- Univ. Grenoble Alpes, CNRS, Grenoble INP,(1) LJK, 38000 Grenoble, France; Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, LIP/PC2S, 38000 Grenoble, France.
| | - Annique Smeding
- Univ. Savoie Mont Blanc, Univ. Grenoble Alpes, LIP/PC2S, 73000 Chambéry, France.
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Davidson C, Shing YL, McKay C, Rafetseder E, Wijeakumar S. The first year in formal schooling improves working memory and academic abilities. Dev Cogn Neurosci 2023; 60:101205. [PMID: 36724671 PMCID: PMC9898018 DOI: 10.1016/j.dcn.2023.101205] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 01/15/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Neurocognition and academic abilities during the period of 4 and 7 years of age are impacted by both the transition from kindergarten to primary school and age-related developmental processes. Here, we used a school cut-off design to tease apart the impact of formal schooling from age, on working memory (WM) function, vocabulary, and numeracy scores. We compared two groups of children with similar age, across two years: first-graders (FG), who were enrolled into primary school the year that they became eligible and kindergarteners (KG), who were deferred school entry until the following year. All children completed a change detection task while brain activation was recorded using portable functional near-infrared spectroscopy, a vocabulary assessment, and a numeracy screener. Our results revealed that FG children showed greater improvement in WM performance and greater engagement of a left-lateralized fronto-parietal network compared to KG children. Further, they also showed higher gains in vocabulary and non-symbolic numeracy scores. This improvement in vocabulary and non-symbolic numeracy scores following a year in primary school was predicted by WM function. Our findings contribute to a growing body of literature examining neurocognitive and academic benefits conferred to children following exposure to formal schooling.
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Affiliation(s)
- Christina Davidson
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
| | - Yee Lee Shing
- Department of Psychology, Goethe University Frankfurt, Germany,Center for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany
| | - Courtney McKay
- Psychology, Faculty of Natural Sciences, University of Stirling, Scotland, UK
| | - Eva Rafetseder
- Psychology, Faculty of Natural Sciences, University of Stirling, Scotland, UK
| | - Sobanawartiny Wijeakumar
- School of Psychology, University of Nottingham, Nottingham, United Kingdom,Psychology, Faculty of Natural Sciences, University of Stirling, Scotland, UK,Correspondence to: School of Psychology, University of Nottingham, NG7 2RD, United Kingdom.
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Kwessi E. Discrete Dynamics of Dynamic Neural Fields. Front Comput Neurosci 2021; 15:699658. [PMID: 34305561 PMCID: PMC8295487 DOI: 10.3389/fncom.2021.699658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/02/2021] [Indexed: 11/13/2022] Open
Abstract
Large and small cortexes of the brain are known to contain vast amounts of neurons that interact with one another. They thus form a continuum of active neural networks whose dynamics are yet to be fully understood. One way to model these activities is to use dynamic neural fields which are mathematical models that approximately describe the behavior of these congregations of neurons. These models have been used in neuroinformatics, neuroscience, robotics, and network analysis to understand not only brain functions or brain diseases, but also learning and brain plasticity. In their theoretical forms, they are given as ordinary or partial differential equations with or without diffusion. Many of their mathematical properties are still under-studied. In this paper, we propose to analyze discrete versions dynamic neural fields based on nearly exact discretization schemes techniques. In particular, we will discuss conditions for the stability of nontrivial solutions of these models, based on various types of kernels and corresponding parameters. Monte Carlo simulations are given for illustration.
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Affiliation(s)
- Eddy Kwessi
- Department of Mathematics, Trinity University, San Antonio, TX, United States
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Buss AT, Magnotta VA, Penny W, Schöner G, Huppert TJ, Spencer JP. How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory. Psychol Rev 2021; 128:362-395. [PMID: 33570976 PMCID: PMC11327926 DOI: 10.1037/rev0000264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Aaron T Buss
- Department of Psychology, University of Tennessee, Knoxville
| | | | - Will Penny
- School of Psychology, University of East Anglia
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5
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Abstract
Working memory is a central cognitive system that plays key role in development, with increases in working memory capacity and speed of processing as children move from infancy through adolescence. Here, I focus on two questions: what neural processes underlie working memory and how do these processes change over development? Answers to these questions lie in computer simulations of artificial neural network models that shed light on how development happens. These models open up new avenues for optimizing clinical interventions aimed at boosting the working memory abilities of at-risk infants.
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Affiliation(s)
- John P Spencer
- School of Psychology, University of East Anglia, Norwich, UK
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Trakoshis S, Martínez-Cañada P, Rocchi F, Canella C, You W, Chakrabarti B, Ruigrok ANV, Bullmore ET, Suckling J, Markicevic M, Zerbi V, Baron-Cohen S, Gozzi A, Lai MC, Panzeri S, Lombardo MV. Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women. eLife 2020; 9:e55684. [PMID: 32746967 PMCID: PMC7402681 DOI: 10.7554/elife.55684] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 06/29/2020] [Indexed: 12/22/2022] Open
Abstract
Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.
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Affiliation(s)
- Stavros Trakoshis
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Department of Psychology, University of CyprusNicosiaCyprus
| | - Pablo Martínez-Cañada
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Optical Approaches to Brain Function Laboratory, Department of Neuroscience and Brain Technologies, Istituto Italiano di TecnologiaGenovaItaly
| | - Federico Rocchi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Carola Canella
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Wonsang You
- Artificial Intelligence and Image Processing Laboratory, Department of Information and Communications Engineering, Sun Moon UniversityAsanRepublic of Korea
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
- Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of ReadingReadingUnited Kingdom
| | - Amber NV Ruigrok
- Autism Research Centre, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Edward T Bullmore
- Cambridgeshire and Peterborough National Health Service Foundation TrustCambridgeUnited Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Marija Markicevic
- Neural Control of Movement Lab, D-HEST, ETH ZurichZurichSwitzerland
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
| | - Valerio Zerbi
- Neural Control of Movement Lab, D-HEST, ETH ZurichZurichSwitzerland
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
| | | | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
- Cambridgeshire and Peterborough National Health Service Foundation TrustCambridgeUnited Kingdom
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Azrieli Adult Neurodevelopmental Centre, and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthTorontoCanada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick ChildrenTorontoCanada
- Department of Psychiatry, Faculty of Medicine, University of TorontoTorontoCanada
- Department of Psychiatry, National Taiwan University Hospital and College of MedicineTaipeiTaiwan
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Autism Research Centre, Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
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Costello MC, Buss AT. Age-related Decline of Visual Working Memory: Behavioral Results Simulated with a Dynamic Neural Field Model. J Cogn Neurosci 2018; 30:1532-1548. [PMID: 29877766 DOI: 10.1162/jocn_a_01293] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Visual working memory (VWM) is essential for executive function and is known to be compromised in older adults. Yet, the cognitive and neural processes associated with these age-related changes remain inconclusive. The purpose of this study was to explore such factors with a dynamic neural field (DNF) model that was manipulated to replicate the behavioral performances of younger and older adults in a change detection task. Although previous work has successfully modeled children and younger adult VWM performance, this study represents the first attempt to model older adult VWM performance within the DNF architecture. In the behavioral task, older adults performed worse than younger adults and exhibited a characteristic response bias that favored "same" over "different" responses. The DNF model was modified to capture the age group differences, with three parameter manipulations producing the best fit for the behavioral performances. The best-fitting model suggests that older adults operate through altered excitatory and inhibitory coupling and decreased inhibitory signals, resulting in wider and weaker neural signals. These results support a dedifferentiation account of brain aging, with older adults operating with wider and weaker neural signals because of decreased intracortical inhibition rather than increased stochastic neural noise.
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Buss AT, Spencer JP. Changes in frontal and posterior cortical activity underlie the early emergence of executive function. Dev Sci 2017; 21:e12602. [PMID: 28913859 DOI: 10.1111/desc.12602] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 06/24/2017] [Indexed: 11/30/2022]
Abstract
Executive function (EF) is a key cognitive process that emerges in early childhood and facilitates children's ability to control their own behavior. Individual differences in EF skills early in life are predictive of quality-of-life outcomes 30 years later (Moffitt et al., 2011). What changes in the brain give rise to this critical cognitive ability? Traditionally, frontal cortex growth is thought to underlie changes in cognitive control (Bunge & Zelazo, 2006; Moriguchi & Hiraki, 2009). However, more recent data highlight the importance of long-range cortical interactions between frontal and posterior brain regions. Here, we test the hypothesis that developmental changes in EF skills reflect changes in how posterior and frontal brain regions work together. Results show that children who fail a "hard" version of an EF task and who are thought to have an immature frontal cortex, show robust frontal activity in an "easy" version of the task. We show how this effect can arise via posterior brain regions that provide on-the-job training for the frontal cortex, effectively teaching the frontal cortex adaptive patterns of brain activity on "easy" EF tasks. In this case, frontal cortex activation can be seen as both the cause and the consequence of rule switching. Results also show that older children have differential posterior cortical activation on "easy" and "hard" tasks that reflects continued refinement of brain networks even in skilled children. These data set the stage for new training programs to foster the development of EF skills in at-risk children.
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Affiliation(s)
- Aaron T Buss
- Department of Psychology, University of Tennessee, Knoxville, TN, USA
| | - John P Spencer
- School of Psychology, University of East Anglia, Norwich, UK
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Schneegans S, Bays PM. Restoration of fMRI Decodability Does Not Imply Latent Working Memory States. J Cogn Neurosci 2017; 29:1977-1994. [PMID: 28820674 DOI: 10.1162/jocn_a_01180] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state.
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Palmeri TJ, Love BC, Turner BM. Model-based cognitive neuroscience. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2017; 76:59-64. [PMID: 30147145 PMCID: PMC6103531 DOI: 10.1016/j.jmp.2016.10.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This special issue explores the growing intersection between mathematical psychology and cognitive neuroscience. Mathematical psychology, and cognitive modeling more generally, has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called model-based cognitive neuroscience, which can both inform cognitive modeling and help to interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states can be used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions about behavior. As examples, brain measures are related to cognitive model parameters fitted to individual participant data, measures of brain dynamics are related to measures of model dynamics, model parameters are constrained by neural measures, model parameters or model states are used in statistical analyses of neural data, or neural and behavioral data are analyzed jointly within a hierarchical modeling framework. We provide an introduction to the field of model-based cognitive neuroscience and to the articles contained within this special issue.
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