The Experts below are selected from a list of 2106 Experts worldwide ranked by ideXlab platform

Vinod Menon - One of the best experts on this subject based on the ideXlab platform.

  • systems neuroscience of Mathematical Cognition and learning basic organization and neural sources of heterogeneity in typical and atypical development
    2018
    Co-Authors: Teresa Iuculano, Aarthi Padmanabhan, Vinod Menon
    Abstract:

    Abstract In this chapter, we take a systems neuroscience approach and review neurocognitive systems involved in Mathematical Cognition and learning, highlighting functional brain circuits that support these processes and sources of heterogeneity that influence their typical or atypical development. We first examine the core neural building blocks of numerical Cognition anchored in posterior parietal and ventral temporal–occipital cortices and then describe how working memory, language, declarative memory, and cognitive control systems facilitate numerical problem-solving and help scaffold Mathematical learning and skill acquisition. We then highlight the contribution of interactive functional circuits to Mathematical Cognition and learning at different stages of development and skill levels. We suggest that Mathematical knowledge serves as a model domain for investigating the ontogenesis of human cognitive and problem-solving skills, and that a systems neuroscience framework can shed light on why some individuals excel and others struggle.

  • Working memory in children's math learning and its disruption in dyscalculia
    Current opinion in behavioral sciences, 2016
    Co-Authors: Vinod Menon
    Abstract:

    Working memory (WM) plays an essential role in children's Mathematical learning. WM influences both the early foundational phases of number knowledge acquisition and subsequent maturation of problem solving skills. The role of individual WM components in Mathematical Cognition depends not only on problem complexity but also on individual differences in Mathematical abilities. Furthermore, the contributions of individual WM components change dynamically over development with visuospatial processes playing an increasingly important role in learning and enhancing Mathematical proficiency. Convergent findings from neuroimaging studies are now providing fundamental insights into the link between WM and Mathematical Cognition, and the mechanisms by which poor WM contributes to learning disabilities. Evidence to date suggests that visuospatial WM is a specific source of vulnerability in children with Mathematical learning disabilities and needs to be considered as a key component in cognitive, neurobiological, and developmental models of typical and atypical Mathematical skill acquisition.

  • memory and cognitive control circuits in Mathematical Cognition and learning
    Progress in Brain Research, 2016
    Co-Authors: Vinod Menon
    Abstract:

    Numerical Cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in Mathematical Cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of Mathematical learning and knowledge acquisition in both children and adults are discussed.

  • Neural Basis of Repetition Priming during Mathematical Cognition: Repetition Suppression or Repetition Enhancement?
    Journal of Cognitive Neuroscience, 2010
    Co-Authors: Valorie N. Salimpoor, Catie Chang, Vinod Menon
    Abstract:

    We investigated the neural basis of repetition priming (RP) during Mathematical Cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning as an alternate model for behavioral facilitation. We examined the neural basis of RP during Mathematical problem solving in the context of these two models of learning. Brain imaging and behavioral data were acquired from 39 adults during novel and repeated presentation of three-operand Mathematical equations. Despite wide-spread decreases in activation during repeat, compared with novel trials, there was no direct relation between behavioral facilitation and the degree of repetition suppression in any brain region. Rather, RT improvements were directly correlated with repetition enhancement in the hippocampus and the posteromedial cortex [posterior cingulate cortex, precuneus, and retrosplenial cortex; Brodmann's areas (BAs) 23, 7, and 30, respectively], regions known to support memory formation and retrieval, and in the SMA (BA 6) and the dorsal midcingulate (“motor cingulate”) cortex (BA 24d), regions known to be important for motor learning. Furthermore, improvements in RT were also correlated with increased functional connectivity of the hippocampus with both the SMA and the dorsal midcingulate cortex. Our findings provide novel support for the hypothesis that repetition enhancement and associated stimulus-response learning may facilitate behavioral performance during problem solving.

  • functional heterogeneity of inferior parietal cortex during Mathematical Cognition assessed with cytoarchitectonic probability maps
    Cerebral Cortex, 2009
    Co-Authors: Sarah S Wu, Tingting Chang, A Majid, Svenja Caspers, Simon B Eickhoff, Vinod Menon
    Abstract:

    Although the inferior parietal cortex (IPC) has been consistently implicated in Mathematical Cognition, the functional roles of its subdivisions are poorly understood. We address this problem using probabilistic cytoarchitectonic maps of IPC subdivisions intraparietal sulcus (IPS), angular gyrus (AG), and supramarginal gyrus. We quantified IPC responses relative to task difficulty and individual differences in task proficiency during mental arithmetic (MA) tasks performed with Arabic (MA-A) and Roman (MA-R) numerals. The 2 tasks showed similar levels of activation in 3 distinct IPS areas, hIP1, hIP2, and hIP3, suggesting their obligatory role in MA. Both AG areas, PGa and PGp, were strongly deactivated in both tasks, with stronger deactivations in posterior area PGp. Compared with the more difficult MA-R task, the MA-A task showed greater responses in both AG areas, but this effect was driven by less deactivation in the MA-A task. AG deactivations showed prominent overlap with lateral parietal nodes of the default mode network, suggesting a nonspecific role in MA. In both tasks, greater bilateral AG deactivation was associated with poorer performance. Our findings suggest a close link between IPC structure and function and they provide new evidence for behaviorally salient functional heterogeneity within the IPC during Mathematical Cognition.

Alessandro Di Nuovo - One of the best experts on this subject based on the ideXlab platform.

  • Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots
    2018 International Joint Conference on Neural Networks (IJCNN), 2018
    Co-Authors: Alessandro Di Nuovo
    Abstract:

    Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model Mathematical Cognition in robots. This paper presents an experimental evaluation of the Long- Short Term Memory networks for modeling the simple Mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.

  • IJCNN - Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots
    2018 International Joint Conference on Neural Networks (IJCNN), 2018
    Co-Authors: Alessandro Di Nuovo
    Abstract:

    Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model Mathematical Cognition in robots. This paper presents an experimental evaluation of the Long- Short Term Memory networks for modeling the simple Mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.

Firat Soylu - One of the best experts on this subject based on the ideXlab platform.

  • CogSci - Mathematical Cognition as Embodied Simulation
    Cognitive Science, 2017
    Co-Authors: Firat Soylu
    Abstract:

    Mathematical Cognition as Embodied Simulation Firat Soylu (fsoylu@indiana.edu) Department of Instructional Systems Technology, Cognitive Science Program, Indiana University, Bloomington W. W. Wright Education Building, Room 2276, 201 North Rose Avenue, Bloomington, IN 47405, USA Abstract Based on behavioral, neuroimaging and neuropsychological data, I argue that a key to understanding Mathematical Cognition is the sharing of neural resources between sensorimotor and Mathematical processes. Mathematical Cognition is embodied in the sense that it is grounded in simulations of sensorimotor processes through the use of neural resources that are also active in bodily perception and action. There are two approaches to the study of embodied Mathematical Cognition: Behavioral, neuroimaging and neuropsychological investigations providing empirical evidence, and the study of conceptual metaphors, focusing on how inferences from physical domains are used to understand abstract Mathematical ideas. The first approach suffers from not providing a unified explanation, while the second approach is criticized for not having empirical validation. I discuss the possible implications of approaching to Mathematical Cognition as embodied simulation in relating disparate findings to provide a more connected picture of how mathematics emerges from the embodied mind. Keywords: embodiment; embodied Cognition; Mathematical Cognition; simulation theories Embodied Cognition is a theoretical stance that argues that cognitive processes are grounded in the body’s interaction with the world. Different approaches in embodied Cognition propose varying levels for bodily involvement in higher Cognition. Clark (1999) has distinguished between simple versus radical embodiment. Simple embodiment focuses on how the body and environment places constraints on a theory of inner organization and processing. Radical embodiment, however, asserts that all cognitive processes are grounded in the sensorimotor system, proposing a profound change in the subject matter and theoretical framework of cognitive science” (p. 348). The fundamental difference between these two approaches is that simple embodiment still relies on internal representations, especially in explaining higher level thinking, whereas radical embodiment entirely rejects the idea of an internal realm and provides a representation free account of cognitive phenomena. I use the term simulation theories of Cognition to refer to theories positing that all cognitive processes are simulations of sensorimotor processes. Note that the term simulation theories is also used to refer to a theory of mind asserting that humans understand other people’s mental states by adopting their perspective (Davies & Stone, 1995), which is different than the usage here. Simulation theories posit a decoupling of sensorimotor functions from their original physical inputs and outputs. For example, consider the case of counting on one’s fingers. In its initial form counting can be done through explicit motor behavior where an observer can see the fingers moving. However, the motor movement of fingers can become gradually more subtle, where at some point it might merely seem like twitching to the observer. We can push the activity inward even further allowing the use of motor programs without any overt behavior. At this point finger counting is a motor simulation. This situation exemplifies how a motor function, without overt behavior, can be the underlying neural mechanism for off-line thinking in the very simple case of counting (Wilson, 2002). Previous theories focused on how conceptual content is represented in the sensorimotor system. Gallese & Lakoff (2005) proposed that embodied simulations are the source of both structural and semantic content in conceptual knowledge. Embodied simulations take place in multimodal sensorimotor networks. Unlike the conventional idea of distinct sensory and motor areas communicating through association areas, multimodality refers to the integration of sensory modalities with one another and also with motor modalities. Barsalou (1999) argued that during perceptual experience association areas in the brain capture bottom-up sensory-motor patterns. Later, during the use of perceptual symbols association areas facilitate some of the same sensory-motor areas in a top down manner. Through experience, memories of the same component are stored in a schematic manner. The memories implement simulators of the perceptual experiences they represent. Simulators can be perceptual, proprioceptive, or introspective. Abstract concepts are grounded in the combinatorial and recursive integration of simulators. Mathematics is often characterized as a challenge to embodiment (Nunez, 2008). Although it is relatively difficult to apply the idea of embodied simulations to explain Mathematical Cognition due to abstract nature of mathematics, there is accumulating evidence for how basic Mathematical processes are grounded in the sensorimotor system. In this paper I review different studies on Mathematical Cognition and discuss some of the challenges in interpreting findings to create a meaningful image of how mathematics can emerge from the embodied mind. Embodiment of Mathematical Thinking Research on embodiment of mathematics is still in its infancy. Mathematical Cognition is a big puzzle with many pieces, each piece requiring us to draw knowledge from a

  • Mathematical Cognition as embodied simulation
    Cognitive Science, 2011
    Co-Authors: Firat Soylu
    Abstract:

    Mathematical Cognition as Embodied Simulation Firat Soylu (fsoylu@indiana.edu) Department of Instructional Systems Technology, Cognitive Science Program, Indiana University, Bloomington W. W. Wright Education Building, Room 2276, 201 North Rose Avenue, Bloomington, IN 47405, USA Abstract Based on behavioral, neuroimaging and neuropsychological data, I argue that a key to understanding Mathematical Cognition is the sharing of neural resources between sensorimotor and Mathematical processes. Mathematical Cognition is embodied in the sense that it is grounded in simulations of sensorimotor processes through the use of neural resources that are also active in bodily perception and action. There are two approaches to the study of embodied Mathematical Cognition: Behavioral, neuroimaging and neuropsychological investigations providing empirical evidence, and the study of conceptual metaphors, focusing on how inferences from physical domains are used to understand abstract Mathematical ideas. The first approach suffers from not providing a unified explanation, while the second approach is criticized for not having empirical validation. I discuss the possible implications of approaching to Mathematical Cognition as embodied simulation in relating disparate findings to provide a more connected picture of how mathematics emerges from the embodied mind. Keywords: embodiment; embodied Cognition; Mathematical Cognition; simulation theories Embodied Cognition is a theoretical stance that argues that cognitive processes are grounded in the body’s interaction with the world. Different approaches in embodied Cognition propose varying levels for bodily involvement in higher Cognition. Clark (1999) has distinguished between simple versus radical embodiment. Simple embodiment focuses on how the body and environment places constraints on a theory of inner organization and processing. Radical embodiment, however, asserts that all cognitive processes are grounded in the sensorimotor system, proposing a profound change in the subject matter and theoretical framework of cognitive science” (p. 348). The fundamental difference between these two approaches is that simple embodiment still relies on internal representations, especially in explaining higher level thinking, whereas radical embodiment entirely rejects the idea of an internal realm and provides a representation free account of cognitive phenomena. I use the term simulation theories of Cognition to refer to theories positing that all cognitive processes are simulations of sensorimotor processes. Note that the term simulation theories is also used to refer to a theory of mind asserting that humans understand other people’s mental states by adopting their perspective (Davies & Stone, 1995), which is different than the usage here. Simulation theories posit a decoupling of sensorimotor functions from their original physical inputs and outputs. For example, consider the case of counting on one’s fingers. In its initial form counting can be done through explicit motor behavior where an observer can see the fingers moving. However, the motor movement of fingers can become gradually more subtle, where at some point it might merely seem like twitching to the observer. We can push the activity inward even further allowing the use of motor programs without any overt behavior. At this point finger counting is a motor simulation. This situation exemplifies how a motor function, without overt behavior, can be the underlying neural mechanism for off-line thinking in the very simple case of counting (Wilson, 2002). Previous theories focused on how conceptual content is represented in the sensorimotor system. Gallese & Lakoff (2005) proposed that embodied simulations are the source of both structural and semantic content in conceptual knowledge. Embodied simulations take place in multimodal sensorimotor networks. Unlike the conventional idea of distinct sensory and motor areas communicating through association areas, multimodality refers to the integration of sensory modalities with one another and also with motor modalities. Barsalou (1999) argued that during perceptual experience association areas in the brain capture bottom-up sensory-motor patterns. Later, during the use of perceptual symbols association areas facilitate some of the same sensory-motor areas in a top down manner. Through experience, memories of the same component are stored in a schematic manner. The memories implement simulators of the perceptual experiences they represent. Simulators can be perceptual, proprioceptive, or introspective. Abstract concepts are grounded in the combinatorial and recursive integration of simulators. Mathematics is often characterized as a challenge to embodiment (Nunez, 2008). Although it is relatively difficult to apply the idea of embodied simulations to explain Mathematical Cognition due to abstract nature of mathematics, there is accumulating evidence for how basic Mathematical processes are grounded in the sensorimotor system. In this paper I review different studies on Mathematical Cognition and discuss some of the challenges in interpreting findings to create a meaningful image of how mathematics can emerge from the embodied mind. Embodiment of Mathematical Thinking Research on embodiment of mathematics is still in its infancy. Mathematical Cognition is a big puzzle with many pieces, each piece requiring us to draw knowledge from a

Stuart R Hameroff - One of the best experts on this subject based on the ideXlab platform.

  • quantum Mathematical Cognition requires quantum brain biology the orch or theory
    Behavioral and Brain Sciences, 2013
    Co-Authors: Stuart R Hameroff
    Abstract:

    : The "Orch OR" theory suggests that quantum computations in brain neuronal dendritic-somatic microtubules regulate axonal firings to control conscious behavior. Within microtubule subunit proteins, collective dipoles in arrays of contiguous amino acid electron clouds enable "quantum channels" suitable for topological dipole "qubits" able to physically represent cognitive values, for example, those portrayed by Pothos & Busemeyer (P&B) as projections in abstract Hilbert space.

Daniel Ansari - One of the best experts on this subject based on the ideXlab platform.

  • challenges in Mathematical Cognition a collaboratively derived research agenda
    Journal of Numerical Cognition 2 (1) pp. 20-41. (2016), 2016
    Co-Authors: Lara Alcock, Daniel Ansari, Sophie Batchelor, Mariejosee Bisson, Bert De Smedt, Camilla K Gilmore, Silke M Gobel, Minna M Hannulasormunen, Jeremy Hodgen, Matthew Inglis
    Abstract:

    This paper reports on a collaborative exercise designed to generate a coherent agenda for research on Mathematical Cognition. Following an established method, the exercise brought together 16 Mathematical Cognition researchers from across the fields of mathematics education, psychology and neuroscience. These participants engaged in a process in which they generated an initial list of research questions with the potential to significantly advance understanding of Mathematical Cognition, winnowed this list to a smaller set of priority questions, and refined the eventual questions to meet criteria related to clarity, specificity and practicability. The resulting list comprises 26 questions divided into six broad topic areas: elucidating the nature of Mathematical thinking, mapping predictors and processes of competence development, charting developmental trajectories and their interactions, fostering conceptual understanding and procedural skill, designing effective interventions, and developing valid and reliable measures. In presenting these questions in this paper, we intend to support greater coherence in both investigation and reporting, to build a stronger base of information for consideration by policymakers, and to encourage researchers to take a consilient approach to addressing important challenges in Mathematical Cognition.

  • challenges in Mathematical Cognition
    Journal of Numerical Cognition, 2016
    Co-Authors: Lara Alcock, Daniel Ansari, Sophie Batchelor, Mariejosee Bisson, Bert De Smedt, Camilla K Gilmore, Silke M Gobel, Minna M Hannulasormunen, Jeremy Hodgen, Matthew Inglis
    Abstract:

    This paper reports on a collaborative exercise designed to generate a coherent agenda for research on Mathematical Cognition. Following an established method, the exercise brought together 16 Mathematical Cognition researchers from across the fields of mathematics education, psychology and neuroscience. These participants engaged in a process in which they generated an initial list of research questions with the potential to significantly advance understanding of Mathematical Cognition, winnowed this list to a smaller set of priority questions, and refined the eventual questions to meet criteria related to clarity, specificity and practicability. The resulting list comprises 26 questions divided into six broad topic areas: elucidating the nature of Mathematical thinking, mapping predictors and processes of competence development, charting developmental trajectories and their interactions, fostering conceptual understanding and procedural skill, designing effective interventions, and developing valid and reliable measures. In presenting these questions in this paper, we intend to support greater coherence in both investigation and reporting, to build a stronger base of information for consideration by policymakers, and to encourage researchers to take a consilient approach to addressing important challenges in Mathematical Cognition.

  • drawing connections between white matter and numerical and Mathematical Cognition a literature review
    Neuroscience & Biobehavioral Reviews, 2015
    Co-Authors: Anna A Matejko, Daniel Ansari
    Abstract:

    Abstract In this review we examine white matter tracts that may support numerical and Mathematical abilities and whether abnormalities in these pathways are associated with deficits in numerical and Mathematical abilities. Diffusion tensor imaging (DTI) yields indices of white matter integrity and can provide information about the axonal organization of the brain. A growing body of research is using DTI to investigate how individual differences in brain microstructures relate to different numerical and Mathematical abilities. Several tracts have been associated with numerical and Mathematical abilities such as the superior longitudinal fasciculus, the posterior segment of the corpus callosum, inferior longitudinal fasciculus, corona radiata, and the corticospinal tract. Impairments in mathematics tend to be associated with atypical white matter structures within similar regions, especially in inferior parietal and temporal tracts. This systematic review summarizes and critically examines the current literature on white matter correlates of numerical and Mathematical abilities, and provides directions for future research.

  • neurocognitive approaches to developmental disorders of numerical and Mathematical Cognition the perils of neglecting the role of development
    Learning and Individual Differences, 2010
    Co-Authors: Daniel Ansari
    Abstract:

    Abstract The present paper provides a critical overview of how adult neuropsychological models have been applied to the study of the atypical development of numerical Cognition. Specifically, the following three assumptions are challenged: 1. Profiles of strength and weaknesses do not change over developmental time. 2. Similar neuronal structures are activated in children and adults, as well as the notion that 3. Similarities in behavioral performance imply equivalence in underlying neurocognitive mechanisms. Data from behavioral and neuroimaging studies with both typically and atypically developing children is reviewed to illustrate the pitfalls of these assumptions. The present review proposes that, instead of resting on adult neuropsychological models, the use of both cross-sectional and longitudinal methods is required to elucidate the age-related changes in brain and behavior that give rise to the breakdown of numeracy and mathematics. Empirical data derived from such studies will generate explanatory models of the development of atypical numerical Cognition.