A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Schneegans, Sebastian; Schöner, Gregor

doi:10.1007/s00422-012-0484-8

Cited by 27 publications

(28 citation statements)

References 59 publications

(100 reference statements)

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“…Subsequent work extended DFT, capturing a wide array of phenomena in the area of spatially-grounded cognition, from infant perseverative reaching (Smith, Thelen, Titzer, & McLin, 1999; Thelen, Schöner, Scheier, & Smith, 2001) to spatial category biases to changes in the metric precision of spatial working memory from childhood to adulthood (Schutte, Spencer, & Schöner, 2003; Simmering, Peterson, Darling, & Spencer, 2008). In the last decade, DFT has been extended into a host of other domains including visual working memory [VWM] (Johnson, Hollingworth, & Luck, 2008; Johnson, Spencer, Luck, & Schöner, 2009; Schneegans, Spencer, Schöner, Hwang, & Hollingworth, 2014), retinal remapping (Schneegans & Schöner, 2012), preferential looking and visual habituation ( Perone, Spencer, & Schöner, 2007; Perone & Spencer, 2008), spatial language (Lipinski, Spencer, & Samuelson, 2010), word learning (Samuelson, Jenkins, & Spencer, 2015), executive function (Buss & Spencer, 2008), and autonomous behavioral organization in cognitive robotics (Sandamirskaya & Schöner, 2010). …”

Section: Overview Of Dynamic Field Theorymentioning

confidence: 99%

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

Wijeakumar

Ambrose

Spencer

et al. 2017

Journal of Mathematical Psychology

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A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

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Section: Overview Of Dynamic Field Theorymentioning

confidence: 99%

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

Wijeakumar

Ambrose

Spencer

et al. 2017

Journal of Mathematical Psychology

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“…This amounts to a coordinate transform (Schneegans and Schöner, 2012). Once implemented within DFT, an operator can become part of a stable coupling from time-varying sensory inputs to motor control.…”

Section: Architectures In Dynamic Field Theorymentioning

confidence: 99%

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

et al. 2016

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Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

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“…Achieving this is a real trick because everything needs to be actively coordinated. For instance, to establish a neural representation in world-centered coordinates requires that you update the relationship between your body and the world every time you move (e.g., Pouget, Deneve, Duhamel, 2002; Schneegans & Schöner, 2012). But we cannot stop there: once you encode the location in a world-centered frame, you need to remember the location during, for instance, a 20 s delay when the toy is occluded by your brother who has come over to interfere with your play time.…”

Section: 1 Application Of Dynamic Field Theory 20: the Developmentmentioning

confidence: 99%

Contributions of dynamic systems theory to cognitive development

Spencer

Austin

Schutte

2012

Cognitive Development

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This paper examines the contributions of dynamic systems theory to the field of cognitive development, focusing on modeling using dynamic neural fields. A brief overview highlights the contributions of dynamic systems theory and the central concepts of dynamic field theory (DFT). We then probe empirical predictions and findings generated by DFT around two examples—the DFT of infant perseverative reaching that explains the Piagetian A-not-B error, and the DFT of spatial memory that explain changes in spatial cognition in early development. A systematic review of the literature around these examples reveals that computational modeling is having an impact on empirical research in cognitive development; however, this impact does not extend to neural and clinical research. Moreover, there is a tendency for researchers to interpret models narrowly, anchoring them to specific tasks. We conclude on an optimistic note, encouraging both theoreticians and experimentalists to work toward a more theory-driven future.

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A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Cited by 27 publications

References 59 publications

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

Contributions of dynamic systems theory to cognitive development

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