The time course of saccadic decision making: Dynamic field theory

Wilimzig, Claudia; Schneider, Stefan; Schöner, Gregor

doi:10.1016/j.neunet.2006.03.003

Cited by 97 publications

(82 citation statements)

References 76 publications

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“…We do not explicitly model the processes of saccade target selection and saccade initiation, although the retinocentric field or the transformation field (when read out along the retinocentric dimension) could provide the input for this. In our previous work, we have provided detailed models of saccade planning that are compatible with our current architecture (Kopecz and Schöner 1995;Wilimzig et al 2006; see also Quaia et al 1999;Trappenberg et al 2001). Here, we only represent the result of this process by applying an input to the appropriate location in the saccade field for every gaze shift required in the simulations.…”

Section: Gaze Update Modulementioning

confidence: 99%

“…2.4, the model does not cover the process of saccade generation itself. Previous models of saccade generation using DNFs (Kopecz and Schöner 1995;Wilimzig et al 2006) have addressed how the metric distance between saccade targets determines whether selection or averaging occurs, including in double-step paradigms (Ottes et al 1984;Aslin and Shea 1987). DNF models have addressed repulsion and attraction effects between metrically close items in related work on spatial working memory (Simmering et al 2008).…”

Section: Trans-saccadic Spatial Memorymentioning

confidence: 99%

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A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Schneegans

Schöner

2012

Biol Cybern

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Whenever we shift our gaze, any location information encoded in the retinocentric reference frame that is predominant in the visual system is obliterated. How is spatial memory retained across gaze changes? Two different explanations have been proposed: Retinocentric information may be transformed into a gaze-invariant representation through a mechanism consistent with gain fields observed in parietal cortex, or retinocentric information may be updated in anticipation of the shift expected with every gaze change, a proposal consistent with neural observations in LIP. The explanations were considered incompatible with each other, because retinocentric update is observed before the gaze shift has terminated. Here, we show that a neural dynamic mechanism for coordinate transformation can also account for retinocentric updating. Our model postulates an extended mechanism of reference frame transformation that is based on bidirectional mapping between a retinocentric and a bodycentered representation and that enables transforming multiple object locations in parallel. The dynamic coupling between the two reference frames generates a shift of the retinocentric representation for every gaze change. We account for the predictive nature of the observed remapping activity by using the same kind of neural mechanism to generate an internal representation of gaze direction that is predictively updated based on corollary discharge signals. We provide evidence for the model by accounting for a series of behavioral and neural experimental observations.

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Section: Gaze Update Modulementioning

confidence: 99%

Section: Trans-saccadic Spatial Memorymentioning

confidence: 99%

A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Schneegans

Schöner

2012

Biol Cybern

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“…The dynamic field framework was originally developed to capture the dynamics of neural activation in visual cortex (Amari, 1977). More recently, this framework has been extended to account for the processes that underlie saccadic eye movements (Kopecz & Schöner, 1995;Wilimzig, Schneider & Schöner, 2006), motor planning (Erlhagen & Schöner, 2002;Schutte & Spencer, 2007b), infants' performance in Piaget's Anot-B task (Thelen, Schöner, Scheier & Smith, 2001), the dynamics of neural activation in motor and premotor cortex (Bastian, Riehle, Erlhagen & Schöner, 1998;Bastian, Schöner & Riehle, 2003), and the behavior of autonomous robots (Bicho, Mallet & Schöner, 2000;Iossifidis & Schöner, 2006;Steinhage & Schöner, 1998).…”

Section: Introductionmentioning

confidence: 99%

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Simmering¹,

Schutte²,

Spencer³

2008

Brain Research

115

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Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks-the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity-generating novel, testable predictions-and generality-spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

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“…Often, models incorporate inhibition without implementing a specific metric or spatial structure. In this case, inhibition acts as a global component where one response option inhibits all others (32,44,50). Other models assign a metric to the interaction among choices characterized by a given distance.…”

Section: Discussionmentioning

confidence: 99%

“…To model competition across all fingers of both hands, inhibition decays for increasingly larger distances but never approaches zero. The involvement of different neural field sites coding for different neurons depends on two factors: their activation level, which is determined by the stimulus input (modeled as Gaussian distributions of activation) (32), and the baseline activity of these field sites (Fig. 3 A and B).…”

Section: Fig 2 Effects Of Coactivation On Rts: Experimental Findingmentioning

confidence: 99%

Cortical topography of intracortical inhibition influences the speed of decision making

Wilimzig

Ragert

Dinse

2012

Proc. Natl. Acad. Sci. U.S.A.

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The neocortex contains orderly topographic maps; however, their functional role remains controversial. Theoretical studies have suggested a role in minimizing computational costs, whereas empirical studies have focused on spatial localization. Using a tactile multiple-choice reaction time (RT) task before and after the induction of perceptual learning through repetitive sensory stimulation, we extend the framework of cortical topographies by demonstrating that the topographic arrangement of intracortical inhibition contributes to the speed of human perceptual decisionmaking processes. RTs differ among fingers, displaying an inverted U-shaped function. Simulations using neural fields show the inverted U-shaped RT distribution as an emergent consequence of lateral inhibition. Weakening inhibition through learning shortens RTs, which is modeled through topographically reorganized inhibition. Whereas changes in decision making are often regarded as an outcome of higher cortical areas, our data show that the spatial layout of interaction processes within representational maps contributes to selection and decisionmaking processes.computational neuroscience | plasticity | cortical reorganization T he mammalian neocortex contains orderly topographic maps of receptor surfaces that emerge early during ontogenetic development (1, 2). However, the functional meaning and relevance of the topographical arrangement are still debated (1-5). There is agreement that topographic mapping is instrumental in minimizing computational costs by keeping the length of axonal and dendritic wiring low, thereby contributing to the compactness of the neural circuitry (1, 6-8). On the other hand, experimental studies have focused on the extent and size of topographic maps, thereby exploring how and where physical stimuli and their attributes are localized and represented in the brain. Cortical lateral interaction seems to play a crucial role in the integration of information from remote skin portions, which is required for localization and discrimination (9-11), contour integration, as well as contextual influences by surrounding elements (12, 13). Accordingly, cortical processing is modulated by nonlinear interactions through range-dependent intracortical excitation and inhibition. The mean-field approach has been shown to provide an adequate and elegant way to model the collective properties of large numbers of interacting neurons (14-16).Decision making has been discussed in the context of contributions of frontal and prefrontal cortical areas (17)(18)(19)(20). Our study provides an extension of the framework of cortical topographies by demonstrating through a combination of psychophysics, plasticity, and modeling that the speed of perceptual decision making is modulated by the topographic layout of intracortical interactions within early sensory cortical areas. We used a tactile multiple-choice reaction time (RT) task that requires selecting a given finger out of all 10 fingers on both hands ( Fig. 1) (21). RTs strongly differ among indivi...

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The time course of saccadic decision making: Dynamic field theory

Cited by 97 publications

References 76 publications

A neural mechanism for coordinate transformation predicts pre-saccadic remapping

A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Cortical topography of intracortical inhibition influences the speed of decision making

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