Control of movement time and sequential action through attractor dynamics : a simulation study demonstrating object interception and coordination

The timing of movements and of action sequences is difficult when on-line coupling to sensory information is a requirement. That requirement arises in most behavior-based robot architectures, in which relatively low-level and often noisy sensor input is used to initiate and steer action. We show how an attractor dynamics approach to the generation of behavior in such architectures can be extended to the timing of motor acts. We propose a two-layer architecture, in which a competitive "neural" dynamics controls the qualitative dynamics of a second, "timing" layer. At that second layer, periodic attractors generate timed movement. By activating such limit cycles over limited time intervals, discrete movements and movement sequences can be obtained. We demonstrate the approach by simulating two tasks that involve control of timing: the interception of moving objects by a simple two-degree-of-freedom robot arm and the temporal coordination of the end-effector motions of two six-degree-of-freedom robot arms.Fundação para a Ciência e a Tecnoloia (FCT

La dynamique des attracteurs comme base de génération de comportements en robotique mobile autonome

L’hypothèse centrale de l’approche dynamique en robotique mobile autonome (Schöner et Dose, 1992 ; Schöner, Dose et Engels, 1995 ; Bicho et Schöner, 1997 ; Steinhage et Schöner, 1998 ; Large, Christensen et Bajcy, 1999 ; Bicho, Mallet et Schöner, 2000) est que le comportement moteur ainsi que les représentations pertinentes nécessaires à sa réalisation doivent, d’une part, être générés de façon continue dans le temps, et, d’autre part, résister aux fluctuations ou perturbations auxquelles tout système réel est exposé. Cela conduit à une conception dans laquelle le comportement et les représentations sont les solutions stables (ou attracteurs) d’un ensemble de systèmes dynamiques, qui traduisent en temps réel l’information sensorielle en contraintes graduées et intégrables. De multiples attracteurs peuvent co-exister en présence de la même situation sensorielle. C’est l’état interne du système autonome qui décidera quel attracteur sera choisi. Le changement du nombre et/ou de la nature des attracteurs à travers des instabilités (ou bifurcations) permet au système autonome de se configurer de manière flexible selon le contexte sensoriel instantané. L’approche est ici présentée au niveau de la génération de comportements moteurs. Dans ce cas, des variables « comportementales » représentent directement un continuum d’états physiques du système qui sont générés par des systèmes de contrôle conventionnels. Par exemple, une variable représentant la direction dans laquelle un véhicule se déplace, peut évoluer dans le temps grâce à un système dynamique qui intègre les contraintes « acquisition de cibles » et « évitement d’obstacles ». La fusion ou sélection parmi ces contraintes est réalisée au moyen d’une dynamique non linéaire bien maîtrisée

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations

Scene memory and spatial inhibition in visual search

Abstract: Any object-oriented action requires that the object be first brought into the attentional foreground, often through visual search. Outside the laboratory, this would always take place in the presence of a scene representation acquired from ongoing visual exploration. The interaction of scene memory with visual search is still not completely understood. Feature integration theory (FIT) has shaped both research on visual search, emphasizing the scaling of search times with set size when searches entail feature conjunctions, and research on visual working memory through the change detection paradigm. Despite its neural motivation, there is no consistently neural process account of FIT in both its dimensions. We propose such an account that integrates (1) visual exploration and the building of scene memory, (2) the attentional detection of visual transients and the extraction of search cues, and (3) visual search itself. The model uses dynamic field theory in which networks of neural dynamic populations supporting stable activation states are coupled to generate sequences of processing steps. The neural architecture accounts for basic findings in visual search and proposes a concrete mechanism for the integration of working memory into the search process. In a behavioral experiment, we address the long-standing question of whether both the overall speed and the efficiency of visual search can be improved by scene memory. We find both effects and provide model fits of the behavioral results. In a second experiment, we show that the increase in efficiency is fragile, and trace that fragility to the resetting of spatial working memory

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory

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)

A three-pillar approach to assessing climate impacts on low flows

The objective of this paper is to present a framework for assessing climate impacts on future low flows that combines different sources of information, termed pillars. To illustrate the framework three pillars are chosen: (a) extrapolation of observed low-flow trends into the future, (b) rainfall–runoff projections based on climate scenarios and (c) extrapolation of changing stochastic rainfall characteristics into the future combined with rainfall–runoff modelling. Alternative pillars could be included in the overall framework. The three pillars are combined by expert judgement based on a synoptic view of data, model outputs and process reasoning. The consistency/inconsistency between the pillars is considered an indicator of the certainty/uncertainty of the projections. The viability of the framework is illustrated for four example catchments from Austria that represent typical climate conditions in central Europe. In the Alpine region where winter low flows dominate, trend projections and climate scenarios yield consistently increasing low flows, although of different magnitudes. In the region north of the Alps, consistently small changes are projected by all methods. In the regions in the south and south-east, more pronounced and mostly decreasing trends are projected but there is disagreement in the magnitudes of the projected changes. The process reasons for the consistencies/inconsistencies are discussed. For an Alpine region such as Austria the key to understanding low flows is whether they are controlled by freezing and snowmelt processes, or by the summer moisture deficit associated with evaporation. It is argued that the three-pillar approach offers a systematic framework of combining different sources of information aimed at more robust projections than that obtained from each pillar alone

Scene memory and spatial inhibition in visual search : A neural dynamic process model and new experimental evidence.

Any object-oriented action requires that the object be first brought into the attentional foreground, often through visual search. Outside the laboratory, this would always take place in the presence of a scene representation acquired from ongoing visual exploration. The interaction of scene memory with visual search is still not completely understood. Feature integration theory (FIT) has shaped both research on visual search, emphasizing the scaling of search times with set size when searches entail feature conjunctions, and research on visual working memory through the change detection paradigm. Despite its neural motivation, there is no consistently neural process account of FIT in both its dimensions. We propose such an account that integrates (1) visual exploration and the building of scene memory, (2) the attentional detection of visual transients and the extraction of search cues, and (3) visual search itself. The model uses dynamic field theory in which networks of neural dynamic populations supporting stable activation states are coupled to generate sequences of processing steps. The neural architecture accounts for basic findings in visual search and proposes a concrete mechanism for the integration of working memory into the search process. In a behavioral experiment, we address the long-standing question of whether both the overall speed and the efficiency of visual search can be improved by scene memory. We find both effects and provide model fits of the behavioral results. In a second experiment, we show that the increase in efficiency is fragile, and trace that fragility to the resetting of spatial working memory

Position and Velocity Coupling of Postural Sway to Somatosensory Drive

, and sinusoidal vertical axis rotation (SVAR) technique rotates Elaine Henson. Position and velocity coupling of postural sway seated subjects at a range of frequencies to measure the gain to somatosensory drive. J. Neurophysiol. 79: 1661Neurophysiol. 79: -1674Neurophysiol. 79: , 1998. and phase of eye movements in the dark as a measure of Light touch contact of a fingertip to a stationary surface provides vestibular function (Howard 1982; whole-body posture body sway relative to the touch plate averaged 20-30Њ at 0.1-Hz Such control theory techniques have not been impledrive and decreased approximately linearly to 0130Њ at 0.8-Hz drive. System gain was Ç1 across frequency. The large phase lags mented for somatosensory function with regard to upright observed cannot be accounted for with velocity coupling alone but stance control. Extensive empiric studies have determined indicate that body sway also was coupled to the position of the that the primary role of somatosensation is to provide infortouch plate. Fitting of a linear second-order model to the data mation concerning contact surface forces and properties such suggests that postural control parameters are not fixed but adapt as texture and friction and the relative configuration of body to the moving frame of reference. Moreover, coupling to both segments (Dietz 1992