Dynamic field theory of movement preparation

Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study

In this paper we present a model for action preparation and decision making in cooperative tasks that is inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. It implements the coordination of actions and goals among the partners as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of others' motor behavior. The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode task-relevant information about action means, task goals and context in the form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic model of joint action is evaluated in a task in which a robot and a human jointly construct a toy object. We show that the highly context sensitive mapping from action observation onto appropriate complementary actions allows coping with dynamically changing joint action situations.

Cortical preparatory activity: representation of movement or first cog in a dynamical machine?

The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a subthreshold form of movement activity: a neuron active during rightward movements becomes modestly active during preparation of a rightward movement. We asked whether this pattern of activity is, in fact, observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning. Yet, somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract "space" that described the population-level pattern of movement activity. In fact, this relationship accounted for preparatory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role.

Visual demand and visual field presentation influence natural scene processing

BACKGROUND

Bottom-up and top-down processes are involved in visual analysis of scenes. Here we examined the influence of top-down visual demand on natural scene processing.

METHODS

We measured accuracy and response time in adults performing two stimuli-equivalent tasks. Unfiltered, low or high spatial frequency (SF) natural scenes were presented in central, left, or right visual fields (CVF, LVF, RVF). The tasks differed only by the instructed visual demand. In the detection task, participants had to decide whether a scene was present or not. In the categorization task, they had to decide whether the scene was a city or a forest.

RESULTS

Higher accuracy was seen for the LVF in the detection task, but for categorization, greater accuracy was seen for the RVF. The interaction between Task and SF revealed coarse-to-fine processing in the categorization task for both accuracy and reaction time, which nearly disappeared in the detection task. Considering the interaction of Task, VF and SF, a left-hemisphere specialisation (i.e., RVF advantage) was observed for the categorisation of HSF scenes for accuracy alone, whereas a LVF advantage was seen for all SFs in the detection task for both accuracy and reaction time.

CONCLUSION

Our results revealed that the nature of top-down visual demand is essential to understanding how visual analysis is achieved in each hemisphere. Moreover, this study examining the effects of visual demand, visual field presentation, and SF content of stimuli through the use of ecological stimuli provides a tool to enrich the clinical examination of visual and neurovisual patients.

Response Competition in the Primary Motor Cortex: Corticospinal Excitability Reflects Response Replacement During Simple Decisions

It has been suggested that, during decisions about actions, multiple options are initially specified in parallel and then gradually eliminated in a competition for overt execution. To further test this hypothesis, we studied the modulation of human corticospinal excitability during the reaction time of the Eriksen flanker task. In the task, subjects responded with finger flexion or extension to a central arrow while ignoring congruent or incongruent flanker arrows. Single-pulse transcranial magnetic stimulation (TMS) was applied over primary motor cortex (M1) at one of five different latencies after stimulus onset, and motor-evoked potentials (MEPs) were measured in the contralateral index finger. During the control (no flankers) and congruent conditions, MEP size in the agonist increased gradually over the course of reaction time, indicating an increase in corticospinal excitability. Conversely, when the same muscle acted as an antagonist, MEP size decreased, suggesting inhibition. Critically, in the incongruent condition, MEPs briefly increased in the muscle corresponding to an initial default response to the flanker arrows and were later replaced by MEPs corresponding to the correct response to the central arrow. Finally, we found that the gradually growing MEPs for the three conditions reached a constant maximum level just before movement initiation. We propose that this dynamic modulation in corticospinal excitability reflects the competition process, leading to the selection of one response and the rejection of the other. Our results suggest that response competition influences activity in primary motor cortex and that its timing directly influences motor output latency.

Schema generation in recurrent neural nets for intercepting a moving target

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

How long does it take to describe what one sees? The first step using picture description tasks

The study explored the dependences between quantifiable features of a picture and the time it takes to describe it. Six native English speakers and six bilinguals watched pictures presented on the monitor and described them "as quickly and accurately as possible". The bilingual participants performed the test twice, in English and in their native language. The pictures could contain one to six objects. There were four series of trials that differed in the number of characteristics of the objects the participants were instructed to describe. Reaction time showed a modest, close to linear scaling with the number of objects. Both reaction time and speech time were significantly longer for the bilingual participants performing in English as compared to their performance in the native language and to the English speaking participants. The difference in reaction time did not depend on the number of objects. Speech time showed a close to linear scaling with the number of objects within each of the four series. The linear regression coefficient in this relationship increased linearly with the number of characteristics of the objects across all series. The results are discussed in relation to speed-accuracy trade-off and different strategies of picture description.

Neural mechanisms for interacting with a world full of action choices

The neural bases of behavior are often discussed in terms of perceptual, cognitive, and motor stages, defined within an information processing framework that was originally inspired by models of human abstract problem solving. Here, we review a growing body of neurophysiological data that is difficult to reconcile with this influential theoretical perspective. As an alternative foundation for interpreting neural data, we consider frameworks borrowed from ethology, which emphasize the kinds of real-time interactive behaviors that animals have engaged in for millions of years. In particular, we discuss an ethologically-inspired view of interactive behavior as simultaneous processes that specify potential motor actions and select between them. We review how recent neurophysiological data from diverse cortical and subcortical regions appear more compatible with this parallel view than with the classical view of serial information processing stages.

Anticipatory planning of sequential hand and finger movements

Hand movements may be anticipatorily planned to reach an immediate target and at the same time facilitate movements to subsequent targets. Researchers have proposed that in anticipatory planning, information about subsequent targets needs to be processed to engage in the planning of the next movement. To test this hypothesis, the authors varied the information 48 participants had about to-be-executed two-step hand and finger movement sequences prior to a choice reaction signal. Movements were initialized faster if participants had advance information about the second target of the sequence than if participants had no advance information at all. The results imply that movement segments to late targets in a movement sequence may be at least partially planned, even if information about earlier targets is not yet available.

A neural mechanism of synergy formation for whole body reaching

The present study proposes a computational model for the formation of whole body reaching synergy, i.e., coordinated movements of lower and upper limbs, characterized by a focal component (the hand must reach a target) and a postural component (the center of mass must remain inside the support base). The model is based on an extension of the equilibrium point hypothesis that has been called Passive Motion Paradigm (PMP), modified in order to achieve terminal attractor features and allow the integration of multiple constraints. The model is a network with terminal attractor dynamics. By simulating it in various conditions it was possible to show that it exhibits many of the spatio-temporal features found in experimental data. In particular, the motion of the center of mass appears to be synchronized with the motion of the hand and with proportional amplitude. Moreover, the joint rotation patterns can be accounted for by a single functional degree of freedom, as shown by principal component analysis. It is also suggested that recent findings in motor imagery support the idea that the PMP network may represent the motor cognitive part of synergy formation, uncontaminated by the effect of execution.

Tests of the dynamic field theory and the spatial precision hypothesis: capturing a qualitative developmental transition in spatial working memory

This study tested a dynamic field theory (DFT) of spatial working memory and an associated spatial precision hypothesis (SPH). Between 3 and 6 years of age, there is a qualitative shift in how children use reference axes to remember locations: 3-year-olds' spatial recall responses are biased toward reference axes after short memory delays, whereas 6-year-olds' responses are biased away from reference axes. According to the DFT and the SPH, quantitative improvements over development in the precision of excitatory and inhibitory working memory processes lead to this qualitative shift. Simulations of the DFT in Experiment 1 predict that improvements in precision should cause the spatial range of targets attracted toward a reference axis to narrow gradually over development, with repulsion emerging and gradually increasing until responses to most targets show biases away from the axis. Results from Experiment 2 with 3- to 5-year-olds support these predictions. Simulations of the DFT in Experiment 3 quantitatively fit the empirical results and offer insights into the neural processes underlying this developmental change.

Dimensional reduction for the inverse problem of neural field theory

Inverse problems in computational neuroscience comprise the determination of synaptic weight matrices or kernels for neural networks or neural fields respectively. Here, we reduce multi-dimensional inverse problems to inverse problems in lower dimensions which can be solved in an easier way or even explicitly through kernel construction. In particular, we discuss a range of embedding techniques and analyze their properties. We study the Amari equation as a particular example of a neural field theory. We obtain a solution of the full 2D or 3D problem by embedding 0D or 1D kernels into the domain of the Amari equation using a suitable path parametrization and basis transformations. Pulses are interconnected at branching points via path gluing. As instructive examples we construct logical gates, such as the persistent XOR and binary addition in neural fields. In addition, we compare results of inversion by dimensional reduction with a recently proposed global inversion scheme for neural fields based on Tikhonov–Hebbian learning. The results show that stable construction of complex distributed processes is possible via neural field dynamics. This is an important first step to study the properties of such constructions and to analyze natural or artificial realizations of neural field architectures.

The self-organization of explicit attitudes

How do minds produce explicit attitudes over several hundred milliseconds? Speeded evaluative measures have revealed implicit biases beyond cognitive control and subjective awareness, yet mental processing may culminate in an explicit attitude that feels personally endorsed and corroborates voluntary intentions. We argue that self-reported explicit attitudes derive from a continuous, temporally dynamic process, whereby multiple simultaneously conflicting sources of information self-organize into a meaningful mental representation. While our participants reported their explicit (like vs. dislike) attitudes toward White versus Black people by moving a cursor to a "like" or "dislike" response box, we recorded streaming x- and y-coordinates from their hand-movement trajectories. We found that participants' hand-movement paths exhibited greater curvature toward the "dislike" response when they reported positive explicit attitudes toward Black people than when they reported positive explicit attitudes toward White people. Moreover, these trajectories were characterized by movement disorder and competitive velocity profiles that were predicted under the assumption that the deliberate attitudes emerged from continuous interactions between multiple simultaneously conflicting constraints.

Simultaneous preparation of multiple potential movements: opposing effects of spatial proximity mediated by premotor and parietal cortex

Neurophysiological studies in monkey have suggested that premotor and motor cortex may prepare for multiple movements simultaneously, sustained by cooperative and competitive interactions within and between the neural populations encoding different actions. Here, we investigate whether competition between alternative movement directions, manipulated in terms of number and spatial angle, is reflected in electroencephalographic (EEG) measures of (pre)motor cortical activity in humans. EEG was recorded during performance of a center-out pointing task in which response signals were preceded by cues providing prior information in the form of arrows pointing to one or more possible movement targets. Delay-period activity in (pre)motor cortex was modulated in the predicted manner by the number of possible movement directions and by the angle separating them. Response latencies, however, were determined not only by the amplitude of movement-preparatory activity, but also by differences in the duration of stimulus evaluation against the visuospatial memory of the cue, reflected in EEG potentials originating from posterior parietal cortex (PPC). Specifically, the spatial proximity of possible movement targets was processed differently by (pre)motor and posterior parietal cortex. Spatial proximity enhanced the amplitude of (pre)motor cortex preparatory activity during the delay period but delayed evaluation of the response signal in the PPC, thus producing opposite effects on response latency. The latter finding supports distributed control of movement decisions in the frontoparietal network, revealing a feature of distributed control that is of potential significance for the understanding of distracter effects in reaching and pointing.

Swing it to the left, swing it to the right: enacting flexible spatial language using a neurodynamic framework

Research is continually expanding the empirical and theoretical picture of embodiment and dynamics in language. To date, however, a formalized neural dynamic framework for embodied linguistic processes has yet to emerge. To advance embodied theories of language, the present work develops a formalized neural dynamic framework of spatial language that explicitly integrates linguistic processes and dynamic sensory-motor systems. We then implement and test our spatial language architecture on a robotic platform continuously linked to real-time camera input. In a suite of tasks using everyday objects we demonstrate the framework's capacity for both contextually-dependent behavioral flexibility and the seamless integration of spatial, non-spatial, and symbolic representations. To our knowledge this is the first unified, neurally-grounded architecture integrating these processes and behaviors.

Corresponding delay-dependent biases in spatial language and spatial memory

The present study addresses the relationship between linguistic and non-linguistic spatial representations. In three experiments we probe spatial language and spatial memory at the same time points in the task sequence. Experiments 1 and 2 show analogous delay-dependent biases in spatial language and spatial memory. Experiment 3 extends this correspondence, showing that additional perceptual structure along the vertical axis reduces delay-dependent effects in both tasks. These results indicate that linguistic and non-linguistic spatial systems depend on shared underlying representational processes. In addition, we also address how these delay-dependent biases can arise within a single theoretical framework without positing differing prototypes for linguistic and non-linguistic spatial systems.

A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory

Many everyday tasks rely on our ability to hold information about a perceived stimulus in mind after that stimulus is no longer visible and to compare this information with incoming perceptual information. This ability has been shown to rely on a short-term form of visual memory that has come to be known as visual working memory. Research and theory at both the behavioral and neural levels has begun to provide important insights into the basic properties of the neuro-cognitive systems underlying specific aspects of this form of memory. However, to date, no neurally-plausible theory has been proposed that addresses both the storage of information in working memory and the comparison process in a single framework. The present paper presents a layered neural field architecture that addresses these limitations. In a series of simulations, we show how the model can be used to capture each of the components underlying performance in simple visual comparison tasks--from the encoding, consolidation, and maintenance of information in working memory, to comparison and updating in response to changed inputs. Importantly, the proposed model demonstrates how these elementary perceptual and cognitive functions emerge from the coordinated activity of an integrated, dynamic neural system.

Stability of localized patterns in neural fields

We investigate two-dimensional neural fields as a model of the dynamics of macroscopic activations in a cortex-like neural system. While the one-dimensional case was treated comprehensively by Amari 30 years ago, two-dimensional neural fields are much less understood. We derive conditions for the stability for the main classes of localized solutions of the neural field equation and study their behavior beyond parameter-controlled destabilization. We show that a slight modification of the original model yields an equation whose stationary states are guaranteed to satisfy the original problem and numerically demonstrate that it admits localized noncircular solutions. Typically, however, only periodic spatial tessellations emerge on destabilization of rotationally invariant solutions.

Redundancy, self-motion, and motor control

Outside the laboratory, human movement typically involves redundant effector systems. How the nervous system selects among the task-equivalent solutions may provide insights into how movement is controlled. We propose a process model of movement generation that accounts for the kinematics of goal-directed pointing movements performed with a redundant arm. The key element is a neuronal dynamics that generates a virtual joint trajectory. This dynamics receives input from a neuronal timer that paces end-effector motion along its path. Within this dynamics, virtual joint velocity vectors that move the end effector are dynamically decoupled from velocity vectors that do not. Moreover, the sensed real joint configuration is coupled back into this neuronal dynamics, updating the virtual trajectory so that it yields to task-equivalent deviations from the dynamic movement plan. Experimental data from participants who perform in the same task setting as the model are compared in detail to the model predictions. We discover that joint velocities contain a substantial amount of self-motion that does not move the end effector. This is caused by the low impedance of muscle joint systems and by coupling among muscle joint systems due to multiarticulatory muscles. Back-coupling amplifies the induced control errors. We establish a link between the amount of self-motion and how curved the end-effector path is. We show that models in which an inverse dynamics cancels interaction torques predict too little self-motion and too straight end-effector paths.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

The dynamic nature of knowledge: insights from a dynamic field model of children's novel noun generalization

This paper examines the tie between knowledge and behavior in a noun generalization context. An experiment directly comparing noun generalizations of children at the same point in development in forced-choice and yes/no tasks reveals task-specific differences in the way children's knowledge of nominal categories is brought to bear in a moment. To understand the cognitive system that produced these differences, the real-time decision processes in these tasks were instantiated in a dynamic field model. The model captures both qualitative and quantitative differences in performance across tasks and reveals constraints on the nature of children's accumulated knowledge. Additional simulations of developmental change in the yes/no task between 2 and 4 years of age illustrate how changes in children's representations translate into developmental changes in behavior. Together, the empirical data and model demonstrate the dynamic nature of knowledge and are consistent with the perspective that knowledge cannot be separated from the task-specific processes that create behavior in the moment.

Cue salience and infant perseverative reaching: tests of the dynamic field theory

Skilled behavior requires a balance between previously successful behaviors and new behaviors appropriate to the present context. We describe a dynamic field model for understanding this balance in infant perseverative reaching. The model predictions are tested with regard to the interaction of two aspects of the typical perseverative reaching task: the visual cue indicating the target and the memory demand created by the delay imposed between cueing and reaching. The memory demand was manipulated by imposing either a 0- or a 3-second delay, and the salience of the cue to reach was systematically varied. Infants demonstrated fewer perseverative errors at 0-delay versus 3-second delay based on the cue salience, such that a more salient visual cue was necessary to overcome a longer delay. These results have important implications for understanding both the basic perceptual-motor processes that produce reaching in infants and skilled flexible behavior in general.

Nature of motor control: perspectives and issues

Four perspectives on motor control provide the framework for developing a comprehensive theory of motor control in biological systems. The four perspectives, of decreasing orthodoxy, are distinguished by their sources of inspiration: neuroanatomy, robotics, self-organization, and ecological realities. Twelve major issues that commonly constrain (either explicitly or implicitly) the understanding of the control and coordination of movement are identified and evaluated within the framework of the four perspectives. The issues are as follows: (1) Is control strictly neural? (2) Is there a divide between planning and execution? (3) Does control entail a frequently involved knowledgeable executive? (4) Do analytical internal models mediate control? (5) Is anticipation necessarily model dependent? (6) Are movements preassembled? (7) Are the participating components context independent? (8) Is force transmission strictly myotendinous? (9) Is afference a matter of local linear signaling? (10) Is neural noise an impediment? (11) Do standard variables (of mechanics and physiology) suffice? (12) Is the organization of control hierarchical?

Similarity effect and optimal control of multiple-choice decision making

Decision making with several choice options is central to cognition. To elucidate the neural mechanisms of such decisions, we investigated a recurrent cortical circuit model in which fluctuating spiking neural dynamics underlie trial-by-trial stochastic decisions. The model encodes a continuous analog stimulus feature and is thus applicable to multiple-choice decisions. Importantly, the continuous network captures similarity between alternatives and possible overlaps in their neural representation. Model simulations accounted for behavioral as well as single-unit neurophysiological data from a recent monkey experiment and revealed testable predictions about the patterns of error rate as a function of the similarity between the correct and actual choices. We also found that the similarity and number of options affect speed and accuracy of responses. A mechanism is proposed for flexible control of speed-accuracy tradeoff, based on a simple top-down signal to the decision circuit that may vary nonmonotonically with the number of choice alternatives.

Mindless reading revisited: an analysis based on the SWIFT model of eye-movement control

In this article, we revisit the mindless reading paradigm from the perspective of computational modeling. In the standard version of the paradigm, participants read sentences in both their normal version as well as the transformed (or mindless) version where each letter is replaced with a z. z-String scanning shares the oculomotor requirements with reading but none of the higher-level lexical and semantic processes. Here we use the z-string scanning task to validate the SWIFT model of saccade generation [Engbert, R., Nuthmann, A., Richter, E., & Kliegl, R. (2005). SWIFT: A dynamical model of saccade generation during reading. Psychological Review, 112(4), 777-813] as an example for an advanced theory of eye-movement control in reading. We test the central assumption of spatially distributed processing across an attentional gradient proposed by the SWIFT model. Key experimental results like prolonged average fixation durations in z-string scanning compared to normal reading and the existence of a string-length effect on fixation durations and probabilities were reproduced by the model, which lends support to the model's assumptions on visual processing. Moreover, simulation results for patterns of regressive saccades in z-string scanning confirm SWIFT's concept of activation field dynamics for the selection of saccade targets.

Towards dynamical system models of language-related brain potentials

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

The dynamic brain: from spiking neurons to neural masses and cortical fields

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

Moving to higher ground: The dynamic field theory and the dynamics of visual cognition

In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the 'binding problem' in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior.

A parallel framework for interactive behavior

Although theoretical models often assume that the basic organization of the nervous system involves separate systems for perception, cognition, and action, neural data often does not fit into any of these conceptual categories. Here, an alternative framework is described, which focuses on interactive behavior and treats it as a continuous competition between representations of currently available potential actions. This suggests a neural organization consisting of two parallel systems: a system for action specification, which uses sensory information to represent currently available potential actions, and a system for action selection, which involves attentional and decisional mechanisms which determine the action that will be performed. It is proposed that neural processing occurs through two waves of activation: an early wave which specifies several potential actions and a later wave of biasing influences which selects one action for execution. A computational model of decision making is described within the context of this proposal, and simulations of neural and behavioral phenomena are presented.

Generality with specificity: the dynamic field theory generalizes across tasks and time scales

A central goal in cognitive and developmental science is to develop models of behavior that can generalize across both tasks and development while maintaining a commitment to detailed behavioral prediction. This paper presents tests of one such model, the Dynamic Field Theory (DFT). The DFT was originally proposed to capture delay-dependent biases in spatial recall and developmental changes in spatial recall performance. More recently, the theory was generalized to adults' performance in a second spatial working memory task, position discrimination. Here we use the theory to predict a specific, complex developmental pattern in position discrimination. Data with 3- to 6-year-old children and adults confirm these predictions, demonstrating that the DFT achieves generality across tasks and time scales, as well as the specificity necessary to generate novel, falsifiable predictions.

Hypothesis regarding the transformation of the intended direction of movement during the production of graphic trajectories: a study of drawing movements in 8- to 12-year-old children

Children from 8 to 12 years of age drew figure-eights and ellipses at a self-chosen tempo on a digitizing tablet. Global aspects (perimeter and average speed) and local aspects (relation between instantaneous speed and curvature) of performance were analyzed across age groups and types of figures. We tested the predictions of the transformation model, which is based on the hypothesis that changing the intended direction of movement is a time-consuming process that affects the evolution in time of the movement trajectory, and compared how well it fitted the data relative to the power law. We found that the relation between speed and curvature was typically better described by the transformation model than by the power law. However, the power law provided a better description when ellipses were drawn at a fast speed. The analyses of the parameters of the transformation model indicate that processing speed increased linearly with age. In addition, the results suggest that the effects of the spring-like properties of the arm were noticeable when ellipses were drawn at a fast speed. This study indicates that both biomechanical properties and central processes have an effect on the kinematics of continuous movements and particularly on the relation between speed and curvature. However, their relative importance varies with the type of figure and average movement speed. In conclusion, the results support the hypothesis that a time-consuming process of transformation of the intended direction of movement is operating during the production of continuous movements and that this process increases in speed between 8 to 12 years of age.

Special Section: Towards an Embodiment of Goals

This paper discusses both a dissociation view and a dynamic view with respect to the study of voluntary, goal-directed behavior. The dissociation view builds on the recently reintroduced ideomotor principle, and conceives of clearly dissociated and hierarchical roles for the planning and control of action. The dynamic view has a more integral and dynamic conception of how planning, control, and timing merge in the guidance of behavior. This view, however, lacks a clear way of encompassing the goaldirectedness of behavior. For behavior to be effective and efficient, sensory information has to play an equally important role in guiding action as goal-related information does. As a third view, a dynamic action-selection approach is introduced by combining aspects of the former two. This model is able to merge ideomotor and sensorimotor processes continuously and in real time. In discussing the action-selection approach, a special emphasis is given to the role of long-term influences like preferences and goals.

Language processing with dynamic fields

We construct a mapping from complex recursive linguistic data structures to spherical wave functions using Smolensky's filler/role bindings and tensor product representations. Syntactic language processing is then described by the transient evolution of these spherical patterns whose amplitudes are governed by nonlinear order parameter equations. Implications of the model in terms of brain wave dynamics are indicated.

Learning to recognize objects on the fly: a neurally based dynamic field approach

Autonomous robots interacting with human users need to build and continuously update scene representations. This entails the problem of rapidly learning to recognize new objects under user guidance. Based on analogies with human visual working memory, we propose a dynamical field architecture, in which localized peaks of activation represent objects over a small number of simple feature dimensions. Learning consists of laying down memory traces of such peaks. We implement the dynamical field model on a service robot and demonstrate how it learns 30 objects from a very small number of views (about 5 per object are sufficient). We also illustrate how properties of feature binding emerge from this framework.

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

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.

Exploring action dynamics as an index of paired-associate learning

Much evidence exists supporting a richer interaction between cognition and action than commonly assumed. Such findings demonstrate that short-timescale processes, such as motor execution, may relate in systematic ways to longer-timescale cognitive processes, such as learning. We further substantiate one direction of this interaction: the flow of cognition into action systems. Two experiments explored match-to-sample paired-associate learning, in which participants learned randomized pairs of unfamiliar symbols. During the experiments, their hand movements were continuously tracked using the Nintendo Wiimote. Across learning, participant arm movements are initiated and completed more quickly, exhibit lower fluctuation, and exert more perturbation on the Wiimote during the button press. A second experiment demonstrated that action dynamics index novel learning scenarios, and not simply acclimatization to the Wiimote interface. Results support a graded and systematic covariation between cognition and action, and recommend ways in which this theoretical perspective may contribute to applied learning contexts.

Effects of arm weight on C-not-B task performance: implications for the motor inhibitory deficit account of search failures

Toddlers have been found to fail on a three-location search task involving the invisible displacements of an object, namely the C-not-B task. In this task, a child is shown the experimenter's hand that contains a toy. The toy then successively disappears under the three cloths (A, B, then C). The examiner silently releases the toy under the second cloth (B). The hidden object makes a bump in the B cloth that covers it. Young children emit a strong bias toward the last cloth that the experimenter's hand passes under, and this has been labeled the C-not-B error. One possible explanation for toddlers' failures in the C-not-B task is that children lack the motor inhibitory mechanisms. To test this hypothesis, the robustness of the C-not-B error was tested, in a first experiment, against variations in body parameters. By putting additional weights on the arm, the C-not-B error was reduced substantially and the C-not-B task had a higher rate of success. Indeed, in contrast to control participants, who ignored a visual clue indicating the correct location of the hidden object and reached for the last location of the experimenter's hand, the participants with arm weights initiated their reaching movements by using the visual clue. The findings from the second control group indicate that the dramatic increase in successful performance by children with arm weights is not merely a consequence of the focus on the attention to arm movements. The motion of the experimenter's hand in space appears to have made the task difficult because toddlers had no problems inferring that a lump under a cloth indicates the existence of an object without actually having watched an object be hidden there, as demonstrated in a second experiment. The findings are consistent with the hypothesis that C-not-B task content activates a prepotent motor response that preempts full consideration of a visual clue indicating the correct location of the hidden object. We propose that the success in the C-not-B task of toddlers with additional arm weights could result from a disruption of automatic hand movement that is triggered by sensory signals, namely salient features of the C-not-B task.

Cortical mechanisms of action selection: the affordance competition hypothesis

At every moment, the natural world presents animals with two fundamental pragmatic problems: selection between actions that are currently possible and specification of the parameters or metrics of those actions. It is commonly suggested that the brain addresses these by first constructing representations of the world on which to build knowledge and make a decision, and then by computing and executing an action plan. However, neurophysiological data argue against this serial viewpoint. In contrast, it is proposed here that the brain processes sensory information to specify, in parallel, several potential actions that are currently available. These potential actions compete against each other for further processing, while information is collected to bias this competition until a single response is selected. The hypothesis suggests that the dorsal visual system specifies actions which compete against each other within the fronto-parietal cortex, while a variety of biasing influences are provided by prefrontal regions and the basal ganglia. A computational model is described, which illustrates how this competition may take place in the cerebral cortex. Simulations of the model capture qualitative features of neurophysiological data and reproduce various behavioural phenomena.

Gain mechanisms for contextually guided visuomotor transformations

A prevailing question in sensorimotor research is the integration of sensory signals with abstract behavioral rules (contexts) and how this results in decisions about motor actions. We used neural network models to study how context-specific visuomotor remapping may depend on the functional connectivity among multiple layers. Networks were trained to perform different rotational visuomotor associations, depending on the stimulus color (a nonspatial context signal). In network I, the context signal was propagated forward through the network (bottom-up), whereas in network II, it was propagated backwards (top-down). During the presentation of the visual cue stimulus, both networks integrate the context with the sensory information via a mechanism similar to the classic gain field. The recurrence in the networks hidden layers allowed a simulation of the multimodal integration over time. Network I learned to perform the proper visuomotor transformations based on a context-modulated memory of the visual cue in its hidden layer activity. In network II, a brief visual response, which was driven by the sensory input, is quickly replaced by a context-modulated motor-goal representation in the hidden layer. This happens because of a dominant feedback signal from the output layer that first conveys context information, and then, after the disappearance of the visual cue, conveys motor goal information. We also show that the origin of the context information is not necessarily closely tied to the top-down feedback. However, we suggest that the predominance of motor-goal representations found in the parietal cortex during context-specific movement planning might be the consequence of strong top-down feedback originating from within the parietal lobe or from the frontal lobe.