The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations

Neural mechanisms of visual motion extrapolation

Because neural processing takes time, the brain only has delayed access to sensory information. When localising moving objects this is problematic, as an object will have moved on by the time its position has been determined. Here, we consider predictive motion extrapolation as a fundamental delay-compensation strategy. From a population-coding perspective, we outline how extrapolation can be achieved by a forwards shift in the population-level activity distribution. We identify general mechanisms underlying such shifts, involving various asymmetries which facilitate the targeted 'enhancement' and/or 'dampening' of population-level activity. We classify these on the basis of their potential implementation (intra- vs inter-regional processes) and consider specific examples in different visual regions. We consider how motion extrapolation can be achieved during inter-regional signaling, and how asymmetric connectivity patterns which support extrapolation can emerge spontaneously from local synaptic learning rules. Finally, we consider how more abstract 'model-based' predictive strategies might be implemented. Overall, we present an integrative framework for understanding how the brain determines the real-time position of moving objects, despite neural delays.

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations

How does the human brain link relational concepts to perceptual experience? For example, a speaker may say "the cup to the left of the computer" to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in neural populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104 simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time- and state-continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of higher cognition.

A dynamic neural field model of continuous input integration

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.

Distinct dynamics of neuronal activity during concurrent motor planning and execution

The smooth conduct of movements requires simultaneous motor planning and execution according to internal goals. So far it remains unknown how such movement plans are modified without interfering with ongoing movements. Previous studies have isolated planning and execution-related neuronal activity by separating behavioral planning and movement periods in time by sensory cues. Here, we separate continuous self-paced motor planning from motor execution statistically, by experimentally minimizing the repetitiveness of the movements. This approach shows that, in the rat sensorimotor cortex, neuronal motor planning processes evolve with slower dynamics than movement-related responses. Fast-evolving neuronal activity precees skilled forelimb movements and is nested within slower dynamics. We capture this effect via high-pass filtering and confirm the results with optogenetic stimulations. The various dynamics combined with adaptation-based high-pass filtering provide a simple principle for separating concurrent motor planning and execution.

Learning words in space and time: Contrasting models of the suspicious coincidence effect

In their 2007b Psychological Review paper, Xu and Tenenbaum found that early word learning follows the classic logic of the "suspicious coincidence effect:" when presented with a novel name ('fep') and three identical exemplars (three Labradors), word learners generalized novel names more narrowly than when presented with a single exemplar (one Labrador). Xu and Tenenbaum predicted the suspicious coincidence effect based on a Bayesian model of word learning and demonstrated that no other theory captured this effect. Recent empirical studies have revealed, however, that the effect is influenced by factors seemingly outside the purview of the Bayesian account. A process-based perspective correctly predicted that when exemplars are shown sequentially, the effect is eliminated or reversed (Spencer, Perone, Smith, & Samuelson, 2011). Here, we present a new, formal account of the suspicious coincidence effect using a generalization of a Dynamic Neural Field (DNF) model of word learning. The DNF model captures both the original finding and its reversal with sequential presentation. We compare the DNF model's performance with that of a more flexible version of the Bayesian model that allows both strong and weak sampling assumptions. Model comparison results show that the dynamic field account provides a better fit to the empirical data. We discuss the implications of the DNF model with respect to broader contrasts between Bayesian and process-level models.

Mathematical Modeling of Working Memory in the Presence of Random Disturbance using Neural Field Equations

In this paper, we describe a neural field model which explains how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events. Moreover, we investigate how noise-induced perturbations may affect the coding process. This is obtained by means of a two-dimensional neural field equation, where one dimension represents the nature of the event (for example, the color of a light signal) and the other represents the moment when the signal has occurred. The additive noise is represented by a Q-Wiener process. Some numerical experiments reported are carried out using a computational algorithm for two-dimensional stochastic neural field equations.

Age-related decline in visual working memory: The effect of nontarget objects during a delayed estimation task

Visual working memory (VWM) is an essential aspect of cognitive functioning that becomes compromised in older adults. A canonical probe of VWM is the change detection task in which participants compare a visually presented stimulus with items being maintained in VWM. Older adults show a decreased ability to detect changes between a stimulus and the contents of VWM compared with younger adults. Previously, we used a dynamic neural field (DNF) model to explore changes in neural connectivity that can explain this pattern of decline in performance. These simulations suggest that older adults have cortical interactions that are more diffuse compared to younger adults. In the current article, we examined the precision of representations in VWM using the delayed-estimation task. Participants are first presented with a memory array. After a delay, a location is cued, and participants click on a color wheel to indicate which color was at that location. The model predicted that older adults should show increased guessing rates and decreased precision (defined as the variability of color responses around the target location) relative to younger adults. The model also predicted that presenting the nontarget items during test should improve the precision of responses for older adults but not for younger adults. Results from two experiments supported these predictions of the model. These findings further advance an emerging theory of the neurocognitive decline of VWM and illustrate how older adults' VWM representations are influenced by the context in which information is being recalled. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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.

Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding

In a novel computer mouse tracking paradigm, participants read a spatial phrase such as "The blue item to the left of the red one" and then see a scene composed of 12 visual items. The task is to move the mouse cursor to the target item (here, blue), which requires perceptually grounding the spatial phrase. This entails visually identifying the reference item (here, red) and other relevant items through attentional selection. Response trajectories are attracted toward distractors that share the target color but match the spatial relation less well. Trajectories are also attracted toward items that share the reference color. A competing pair of items that match the specified colors but are in the inverse spatial relation increases attraction over-additively compared to individual items. Trajectories are also influenced by the spatial term itself. While the distractor effect resembles deviation toward potential targets in previous studies, the reference effect suggests that the relevance of the reference item for the relational task, not its role as a potential target, was critical. This account is supported by the strengthened effect of a competing pair. We conclude, therefore, that the attraction effects in the mouse trajectories reflect the neural processes that operate on sensorimotor representations to solve the relational task. The paradigm thus provides an experimental window through motor behavior into higher cognitive function and the evolution of activation in modal substrates, a longstanding topic in the area of embodied cognition.

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

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.

Moving Word Learning to a Novel Space: A Dynamic Systems View of Referent Selection and Retention

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child's active engagement with objects and people supports referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children's knowledge of words and objects grows. Together this work suggests that understanding systems for perception, action, attention, and memory, and their complex interaction, is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits.

Grounding cognitive-level processes in behavior: the view from dynamic systems theory

Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory (DST) focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory (DFT). We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding of behavior and cognition that results from a DST perspective. These point to a central challenge for cognitive science research as defined by Marr-emergence. We argue that appreciating emergence raises questions about the utility of computational-level analyses and opens the door to insights concerning the origin of novel forms of behavior and thought (e.g., a new chess strategy). We contend this is one of the most fundamental questions about cognition and behavior.

Beyond slots and resources: grounding cognitive concepts in neural dynamics

Research over the past decade has suggested that the ability to hold information in visual working memory (VWM) may be limited to as few as three to four items. However, the precise nature and source of these capacity limits remains hotly debated. Most commonly, capacity limits have been inferred from studies of visual change detection, in which performance declines systematically as a function of the number of items that participants must remember. According to one view, such declines indicate that a limited number of fixed-resolution representations are held in independent memory "slots." Another view suggests that such capacity limits are more apparent than real, but emerge as limited memory resources are distributed across more to-be-remembered items. Here we argue that, although both perspectives have merit and have generated and explained impressive amounts of empirical data, their central focus on the representations--rather than processes--underlying VWM may ultimately limit continuing progress in this area. As an alternative, we describe a neurally grounded, process-based approach to VWM: the dynamic field theory. Simulations demonstrate that this model can account for key aspects of behavioral performance in change detection, in addition to generating novel behavioral predictions that have been confirmed experimentally. Furthermore, we describe extensions of the model to recall tasks, the integration of visual features, cognitive development, individual differences, and functional imaging studies of VWM. We conclude by discussing the importance of grounding psychological concepts in neural dynamics, as a first step toward understanding the link between brain and behavior.

The emergent executive: a dynamic field theory of the development of executive function

Executive function (EF) is a central aspect of cognition that undergoes significant changes in early childhood. Changes in EF in early childhood are robustly predictive of academic achievement and general quality of life measures later in adulthood. We present a dynamic neural field (DNF) model that provides a process-based account of behavior and developmental change in a key task used to probe the early development of executive function—the Dimensional Change Card Sort (DCCS) task. In the DCCS, children must flexibly switch from sorting cards either by shape or color to sorting by the other dimension. Typically, 3-year-olds, but not 5-year-olds, lack the flexibility to do so and perseverate on the first set of rules when instructed to switch. Using the DNF model, we demonstrate how rule-use and behavioral flexibility come about through a form of dimensional attention. Further, developmental change is captured by increasing the robustness and precision of dimensional attention. Note that although this enables the model to effectively switch tasks, the dimensional attention system does not “know” the details of task-specific performance. Rather, correct performance emerges as a property of system–wide interactions. We show how this captures children’s behavior in quantitative detail across 14 versions of the DCCS task. Moreover, we successfully test a set of novel predictions with 3-year-old children from a version of the task not explained by other theories.

Integration of memory, perception and attention in episode processing

The goal of this paper is to examine abstract, non-neuronal level concepts and processes of cognition and to introduce a model of episode processing which includes processing of perception and memory for ordered events, attentional processes, forgetting (including both constant and non-constant time-based decay), confusions and distinctiveness between items, and false memories and their suppression.

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009)

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

Cortical control of arm movements: a dynamical systems perspective

Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.

Modeling the influences of nanoparticles on neural field oscillations in thalamocortical networks

The purpose of this study is twofold. First, we present a simplified multiscale modeling approach integrating activity on the scale of ionic channels into the spatiotemporal scale of neural field potentials: Resting upon a Hodgkin-Huxley based single cell model we introduced a neuronal feedback circuit based on the Llinás-model of thalamocortical activity and binding, where all cell specific intrinsic properties were adopted from patch-clamp measurements. In this paper, we expand this existing model by integrating the output to the spatiotemporal scale of field potentials. Those are supposed to originate from the parallel activity of a variety of synchronized thalamocortical columns at the quasi-microscopic level, where the involved neurons are gathered together in units. Second and more important, we study the possible effects of nanoparticles (NPs) that are supposed to interact with thalamic cells of our network model. In two preliminary studies we demonstrated in vitro and in vivo effects of NPs on the ionic channels of single neurons and thereafter on neuronal feedback circuits. By means of our new model we assumed now NPs induced changes on the ionic currents of the involved thalamic neurons. Here we found extensive diversified pattern formations of neural field potentials when comparing to the modeled activity without neuromodulating NPs addition. This model provides predictions about the influences of NPs on spatiotemporal neural field oscillations in thalamocortical networks. These predictions can be validated by high spatiotemporal resolution electrophysiological measurements like voltage sensitive dyes and multiarray recordings.

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.

Decision making requires the selection between alternative actions. It has been suggested that action selection is not separate from motor preparation of the according actions, but rather that the selection emerges from the competition between different movement plans. We expand on this idea, and ask how action selection mechanisms interact with the learning of new action choices. We present a neurodynamic model that provides an integrated account of action selection and the learning of sensorimotor associations. The model explains recent electrophysiological findings from monkeys' sensorimotor cortex, and correctly predicted a newly described characteristic pattern of their choice errors. Based on the model, we present a theory of how geometrical sensorimotor mapping rules can be learned by association without the need for an explicit representation of the transformation rule, and how the learning history of these associations can have a direct influence on later decision making.

A neurobehavioral model of flexible spatial language behaviors

We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is linked to relational spatial descriptions through a neural mechanism for reference frame transformations. We demonstrate that the system can extract spatial relations from visual scenes, select items based on relational spatial descriptions, and perform reference object selection in a single unified architecture. We further show that the performance of the system is consistent with behavioral data in humans by simulating results from 2 independent empirical studies, 1 spatial term rating task and 1 study of reference object selection behavior. The architecture we present thereby achieves a high degree of task flexibility under realistic stimulus conditions. At the same time, it also provides a detailed neural grounding for complex behavioral and cognitive processes.

Orientation selective or not? - Measuring significance of tuning to a circular parameter

Orientation and direction tuning are among the most studied features of the visual system and are routinely measured during experiments to estimate the quality of neuronal responses. However, standard approaches to report orientation selectivity are only narrowly quantitative and strongly depend on the signal quality, while the more sophisticated ones are computationally exhaustive, making them difficult to use during ongoing experiments. We propose a fast and efficient method for reporting the reliability of coding applicable to any circular parameter. Similar to standard deviation in the linear statistics, reproducibility measures trial-to-trial variability of a circular response parameter. Reproducibility is a normalized measure easily transformed to p-values, which provide explicit information about significance of the estimated orientation preference. The proposed approach is applicable to a wide range of signal types. Here, we discuss examples from optical imaging and electrophysiological recordings, and provide a more thorough examination based on tuning curves modeled in silico.

Biased feedback in spatial recall yields a violation of delta rule learning

This study investigates whether inductive processes influencing spatial memory performance generalize to supervised learning scenarios with differential feedback. After providing a location memory response in a spatial recall task, participants received visual feedback showing the target location. In critical blocks, feedback was systematically biased either 4 degrees toward the vertical axis (toward condition) or 4 degrees farther away from the vertical axis (away condition). Results showed that the weaker teaching signal (i.e., a smaller difference between the remembered location and the feedback location) produced a stronger experience-dependent change over blocks in the away condition than in the toward condition. This violates delta rule learning. Subsequent simulations of the dynamic field theory of spatial cognition provide a theoretically unified account of these results.

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.

A dynamic neural field model of mesoscopic cortical activity captured with voltage-sensitive dye imaging

A neural field model is presented that captures the essential non-linear characteristics of activity dynamics across several millimeters of visual cortex in response to local flashed and moving stimuli. We account for physiological data obtained by voltage-sensitive dye (VSD) imaging which reports mesoscopic population activity at high spatio-temporal resolution. Stimulation included a single flashed square, a single flashed bar, the line-motion paradigm--for which psychophysical studies showed that flashing a square briefly before a bar produces sensation of illusory motion within the bar--and moving squares controls. We consider a two-layer neural field (NF) model describing an excitatory and an inhibitory layer of neurons as a coupled system of non-linear integro-differential equations. Under the assumption that the aggregated activity of both layers is reflected by VSD imaging, our phenomenological model quantitatively accounts for the observed spatio-temporal activity patterns. Moreover, the model generalizes to novel similar stimuli as it matches activity evoked by moving squares of different speeds. Our results indicate that feedback from higher brain areas is not required to produce motion patterns in the case of the illusory line-motion paradigm. Physiological interpretation of the model suggests that a considerable fraction of the VSD signal may be due to inhibitory activity, supporting the notion that balanced intra-layer cortical interactions between inhibitory and excitatory populations play a major role in shaping dynamic stimulus representations in the early visual cortex.

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.

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.

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.

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.

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.

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.

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.

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.

Dynamic instabilities as mechanisms for emergence

That competences may emerge given appropriate environmental and behavioral context is a long-standing theme in developmental research. Work in the motor domain, but also in cognitive development, has made it possible to transform this idea into a mechanistic account closely linked to empirical evidence. In dynamic systems thinking, such capacities as keeping a motor goal in mind, remembering a location, or resisting a motor habit, are all understood in terms of the generation of stable patterns of neuronal activation. These may be input-driven, but also be stabilized by interactions within neuronal representations. A key theoretical insight is that whether a particular pattern of activation is stable or not is not determined by any single factor, learning process, or structural parameter. Instead, ongoing activity, recent activation history, current input, all may affect when a particular dynamic regime is reachable. In spite of such broad interdependence, sharp transitions may characterize the onset of a skill in any given context. Dynamic instabilities are the mechanistic basis for this phenomenon and thus form the basis for understanding development in terms of emergence. We exemplify the concepts of instability and emergence around the phenomenon of infant perseverative reaching and discuss implications for identifying key markers of development and their link to neuronal processes.

Reference-related inhibition produces enhanced position discrimination and fast repulsion near axes of symmetry

Models proposed to account for reference frame effects in spatial cognition often account for performance in some tasks well, but fail to generalize to other tasks. Here, we demonstrate that a new process account of spatial working memory--the dynamic field theory (DFT)--can bridge the gap between perceptual and memory processes in position discrimination and spatial recall, highlighting that the processes underlying spatial recall also operate in position discrimination. In six experiments, we tested two novel predictions of the DFT: first, that discrimination is enhanced near symmetry axes, especially when the perceptual salience of the axis is increased; and second, that performance far from a reference axis depends on the direction in which the second stimulus is presented. The DFT also predicts the magnitude of this direction-dependent modulation. These effects arise from reference-related inhibition in the theory. We discuss how the processes captured by the DFT relate to existing psychophysical models and operate across a diverse array of spatial tasks.

Moving Toward a Grand Theory of Development: In Memory of Esther Thelen

This paper is in memory of Esther Thelen, who passed away while President of the Society for Research in Child Development. A survey of Esther Thelen's career reveals a trajectory from early work on simple movements like stepping, to the study of goal-directed reaching, to work on the embodiment of cognition, and, ultimately, to a grand theory of development--dynamic systems theory. Four central concepts emerged during her career: (1) a new emphasis on time; (2) the proposal that behavior is softly assembled from the interaction of multiple subsystems; (3) the embodiment of perception, action, and cognition; and (4) a new respect for individuality. Esther Thelen communicated these ideas to scientists and practitioners alike, so the ultimate benefactors of her work were children.

The time course of saccadic decision making: dynamic field theory

Making a saccadic eye movement involves two decisions, the decision to initiate the saccade and the selection of the visual target of the saccade. Here we provide a theoretical account for the time-courses of these two processes, whose instabilities are the basis of decision making. We show how the cross-over from spatial averaging for fast saccades to selection for slow saccades arises from the balance between excitatory and inhibitory processes. Initiating a saccade involves overcoming fixation, as can be observed in the countermanding paradigm, which we model accounting both for the temporal evolution of the suppression probability and its dependence on fixation activity. The interaction between the two forms of decision making is demonstrated by predicting how the cross-over from averaging to selection depends on the fixation stimulus in gap-step-overlap paradigms. We discuss how the activation dynamics of our model may be mapped onto neuronal structures including the motor map and the fixation cells in superior colliculus.

A dynamic model for action understanding and goal-directed imitation

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.