A vector-integration-to-endpoint model for performance of viapoint movements

Bullock, Daniel; Bongers, Raoul M.; Lankhorst, Marnix; Beek, Peter J.

doi:10.1016/s0893-6080(98)00109-9

Cited by 34 publications

(17 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Alternate approaches to the anticipatory initiation of selection in the context of prospective motor control for piano key presses and visually guided catching or hitting have recently been proposed that utilize an explicit computation of an estimated time-to-contact (TC) signal (e.g., Bullock et al, 1999;Dessing, Caljouw, Peper & Beek, 2004;Dessing et al, 2005;Jacobs & Bullock, 1998;Lee, 1976). While remaining agnostic as whether such a signal is computed in the brain, the LIST PARSE model utilizes a somewhat more direct means of controlling motor plan selection in which movement kinematics (velocity outflow signals) produce a deceleration computation that directly drive the gating of plan selection by way of the rehearsal signal (R).…”

Section: Figure 15 (A) Profile Of Movement Kinematics (Velocity and mentioning

confidence: 99%

Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: Toward a unified theory of how the cerebral cortex works.

Grossberg¹,

Pearson²

2008

Psychological Review

126

183

View full text Add to dashboard Cite

How do the layered circuits of prefrontal and motor cortex carry out working memory storage, sequence learning, and voluntary sequential item selection and performance? A neural model called LIST PARSE is presented to explain and quantitatively simulate cognitive data about both immediate serial recall and free recall, including bowing of the serial position performance curves, error-type distributions, temporal limitations upon recall, and list length effects. The model also qualitatively explains cognitive effects related to attentional modulation, temporal grouping, variable presentation rates, phonemic similarity, presentation of non-words, word frequency/item familiarity and list strength, distracters and modality effects. In addition, the model quantitatively simulates neurophysiological data from the macaque prefrontal cortex obtained during sequential sensory-motor imitation and planned performance. The article further develops a theory concerning how the cerebral cortex works by showing how variations of the laminar circuits that have previously clarified how the visual cortex sees can also support cognitive processing of sequentially organized behaviors. 2 INTRODUCTIONIntelligent behavior depends upon the capacity to think about, plan, execute, and evaluate sequences of events. Whether we learn to understand and speak a language, solve a mathematics problem, cook an elaborate meal, or merely dial a phone number, multiple events in a specific temporal order must somehow be kept in mind temporarily in working memory. Once events are stored temporarily in a working memory, they are then grouped, or chunked, through learning into unitized representations that encode whole sequences of events; e.g., word and action sequences. How these working memory sequences and unitized plans interact during cognitive information processing and motor performance remains one of the most important problems confronting cognitive scientists and neuroscientists.This article introduces the LIST PARSE (Laminar Integrated Storage of Temporal Patterns for Associative Retrieval, Sequencing and Execution) model, which proposes how the layered circuits of prefrontal and motor cortex may be organized to achieve processes of working memory storage, sequence learning, and motor planning and execution during both cognitive and sensory-motor tasks. A schematic of the model is shown in Figure 1. The model makes predictions about the laminar organization of such cortical circuits that go beyond present neurophysiological and anatomical knowledge. It formulates these predictions by integrating several sorts of convergent constraints: extensive behavioral and neuroimaging data about cognitive information processing in humans; behavioral and neurobiological data about sensorymotor storage and performance of familiar sequential actions in monkeys; anatomical data about the laminar circuits that are shared by granular neocortical areas and connectivity specific to the lateral prefrontal cortex; laminar models of visual cortex that can e...

show abstract

Section: Figure 15 (A) Profile Of Movement Kinematics (Velocity and mentioning

confidence: 99%

Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: Toward a unified theory of how the cerebral cortex works.

Grossberg¹,

Pearson²

2008

Psychological Review

126

183

View full text Add to dashboard Cite

show abstract

“…We have used these ideas to build a neural network model of how we voluntarily vary speed without disrupting the form-distance and direction-of a planned point-topoint movement. The name of this circuit is the VITE model, which has now undergone several stages of progressive elaboration to explain a diverse array of data sets (Bullock & Grossberg, 1988a, b;Bullock, Bongers, Lankhorst, and Beck, 1999;Bullock, Grossberg & Guenther, 1993;Bullock, Cisek & Grossberg, 1998;Cisek, Grossberg, and Bullock, 1998;Jacobs and Bullock, !998). Here "VITE" is after the French for "quickly" (because the model seeks to explain voluntary speeding of movement), but is also an acronym for Vector Integration To Endpoint, as explained below.…”

Section: A Volition-deliberation Nexus and Voluntary Trajectory Genermentioning

confidence: 99%

Cortical Models for Movement Control

Bullock

2001

Mathematical Modelling: Theory and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the addition of sensitivity to relative velocity, and appropriate coupling to TTC, the new velocity integration to endpoint model of Dessing and colleagues 43 explains a range of human reaching data that is -literally -beyond the reach of prior models. Neural evidence for the model's assumption ofTTC cells is abundant 43 • 44 , and TTC is prominent in other sensmy-motor timing models, including VITE-based models of viapoint movements 45 and legato atticulation 46 ' 47 by pianists (see Figure 2). …”

Section: Coordination Of Rates and Completion Times In Voluntary Actionmentioning

confidence: 99%

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Grady¹,

Schwartz²

2006

SIAM J. Sci. Comput.

View full text Add to dashboard Cite

Aclmowlcdgcments: Preparation of this article was partially supported by NIMH RO I DC02852. Neural models of queuing and timingTeaser Sentence: Fifty years after Lashley inferred parallel cerebral representations of serial plans, and twenty-five years after Grossberg proposed a parallel competitive queuing model, confinnatmy neurophysiological data arrive -but other new data cast doubt on network-delay models of cerebellar adaptive timing.Abstract. Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a finer scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under-or over-shoot the amounts needed for precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine -the cerebellum -is needed to amplify differences within, and continuously search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.

show abstract

A vector-integration-to-endpoint model for performance of viapoint movements

Cited by 34 publications

References 107 publications

Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: Toward a unified theory of how the cerebral cortex works.

Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: Toward a unified theory of how the cerebral cortex works.

Cortical Models for Movement Control

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Contact Info

Product

Resources

About