The mechanisms of movement preparation: a precuing study

Bock, Otmar; Eversheim, Udo

doi:10.1016/s0166-4328(99)00134-5

Cited by 37 publications

(55 citation statements)

References 18 publications

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“…Figure 7b shows the average RT from model simulations in four conditions: three cues spanning a large angle; two cues spanning that same large angle; two cues spanning a small angle; and no cues at all. This simulation qualitatively reproduces the result shown in Figure 3 of Bock and Eversheim (2000). The present model reproduces this result because reaction time is determined by the level of activity in a distributed population, and this is itself dependent not only on the number of targets but also on the angle subtended by the directions of the movements toward them.…”

Section: Simulations Of Behavioral Phenomenasupporting

confidence: 84%

“…These findings are compatible with many models of decision making (Shafir and Tversky, 1995;Roe et al, 2001;Usher and McClelland, 2001). However, Bock and Eversheim (2000) showed that it is not the number of cues that determines RTs but rather the spatial angle that they subtend. In other words, the RT is the same whether two or three targets span a given angle.…”

Section: Simulations Of Behavioral Phenomenamentioning

confidence: 99%

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Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model

Cisek¹

2006

J. Neurosci.

291

303

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To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making and motor planning have been developed primarily independently. However, neural data suggests that these processes involve the same brain regions and are performed in an integrated manner. Here, a computational model is described that addresses both the question of how different potential actions are specified and how the brain decides between them. In the model, multiple potential actions are simultaneously represented as continuous regions of activity within populations of cells in frontoparietal cortex. These representations engage in a competition for overt execution that is biased by modulatory influences from prefrontal cortex. The model neural populations exhibit activity patterns that correlate with both the spatial metrics of potential actions and their associated decision variables, in a manner similar to activities in parietal, prefrontal, and premotor cortex. The model therefore suggests an explanation for neural data that have been hard to account for in terms of serial theories that propose that decision making occurs before action planning. In addition to simulating the activity of individual neurons during decision tasks, the model also reproduces key aspects of the spatial and temporal statistics of human choices and makes a number of testable predictions.

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Section: Simulations Of Behavioral Phenomenasupporting

confidence: 84%

Section: Simulations Of Behavioral Phenomenamentioning

confidence: 99%

Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model

Cisek¹

2006

J. Neurosci.

291

303

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show abstract

“…The program is then translated into a motor command that controls muscle activity (Keele, 1968;Rosenbaum, 1980). The precuing paradigm (Rosenbaum 1980(Rosenbaum , 1983 has been commonly used to characterize the processes related to the planning of distinct movement parameters (Bock & Eversheim, 2000;Favilla & De Cecco, 1996;Le´pine et al, 1989). In the precuing reaction time (RT) task, a preparatory signal (or precue) provides advanced information about one or several parameters of a movement (e.g., direction, amplitude, or force) that has to be executed as fast as possible after an imperative GO signal.…”

mentioning

confidence: 99%

Planning of visually guided reach‐to‐grasp movements: Inference from reaction time and contingent negative variation (CNV)

Zaepffel

Brochier

2011

Psychophysiology

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We performed electroencephalogram (EEG) recording in a precuing task to investigate the planning processes of reach‐to‐grasp movements in human. In this reaction time (RT) task, subjects had to reach, grasp, and pull an object as fast as possible after a visual GO signal. We manipulated two parameters: the hand shape for grasping (precision grip or side grip) and the force required to pull the object (high or low). Three seconds before the GO onset, a cue provided advance information about force, grip, both parameters, or no information at all. EEG data show that reach‐to‐grasp movements generate differences in the topographic distribution of the late Contingent Negative Variation (ICNV) amplitude between the 4 precuing conditions. Along with RT data, it confirms that two distinct functional networks are involved with different time courses in the planning of grip and force. Finally, we outline the composite nature of the lCNV that might reflect both high‐ and low‐level planning processes.

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“…Spatially nearby targets have overlapping population peaks, and the time taken to reach a quenching threshold is markedly reduced. In general, the model closely captured in vivo electrophysiological (Cisek and Kalaska, 2005) and behavioral reaction time data (Bock and Eversheim, 2000), giving it an additional stamp of credence.…”

mentioning

confidence: 77%