Effort, success, and nonuse determine arm choice

Schweighofer, Nicolas; Xiao, Yupeng; Kim, Sujin; Yoshioka, Toshinori; Gordon, James; Osu, Rieko

doi:10.1152/jn.00593.2014

Cited by 55 publications

(83 citation statements)

References 40 publications

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“…The longer preferred durations than the duration that minimizes variance may be due to effort minimization, which tends to lengthen movement duration. Our between-target results, as well as those from previous studies (e.g., Gordon et al 1994;Park et al 2016) showing that subjects slowed down for movements toward the 135°target, which require greatest (estimated) effort because of greater inertia at the hand (see Schweighofer et al 2015), are consistent with this view.…”

Section: Discussionsupporting

confidence: 91%

“…1 For instance, movements performed by the right hand to leftward targets are slower than movements to rightward targets (Gordon et al 1994;Park et al 2016). This can be attributed to greater effort (or equivalently signal-dependent noise) for leftward targets because of greater inertia at the hand for these movements (Cos et al 2011;Guigon et al 2007;Schweighofer et al 2015).…”

Section: New and Noteworthymentioning

confidence: 99%

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The duration of reaching movement is longer than predicted by minimum variance

Wang

Xiao

Burdet

et al. 2016

Journal of Neurophysiology

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Whether the CNS minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here, we conducted an experiment in which subjects performed arm-reaching movements without visual feedback in fast, medium, slow, and preferred duration conditions. Results show that (i) total endpoint variance was smallest in the medium duration condition, and (ii) subjects preferred to carry out movements that were slower than this medium duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as endpoint error due to both signal-dependent and constant noise

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Section: Discussionsupporting

confidence: 91%

Section: New and Noteworthymentioning

confidence: 99%

The duration of reaching movement is longer than predicted by minimum variance

Wang

Xiao

Burdet

et al. 2016

Journal of Neurophysiology

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“…A final set of predictions is tested. Intermanual differences in biomechanical and energetic consequences, related to the inertial properties of the arm (Gordon et al, 1994), bias both hand (Habagishi et al, 2014;Schweighofer et al, 2015) and arm-movement (Sabes and Jordan, 1997;Cos et al, 2011;Dounskaia et al, 2011) choices. When reaching to targets in either hemispace, the hand that is on the same side of space as the target is favoured, and this bias increases with target laterality (Stins et al, 2001;Oliveira et al, 2010;Valyear et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The neural basis of hand choice: An fMRI investigation of the Posterior Parietal Interhemispheric Competition model

2019

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The current study investigates a new neurobiological model of human hand choice: The Posterior Parietal Interhemispheric Competition (PPIC) model. The model specifies that neural populations in bilateral posterior intraparietal and superior parietal cortex (pIP-SPC) encode actions in hand-specific terms, and compete for selection across and within hemispheres. Actions with both hands are encoded bilaterally, but the contralateral hand is overrepresented. We use a novel fMRI paradigm to test the PPIC model. Participants reach to visible targets while in the scanner, and conditions involving free choice of which hand to use (Choice) are compared with when hand-use is instructed. Consistent with the PPIC model, bilateral pIP-SPC is preferentially responsive for the Choice condition, and for actions made with the contralateral hand. In the right pIP-SPC, these effects include anterior intraparietal and superior parieto-occipital cortex. Left dorsal premotor cortex, and an area in the right lateral occipitotemporal cortex show the same response pattern, while the left inferior parietal lobule is preferentially responsive for the Choice condition and when using the ipsilateral hand. Behaviourally, hand choice is biased by target location-for targets near the left/right edges of the display, the hand in ipsilateral hemispace is favoured. Moreover, consistent with a competitive process, response times are prolonged for choices to more ambiguous targets, where hand choice is relatively unbiased, and fMRI responses in bilateral pIP-SPC parallel this pattern. Our data provide support for the PPIC model, and reveal a selective network of brain areas involved in free hand choice, including bilateral posterior parietal cortex, left-lateralized inferior parietal and dorsal premotor cortices, and the right lateral occipitotemporal cortex.

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“…2015). Hand choice is known to be affected by a number of factors, including the relative position of each hand to the object (Coelho et al 2013;Oliveira et al 2010;Przybyla et al 2013), biomechanical costs (Schweighofer et al 2015), recent choice success (Stoloff et al 2011), handedness (Bryden et al 2000;Gabbard et al 2003;Scharoun et al 2016), and skill demands of a task (Mamolo et al 2006).…”

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confidence: 99%

“…In the deliberations preceding hand choice, the brain may assign a value (the "action value"), or cost, to each of the above factors and compare their summed value between the two hands. Recently, Schweighofer et al (2015) quantified the relative contribution of a number of these factors, including expected biomechanical costs, expected task success, and overall handedness, on the hand choices for reaches to different targets. Their model attributes the behavioral observation that subjects choose their hand ipsilateral to a target to the associated biomechanical costs.…”

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confidence: 99%

Decisions in motion: passive body acceleration modulates hand choice

Bakker

Weijer

Beers

et al. 2017

Journal of Neurophysiology

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In everyday life, we frequently have to decide which hand to use for a certain action. It has been suggested that for this decision the brain calculates expected costs based on action values, such as expected biomechanical costs, expected success rate, handedness, and skillfulness. Although these conclusions were based on experiments in stationary subjects, we often act while the body is in motion. We investigated how hand choice is affected by passive body motion, which directly affects the biomechanical costs of the arm movement due to its inertia. With the use of a linear motion platform, 12 right-handed subjects were sinusoidally translated (0.625 and 0.5 Hz). At 8 possible motion phases, they had to reach, using either their left or right hand, to a target presented at 1 of 11 possible locations. We predicted hand choice by calculating the expected biomechanical costs under different assumptions about the future acceleration involved in these computations, being the forthcoming acceleration during the reach, the instantaneous acceleration at target onset, or zero acceleration as if the body were stationary. Although hand choice was generally biased to use of the dominant hand, it also modulated sinusoidally with the motion, with the amplitude of the bias depending on the motion's peak acceleration. The phase of hand choice modulation was consistent with the cost model that took the instantaneous acceleration signal at target onset. This suggests that the brain relies on the bottom-up acceleration signals, and not on predictions about future accelerations, when deciding on hand choice during passive whole body motion. Decisions of hand choice are a fundamental aspect of human behavior. Whereas these decisions are typically studied in stationary subjects, this study examines hand choice while subjects are in motion. We show that accelerations of the body, which differentially modulate the biomechanical costs of left and right hand movements, are also taken into account when deciding which hand to use for a reach, possibly based on bottom-up processing of the otolith signal.

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Effort, success, and nonuse determine arm choice

Cited by 55 publications

References 40 publications

The duration of reaching movement is longer than predicted by minimum variance

The duration of reaching movement is longer than predicted by minimum variance

The neural basis of hand choice: An fMRI investigation of the Posterior Parietal Interhemispheric Competition model

Decisions in motion: passive body acceleration modulates hand choice

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