Flexible strategies for sensory integration during motor planning

Sober, Samuel J.; Sabes, Philip N.

doi:10.1038/nn1427

Cited by 409 publications

(382 citation statements)

References 32 publications

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“…Other V6A cells may use the visual information to monitor the ongoing arm movement, as well as hand/object interaction. These data provide empirical support for computational models suggesting task-dependent reweighting of sensory signals dictated by the information content of the visual feedback when the action occurs (Sober and Sabes, 2005;McGuire and Sabes, 2009). …”

Section: Discussionsupporting

confidence: 57%

Reaching Activity in the Medial Posterior Parietal Cortex of Monkeys Is Modulated by Visual Feedback

Bosco

Breveglieri²,

Chinellato³

et al. 2010

J. Neurosci.

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Reaching and grasping an object is an action that can be performed in light, as well as in darkness. Area V6A is a visuomotor area of the medial posterior parietal cortex involved in the control of reaching movements. It contains reaching neurons as well as neurons modulated by passive somatosensory and visual stimulations. In the present work we analyze the effect of visual feedback on reaching activity of V6A neurons. Three macaques were trained to execute reaching movements in two conditions: in darkness, where only the reaching target was visible, and in full light, where the monkey also saw its own moving arm and the environment. Approximately 85% of V6A neurons (127/149) were significantly related to the task in at least one of the two conditions. The majority of task-related cells (69%) showed reach-related activity in both visual conditions, some were modulated only in light (15%), while others only in dark (16%). The sight of the moving arm often changed dramatically the cell's response to arm movements. In some cases the reaching activity was enhanced and in others it was reduced or disappeared altogether. These neuronal properties may represent differences in the degree to which cells are influenced by feedback control versus feedforward movement planning. On average, reach-related modulations were stronger in light than in dark, a phenomenon similar to that observed in brain imaging experiments in the human medial posterior parietal cortex, a region likely homologous to macaque area V6A.

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Section: Discussionsupporting

confidence: 57%

Reaching Activity in the Medial Posterior Parietal Cortex of Monkeys Is Modulated by Visual Feedback

Bosco

Breveglieri²,

Chinellato³

et al. 2010

J. Neurosci.

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“…First, the reduced speed and completeness of learning in response to larger errors might reflect a change in multisensory integration. Psychophysical studies of human motor control have shown that when integrating sensory signals, subjects can flexibly weight sensory inputs (18,19) and that such weighting appears to be based on the variability of the available sensory signals, as predicted by theories of optimal motor control (20). Such a flexible weighting strategy might account for the inverse relationship between the size of sensory pitch shifts and the magnitude of the behavioral responses.…”

Section: Discussionmentioning

confidence: 99%

Vocal learning is constrained by the statistics of sensorimotor experience

Sober

Brainard

2012

Proc. Natl. Acad. Sci. U.S.A.

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The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.multisensory integration | sensorimotor learning | motor variability E rror correction based on sensory feedback is a ubiquitous mechanism for maintaining behavioral performance (1-4). However, sensory information is vulnerable to environmental noise and to errors in sensory encoding arising at the periphery or during central processing of the sensory signal (5). Such contamination can result in large mismatches between the actual and expected sensory feedback that do not necessarily reflect errors in performance. The brain must therefore decide whether to modify behavior based on sensory feedback (and risk "adapting" to signals that do not accurately reflect performance) or ignore sensory input (and risk leaving errors uncorrected). This problem is especially important in vocal behaviors, in which performance can have significant consequences for the success of an organism and auditory feedback is vulnerable to extrinsic acoustic signals and errors in auditory encoding.During development, both humans and songbirds learn by processes of vocal imitation that rely heavily on auditory feedback (2). Early vocalizations bear little resemblance to mature song or speech, resulting in large differences (errors) between experienced auditory feedback and the sensory goal. With practice, vocalizations become m...

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“…Other bottom-up influences such as the reliability of sensory (Ernst & Banks, 2002) or sensimotor (Körding & Wolpert, 2004) information, or target modality (Sober & Sabes, 2005) may influence our model's weights, too, but were not within the scope of the current study. Moreover, the present parameter estimates invite the speculation that bottom-up influences, such as the number of stimuli, are reflected in the anatomical weights, whereas top-down influences, such as task instructions, bear on the external weights: Indeed, the anatomical representation of touch is associated with the primary somatosensory cortices, that store all sensory characteristics of a touch.…”

Section: Weighted Integration Accounts For Previous Findingsmentioning

confidence: 93%

“…Yet, to plan actions toward the object, its location must be transformed into another, posture-dependent reference frame (Pouget et al, 2002;Sober & Sabes, 2005). Any perceived tactile stimulus seems to be transformed into an external-spatial reference frame (Driver & Spence, 1998;Yamamoto & Kitazawa, 2001a;Shore et al, 2002;Spence et al, 2004;Soto-Faraco et al, 2004;Röder et al, 2004;Schicke & Röder, 2006;Heed & Azañón, 2014;Heed et al, 2015), even when such recoding is currently not required (Kitazawa, 2002;Azañón et al, 2010a).…”

Section: Introductionmentioning

confidence: 99%

Integration of anatomical and external response mappings explains crossing effects in tactile localization: A probabilistic modeling approach

2015

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To act upon a tactile stimulus its original skinbased, anatomical spatial code has to be transformed into an external, posture-dependent reference frame, a process known as tactile remapping. When the limbs are crossed, anatomical and external location codes are in conflict, leading to a decline in tactile localization accuracy. It is unknown whether this impairment originates from the integration of the resulting external localization response with the original, anatomical one or from a failure of tactile remapping in crossed postures. We fitted probabilistic models based on these diverging accounts to the data from three tactile localization experiments. Hand crossing disturbed tactile left-right location choices in all experiments. Furthermore, the size of these crossing effects was modulated by stimulus configuration and task instructions. The best model accounted for these results by integration of the external response mapping with the original, anatomical one, while applying identical integration weights for uncrossed and crossed postures. Thus, the model explained the data without assuming failures of remapping. Moreover, performance differences across tasks were accounted for by non-individual parameter adjustments, indicating that individual participants' task adaptation results from one common functional mechanism. These results suggest that remapping is an automatic and accurate process, and that the observed localization impairments in touch result from a cognitively controlled integration process that combines anatomically and externally coded responses.

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Flexible strategies for sensory integration during motor planning

Cited by 409 publications

References 32 publications

Reaching Activity in the Medial Posterior Parietal Cortex of Monkeys Is Modulated by Visual Feedback

Reaching Activity in the Medial Posterior Parietal Cortex of Monkeys Is Modulated by Visual Feedback

Vocal learning is constrained by the statistics of sensorimotor experience

Integration of anatomical and external response mappings explains crossing effects in tactile localization: A probabilistic modeling approach

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