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Record W4315479545 · doi:10.3389/fnins.2022.1124062

Editorial: Response to an object near the head/body: Multisensory coding and motor processing guided by sensory systems

2023· editorial· en· W4315479545 on OpenAlexaff
D. O. Kim, Nicholas P. Holmes, Gerome A. Manson, Pavel Zahorik

Bibliographic record

VenueFrontiers in Neuroscience · 2023
Typeeditorial
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsQueen's University
Fundersnot available
KeywordsSensory systemComputer scienceCoding (social sciences)Motor systemNeurosciencePhysical medicine and rehabilitationCommunicationPsychologyComputer visionMedicineMathematics

Abstract

fetched live from OpenAlex

Detecting and responding to objects near the body in peripersonal space is essential for successful interactions with the environment. Presently, we lack knowledge about the neural coding of the distance of sounds relative to the head, or about the proprioceptive coding of 3dimensional positions of objects relative to the body. Neural coding of nearby objects is an important subject for neuroscience, and there is a great need to advance knowledge about how sensory and motor systems mediate peripersonal behavior. The present Research Topic drew investigators' attention to the sensory and sensorimotor mechanisms underlying both the perception of objects, and the performance of actions near the body, and to facilitate efforts toward making progress in this area.We assembled seven original studies and one review article that address various aspects of the Research Topic. This expands the current literature on the sensory and sensorimotor mechanisms underlying behaviors dealing with objects and stimuli near the body.Effects of active and guided exploration for a sound source on the perceived distance in peripersonal space (0.4 -1.5 m from the head) were investigated by Hug et al. This study is a welcome addition to the present topic areas because it engaged multisensory systems (auditory, tactile, and proprioceptive) and sensorimotor processes. The participants who could actively explore the sound source improved their distance judgements whereas the performance of those whose arms were guided by the experimenter were less accurate than the active group.Zahorik studied audiovisual distance perception in which visual distance "captured" the perceived distance to a sound source. This capture was asymmetric, however. Sound sources farther than the visual target were more frequently judged to be at the distance of the visual target than closer sources. Such results, though fundamentally different than the well-known directional ventriloquist effects, are shown to be predictable by a computational model of auditory-visual distance perception where distance is perceived on a logarithmic scale, and auditory percepts are less precise than visual percepts. These results fill a gap in knowledge in the audiovisual distance perception.Mooti and Park considered the multisensory factors underlying the perception of head and trunk orientation. In humans, the perception of head-trunk orientation is inherently multisensory (visual, vestibular, proprioceptive). The relative contributions of the sensory systems in dynamic head-trunk orientation situations are more complicated and less studied than in static situations. The authors addressed the gap in knowledge in the horizontal (yaw) dimension. The results suggest that cervical proprioception is the primary determinant of perceived head-trunk orientation, but that either visual or vestibular information can provide additional information to improve head-trunk orientation accuracy.Although human reaching behaviors to nearby objects are typically accurate, errors can arise from the initial encoding of the hand or goal location (sensory), the transformation of sensory signals into motor commands (planning), or the movements themselves (execution). Phataraphruk et al. reasoned that initial arm posture would affect execution noise and reach accuracy, and that this would be more prominent when vision was unavailable. Indeed, the size and shape of the distributions of reach responses were determined by more complex interactions involving initial arm posture. Thus, these results provide insight into the multifactorial and multisensory aspects of human reaching behavior.How the brain integrates information from the eyes and body is the fundamental neuroscience problem tackled by Hsiao et al. They used virtual-reality methods to manipulate the visual feedback of hand position as people moved. In the study, the real and virtual positions of the hand drifted apart gradually. Participants often made movements according to a combination of the visual and bodily information. Sometimes they became aware of the discrepancy and other times not. They found that small discrepancies were often unnoticed, yet could still affect movement. Larger discrepancies were more often noticed, and participants recalibrated their movements.When we place our hand near an object, is our attention allocated automatically to that object? This question is addressed by Reed and colleagues. Under divided attention, a visual cue presented before a target led to smaller visual-evoked potentials when either the hand or a neutral block was near the target compared to far away. When participants were cued to focus their attention on one side of space, the anchoring effects of the hand or block were not observed. These results raise new questions about how hand location influences visual processing, and about when and where in the brain these effects occur. Kuroda et al. used a combination of virtual reality and stationary cycling to investigate the visual and proprioceptive contributions to the perception of a passable width during selfmotion. The authors replicated previous findings by showing that participants perception of a passable width narrowed as perceived self-motion speed increased. The authors found that optic flow altered judgments of passable width even if participants were not pedaling. The results suggest that visual information about perceived self-motion may contribute more than proprioception to perceptual judgments of passable width.Lohse et al. reviewed the interactions between the auditory and somatosensory systems, and between the auditory and the motor systems. The review highlights "the importance of considering both multisensory context and movement-related activity in order to understand how the auditory cortex operates during natural behaviors".There is a continued need for further research in the areas of multisensory and sensorimotor processes that mediate peripersonal-space behavior. For example, the proprioceptive mechanisms that underlie coding of 3-dimensional coordinates of an object near the body and those of body parts remain to be identified, and how the motor and proprioceptive systems jointly utilize such 3-dimensional information in planning and executing movements of body parts to reach and grasp the target object has yet to be determined. Of additional interest is how information from multiple sensory modalities is combined and weighted to account for differences in the capabilities of each modality. For example, when a salient nearby object is behind, vision is not available. Thus, auditory and vibrotactile detection of such invisible objects would be of vital importance to an organism.(990 words in main body; 1000-word limit)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.001
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0060.001
Research integrity0.0170.017
Insufficient payload (model declined to judge)0.0180.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.330
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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