Visual context influences how humans walk on winding paths

During walking, proactive balance control mechanisms enable individuals to anticipate and respond to changes in their environment, such as terrain layout or obstacles. These mechanisms rely on sensory inputs, particularly vision, to adjust gait patterns in advance. Visuomotor coupling integrates information from central and peripheral vision to guide locomotion. Central vision provides detailed information about the walking path, including surface characteristics and layout. Peripheral vision processes environmental landmarks to support spatial orientation, depth perception, and self-motion. Together, these inputs allow the nervous system to plan and execute gait adjustments. Disruptions to visual information, whether due to reduced visual acuity, contrast sensitivity, or environmental conditions, can significantly alter walking behavior and challenge proactive balance control. The study associated with these data was designed to investigate how changes in the availability of central an..., This experiment included data from 28 healthy human adult participants (16F/12M; Age 26.2±4.2yrs).  Data regarding their baseline demographics and relevant assessment scores are provided (*.xlsx file). 
Participants walked on a motorized treadmill in a Motek M-Gait virtual reality system (https://www.motekmedical.com/). They walked on 4 distinct walking paths, varying by 2 shapes (straight and winding) and 2 color contrasts (high and low). Each path was presented within two distinct virtual environments. Detailed descriptions of the walking paths and environments are provided in the associated README file. Each participant performed two experimental trials (3 min long each) for all 8 walking conditions.
For each trial performed by each participant, motion capture data were recorded with a 10-camera Vicon system (https://www.vicon.com/).  These data were cleaned using Vicon Nexus software, and further processed in Matlab (https://www.mathworks.com/). All marker trajectorie..., # Data: Visual Context Influences How Walk on Winding Paths

**[DOI Link:** **[https://doi.org/10.5061/dryad.7sqv9s56j](https://doi.org/10.5061/dryad.7sqv9s56j)]**

## Description of the Data and File Structure

We collected data from 28 young, healthy human adult participants (16F/12M; Age 26.2±4.2yrs). Age, height, body mass and leg length (measure from greater trochanter to lateral malleolus) of each participant were recorded. Additionally, participants completed three assessments: a contrast sensitivity test (logCSWeber), four-choice reaction time test (4CRT), and four-square step test (FSST).

Participants walked on a 1.2 m wide motorized treadmill in a Motek M-Gait virtual reality system ([https://www.motekmedical.com/](https://www.motekmedical.com/)). They walked on each of two 0.45 m wide virtual paths: *Straight* (STR) and *Winding* (HIF). The pseudo-randomly oscillating path was created from a sum of three sin waves with incommensurate frequencies:

z(x)= 0.22 sin(A·0.46875x)..., For this study, human participants gave explicit consent to publish their de-identified data in the public domain.

Primary data are electronic (motion capture) and therefore contain no identifiers. No identifiers are used in naming of files etc (all files are named by participant number - randomly assigned). Demographic and anthropometric data for participants (see Excel spreadsheet) are kept to the minimum necessary to process / analyze the data. Variables not directly necessary for data processing are reported as aggregate values (mean ± s.d.).