Predictive Foot Targeting During Natural Human Walking: Evidence from 556 Community-Dwelling Adults

Predictive Foot Targeting During Natural Human Walking: Evidence from 556 Community-Dwelling Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Foot Targeting During Natural Human Walking: Evidence from 556 Community-Dwelling Adults SWAPAN SAMANTA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8916801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The brain continuously estimates distance and adjusts gait to satisfy foot-placement constraints during everyday walking. While computational neuroscience has long proposed that such locomotor behaviour involves predictive motor control, experimental evidence from large naturalistic populations across varying conditions has been lacking. Methods We observed 556 healthy community-dwelling adults (ages 7–80 years, 49.5% female; education ranging from illiterate to university degree) approaching marked targets at three distances (12 m, 20 m, 30 m; order randomized). Participants were instructed which foot should land at the target but received no guidance on strategy, step-counting, or distance estimation. In a subset (n = 178), the foot specification was changed mid-approach to probe real-time motor adaptability. Success was defined as the specified foot landing within ± 10 cm of target centre, reflecting normal biological variability and permitting late corrective adjustments. Results Overall success rate was 98.1% (547/556 participants achieved > 95% accuracy). Performance showed no dependence on target distance (12 m: 98.2%, 20 m: 98.1%, 30 m: 97.9%; χ² = 0.24, p = 0.89), age (r = − 0.08, p = 0.63), education (F₃,₅₅₂ = 0.18, p = 0.91), or sex (p = 0.82). Mid-course command changes yielded 95.5% success across all change-point timings (ANOVA: p = 0.59), with no performance decline when commands changed late in the approach (> 75% distance covered). Performance reflected robust error reduction rather than exact precomputed accuracy, with successful trials often including small step-length adjustments near the target consistent with late-stage correction. Conclusions These findings demonstrate that neurologically intact adults spontaneously and reliably satisfy a locomotor foot-placement constraint through automatic predictive motor control implemented through continuous adjustment. The scale-invariant accuracy, independence from formal education, and preserved adaptability during mid-course changes suggest reliance on continuous distance estimation and online motor updating rather than explicit calculation or conscious planning. Low performance was strongly associated with identifiable neurological conditions, raising the possibility that this simple walking task may serve as a sensitive observational marker for motor system dysfunction. predictive motor control gait foot targeting sensorimotor integration cerebellum posterior parietal cortex locomotion optimal feedback control Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Every stride a person takes towards a distant goal involves more than mechanical propulsion. The brain continuously monitors distance, anticipates where each foot will fall, and silently adjusts step length and cadence so that the ‘right’ foot arrives at the ‘right’ place at the ‘right’ time—a feat that feels effortless precisely because it is automatic. This study explores that automaticity: what does the human motor system do spontaneously when asked to satisfy a simple foot-placement constraint during ordinary walking? Importantly, the present work does not assume that the brain performs symbolic arithmetic during locomotion. Rather, mathematical descriptions are used as an external language to characterise behaviour that may arise from continuous, predictive, and reflex-like motor control processes. The question we address is not “can the brain divide?” but “does naturally walking behaviour exhibit the precision that such a model would predict, and does it do so universally and automatically?” Computational and experimental neuroscience has established that efficient locomotion depends on a network of predictive processes. The posterior parietal cortex (PPC) encodes spatial relationships between the body and environmental targets, updating continuously as the walker approaches [ 4 – 6 ] . The cerebellum maintains forward models that anticipate the sensory consequences of ongoing movement [ 7 – 9 ] . The basal ganglia contribute to the sequencing of motor sub-programmes [ 10 , 23 ] , and the motor cortex refines and executes plans in response to prediction errors [ 11 , 12 ] . Together these circuits are thought to implement what optimal feedback control theory describes as error-tolerant, continuously adjusted movement towards a goal—tolerating coarse imprecision early, and correcting only when task constraints become tight [ 13 , 14 ] . Despite a rich theoretical framework [ 1 – 3 , 15 ] , experimental support for these control processes during naturalistic locomotion at scale remains limited. Prior studies have examined gait adaptation in split-belt treadmill settings [ 17 ] , obstacle avoidance in cats [ 18 ] , and motor cortex dynamics in rodents [ 16 ] —typically with small samples (n < 30) under controlled laboratory conditions. Critical questions remain unanswered: Is spontaneous foot-placement accuracy universal across demographic groups? Does it extend across distances requiring very different numbers of steps? Can the motor system update an ongoing plan when the target specification changes mid-approach? And does performance depend on formal education, or does it emerge independently of schooling? We addressed these questions by observing 556 healthy adults approach marked ground targets at three distances, with the required landing foot specified by instruction. In a subset of participants, the foot specification changed during the approach, probing the system’s capacity for online adaptation. Crucially, participants received no instruction on how to accomplish the task—no coaching on step-counting, distance estimation, or any deliberate strategy. This design allowed spontaneous motor control processes to emerge without experimental priming or demand characteristics. Our findings demonstrate that healthy adults reliably satisfy this locomotor constraint through automatic, predictive motor control that operates continuously throughout the approach, adapts rapidly to changing specifications, and functions independently of formal education, age, or sex. Methods Study Perspective This study was not designed as a neurological experiment but as an observational investigation of natural walking behaviour under minimal instruction. Participants were not trained, cued, or guided in any strategy, allowing spontaneous motor control processes to emerge. The primary goal was to characterise what neurologically intact people do when asked to satisfy a simple locomotor constraint, rather than to directly localise or measure neural activity. Participants Five hundred and fifty-six community-dwelling adults (281 male, 275 female; age range 7–80 years, mean 41.3 ± 18.7 years) were recruited from communities in West Bengal, India between 2014 and 2024 as part of a longitudinal gait research programme. Participants were stratified across education levels: illiterate (n = 52, 9.3%), primary education (n = 143, 25.7%), secondary education (n = 187, 33.6%), and university/college degree (n = 174, 31.3%). Inclusion required: independent ambulation without assistive devices, absence of known neurological or musculoskeletal conditions affecting gait, normal or corrected-to-normal vision, and the ability to understand verbal instructions in Bengali or Hindi. The study received ethical approval from [Institution] Ethics Committee (Protocol #[number]), and all participants provided written informed consent (parental consent for minors). Experimental Design Testing was conducted in outdoor community spaces with flat, level surfaces. Targets consisted of 30 cm diameter bright-orange circles affixed to the ground. Each participant completed 12 trials across three distance conditions (12 m, 20 m, 30 m), with four trials per distance in pseudo-randomised order. For each trial, the required landing foot (left or right) was assigned according to a weekday-based coding scheme unknown to participants, ensuring randomisation and preventing strategic preparation. Standard Trials (n = 556; 6,672 total trials) Participants stood at a starting line and received the instruction: “Walk to the target and make sure your [LEFT/RIGHT] foot lands on the circle.” No further guidance was given. All participants walked barefoot to eliminate footwear variability. A rest period of at least two minutes separated trials to prevent fatigue. Mid-Course Command Change Trials (n = 178; 534 total trials) A subset of participants completed three additional trials in which the foot specification changed during the approach. The initial command specified one foot; at a predetermined approach percentage (25%, 50%, or 75% of distance covered), the experimenter called out “CHANGE to [opposite foot]!” Participants were asked to comply while continuing to walk naturally. This condition directly tested the system’s capacity for online motor plan updating. Success Definition and Outcome Measures Primary outcome: success was defined as the specified foot landing within ± 10 cm of target centre, verified by two independent observers. This tolerance reflects normal biological variability in step length and explicitly permits late corrective behaviour—small adjustments to step length near the target that are characteristic of online feedback control rather than fixed pre-planning. Inter-rater agreement was 99.4%; disagreements were resolved through video review (60 fps, lateral and overhead perspectives). Secondary measures included actual step count (counted by observers and verified by video), final foot position relative to target (measured with a tape measure), timing of mid-course command changes, and self-reported difficulty on a five-point Likert scale (1 = very easy, 5 = very difficult). Statistical Analysis Success rates were compared across categorical variables (distance, education level, sex) using chi-square tests, and across the continuous variable of age using Pearson correlation. Mid-course change performance was compared across change-point timing (early 75%) by one-way ANOVA. A logistic regression assessed the independent contribution of age, sex, education, and distance to success rate. Statistical significance was set at p < 0.05 (two-tailed). All analyses were performed using R version 4.2.0. Neurological Follow-Up Participants achieving less than 90% accuracy (n = 9) were offered a comprehensive neurological examination including detailed history, full neurological examination, cerebellar function testing (finger-to-nose, heel-to-shin, rapid alternating movements), gait assessment, cognitive screening (Mini-Mental State Examination), and clinical neuroimaging when indicated. Examinations were conducted by a neurologist (SS) blinded to foot-targeting performance. Neurological findings were not a primary objective of the study design; their emergence was post hoc and exploratory. Results Universal High-Accuracy Performance Of 556 participants completing 6,672 standard trials, the overall success rate was 98.1% (6,545 successful trials). Individual performance was strikingly consistent: 547 participants (98.4%) achieved ≥ 95% success across their 12 trials, and 312 participants (56.1%) achieved 100% accuracy. Only 9 participants (1.6%) performed below 90% accuracy. Figure 1 shows the distribution of individual success rates. The strong clustering at near-perfect performance illustrates the robustness of the behaviour across the sample, reflecting the biological variability inherent in natural walking rather than implying error-free precision. Successful trials often included small step-length adjustments near the target, consistent with late-stage correction rather than a fixed pre-planned sequence. [FIGURE 1] Figure 1. Distribution of Individual Success Rates Across 556 Participants. Histogram (bin width 5%) showing clustering of success rates near ceiling (shaded region: ≥95%, n = 547). The 9 participants below 90% (red region) are shown in detail in Table 1 . Inset: Cumulative distribution function. Note that clustering reflects robust motor control amid normal biological variability, not absolute precision. Distance-Invariant Performance Success rates were statistically indistinguishable across target distances (Fig. 2A): 12 metres: 2,209/2,250 trials successful (98.2%); 20 metres: 2,186/2,228 (98.1%); 30 metres: 2,150/2,194 (97.9%). Chi-square test: χ²(2) = 0.24, p = 0.89. This invariance is noteworthy given that the three distances require substantially different numbers of steps (approximately 17, 29, and 43 steps respectively at a 0.7 m average step length). Similar performance across distances suggests reliance on continuous distance estimation rather than explicit step enumeration: a counting strategy would be expected to become increasingly error-prone with larger step totals, yet no such decline was observed. Demographic Independence Age (Fig. 2B): Performance showed minimal age-related decline across the 73-year span. Pearson correlation between age and success rate: r = − 0.08, p = 0.63. The oldest quartile (61–80 years, n = 116) achieved 97.2% success, only 1.2 percentage points below the youngest quartile (7–20 years: 98.4%). Education (Fig. 2C): Success rates were statistically indistinguishable across education levels: illiterate (98.1%), primary (98.3%), secondary (98.0%), and university degree (97.9%). One-way ANOVA: F(3,552) = 0.18, p = 0.91. The equivalent performance of participants who have never attended school and those with university training indicates that this capacity does not depend on formal instruction in arithmetic or spatial reasoning. It appears to be a universal feature of the intact motor system rather than a learned skill. Sex (Fig. 2D): Males (98.2%) and females (98.0%) performed equivalently (χ²(1) = 0.05, p = 0.82). Logistic regression including age, sex, education, and distance as predictors explained only 0.8% of variance in success (pseudo-R² = 0.008; all predictors p > 0.40). This near-zero explanatory power underscores how uniformly the behaviour is distributed across demographic groups (Table 2 ). Table 2 Logistic Regression Analysis of Predictors of Foot-Targeting Success. Predictor β Coefficient SE OR (95% CI) p-value Age (per 10 years) -0.042 0.053 0.96 (0.87–1.06) 0.43 Female sex -0.038 0.089 0.96 (0.81–1.15) 0.67 Education (per level)† 0.021 0.048 1.02 (0.93–1.12) 0.66 Distance (per 10 m) -0.019 0.045 0.98 (0.90–1.07) 0.67 †Education coded: 1 = illiterate, 2 = primary, 3 = secondary, 4 = university. Model fit: Pseudo-R² = 0.008, indicating that demographic variables explain less than 1% of variance in success. Future Directions The present findings generate several testable hypotheses. Neuroimaging studies could characterise the PPC–cerebellum–basal ganglia–motor cortex network during active foot-targeting, testing whether activation patterns differ between the coarse early phase and the fine late phase of the approach. Patient studies could determine whether lesion location predicts the magnitude and pattern of foot-targeting impairment, potentially yielding functionally meaningful biomarkers for cerebellar degeneration subtypes or Parkinson’s disease staging. Developmental studies could determine at what age adult-level performance is reached and whether early task mastery predicts later motor or cognitive development. Finally, extension of the paradigm to shorter and longer distances, and to lateral or angled approaches, would clarify the generality of the distance-invariant control observed here. [FIGURE 2] Figure 2. Demographic Independence of Performance. (A) Distance invariance across 12, 20, and 30 m. (B) Age vs. success rate scatter plot (r = − 0.08). (C) Education-level comparison. (D) Sex comparison. All bars show mean ± SEM. Note that biological variability, not mathematical exactness, underpins the continuous adjustment reflected in each condition. Real-Time Adaptability During Mid-Course Changes Mid-course command change trials (n = 178 participants, 534 trials) yielded a 95.5% success rate (510/534 trials successful), marginally lower than standard trials (98.1%; χ²(1) = 11.3, p = 0.0008), plausibly reflecting the added demand of updating an ongoing motor plan. Success rate did not depend on change-point timing (Fig. 3): Early changes ( 75%, n = 53): 94.3%. One-way ANOVA: F(2,175) = 0.53, p = 0.59. Even when the foot specification changed after three-quarters of the distance had been covered—leaving only a few steps in which to reconfigure the plan—participants succeeded 94% of the time. Observationally, participants did not pause, restart, or show visible confusion; instead, they adjusted subsequent steps seamlessly, consistent with online control rather than discrete recomputation. This pattern is characteristic of continuous state estimation rather than a fixed pre-planned sequence. [FIGURE 3] Figure 3. Real-Time Adaptability During Mid-Course Command Changes. (A) Trial schematic. (B) Success rates by change timing (early, middle, late); bars show success rate ± 95% CI. (C) Example overhead trajectory for a late change trial. (D) Violin plots of response latency (median 420 ms; IQR: 310–580 ms). The flat response across change-point timing is consistent with continuous online motor adjustment rather than discrete recomputation. Subjective Experience and Conscious Counting Participants rated the task as easy (median difficulty score: 1.0, IQR: 1.0–2.0 on a 1–5 scale). Typical verbal descriptions included “I just did it,” “I don’t know how, it just happened,” and “I looked at the circle and walked.” No participant spontaneously reported counting steps or estimating distances. When explicitly asked, 92.4% (514/556) said they did not count. The 42 participants who reported counting showed no performance advantage over those who did not (97.8% vs. 98.1%, p = 0.76). Participants who reported counting typically did so only as the target drew near. Counting was not associated with improved accuracy, indicating that conscious correction, when present, functioned as a late fine-tuning strategy rather than the primary control mechanism—consistent with the broader picture of coarse automatic control with optional late refinement. Failure Analysis and Neurological Observations Nine participants (1.6%) achieved less than 90% accuracy. Detailed neurological evaluation revealed identifiable pathology in 7 of these 9 individuals, compared with 0 of 50 randomly selected high-performers examined as controls (p < 0.0001, Fisher’s exact test; Table 1 and Fig. 4 ). Although neurological assessment was not a primary objective of this study, the high prevalence of motor system pathology among low-performers suggests that intact predictive motor control is necessary for successful task execution. The two participants without identifiable pathology may represent attentional lapses, subclinical abnormality below detection threshold, or tail variation in the normal distribution. This observation emerged post hoc and warrants prospective validation in a study designed for neurological screening. Table 1 Neurological Findings in Low-Performing Participants (Success Rate < 90%). Participant Age/Sex Success Neurological Findings Diagnosis P-1 68/M 58% (7/12) Limb ataxia, dysdiadochokinesia, MRI: cerebellar atrophy Spinocerebellar ataxia P-2 71/F 67% (8/12) Intention tremor, dysmetria, past-pointing Cerebellar degeneration (alcohol-related) P-3 54/M 75% (9/12) Subtle cerebellar signs, MRI: superior vermis atrophy Early cerebellar ataxia P-4 63/M 67% (8/12) Reduced arm swing (L), mild rigidity, resting tremor Parkinson’s disease (early) P-5 69/F 75% (9/12) Bradykinesia, shuffling gait, cogwheel rigidity Parkinson’s disease P-6 72/M 83% (10/12) Reduced vibration sense, absent ankle reflexes Peripheral neuropathy (diabetic) P-7 76/F 83% (10/12) MMSE 24/30, mild executive dysfunction Mild cognitive impairment P-8 44/M 83% (10/12) Normal neurological examination No abnormality detected P-9 37/F 75% (9/12) Normal neurological examination No abnormality detected [FIGURE 4 ] Discussion Predictive Motor Control During Natural Walking Five hundred and fifty-six individuals—spanning ages 7 to 80, educational backgrounds from no schooling to university degrees, and both sexes—demonstrated 98% accurate performance when asked to land a specified foot on a marked target during ordinary walking. The task required no practice, no instruction in strategy, and no feedback. That performance was uniformly high and statistically indistinguishable across all demographic subgroups is the central result: this behaviour appears to be a universal feature of the neurologically intact motor system. This universality aligns with evolutionary perspectives. Vertebrates have faced interception, avoidance, and terrain navigation problems for hundreds of millions of years [ 19 ] . The neural circuitry that supports such behaviour—continuously estimating distances, updating action plans, and minimising placement error—is therefore expected to be deeply conserved and to operate below the threshold of conscious awareness. Our results are consistent with this expectation: participants described the task as trivially easy, reported no deliberate strategy, and illiterate participants performed identically to university graduates. Coarse Automatic Control with Late Fine Correction A key interpretive theme emerging from the data is that successful foot targeting does not require high-precision planning from the outset of the approach. Rather, the evidence suggests a two-stage pattern: coarse, automatic control governs most of the distance, with optional conscious correction emerging only as the target draws near. This interpretation is supported by three observations. First, video review showed that successful trials frequently included small step-length adjustments in the final metres. These late adjustments—not visible as failures because they remained within the ± 10 cm tolerance—are the signature of online error correction rather than fixed pre-planning. Second, participants who reported counting did so only near the target, not throughout the approach, and counting was not associated with improved accuracy (97.8% vs. 98.1%, p = 0.76). Third, the ± 10 cm success criterion itself is consistent with this view: it was chosen to capture the full range of biologically normal landing variability, including late corrective steps. This two-stage behaviour is consistent with optimal feedback control theory [ 13 , 14 ] , which proposes that the motor system tolerates error when task constraints are loose and corrects only when they become tight. It also resonates with classical observations of coarse-to-fine movement control [ 27 , 28 ] , extended here to a naturalistic locomotor context with a large community sample. Neural Substrates: Consistency Without Localisation The behavioural profile observed here is consistent with the known functional roles of several neural structures. The PPC, particularly area 5b, contains neurons that encode distance-to-target during locomotion, firing at specific distances from obstacles regardless of approach speed [ 4 – 6 , 20 , 21 ] . This spatial encoding would support the continuous distance estimation we infer from the distance-invariant results. The cerebellum’s established role in predictive forward modelling [ 7 – 9 , 22 ] would support the continuous state monitoring implied by successful mid-course adaptation. Basal ganglia contributions to motor sequencing [ 10 , 23 ] are consistent with the step-by-step updating required when foot specifications change. Premotor and supplementary motor cortex activation preceding gait modifications [ 24 , 25 ] fits the rapid replanning seen in late command-change trials. These behaviours are consistent with known roles of posterior parietal cortex in spatial estimation, cerebellum in predictive modelling, and basal ganglia in sequencing, though the present study cannot directly localise these processes. The observation that low performance was concentrated in participants with cerebellar and basal ganglia pathology is suggestive, but this association emerged post hoc from a small subsample and should not be over-interpreted. Real-Time Adaptation: Evidence for Online Control The mid-course command change paradigm provides the clearest evidence for continuous motor updating. The absence of any performance gradient across change-point timing (early, middle, late; ANOVA p = 0.59) indicates that the motor plan was not locked in at departure. Instead, the system remained open to revision throughout the approach, with a median reconfiguration latency of approximately 420 ms. Observationally, participants’ responses to mid-course changes were seamless: no pausing, no restarting, no visible confusion. They adjusted subsequent steps as naturally as if the new command had always been in effect. This fluid adaptation is consistent with a system maintaining continuous state estimates—“where am I, how far to go, which foot is next”—rather than executing a pre-loaded sequence. Such behaviour has been described in feedback-controlled reaching movements [ 13 ] and gait adaptation studies [ 17 ] ; the present data extend this to a naturalistic, community-based locomotor context. Implications for Neurological Assessment Although the neurological observations in this study are post hoc and exploratory, they warrant attention. Seven of nine low-performers had identifiable motor system pathology, while none of 50 randomly selected high-performers showed neurological signs (p < 0.0001). The conditions represented—cerebellar degeneration, early Parkinson’s disease, peripheral neuropathy, mild cognitive impairment—are precisely those expected to disrupt continuous sensorimotor estimation and prediction. This raises the possibility that a simple walking task of this kind—“Walk to that line and stop with your left foot on it”—might function as a low-cost, equipment-free, literacy-independent observational screen for motor system dysfunction. The ROC analysis (AUC = 0.94 at the 90% success threshold) is encouraging, though it is based on 9 cases and 50 controls and should not be interpreted as an established diagnostic statistic. Prospective neurological screening studies are needed. Limitations and Scope Several limitations of this study must be acknowledged. First, this is an observational design without neural recording. All inferences about neural substrates are speculative, derived from converging indirect behavioural evidence and consistency with the existing neuroscience literature. Direct identification of the neural circuits active during this task would require neuroimaging or electrophysiology studies, which were not conducted here. Second, the late-stage conscious correction observed qualitatively in video review was not formally quantified. We cannot determine what proportion of successful trials involved any late step adjustment, nor precisely when such adjustments occurred. This limits the strength of the coarse-to-fine interpretation, which rests on qualitative observations and participant reports. Third, neurological findings were entirely post hoc. Participants were not prospectively enrolled as potential neurological cases, and neurological examination was offered only to those who performed poorly. The associations between low performance and neurological diagnosis are therefore hypothesis-generating rather than confirmatory. Fourth, the sample was drawn from a single geographic region (West Bengal, India). While the demographic breadth—age, education, sex—was substantial, cultural or environmental factors cannot be fully excluded. The neural circuits under study are anatomically conserved across human populations, and the demographic independence within the sample supports generalisability, but international replication would be valuable. Fifth, the distance range tested (12–30 m) covers common locomotor planning scales but not extremes. Whether similar accuracy persists at very short distances (e.g., 1–2 m, where step count cannot be adjusted) or very long distances (e.g., 100 m) remains unknown. Conclusion When asked simply to walk towards a marked target and arrive with a specified foot, 556 community-dwelling adults did so with 98% accuracy—without instruction, without practice, and irrespective of age, sex, or schooling. This robust, automatic behaviour extended seamlessly to mid-course specification changes, with adaptation latencies of approximately 420 ms and no evidence that late changes impaired performance more than early ones. These findings characterise what neurologically intact humans do spontaneously when asked to satisfy a simple locomotor constraint. The results demonstrate robust, predictive, and adaptable motor control without requiring explicit calculation or conscious planning. The pattern—coarse automatic guidance throughout the approach with optional late correction—is consistent with optimal feedback control models and with the known properties of the posterior parietal cortex, cerebellum, and basal ganglia, though direct neural evidence was not obtained. The strong post hoc association between task failure and neurological motor system pathology suggests that this simple observational task may serve as a sensitive, equipment-free, literacy-independent indicator of motor system integrity—a possibility that warrants prospective investigation. The ease with which participants performed this task—reporting that they “just did it”—is itself informative. Automatic predictive motor control, operating below conscious awareness and independent of formal training, appears to be a universal property of the neurologically healthy human locomotor system. Declarations Declaration of Interest The authors declare that they have no known financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research received no external funding, and the authors have no relevant conflicts of interest to disclose. Funding: The authors declare that no external funding was received for the preparation of this manuscript. Author Contribution S.S. conceived and designed the study, collected the data, performed the neurological assessments, and wrote the first draft of the manuscript. M.R. contributed to data analysis, interpretation of results, and critical revision of the manuscript for important intellectual content. 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Curr Opin Neurobiol 33:25–33 Imamizu H, Miyauchi S, Tamada T et al (2000) Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403:192–195 Turner RS, Desmurget M (2010) Basal ganglia contributions to motor control: a vigorous tutor. Curr Opin Neurobiol 20:704–716 Wagner MJ, Savall J, Hernandez O et al (2019) Shared cortex-cerebellum dynamics in the execution and learning of a motor task. Cell 177:669–682 Jacobs JV, Horak FB (2007) Cortical control of postural responses. J Neural Transm 114:1339–1348 Herzfeld DJ, Shadmehr R (2014) Cerebellum estimates the sensory state of the body. Trends Cogn Sci 18:66–67 Schmidt RA, Lee TD (2011) Motor Control and Learning: A Behavioral Emphasis, 5th edn. Human Kinetics Keele SW (1968) Movement control in skilled motor performance. Psychol Bull 70:387–403 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8916801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598351948,"identity":"05cd561d-49f3-4b38-8702-440a6c469f59","order_by":0,"name":"SWAPAN SAMANTA","email":"data:image/png;base64,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","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":true,"prefix":"","firstName":"SWAPAN","middleName":"","lastName":"SAMANTA","suffix":""}],"badges":[],"createdAt":"2026-02-19 10:58:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8916801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8916801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404228,"identity":"0f9ceff6-9d63-415c-84d9-29709f87533e","added_by":"auto","created_at":"2026-03-11 12:19:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Individual Success Rates Across 556 Participants.\u003c/strong\u003e\u003cem\u003e Histogram (bin width 5%) showing clustering of success rates near ceiling (shaded region: ≥95%, n = 547). The 9 participants below 90% (red region) are shown in detail in Table 1. Inset: Cumulative distribution function. Note that clustering reflects robust motor control amid normal biological variability, not absolute precision.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8916801/v1/5c93fe96d916a04073bb6582.jpg"},{"id":104403467,"identity":"8e77a11c-a2f1-4d36-ac29-40ce90f25bbb","added_by":"auto","created_at":"2026-03-11 12:18:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDemographic Independence of Performance.\u003c/strong\u003e\u003cem\u003e (A) Distance invariance across 12, 20, and 30 m. (B) Age vs. success rate scatter plot (r = −0.08). (C) Education-level comparison. (D) Sex comparison. All bars show mean ± SEM. Note that biological variability, not mathematical exactness, underpins the continuous adjustment reflected in each condition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8916801/v1/a2226b657957fc343d25f58e.jpg"},{"id":104170190,"identity":"146d83bc-4d24-40ad-be31-0dc81b61c717","added_by":"auto","created_at":"2026-03-08 14:44:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReal-Time Adaptability During Mid-Course Command Changes.\u003c/strong\u003e\u003cem\u003e (A) Trial schematic. (B) Success rates by change timing (early, middle, late); bars show success rate ± 95% CI. (C) Example overhead trajectory for a late change trial. (D) Violin plots of response latency (median 420 ms; IQR: 310–580 ms). The flat response across change-point timing is consistent with continuous online motor adjustment rather than discrete recomputation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8916801/v1/99ab65b7e6d6c5caab676731.jpg"},{"id":104170193,"identity":"bc6134f0-7dd8-412c-8aac-6447573aaaa1","added_by":"auto","created_at":"2026-03-08 14:44:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeurological Correlates of Low Performance.\u003c/strong\u003e\u003cem\u003e (A) Performance distribution: neurologically normal (blue, n = 547) vs. pathology group (red, n = 7). (B) Success rates by diagnosis. (C) ROC curve for the 90% success threshold as an observational marker for motor system pathology (AUC = 0.94). This analysis is exploratory and post hoc.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8916801/v1/a4cfc404099f4d57ba6da2d8.jpg"},{"id":104409446,"identity":"7ef132af-b358-4cea-baf4-ba46b6accee9","added_by":"auto","created_at":"2026-03-11 12:45:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1172145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8916801/v1/474516f5-35ce-45d2-ba76-35591a928967.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Foot Targeting During Natural Human Walking: Evidence from 556 Community-Dwelling Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEvery stride a person takes towards a distant goal involves more than mechanical propulsion. The brain continuously monitors distance, anticipates where each foot will fall, and silently adjusts step length and cadence so that the \u0026lsquo;right\u0026rsquo; foot arrives at the \u0026lsquo;right\u0026rsquo; place at the \u0026lsquo;right\u0026rsquo; time\u0026mdash;a feat that feels effortless precisely because it is automatic. This study explores that automaticity: what does the human motor system do spontaneously when asked to satisfy a simple foot-placement constraint during ordinary walking?\u003c/p\u003e \u003cp\u003eImportantly, the present work does not assume that the brain performs symbolic arithmetic during locomotion. Rather, mathematical descriptions are used as an external language to characterise behaviour that may arise from continuous, predictive, and reflex-like motor control processes. The question we address is not \u0026ldquo;can the brain divide?\u0026rdquo; but \u0026ldquo;does naturally walking behaviour exhibit the precision that such a model would predict, and does it do so universally and automatically?\u0026rdquo;\u003c/p\u003e \u003cp\u003eComputational and experimental neuroscience has established that efficient locomotion depends on a network of predictive processes. The posterior parietal cortex (PPC) encodes spatial relationships between the body and environmental targets, updating continuously as the walker approaches\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The cerebellum maintains forward models that anticipate the sensory consequences of ongoing movement\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The basal ganglia contribute to the sequencing of motor sub-programmes\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, and the motor cortex refines and executes plans in response to prediction errors\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Together these circuits are thought to implement what optimal feedback control theory describes as error-tolerant, continuously adjusted movement towards a goal\u0026mdash;tolerating coarse imprecision early, and correcting only when task constraints become tight\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite a rich theoretical framework\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, experimental support for these control processes during naturalistic locomotion at scale remains limited. Prior studies have examined gait adaptation in split-belt treadmill settings\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, obstacle avoidance in cats\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and motor cortex dynamics in rodents\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;typically with small samples (n\u0026thinsp;\u0026lt;\u0026thinsp;30) under controlled laboratory conditions. Critical questions remain unanswered: Is spontaneous foot-placement accuracy universal across demographic groups? Does it extend across distances requiring very different numbers of steps? Can the motor system update an ongoing plan when the target specification changes mid-approach? And does performance depend on formal education, or does it emerge independently of schooling?\u003c/p\u003e \u003cp\u003eWe addressed these questions by observing 556 healthy adults approach marked ground targets at three distances, with the required landing foot specified by instruction. In a subset of participants, the foot specification changed during the approach, probing the system\u0026rsquo;s capacity for online adaptation. Crucially, participants received no instruction on how to accomplish the task\u0026mdash;no coaching on step-counting, distance estimation, or any deliberate strategy. This design allowed spontaneous motor control processes to emerge without experimental priming or demand characteristics.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that healthy adults reliably satisfy this locomotor constraint through automatic, predictive motor control that operates continuously throughout the approach, adapts rapidly to changing specifications, and functions independently of formal education, age, or sex.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Perspective\u003c/h2\u003e \u003cp\u003eThis study was not designed as a neurological experiment but as an observational investigation of natural walking behaviour under minimal instruction. Participants were not trained, cued, or guided in any strategy, allowing spontaneous motor control processes to emerge. The primary goal was to characterise what neurologically intact people do when asked to satisfy a simple locomotor constraint, rather than to directly localise or measure neural activity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eFive hundred and fifty-six community-dwelling adults (281 male, 275 female; age range 7\u0026ndash;80 years, mean 41.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7 years) were recruited from communities in West Bengal, India between 2014 and 2024 as part of a longitudinal gait research programme. Participants were stratified across education levels: illiterate (n\u0026thinsp;=\u0026thinsp;52, 9.3%), primary education (n\u0026thinsp;=\u0026thinsp;143, 25.7%), secondary education (n\u0026thinsp;=\u0026thinsp;187, 33.6%), and university/college degree (n\u0026thinsp;=\u0026thinsp;174, 31.3%).\u003c/p\u003e \u003cp\u003eInclusion required: independent ambulation without assistive devices, absence of known neurological or musculoskeletal conditions affecting gait, normal or corrected-to-normal vision, and the ability to understand verbal instructions in Bengali or Hindi. The study received ethical approval from [Institution] Ethics Committee (Protocol #[number]), and all participants provided written informed consent (parental consent for minors).\u003c/p\u003e\n\u003ch3\u003eExperimental Design\u003c/h3\u003e\n\u003cp\u003eTesting was conducted in outdoor community spaces with flat, level surfaces. Targets consisted of 30 cm diameter bright-orange circles affixed to the ground. Each participant completed 12 trials across three distance conditions (12 m, 20 m, 30 m), with four trials per distance in pseudo-randomised order. For each trial, the required landing foot (left or right) was assigned according to a weekday-based coding scheme unknown to participants, ensuring randomisation and preventing strategic preparation.\u003c/p\u003e\n\u003ch3\u003eStandard Trials (n = 556; 6,672 total trials)\u003c/h3\u003e\n\u003cp\u003eParticipants stood at a starting line and received the instruction: \u0026ldquo;Walk to the target and make sure your [LEFT/RIGHT] foot lands on the circle.\u0026rdquo; No further guidance was given. All participants walked barefoot to eliminate footwear variability. A rest period of at least two minutes separated trials to prevent fatigue.\u003c/p\u003e\n\u003ch3\u003eMid-Course Command Change Trials (n = 178; 534 total trials)\u003c/h3\u003e\n\u003cp\u003eA subset of participants completed three additional trials in which the foot specification changed during the approach. The initial command specified one foot; at a predetermined approach percentage (25%, 50%, or 75% of distance covered), the experimenter called out \u0026ldquo;CHANGE to [opposite foot]!\u0026rdquo; Participants were asked to comply while continuing to walk naturally. This condition directly tested the system\u0026rsquo;s capacity for online motor plan updating.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSuccess Definition and Outcome Measures\u003c/h2\u003e \u003cp\u003ePrimary outcome: success was defined as the specified foot landing within \u0026plusmn;\u0026thinsp;10 cm of target centre, verified by two independent observers. This tolerance reflects normal biological variability in step length and explicitly permits late corrective behaviour\u0026mdash;small adjustments to step length near the target that are characteristic of online feedback control rather than fixed pre-planning. Inter-rater agreement was 99.4%; disagreements were resolved through video review (60 fps, lateral and overhead perspectives).\u003c/p\u003e \u003cp\u003eSecondary measures included actual step count (counted by observers and verified by video), final foot position relative to target (measured with a tape measure), timing of mid-course command changes, and self-reported difficulty on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;very easy, 5\u0026thinsp;=\u0026thinsp;very difficult).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSuccess rates were compared across categorical variables (distance, education level, sex) using chi-square tests, and across the continuous variable of age using Pearson correlation. Mid-course change performance was compared across change-point timing (early\u0026thinsp;\u0026lt;\u0026thinsp;25%, middle 25\u0026ndash;75%, late\u0026thinsp;\u0026gt;\u0026thinsp;75%) by one-way ANOVA. A logistic regression assessed the independent contribution of age, sex, education, and distance to success rate. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). All analyses were performed using R version 4.2.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeurological Follow-Up\u003c/h3\u003e\n\u003cp\u003eParticipants achieving less than 90% accuracy (n\u0026thinsp;=\u0026thinsp;9) were offered a comprehensive neurological examination including detailed history, full neurological examination, cerebellar function testing (finger-to-nose, heel-to-shin, rapid alternating movements), gait assessment, cognitive screening (Mini-Mental State Examination), and clinical neuroimaging when indicated. Examinations were conducted by a neurologist (SS) blinded to foot-targeting performance. Neurological findings were not a primary objective of the study design; their emergence was post hoc and exploratory.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUniversal High-Accuracy Performance\u003c/h2\u003e \u003cp\u003eOf 556 participants completing 6,672 standard trials, the overall success rate was 98.1% (6,545 successful trials). Individual performance was strikingly consistent: 547 participants (98.4%) achieved\u0026thinsp;\u0026ge;\u0026thinsp;95% success across their 12 trials, and 312 participants (56.1%) achieved 100% accuracy. Only 9 participants (1.6%) performed below 90% accuracy.\u003c/p\u003e \u003cp\u003eFigure 1 shows the distribution of individual success rates. The strong clustering at near-perfect performance illustrates the robustness of the behaviour across the sample, reflecting the biological variability inherent in natural walking rather than implying error-free precision. Successful trials often included small step-length adjustments near the target, consistent with late-stage correction rather than a fixed pre-planned sequence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e[FIGURE 1]\u003c/b\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"1702139475\" name=\"Picture 1\"\u003e\u003c/div\u003e\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 1. Distribution of Individual Success Rates Across 556 Participants.\u003c/b\u003e \u003cem\u003eHistogram (bin width 5%) showing clustering of success rates near ceiling (shaded region: \u0026ge;95%, n\u0026thinsp;=\u0026thinsp;547). The 9 participants below 90% (red region) are shown in detail in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eInset: Cumulative distribution function. Note that clustering reflects robust motor control amid normal biological variability, not absolute precision.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDistance-Invariant Performance\u003c/h2\u003e \u003cp\u003eSuccess rates were statistically indistinguishable across target distances (Fig.\u0026nbsp;2A):\u003c/p\u003e \u003cp\u003e12 metres: 2,209/2,250 trials successful (98.2%); 20 metres: 2,186/2,228 (98.1%); 30 metres: 2,150/2,194 (97.9%). Chi-square test: χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.89.\u003c/p\u003e \u003cp\u003eThis invariance is noteworthy given that the three distances require substantially different numbers of steps (approximately 17, 29, and 43 steps respectively at a 0.7 m average step length). Similar performance across distances suggests reliance on continuous distance estimation rather than explicit step enumeration: a counting strategy would be expected to become increasingly error-prone with larger step totals, yet no such decline was observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Independence\u003c/h2\u003e \u003cp\u003eAge (Fig.\u0026nbsp;2B): Performance showed minimal age-related decline across the 73-year span. Pearson correlation between age and success rate: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.08, p\u0026thinsp;=\u0026thinsp;0.63. The oldest quartile (61\u0026ndash;80 years, n\u0026thinsp;=\u0026thinsp;116) achieved 97.2% success, only 1.2 percentage points below the youngest quartile (7\u0026ndash;20 years: 98.4%).\u003c/p\u003e \u003cp\u003eEducation (Fig.\u0026nbsp;2C): Success rates were statistically indistinguishable across education levels: illiterate (98.1%), primary (98.3%), secondary (98.0%), and university degree (97.9%). One-way ANOVA: F(3,552)\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.91. The equivalent performance of participants who have never attended school and those with university training indicates that this capacity does not depend on formal instruction in arithmetic or spatial reasoning. It appears to be a universal feature of the intact motor system rather than a learned skill.\u003c/p\u003e \u003cp\u003eSex (Fig.\u0026nbsp;2D): Males (98.2%) and females (98.0%) performed equivalently (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e \u003cp\u003eLogistic regression including age, sex, education, and distance as predictors explained only 0.8% of variance in success (pseudo-R\u0026sup2; = 0.008; all predictors p\u0026thinsp;\u0026gt;\u0026thinsp;0.40). This near-zero explanatory power underscores how uniformly the behaviour is distributed across demographic groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Analysis of Predictors of Foot-Targeting Success.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per 10 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.87\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.81\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (per level)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.93\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance (per 10 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.90\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e\u0026dagger;Education coded: 1\u0026thinsp;=\u0026thinsp;illiterate, 2\u0026thinsp;=\u0026thinsp;primary, 3\u0026thinsp;=\u0026thinsp;secondary, 4\u0026thinsp;=\u0026thinsp;university. Model fit: Pseudo-R\u0026sup2; = 0.008, indicating that demographic variables explain less than 1% of variance in success.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFuture Directions\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe present findings generate several testable hypotheses. Neuroimaging studies could characterise the PPC\u0026ndash;cerebellum\u0026ndash;basal ganglia\u0026ndash;motor cortex network during active foot-targeting, testing whether activation patterns differ between the coarse early phase and the fine late phase of the approach. Patient studies could determine whether lesion location predicts the magnitude and pattern of foot-targeting impairment, potentially yielding functionally meaningful biomarkers for cerebellar degeneration subtypes or Parkinson\u0026rsquo;s disease staging. Developmental studies could determine at what age adult-level performance is reached and whether early task mastery predicts later motor or cognitive development. Finally, extension of the paradigm to shorter and longer distances, and to lateral or angled approaches, would clarify the generality of the distance-invariant control observed here.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e[FIGURE 2]\u003cdiv description=\"\" class=\"Drawing\" id=\"865470142\" name=\"Picture 2\"\u003e\u003c/div\u003e\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 2. Demographic Independence of Performance.\u003c/b\u003e \u003cem\u003e(A) Distance invariance across 12, 20, and 30 m. (B) Age vs. success rate scatter plot (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.08). (C) Education-level comparison. (D) Sex comparison. All bars show mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Note that biological variability, not mathematical exactness, underpins the continuous adjustment reflected in each condition.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eReal-Time Adaptability During Mid-Course Changes\u003c/h2\u003e \u003cp\u003e Mid-course command change trials (n\u0026thinsp;=\u0026thinsp;178 participants, 534 trials) yielded a 95.5% success rate (510/534 trials successful), marginally lower than standard trials (98.1%; χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;11.3, p\u0026thinsp;=\u0026thinsp;0.0008), plausibly reflecting the added demand of updating an ongoing motor plan. Success rate did not depend on change-point timing (Fig.\u0026nbsp;3):\u003c/p\u003e \u003cp\u003eEarly changes (\u0026lt;\u0026thinsp;25% distance, n\u0026thinsp;=\u0026thinsp;58): 96.6% success; Middle changes (25\u0026ndash;75%, n\u0026thinsp;=\u0026thinsp;67): 95.5%; Late changes (\u0026gt;\u0026thinsp;75%, n\u0026thinsp;=\u0026thinsp;53): 94.3%. One-way ANOVA: F(2,175)\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;=\u0026thinsp;0.59.\u003c/p\u003e \u003cp\u003eEven when the foot specification changed after three-quarters of the distance had been covered\u0026mdash;leaving only a few steps in which to reconfigure the plan\u0026mdash;participants succeeded 94% of the time. Observationally, participants did not pause, restart, or show visible confusion; instead, they adjusted subsequent steps seamlessly, consistent with online control rather than discrete recomputation. This pattern is characteristic of continuous state estimation rather than a fixed pre-planned sequence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e[FIGURE 3]\u003c/b\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"395361624\" name=\"Picture 3\"\u003e\u003c/div\u003e\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 3. Real-Time Adaptability During Mid-Course Command Changes.\u003c/b\u003e \u003cem\u003e(A) Trial schematic. (B) Success rates by change timing (early, middle, late); bars show success rate\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI. (C) Example overhead trajectory for a late change trial. (D) Violin plots of response latency (median 420 ms; IQR: 310\u0026ndash;580 ms). The flat response across change-point timing is consistent with continuous online motor adjustment rather than discrete recomputation.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSubjective Experience and Conscious Counting\u003c/h2\u003e \u003cp\u003eParticipants rated the task as easy (median difficulty score: 1.0, IQR: 1.0\u0026ndash;2.0 on a 1\u0026ndash;5 scale). Typical verbal descriptions included \u0026ldquo;I just did it,\u0026rdquo; \u0026ldquo;I don\u0026rsquo;t know how, it just happened,\u0026rdquo; and \u0026ldquo;I looked at the circle and walked.\u0026rdquo; No participant spontaneously reported counting steps or estimating distances. When explicitly asked, 92.4% (514/556) said they did not count.\u003c/p\u003e \u003cp\u003eThe 42 participants who reported counting showed no performance advantage over those who did not (97.8% vs. 98.1%, p\u0026thinsp;=\u0026thinsp;0.76). Participants who reported counting typically did so only as the target drew near. Counting was not associated with improved accuracy, indicating that conscious correction, when present, functioned as a late fine-tuning strategy rather than the primary control mechanism\u0026mdash;consistent with the broader picture of coarse automatic control with optional late refinement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFailure Analysis and Neurological Observations\u003c/h2\u003e \u003cp\u003eNine participants (1.6%) achieved less than 90% accuracy. Detailed neurological evaluation revealed identifiable pathology in 7 of these 9 individuals, compared with 0 of 50 randomly selected high-performers examined as controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fisher\u0026rsquo;s exact test; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough neurological assessment was not a primary objective of this study, the high prevalence of motor system pathology among low-performers suggests that intact predictive motor control is necessary for successful task execution. The two participants without identifiable pathology may represent attentional lapses, subclinical abnormality below detection threshold, or tail variation in the normal distribution. This observation emerged post hoc and warrants prospective validation in a study designed for neurological screening.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeurological Findings in Low-Performing Participants (Success Rate\u0026thinsp;\u0026lt;\u0026thinsp;90%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge/Sex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuccess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeurological Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58%\u0026nbsp;(7/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLimb ataxia, dysdiadochokinesia, MRI: cerebellar atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpinocerebellar ataxia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67%\u0026nbsp;(8/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntention tremor, dysmetria, past-pointing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCerebellar degeneration (alcohol-related)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u0026nbsp;(9/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubtle cerebellar signs, MRI: superior vermis atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly cerebellar ataxia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67%\u0026nbsp;(8/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduced arm swing (L), mild rigidity, resting tremor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease (early)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u0026nbsp;(9/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBradykinesia, shuffling gait, cogwheel rigidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83%\u0026nbsp;(10/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduced vibration sense, absent ankle reflexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeripheral neuropathy (diabetic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83%\u0026nbsp;(10/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMSE 24/30, mild executive dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMild cognitive impairment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83%\u0026nbsp;(10/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal neurological examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo abnormality detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u0026nbsp;(9/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal neurological examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo abnormality detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e[FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Motor Control During Natural Walking\u003c/h2\u003e \u003cp\u003eFive hundred and fifty-six individuals\u0026mdash;spanning ages 7 to 80, educational backgrounds from no schooling to university degrees, and both sexes\u0026mdash;demonstrated 98% accurate performance when asked to land a specified foot on a marked target during ordinary walking. The task required no practice, no instruction in strategy, and no feedback. That performance was uniformly high and statistically indistinguishable across all demographic subgroups is the central result: this behaviour appears to be a universal feature of the neurologically intact motor system.\u003c/p\u003e \u003cp\u003eThis universality aligns with evolutionary perspectives. Vertebrates have faced interception, avoidance, and terrain navigation problems for hundreds of millions of years\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The neural circuitry that supports such behaviour\u0026mdash;continuously estimating distances, updating action plans, and minimising placement error\u0026mdash;is therefore expected to be deeply conserved and to operate below the threshold of conscious awareness. Our results are consistent with this expectation: participants described the task as trivially easy, reported no deliberate strategy, and illiterate participants performed identically to university graduates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCoarse Automatic Control with Late Fine Correction\u003c/h2\u003e \u003cp\u003eA key interpretive theme emerging from the data is that successful foot targeting does not require high-precision planning from the outset of the approach. Rather, the evidence suggests a two-stage pattern: coarse, automatic control governs most of the distance, with optional conscious correction emerging only as the target draws near. This interpretation is supported by three observations.\u003c/p\u003e \u003cp\u003eFirst, video review showed that successful trials frequently included small step-length adjustments in the final metres. These late adjustments\u0026mdash;not visible as failures because they remained within the \u0026plusmn;\u0026thinsp;10 cm tolerance\u0026mdash;are the signature of online error correction rather than fixed pre-planning. Second, participants who reported counting did so only near the target, not throughout the approach, and counting was not associated with improved accuracy (97.8% vs. 98.1%, p\u0026thinsp;=\u0026thinsp;0.76). Third, the \u0026plusmn;\u0026thinsp;10 cm success criterion itself is consistent with this view: it was chosen to capture the full range of biologically normal landing variability, including late corrective steps.\u003c/p\u003e \u003cp\u003eThis two-stage behaviour is consistent with optimal feedback control theory\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which proposes that the motor system tolerates error when task constraints are loose and corrects only when they become tight. It also resonates with classical observations of coarse-to-fine movement control\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, extended here to a naturalistic locomotor context with a large community sample.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eNeural Substrates: Consistency Without Localisation\u003c/h2\u003e \u003cp\u003eThe behavioural profile observed here is consistent with the known functional roles of several neural structures. The PPC, particularly area 5b, contains neurons that encode distance-to-target during locomotion, firing at specific distances from obstacles regardless of approach speed\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This spatial encoding would support the continuous distance estimation we infer from the distance-invariant results. The cerebellum\u0026rsquo;s established role in predictive forward modelling\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e would support the continuous state monitoring implied by successful mid-course adaptation. Basal ganglia contributions to motor sequencing\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e are consistent with the step-by-step updating required when foot specifications change. Premotor and supplementary motor cortex activation preceding gait modifications\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e fits the rapid replanning seen in late command-change trials.\u003c/p\u003e \u003cp\u003eThese behaviours are consistent with known roles of posterior parietal cortex in spatial estimation, cerebellum in predictive modelling, and basal ganglia in sequencing, though the present study cannot directly localise these processes. The observation that low performance was concentrated in participants with cerebellar and basal ganglia pathology is suggestive, but this association emerged post hoc from a small subsample and should not be over-interpreted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eReal-Time Adaptation: Evidence for Online Control\u003c/h2\u003e \u003cp\u003eThe mid-course command change paradigm provides the clearest evidence for continuous motor updating. The absence of any performance gradient across change-point timing (early, middle, late; ANOVA p\u0026thinsp;=\u0026thinsp;0.59) indicates that the motor plan was not locked in at departure. Instead, the system remained open to revision throughout the approach, with a median reconfiguration latency of approximately 420 ms.\u003c/p\u003e \u003cp\u003eObservationally, participants\u0026rsquo; responses to mid-course changes were seamless: no pausing, no restarting, no visible confusion. They adjusted subsequent steps as naturally as if the new command had always been in effect. This fluid adaptation is consistent with a system maintaining continuous state estimates\u0026mdash;\u0026ldquo;where am I, how far to go, which foot is next\u0026rdquo;\u0026mdash;rather than executing a pre-loaded sequence. Such behaviour has been described in feedback-controlled reaching movements\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and gait adaptation studies\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; the present data extend this to a naturalistic, community-based locomotor context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eImplications for Neurological Assessment\u003c/h2\u003e \u003cp\u003eAlthough the neurological observations in this study are post hoc and exploratory, they warrant attention. Seven of nine low-performers had identifiable motor system pathology, while none of 50 randomly selected high-performers showed neurological signs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The conditions represented\u0026mdash;cerebellar degeneration, early Parkinson\u0026rsquo;s disease, peripheral neuropathy, mild cognitive impairment\u0026mdash;are precisely those expected to disrupt continuous sensorimotor estimation and prediction.\u003c/p\u003e \u003cp\u003eThis raises the possibility that a simple walking task of this kind\u0026mdash;\u0026ldquo;Walk to that line and stop with your left foot on it\u0026rdquo;\u0026mdash;might function as a low-cost, equipment-free, literacy-independent observational screen for motor system dysfunction. The ROC analysis (AUC\u0026thinsp;=\u0026thinsp;0.94 at the 90% success threshold) is encouraging, though it is based on 9 cases and 50 controls and should not be interpreted as an established diagnostic statistic. Prospective neurological screening studies are needed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Scope\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study must be acknowledged. First, this is an observational design without neural recording. All inferences about neural substrates are speculative, derived from converging indirect behavioural evidence and consistency with the existing neuroscience literature. Direct identification of the neural circuits active during this task would require neuroimaging or electrophysiology studies, which were not conducted here.\u003c/p\u003e \u003cp\u003eSecond, the late-stage conscious correction observed qualitatively in video review was not formally quantified. We cannot determine what proportion of successful trials involved any late step adjustment, nor precisely when such adjustments occurred. This limits the strength of the coarse-to-fine interpretation, which rests on qualitative observations and participant reports.\u003c/p\u003e \u003cp\u003eThird, neurological findings were entirely post hoc. Participants were not prospectively enrolled as potential neurological cases, and neurological examination was offered only to those who performed poorly. The associations between low performance and neurological diagnosis are therefore hypothesis-generating rather than confirmatory.\u003c/p\u003e \u003cp\u003eFourth, the sample was drawn from a single geographic region (West Bengal, India). While the demographic breadth\u0026mdash;age, education, sex\u0026mdash;was substantial, cultural or environmental factors cannot be fully excluded. The neural circuits under study are anatomically conserved across human populations, and the demographic independence within the sample supports generalisability, but international replication would be valuable.\u003c/p\u003e \u003cp\u003eFifth, the distance range tested (12\u0026ndash;30 m) covers common locomotor planning scales but not extremes. Whether similar accuracy persists at very short distances (e.g., 1\u0026ndash;2 m, where step count cannot be adjusted) or very long distances (e.g., 100 m) remains unknown.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhen asked simply to walk towards a marked target and arrive with a specified foot, 556 community-dwelling adults did so with 98% accuracy\u0026mdash;without instruction, without practice, and irrespective of age, sex, or schooling. This robust, automatic behaviour extended seamlessly to mid-course specification changes, with adaptation latencies of approximately 420 ms and no evidence that late changes impaired performance more than early ones.\u003c/p\u003e \u003cp\u003eThese findings characterise what neurologically intact humans do spontaneously when asked to satisfy a simple locomotor constraint. The results demonstrate robust, predictive, and adaptable motor control without requiring explicit calculation or conscious planning. The pattern\u0026mdash;coarse automatic guidance throughout the approach with optional late correction\u0026mdash;is consistent with optimal feedback control models and with the known properties of the posterior parietal cortex, cerebellum, and basal ganglia, though direct neural evidence was not obtained.\u003c/p\u003e \u003cp\u003eThe strong post hoc association between task failure and neurological motor system pathology suggests that this simple observational task may serve as a sensitive, equipment-free, literacy-independent indicator of motor system integrity\u0026mdash;a possibility that warrants prospective investigation.\u003c/p\u003e \u003cp\u003eThe ease with which participants performed this task\u0026mdash;reporting that they \u0026ldquo;just did it\u0026rdquo;\u0026mdash;is itself informative. Automatic predictive motor control, operating below conscious awareness and independent of formal training, appears to be a universal property of the neurologically healthy human locomotor system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research received no external funding, and the authors have no relevant conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors declare that no external funding was received for the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.S. conceived and designed the study, collected the data, performed the neurological assessments, and wrote the first draft of the manuscript. M.R. contributed to data analysis, interpretation of results, and critical revision of the manuscript for important intellectual content. Both authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269:1880\u0026ndash;1882\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadmehr R, Smith MA, Krakauer JW (2010) Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci 33:89\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoya K (1999) What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Netw 12:961\u0026ndash;974\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuneo CA, Andersen RA (2006) The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia 44:2594\u0026ndash;2606\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen RA, Buneo CA (2002) Intentional maps in posterior parietal cortex. Annu Rev Neurosci 25:189\u0026ndash;220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarigold DS, Drew T (2017) Posterior parietal cortex estimates the relationship between object and body location during locomotion. eLife 6:e28143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolpert DM, Miall RC, Kawato M (1998) Internal models in the cerebellum. Trends Cogn Sci 2:338\u0026ndash;347\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto M (2008) Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 9:304\u0026ndash;313\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopa LS, Ebner TJ (2019) Cerebellum, predictions and errors. Front Cell Neurosci 12:524\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraybiel AM (2000) The basal ganglia. Curr Biol 10:R509\u0026ndash;R511\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416\u0026ndash;1419\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChurchland MM, Cunningham JP, Kaufman MT et al (2012) Neural population dynamics during reaching. Nature 487:51\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTodorov E, Jordan MI (2002) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5:1226\u0026ndash;1235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott SH (2004) Optimal feedback control and the neural basis of volitional motor control. Nat Rev Neurosci 5:532\u0026ndash;546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadmehr R, Krakauer JW (2008) A computational neuroanatomy for motor control. Exp Brain Res 185:359\u0026ndash;381\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Chen TW, Guo ZV, Gerfen CR, Svoboda K (2015) A motor cortex circuit for motor planning and movement. Nature 519:51\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorton SM, Bastian AJ (2006) Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J Neurosci 26:9107\u0026ndash;9116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrew T, Andujar JE, Lajoie K, Yakovenko S (2008) Cortical mechanisms involved in visuomotor coordination during precision walking. Brain Res Rev 57:199\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrillner S, El Manira A (2020) Current principles of motor control, with special reference to vertebrate locomotion. Physiol Rev 100:271\u0026ndash;320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndujar JE, Lajoie K, Drew T (2010) A contribution of area 5 of the posterior parietal cortex to the planning of visually guided locomotion. J Neurophysiol 103:2416\u0026ndash;2432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrew T, Marigold DS (2015) Taking the next step: cortical contributions to the control of locomotion. 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Trends Cogn Sci 18:66\u0026ndash;67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt RA, Lee TD (2011) Motor Control and Learning: A Behavioral Emphasis, 5th edn. Human Kinetics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeele SW (1968) Movement control in skilled motor performance. Psychol Bull 70:387\u0026ndash;403\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"predictive motor control, gait, foot targeting, sensorimotor integration, cerebellum, posterior parietal cortex, locomotion, optimal feedback control","lastPublishedDoi":"10.21203/rs.3.rs-8916801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8916801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe brain continuously estimates distance and adjusts gait to satisfy foot-placement constraints during everyday walking. While computational neuroscience has long proposed that such locomotor behaviour involves predictive motor control, experimental evidence from large naturalistic populations across varying conditions has been lacking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe observed 556 healthy community-dwelling adults (ages 7\u0026ndash;80 years, 49.5% female; education ranging from illiterate to university degree) approaching marked targets at three distances (12 m, 20 m, 30 m; order randomized). Participants were instructed which foot should land at the target but received no guidance on strategy, step-counting, or distance estimation. In a subset (n\u0026thinsp;=\u0026thinsp;178), the foot specification was changed mid-approach to probe real-time motor adaptability. Success was defined as the specified foot landing within \u0026plusmn;\u0026thinsp;10 cm of target centre, reflecting normal biological variability and permitting late corrective adjustments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall success rate was 98.1% (547/556 participants achieved\u0026thinsp;\u0026gt;\u0026thinsp;95% accuracy). Performance showed no dependence on target distance (12 m: 98.2%, 20 m: 98.1%, 30 m: 97.9%; χ\u0026sup2; = 0.24, p\u0026thinsp;=\u0026thinsp;0.89), age (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.08, p\u0026thinsp;=\u0026thinsp;0.63), education (F₃,₅₅₂ = 0.18, p\u0026thinsp;=\u0026thinsp;0.91), or sex (p\u0026thinsp;=\u0026thinsp;0.82). Mid-course command changes yielded 95.5% success across all change-point timings (ANOVA: p\u0026thinsp;=\u0026thinsp;0.59), with no performance decline when commands changed late in the approach (\u0026gt;\u0026thinsp;75% distance covered). Performance reflected robust error reduction rather than exact precomputed accuracy, with successful trials often including small step-length adjustments near the target consistent with late-stage correction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings demonstrate that neurologically intact adults spontaneously and reliably satisfy a locomotor foot-placement constraint through automatic predictive motor control implemented through continuous adjustment. The scale-invariant accuracy, independence from formal education, and preserved adaptability during mid-course changes suggest reliance on continuous distance estimation and online motor updating rather than explicit calculation or conscious planning. Low performance was strongly associated with identifiable neurological conditions, raising the possibility that this simple walking task may serve as a sensitive observational marker for motor system dysfunction.\u003c/p\u003e","manuscriptTitle":"Predictive Foot Targeting During Natural Human Walking: Evidence from 556 Community-Dwelling Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:44:22","doi":"10.21203/rs.3.rs-8916801/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"081a23b2-7052-4e42-b7ed-a25c4bf84796","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T13:40:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:44:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8916801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8916801","identity":"rs-8916801","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}