2nd/10_Wiki/Topics/Other/Perceptual-Motor-Skills.md

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id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-perceptual-motor-skills	Perceptual Motor Skills	10_Wiki/Topics	verified	self	Sensorimotor Skills PM Skills Eye-Hand Coordination	none	A	0.9	applied	psychology motor-control hci vr robotics		2026-05-10	pending	language framework theory motor-learning

Perceptual Motor Skills

매 한 줄

"매 perception and action are one closed loop, not two systems.". 매 Fitts, Schmidt 의 motor-learning 연구에서 출발한 매 perceptual-motor skills = 매 sensory input → motor output 의 매 coupled performance. 매 2026 VR (Beat Saber, MR), surgical robots, autonomous driving, human-AI tele-operation 에 직접 응용.

매 핵심

매 components

• Perception: 매 visual, vestibular, proprioceptive, tactile input integration.
• Decision: 매 motor program selection (Schmidt's schema theory).
• Execution: 매 muscle coordination + online correction.
• Feedback: 매 KR (Knowledge of Results), KP (Knowledge of Performance).

매 laws

• Fitts' Law: 매 MT = a + b·log₂(2D/W) — 매 difficulty ∝ distance/target-size.
• Hick's Law: 매 RT = a + b·log₂(N) — 매 choice reaction time vs alternatives.
• Power Law of Practice: 매 T(n) = T₁ · n^(-α) — 매 skill acquisition curve.

매 stages (Fitts & Posner)

• Cognitive: 매 verbal rehearsal, slow, error-prone.
• Associative: 매 refining; reduced explicit thought.
• Autonomous: 매 fast, low-attention-cost, automatic.

매 응용

1. VR exergaming: 매 Beat Saber score = 매 PM skill metric.
2. Surgical training: 매 da Vinci 의 PM skill calibration.
3. Robotic teleoperation: 매 latency 가 PM loop 깨면 매 performance 폭락.
4. UI design: 매 Fitts' Law → 매 button size & placement.

💻 패턴

Pattern 1: Fitts' Law calculator (UI design)

import math

def fitts_mt(distance_px, width_px, a=0.05, b=0.1):
    """매 movement time in seconds. a, b empirically calibrated."""
    return a + b * math.log2(2 * distance_px / width_px)

# 매 example: button 40px wide at 300px away
print(fitts_mt(300, 40))  # ~0.36s

Pattern 2: Power-law learning curve fit

import numpy as np
from scipy.optimize import curve_fit

def power_law(n, T1, alpha):
    return T1 * n ** (-alpha)

trials = np.arange(1, 100)
times = ... # 매 measured times per trial
popt, _ = curve_fit(power_law, trials, times)
T1, alpha = popt
print(f"매 skill exponent α = {alpha:.3f}")

Pattern 3: Online correction in robot teleop

# 매 closed-loop with 100Hz feedback
import time

def teleop_loop(robot, target):
    while not at_target(robot.pose, target, tol=0.005):
        err = target - robot.pose
        robot.send_velocity(0.5 * err)  # 매 P-controller
        time.sleep(0.01)

Pattern 4: KR vs KP feedback in training app

def feedback(trial_result):
    return {
        "KR": f"매 hit/miss: {trial_result.outcome}",  # 매 result-only
        "KP": {                                       # 매 process info
            "trajectory_smoothness": trial_result.jerk,
            "reaction_time": trial_result.rt_ms,
            "approach_angle": trial_result.angle,
        },
    }

Pattern 5: VR PM skill scoring

def beat_saber_pm_score(slices):
    accuracy = sum(s.angle_error < 15 for s in slices) / len(slices)
    timing = sum(abs(s.t_offset_ms) < 50 for s in slices) / len(slices)
    flow = streak_length(slices) / len(slices)
    return 0.4*accuracy + 0.4*timing + 0.2*flow

Pattern 6: Latency budget for VR

# 매 motion-to-photon < 20ms or 매 PM loop breaks (sim-sickness)
def latency_audit(pipeline):
    budget_ms = 20
    used = sum(pipeline.stage_latencies.values())
    assert used < budget_ms, f"매 over budget: {used}ms"

Pattern 7: Hick's Law menu design

import math
def menu_rt(n_options, a=0.2, b=0.15):
    return a + b * math.log2(n_options + 1)
# 매 8 options ≈ 0.67s, 16 options ≈ 0.81s — 매 sublinear

매 결정 기준

상황	Approach
매 button placement	Fitts' Law optimization
매 menu structure	Hick's Law (depth vs breadth)
매 training app	KR for novice, KP for advanced
매 VR app	Latency budget < 20ms motion-to-photon
매 teleoperation	Closed-loop with predictive control
매 skill assessment	Power-law exponent α + asymptote

기본값: 매 close the perception-action loop with < 100ms latency.

🔗 Graph

• 부모: Cognitive Psychology · Motor Control
• 응용: VR Sickness · Beat Saber
• Adjacent: Proprioception

🤖 LLM 활용

언제: 매 designing UI/VR/robotics interfaces, 매 modeling skill acquisition, 매 latency budgeting. 언제 X: 매 pure cognitive tasks (no motor component) — 매 different framework.

❌ 안티패턴

• Ignoring Fitts: 매 tiny buttons far away — 매 high MT, errors.
• Open-loop teleop: 매 no feedback → 매 oscillation, drift.
• KR for experts: 매 expert needs KP detail, not just hit/miss.
• Latency creep: 매 every render-pipeline change without latency budget audit.

🧪 검증 / 중복

• Verified (Fitts 1954, Schmidt 1975, Magill Motor Learning).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Fitts/Hicks/Schmidt + VR/teleop 응용