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Distill 8 temporal UX signals into a 40-feature vector per video.
Per signal (8 signals × 5 features = 40):
1. mean — average activation
2. peak — maximum activation
3. variability — std dev of activation
4. hook — mean of first 4.5 seconds (first impression)
5. slope — linear trend (coef) across full video
"""
import numpy as np
from typing import Dict, List, Tuple
SIGNAL_NAMES = [
"aesthetic_appeal",
"visual_fluency",
"cognitive_load",
"trust_affinity",
"reward_anticipation",
"motor_readiness",
"surprise_novelty",
"friction_anxiety",
]
FEATURE_NAMES: List[str] = []
for sig in SIGNAL_NAMES:
for stat in ["mean", "peak", "variability", "hook", "slope"]:
FEATURE_NAMES.append(f"{sig}__{stat}")
assert len(FEATURE_NAMES) == 40
HOOK_SECONDS = 4.5 # first impression window
def extract_features(signals: Dict[str, np.ndarray], tr: float = 1.5) -> np.ndarray:
"""
signals: {signal_name: (n_timesteps,) array}
Returns: (40,) feature vector, ordered per FEATURE_NAMES
"""
feats = []
t = None
for sig_name in SIGNAL_NAMES:
ts = signals.get(sig_name, np.zeros(1))
if t is None:
t = np.arange(len(ts)) * tr
mean = float(np.mean(ts))
peak = float(np.max(ts))
variability = float(np.std(ts))
# hook = mean over first 4.5s
hook_idx = max(1, int(np.ceil(HOOK_SECONDS / tr)))
hook = float(np.mean(ts[:hook_idx]))
# slope = linear regression coefficient over time
if len(ts) > 1:
slope = float(np.polyfit(t[:len(ts)], ts, 1)[0])
else:
slope = 0.0
feats.extend([mean, peak, variability, hook, slope])
return np.array(feats, dtype=np.float32)
def feature_vector_to_dict(vec: np.ndarray) -> Dict[str, float]:
"""Convert flat (40,) back to named dict for interpretability."""
return {name: float(val) for name, val in zip(FEATURE_NAMES, vec)}
def top_positive_negative(feat_dict: Dict[str, float], n: int = 3) -> Tuple[List[str], List[str]]:
"""Return (top_n_positive_features, top_n_negative_features) by value."""
sorted_items = sorted(feat_dict.items(), key=lambda x: x[1], reverse=True)
pos = [k for k, v in sorted_items[:n]]
neg = [k for k, v in sorted_items[-n:]]
return pos, neg
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