File size: 3,408 Bytes
9635a89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | """Tests for the cognitive load scorer."""
import numpy as np
import pytest
def _make_roi_indices():
"""Create minimal ROI indices for testing."""
return {
# Executive
"46": np.array([0, 1]),
"FEF": np.array([2, 3]),
"p32pr": np.array([4]),
# Visual
"V1": np.array([10, 11, 12]),
"V2": np.array([13, 14]),
"MT": np.array([15, 16]),
# Auditory
"A1": np.array([20, 21, 22]),
"LBelt": np.array([23]),
# Language
"44": np.array([30, 31]),
"45": np.array([32, 33]),
"TPOJ1": np.array([34]),
}
class TestCognitiveLoadScorer:
def test_basic_scoring(self):
from cortexlab.analysis.cognitive_load import CognitiveLoadScorer
roi_indices = _make_roi_indices()
scorer = CognitiveLoadScorer(roi_indices)
# Create predictions with 50 vertices, 10 timepoints
predictions = np.random.randn(10, 50)
result = scorer.score_predictions(predictions)
assert 0.0 <= result.overall_load <= 1.0
assert 0.0 <= result.visual_complexity <= 1.0
assert 0.0 <= result.auditory_demand <= 1.0
assert 0.0 <= result.language_processing <= 1.0
assert 0.0 <= result.executive_load <= 1.0
def test_timeline_length(self):
from cortexlab.analysis.cognitive_load import CognitiveLoadScorer
roi_indices = _make_roi_indices()
scorer = CognitiveLoadScorer(roi_indices)
predictions = np.random.randn(15, 50)
result = scorer.score_predictions(predictions, tr_seconds=1.5)
assert len(result.timeline) == 15
# Check timeline timestamps
assert result.timeline[0][0] == 0.0
assert abs(result.timeline[1][0] - 1.5) < 1e-6
def test_single_timepoint(self):
from cortexlab.analysis.cognitive_load import CognitiveLoadScorer
roi_indices = _make_roi_indices()
scorer = CognitiveLoadScorer(roi_indices)
predictions = np.random.randn(50) # 1D input
result = scorer.score_predictions(predictions)
assert len(result.timeline) == 1
assert 0.0 <= result.overall_load <= 1.0
def test_high_visual_activation(self):
from cortexlab.analysis.cognitive_load import CognitiveLoadScorer
roi_indices = _make_roi_indices()
scorer = CognitiveLoadScorer(roi_indices, baseline_activation=0.1)
# Create predictions with very high activation in visual ROIs
predictions = np.zeros((5, 50))
for roi in ["V1", "V2", "MT"]:
predictions[:, roi_indices[roi]] = 10.0
result = scorer.score_predictions(predictions)
# Visual complexity should be highest (capped at 1.0 due to normalization)
assert result.visual_complexity > 0.5
def test_custom_cognitive_map(self):
from cortexlab.analysis.cognitive_load import CognitiveLoadScorer
roi_indices = {"V1": np.array([0, 1, 2]), "A1": np.array([3, 4])}
custom_map = {
"visual_complexity": ["V1"],
"auditory_demand": ["A1"],
}
scorer = CognitiveLoadScorer(roi_indices, cognitive_map=custom_map)
predictions = np.random.randn(5, 10)
result = scorer.score_predictions(predictions)
assert result.executive_load == 0.0 # Not in custom map
assert result.language_processing == 0.0
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