Raw Data: Stereo-anomaly is found more frequently in tasks that require discrimination between depths

Data serialised to a JSON file, suitable to be loaded into Python as a dictionary. Structure is then as follows (if it were loaded to a dictionary "data"):
data[deidentifiedParticipantCode][taskStr][repetitionStr]
Where: deidentifiedParticipantCode is of the format: "Participant_001" to "Participant_228",
taskStr is either "detection" or "discrimination"
repetitionStr is either "test" or "retest"
Indexing at those three levels will return a single measurement result, which conforms to:
"type": "object",
"required": ["data", "log2Threshold", "log2ThresholdStdErr", "overallPCorrect", "overallPCorrectCI"],
"properties": {
"data": {
"description": "Raw data collected in the measurement",
"type": "object",
"required": ["log2Disparity", "nCorrect", "nTrials"],
"properties": {
"log2Disparity": {
"description": "log2-transformed disparity values",
"type": "array",
"items": {
"type": "number"
}
},
"nCorrect": {
"description": "Number of correct responses at each disparity",
"type": "array",
"items": {
"type": "string"
}
},
"nTrials": {
"description": "Number of trials collected at each disparity",
"type": "array",
"items": {
"type": "string"
}
}
}
},
"log2Threshold": {
"description": "Stereoacuity threshold in log2 disparity",
"type": "number"
},
"log2ThresholdStdErr": {
"description": "Standard error of log2Threshold",
"type": "number"
},
"overallPCorrect": {
"description": "Overall proportion of correct responses",
"type": "number"
},
"overallPCorrectCI": {
"description": "Lower and upper limits of the 95% confidence interval for overallPCorrect",
"type": "array",
"items": {
"type": "number"
}
}
}