Abstract
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, there is a need for methods that allow to interpret trained decoding models. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0–9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. The present results confirm previous findings insofar as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and topography varied considerably between individuals, pointing to more pronounced inter-individual differences than previously reported.
Highlights
The application of statistical algorithms to neural data is becoming an increasingly popular tool for explaining the link between biology and psychology [1, 2]
We demonstrate the use of grouped model reliance as a generally applicable method for interpreting neural decoding models
Trained decoding models consistently relied on predictor variables from the alpha frequency band, which is in line
Summary
The application of statistical algorithms to neural data is becoming an increasingly popular tool for explaining the link between biology and psychology [1, 2]. Supervised learning algorithms, in particular methods such as random forest [3] and support vector machine (SVM) [4] algorithms, are frequently utilized to decode various psychological phenomena, related to functions such as perception, attention, and memory, with promising success [5,6,7,8,9]. While these algorithms are optimized to provide accurate predictions, their interpretability is often not given. It should be noted that care has to be taken when trying to interpret decoding models as “reading the code of the brain”, since a decoding model alone does not provide a computational account of information processing in the brain [10]
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