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Informative and efficient Bayesian hypothesis tests for lesion studies

Dr. Frederik Aust
University of Amsterdam ~ Psychological Methods
Dr. Julia Haaf
University of Amsterdam ~ Psychological Methods
Prof. Edward de Haan
University of Amsterdam
Eric-Jan Wagenmakers
University of Amsterdam ~ Psychological Methods

Experimental studies of brain lesions can reveal the neural underpinnings of behavior and inform theories of cognitive processes. But standard pre-post analysis methods used in lesion studies make an unnecessarily permissive assumption: They assume that some individuals' abilities will be better after lesions have been applied. This assumption is ethically and scientifically problematic: (1) it contributes to the pervasive low statistical sensitivity of lesion studies (wasting animal lives), and (2) it limits inferences to population averages when researchers are seeking insights that apply to each individual. These problems are exacerbated when researchers infer lesion-spared abilities from non-significant p-values. We propose Bayesian hypothesis tests that incorporate constraints on individual differences and can quantify evidence of spared abilities. Our tests reflect researchers' substantive knowledge and appropriately constrain permissible outcomes: (1) carefully applied lesions impair each individual's ability and (2) the magnitude of impairment correlates with pre-lesion ability. As a result, our tailored Bayesian hypothesis tests (1) increase statistical sensitivity (saving animal lives), (2) warrant inference at the level of individuals, and (3) can quantify evidence for spared abilities. In a series of simulation studies, we compare the performance of our tests with standard procedures. We quantify the gains in evidence and the resulting sample size savings for sequential designs. Of course, there is no free lunch. The increase in statistical sensitivity is the result of additional assumptions; violations of these assumptions can lead to biased inference. We explore the consequences of violating assumptions about response distributions and the structure of individual differences.



Bayesian statistics
hierarchical models
brain lesions
individual differences

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Cite this as:

Aust, F., Haaf, J. M., de Haan, E., & Wagenmakers, E.-J. (2023, July). Informative and efficient Bayesian hypothesis tests for lesion studies. Abstract published at MathPsych/ICCM/EMPG 2023. Via