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This paper investigates whether Brain Score, a metric comparing language model representations to human fMRI activations during reading, is truly capturing human-like language processing or merely structural similarity. The researchers train language models on diverse natural languages and non-linguistic structured data (genome, Python, nested parentheses), finding that models trained on different languages and even non-linguistic sequences achieve similar Brain Score performance, suggesting the metric may not be sensitive enough to distinguish human-specific processing.