TL;DR: 52 schizophrenics & controls were asked to name as many words as they could either belonging to the category “animals” (category fluency) or starting with the letter “p” (letter fluency) in 5 min. Authors then studied the trajectory of the words in the FastText latent space and its relationship to some magnetoencephalography measures.


Schizophrenia is a debilitating neuropsychiatric disorder whose core clinical features are thought to reflect abnormalities in internal conceptual representations (“cognitive maps”). The current work provides a language-based computational assay of conceptual disorganization in schizophrenia and relates this to neural signatures of cognitive map representation measured using magnetoencephalography (MEG). At a behavioral level, patients with schizophrenia showed reduced semantically guided word sampling during a verbal fluency task (a marker of “looser” conceptual organization). At a neural level, between-participant variance in this effect correlated with the strength of an MEG signature of hippocampal ripple power (measured in a separate task), known to be involved in cognitive map stabilization. These findings shed light on the neural basis of semantic representation in schizophrenia.


Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia—from thought disorder to delusions—are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant’s verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia.

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