Alessandro Acciai

This study investigates the potential use of current neural language models in psychological practice, particularly in diagnosing patients through the analysis of textual content. The growing interest in the capabilities of neural language models, especially in areas where natural language serves as a primary information source, and specifically by psychology, is the motivation behind this study. As a case study, we tackled the assessment of coherence in autobiographical narratives, a diagnostic methodology widely recognized in psychology for its pivotal role in understanding personal identity. This is a particularly intriguing case study for two reasons. On the one hand, it demonstrates how these tools can be a valid aid in psychological practice, in a task notoriously burdensome and time-consuming for human operators. On the other hand, it tests the ability of neural language models to grasp deep, global conceptual aspects from a text, a task not easily accomplished even by professional psychologists. Coherence is evaluated here quantitatively using the standard procedure known as the Narrative Coherence Coding Scheme. The results obtained are particularly promising, especially when using models subjected to fine-tuning with some examples of stories and their evaluations. However, even without fine-tuning, models prompted with appropriate prompts also perform acceptably.

This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural language models are being applied in cognitive neuroscience for simulative purposes is then presented; after recalling the main epistemological features of the simulative method in artificial intelligence, it is finally highlighted how the epistemic opacity of neural language models is tackled by using the brain itself to simulate the neural language model and to test hypotheses about it, in what is called here a co-simulation.