The Anterior Insula Engages in Feature- and Context-Level Predictive Coding Processes for Recognition Judgments.

Abstract

Predictive coding mechanisms facilitate detection and perceptual recognition, thereby influencing recognition judgements, and, broadly, perceptual decision-making. The anterior insula (AI) has been shown to be involved in reaching a decision about discrimination and recognition, as well as to coordinate brain circuits related to reward-based learning. Yet, experimental studies in the context of recognition and decision-making, targeting this area and based on formal trial-by-trial predictive coding computational quantities, are sparse. The present study goes beyond previous investigations and provides a predictive coding computational account of the role of the AI in recognition-related decision-making, by leveraging Zaragoza-Jimenez et al. (2023) open fMRI dataset (17 female, 10 male participants) and computational modeling, characterized by a combination of view-independent familiarity learning and contextual learning. Using model-based fMRI, we show that, in the context a two-option forced-choice identity recognition task, the AI engages in feature-level (i.e., view-independent familiarity) updating and error signaling processes and context-level familiarity updating to reach a recognition judgment. Our findings highlight that an important functional property of the AI is to update the level of familiarity of a given stimulus while also adapting to task-relevant, contextual information. Ultimately, these expectations, combined with input visual signals through reciprocally interconnected feedback and feedforward processes, facilitate recognition judgments, thereby guiding perceptual decision-making.