The anterior insula engages in feature- and context-level predictive coding processes for recognition judgments

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 provide 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 modelling, 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 judgements, thereby guiding perceptual decision making. Significance statement Despite the renowned role of the anterior insula (AI) within the Salience Network and Error-Monitoring Network, studies with a focus on this area and based on formal trial-by-trial predictive coding computational quantities are sparse. The present study provides a formal predictive coding computational account of the AI involvement in recognition-related decision-making. The present results demonstrate that AI activity reflects its engagement in encoding and updating the strength of an agent’s belief in the statistical dependencies within the environment, thereby guiding perceptual decision-making. This underscores the pivotal role of the AI in integrating sensory information and mediating recognition-related decision-making processes. Overall, the findings highlight the AI's function in updating familiarity levels of stimuli and processing contextual information, ultimately facilitating recognition judgments.