Artificial intelligence (AI) is rapidly transforming our understanding of human cognition by integrating insights from perception studies. Perception is the biological process whereby organisms interpret external stimuli via sensory organs, initiating neural signals. Building on this, cognition encompasses higher-order processes such as reasoning and decision-making, yielding essential insights into human mental functions. The current challenge facing researchers is the accurate simulation of these complex perceptual and cognitive processes, which remains an ongoing quest for multidisciplinary fields such as neuroscience, cognitive psychology, AI, and bionics. The aim is to refine existing models and identify new approaches that improve predictive accuracy regarding human behavior, intentions, and emotions.

This Research Topic aims to propel the field of cognitive simulation forward by fostering the integration of AI with multidisciplinary theories and techniques. It seeks to explore how these integrated approaches can accelerate the development of cognitive computing models that are capable of simulating and predicting human cognitive behaviors more effectively. By advancing research and applications in this area, this Research Topic hopes to bridge significant gaps in our understanding of cognitive processes and their artificial replication.

To gather further insights into the boundaries of cognitive simulation technologies and their applications, we welcome articles addressing, but not limited to, the following themes:

- Neurocognitive modeling using AI: Integration of EEG, fNIRS, or fMRI data with machine learning techniques to model attentional, emotional, or memory-related processes.

- Bionic perception and simulation: Use of AI-driven sensory devices (e.g., artificial olfaction or vision systems) for cognitive modeling and interaction.

- Multimodal information fusion: Combining visual, auditory, taste, olfactory and physiological data for comprehensive simulation of human perceptual states.

- Theoretical advancements: Development of new computational theories or hybrid architectures (e.g., Bayesian-AI or neural-symbolic models) for cognitive simulation.

- Technological implementations: Design of advanced detection systems or neuroadaptive devices that enhance the fidelity of human-machine interaction.

We particularly encourage submissions that present empirical studies, simulation results, theoretical models, or proof-of-perception (cognition) systems demonstrating how AI can extend and refine perception (cognition) simulation frameworks.

Are you interested in contributing a manuscript to this Research Topic?

Integrating AI and Cognitive Simulation: Interdisciplinary Approaches