Formal models and quantitative measures of multisensory integration: a selective overview

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Science

Background:

• Multisensory integration (MI) is the neural process of combining unisensory signals into a novel percept.
• Research in MI has rapidly expanded, necessitating a review of current theoretical and empirical approaches.
• Formal modeling offers a quantitative framework for understanding the mechanisms underlying MI.

Purpose of the Study:

• To provide a selective overview of formal modeling approaches to multisensory integration.
• To identify essential concepts and quantitative measures used in MI modeling.
• To discuss the strengths, weaknesses, and future directions for a unified theory of MI.

Main Methods:

• Review of formal models and measures for behavioral paradigms (e.g., localization, temporal order judgment, reaction times).
• Inclusion of concepts for modeling single-cell spike rates.
• Identification of underlying concepts such as Bayesian causal inference, probability summation, coactivation, and integration time windows.

Main Results:

• Essential concepts like Bayesian causal inference and probability summation are fundamental to most MI models.
• Quantitative indexes for measuring MI strength across paradigms have been discussed.
• A range of modeling approaches exist, each with specific strengths and limitations.

Conclusions:

• Formal modeling provides valuable tools for quantifying and understanding multisensory integration.
• Despite progress, obstacles remain in developing a unified theory of multisensory integration.
• Further research is needed to reconcile different modeling approaches and experimental findings.