Analyzing multisensory integration: dos and donts

Multisensory perception is a cornerstone paradigm for understanding how the brain constructs coherent representations of the world from noisy, fragmented sensory inputs. For decades, researchers have used the magnitude of crossmodal illusions, the width of the temporal binding window, and related behavioral indices as direct proxies for integration strength, and have leveraged these measures to compare multisensory function across developmental, clinical, and aging populations. Here we argue that this descriptive practice is fundamentally compromised: behavioral readouts of multisensory integration are composite measures jointly shaped by unisensory precision, amodal priors, and the binding process itself, and cannot be interpreted in isolation. Drawing on simulations within a Bayesian Causal Inference framework, we show how identical behavioral patterns can arise from very different underlying causes, leading to systematic misattribution of group differences to deficits or enhancements in integration. We review complementary computational frameworks, including drift diffusion, multisensory correlation detection, and statistical facilitation models, and outline their respective explanatory limits. Finally, we provide a model-based inference pipeline, from experimental design and unisensory baselines to parameter estimation and interpretation, that disentangles sensory fidelity, prior expectations, and integrative tendency. Adopting this normative approach is essential for cumulative progress in basic multisensory research and for its translation to neuropsychiatric assessment, lifespan research, and artificial perceptual systems.