Dataset for: A biologically inspired neurocomputational model for audio-visual integration and causal inference

Recently, experimental and theoretical research has focused on the brain’s abilities to extract information from a noisy sensory environment and how cross-modal inputs are processed to solve the causal inference problem to provide the best estimate of external events. Despite the empirical evidence suggesting that the nervous system uses a statistically optimal and probabilistic approach in addressing these problems, little is known about the brain’s architecture needed to implement these computations. The aim of this work is to realize a mathematical model, based on physiologically plausible hypotheses, to analyze the neural mechanisms underlying multisensory perception and causal inference. The model consists of three layers topologically organized: two encode auditory and visual stimuli, separately, and are reciprocally connected via excitatory synapses and send excitatory connections to the third downstream layer. This synaptic organization realizes two mechanisms of cross-modal interactions: the first is responsible for the sensory representation of the external stimuli while the second solves the causal inference problem. We tested the network by comparing its results to behavioral data reported in the literature. Among others, the network can account for the ventriloquism illusion, the pattern of sensory bias and the percept of unity as a function of the spatial auditory-visual distance, and the dependence of the auditory error on the causal inference. Finally, simulations results are consistent with probability matching as the perceptual strategy used in auditory-visual spatial localization tasks, agreeing with the behavioral data. The model makes untested predictions that can be investigated in future behavioral experiments.

近年来，实验与理论研究的核心聚焦于大脑从噪声感官环境中提取信息的能力，以及跨模态输入（cross-modal inputs）如何被处理以解决因果推断（causal inference）问题，从而实现对外部事件的最优估计。尽管已有实验证据表明，神经系统在解决此类问题时采用了统计最优与概率化的方法，但学界对实现这些计算所需的大脑神经架构仍知之甚少。
本研究旨在基于生理学合理假设构建数学模型，以解析多感官知觉（multisensory perception）与因果推断背后的神经机制。该模型包含三层拓扑结构化网络：其中两层分别编码听觉与视觉刺激，二者通过兴奋性突触（excitatory synapses）实现双向连接，并均向第三层下游网络发送兴奋性投射。这种突触组织实现了两种跨模态交互机制：其一负责外部刺激的感官表征，其二则用于解决因果推断问题。
我们通过将该网络的输出与文献中报道的行为学数据进行对比，对其开展了验证。具体而言，该网络能够复现腹语错觉（ventriloquism illusion）、感官偏差模式以及随听视空间距离变化的统一知觉，并可解释听觉误差与因果推断之间的关联。最后，仿真结果与听视空间定位任务中采用的概率匹配（probability matching）知觉策略相符，与已有行为学数据一致。该模型还提出了若干尚未验证的预测，可在未来的行为学实验中开展验证。