Lateralization of complex high-frequency sounds is conveyed by interaural level differences (ILDs) and interaural time differences (ITDs) in the envelope. In this work, the authors constructed an auditory model and simulate data from three previous behavioral studies obtained with, in total, over 1000 different amplitude-modulated stimuli. The authors combine a well-established auditory periphery model with a functional count-comparison model for binaural excitatory–inhibitory (EI) interaction. After parameter optimization of the EI-model stage, the hemispheric rate-difference between pairs of EI-model neurons relates linearly with the extent of laterality in human listeners. If a certain ILD and a certain envelope ITD each cause a similar extent of laterality, they also produce a similar rate difference in the same model neurons. After parameter optimization, the model accounts for 95.7% of the variance in the largest dataset, in which amplitude modulation depth, rate of modulation, modulation exponent, ILD, and envelope ITD were varied. The model also accounts for 83% of the variances in each of the other two datasets using the same EI model parameters.

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Elucidating causal, neurobiological underpinnings of behaviour is an ultimate goal of every neuroscientific study. However, due to the complexity of the brain as well as the complexity of the human environment, finding a~causal architecture that underlies behaviour remains a~formidable challenge. In this manuscript, we review the logical and conceptual issues with respect to causal research in neuroscience.First, we review the state of the art interventional and computational approaches to infer causal brain-behaviour relationships. We provide an~overview of potential issues, flaws, and confounds in these studies. We conclude that studies on the causal structure underlying behaviour should be performed by accumulating evidence coming from several lines of experimental and modelling studies. Lastly, we also propose computational models including artificial neuronal networks and simulated animats as a~potential breakthrough to causal brain-behaviour investigations.