Diederich, A. | Search bioRxiv

bioRxiv2018

Multisensory perception reflects individual differences in processing temporal correlations

Sensory signals originating from a single event, such as audiovisual speech, are temporally correlated. Correlated signals are known to facilitate multisensory integration and binding. We sought to further elucidate the nature of this relationship, hypothesizing that multisensory perception will vary with the strength of audiovisual correlation. Human participants detected near-threshold amplitude modulations in auditory and/or visual stimuli. During audiovisual trials, the frequency and phase of auditory modulations were varied, producing signals with a range of correlations. After accounting for individual differences which likely reflect relative temporal processing abilities of participants auditory and visual systems, we found that multisensory perception varied linearly with strength of correlation. Diffusion modelling confirmed this and revealed that correlation is supplied to the decisional system as sensory evidence. These data implicate correlation as an important cue in audiovisual feature integration and binding and suggest correlational strength as an important factor for flexibility in these processes.

neuroscience↗

bioRxiv2017

Race Model for the Stop Signal Paradigm Revisited: Perfect Negative Dependence

The ability to inhibit our responses voluntarily is an important case of cognitive control. The stop-signal paradigm is a popular tool to study response inhibition. Participants perform a response time task (go task) and, occasionally, the go stimulus is followed by a stop signal after a variable delay, indicating subjects to withhold their response (stop task). The main interest of modeling is in estimating the unobservable stop-signal processing time, that is, the covert latency of the stopping process as a characterization of the response inhibition mechanism. In the independent race model the stop-signal task is represented as a race between stochastically independent go and stop processes. Without making any specific distributional assumptions about the processing times, the model allows to estimate the mean time to cancel a response. However, neurophysiological studies on countermanding saccadic eye movements have shown that neural correlates of go and stop processes consist of networks of mutually interacting gaze-shifting and gaze-holding neurons. This poses a major challenge in formulating linking propositions between the behavioral and neural findings. Here we propose a dependent race model that postulates perfect negative stochastic dependence between go and stop activations. The model is consistent with the concept of interacting processes while retaining the simplicity and elegance of the distribution-free independent race model. For mean data, the dependent models predictions remain identical to those of the independent model. The resolution of this apparent paradox advances the understanding of mechanisms of response inhibition and paves the way for modeling more complex situations.

neuroscience↗