Novel neuro-cognitive models can explore spatial attention's effect on perceptual decision making
Traditional cognitive models, such as sequential sampling models (SSM), are unable to address some research questions about cognition and related neural mechanisms since they rely solely on behavior and lack explanations of brain data. Electroencephalography (EEG) can help a popular model of SSMs, the drift-diffusion model, to unravel latent processes that have not been previously discovered. For instance, although perceptual decision making often involves both decision processes and non-decision processes, researchers often seek to anatomize the subprocesses of decision variables while being less aware of the impact of some cognitive manipulations on the non-decision processes. We propose neuro-cognitive models that can utilize behavioral data and neural signals simultaneously to constrain two distinct groups of parameters as well as predict the onset time of evidence accumulation by single-trial EEG in occipito-parietal areas. Specifically, we strove to investigate a research question, why does performance in decision making improve when participants are exposed to top-down spatial orientation cues before the appearance of stimuli? Using a public dataset, we found evidence that spatial top-down attention can manipulate both visual encoding time (VET) and other non-decision times, but not decision variables such as evidence accumulation during a face/car perceptual decision-making task. To make this inference we used "directed'' neurocognitive models that have previously been used to find single-trial relationships between EEG measures and cognitive parameters. However we have also tested new "integrated'' models that predict EEG measures. These integrated neurocognitive models predict two sources of variance in EEG measures across single-trials: variance related to trial-by-trial changes in non-decision times versus measurement error in the observed EEG measures. We discuss how we fit these two classes of models and how they can be used to answer similar questions in future work.
There is nothing here yet. Be the first to create a thread.