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Full-Text Articles in Psychology
Modeling Distribution Learning In Visual Search, Andrey Chetverikov
Modeling Distribution Learning In Visual Search, Andrey Chetverikov
MODVIS Workshop
Chetverikov, Campana, and Kristjansson (2017) used visual search to demonstrate that human observers are able to extract statistical distributions of visual features. Observers searched for an odd-one-out target with distractors randomly drawn from the same distribution over the course of several “prime” trials. Then, on test trials parameters of the target and distractors changed and response times (RT) were analyzed as a function of the distance between the target position in feature space and the mean of distractor features during prime trials. The resulting RT curves followed the probability density of prime distractor distributions. This approach provides a detailed estimation …
Modeling The Neural Circuitry Underlying The Behavioral And Eeg Correlates Of Attentional Capture, Chloe Callahan-Flintoft, Brad Wyble
Modeling The Neural Circuitry Underlying The Behavioral And Eeg Correlates Of Attentional Capture, Chloe Callahan-Flintoft, Brad Wyble
MODVIS Workshop
The Reactive-Convergent Gradient Field model (R-CGF) is a unique approach to modeling spatial attention in that it links neural mechanisms to event related potentials (ERPs) from scalp EEG. This model was developed with the aim of explaining different, sometimes conflicting, findings in the attention literature. Specifically, this model address conflicting findings showing both simultaneous and serial deployment of attention. Another argument addressed by the model is whether attention to a location invokes a suppression of the spatial surround, or the selective inhibition of distractors. With the R-CGF, we have found that these results are not as incompatible as they appear …