A neural network created to model visual search results from the Vision Lab at University of Illinois.
Buetti et al. (2016) reported that, contrary to what was previously thought, systematic variability exists in efficient ("popout") search. Reaction times increased logarithmically as a function of set size, suggesting that Stage 1 of visual search is exhaustive.
This neural network is an attempt to model the cognitive processes reported in papers from our lab:
Buetti, S., Cronin, D. A., Madison, A. M., Wang, Z., & Lleras, A. (2016). Towards a better understanding of parallel visual processing in human vision: Evidence for exhaustive analysis of visual information. Journal of Experimental Psychology: General, 145(6), 672.
Wang, Z., Buetti, S., & Lleras, A. (2017). Predicting search performance in heterogeneous visual search scenes with real-world objects. Collabra: Psychology, 3(1).
Madison, A., Lleras, A., Buetti, S. (2017). The role of crowding in parallel search: peripheral pooling is not responsible for logarithmic efficiency in parallel search. Attention, Perception & Psychophysics.
Wang, Z., Lleras, A., & Buetti, S. (2018). Parallel, exhaustive processing underlies logarithmic search functions: Visual search with cortical magnification. Psychonomic bulletin & review, 1-8.
Ng, G. J. P., Lleras, A., & Buetti, S. (2018). Fixed-target efficient search has logarithmic efficiency with and without eye movements. Attention, Perception, & Psychophysics, 1-11.