September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
The relation of object substitution masking (OSM) and attention dynamics: A neuro-computational modeling study
Author Affiliations
  • Frederik Beuth
    Artificial Intelligence, Chemnitz University of Technology, Germany
  • Fred Hamker
    Artificial Intelligence, Chemnitz University of Technology, Germany
Journal of Vision September 2015, Vol.15, 1229. doi:10.1167/15.12.1229
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      Frederik Beuth, Fred Hamker; The relation of object substitution masking (OSM) and attention dynamics: A neuro-computational modeling study. Journal of Vision 2015;15(12):1229. doi: 10.1167/15.12.1229.

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Abstract

Although object substitution masking (OSM; DiLollo et al., 2000, J Exp Psychol Gen) has been discussed being affected by attention (P├Áder, 2012, J Exp Psychol Gen), OSM is not considered emerging from attentive dynamics and there is little overlap between both fields of research. By means of a neuro-computational modeling study, we demonstrate that OSM can be fully explained by attentive dynamics. The model is inspired by previous systems level models of attention (Hamker, 2005, Cerebral Cortex; Zirnsak et al., 2011, Eur J Neurosci) and includes the ventral stream, particularly area V4 and the frontal eye field (FEF). It simulates the firing rates of neurons over time, and models accurate signal timings in the visual system (Schmolesky et al., 1998, J Neurophysiol; Thomson et al., 2002, Cerebral cortex). It is first shown to fit data from common visual attention experiments, like biased competition and visual search. Next we show that the same model reproduces typical OSM data (e.g. DiLollo et al., 2000, J Exp Psychol Gen, and Argyropoulos et al., 2013, J of Exp Psychology). OSM is explained based on two model mechanisms. Similar as in attentional biased competition, the target and mask compete for a visual representation by means of suppressive connections. This competition mechanism accounts for the mask duration dependency in OSM. OSM also requires a high number of distractors (set size effect) like in visual search paradigms. Our model explains this observation by spatially reentrant processing between FEF and V4. We conclude that OSM can be accounted for by well-known attentional mechanisms within a unified model. Contrary to existing theories of OSM, our model is grounded on a large set of physiological and neuroanatomical data.

Meeting abstract presented at VSS 2015

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