Theories of attention and learning differ in the roles they prescribe to memory representations. Theories of attention almost universally propose that visual working memory representations determine which objects are the focuses of processing in complex visual scenes. However, models of learning propose that after a handful of trials, the control of visual cognition should shift from being driven by visual working memory to representations in long-term memory. In this project we will bring these ideas from models of learning to bear on the processing of visual information. To test these model predictions in a definitive manner, we will develop methods of measuring electrical brain activity that we can use to track what information is being actively held in visual working memory, simultaneously with independent measures of the representations in visual long-term memory. Our preliminary data indicate that these electrophysiological tools can provide trial-by-trial indices of memory representations controlling visual cognition. Our integrated modeling and neuroscientific approach will allow us to more quickly lay a theoretical foundation for understanding the top-down control of visual processing. This can then be used to understand the nature of deficits in clinical populations and to refine potential treatments.
This project will use electrophysiological measures of brain activity to develop and test a model of attention. The overarching goal will be to understand the top-down control of visual attention and determine the nature of the representations that control visual cognition. This will involve developing a set of neural measures that can simultaneously track the maintenance of representations in visual working memory and the accrual of representations in visual long-term memory that are used to control the processing of task-relevant inputs to the visual system.
| Servant, Mathieu; Cassey, Peter; Woodman, Geoffrey F et al. (2018) Neural bases of automaticity. J Exp Psychol Learn Mem Cogn 44:440-464 |
| Heritage, Allan J; Long, Laura J; Woodman, Geoffrey F et al. (2018) Personality correlates of individual differences in the recruitment of cognitive mechanisms when rewards are at stake. Psychophysiology 55: |
| Servant, Mathieu; van Wouwe, Nelleke; Wylie, Scott A et al. (2018) A model-based quantification of action control deficits in Parkinson's disease. Neuropsychologia 111:26-35 |
| Rugo, Kelsi F; Tamler, Kendall N; Woodman, Geoffrey F et al. (2017) Recognition-induced forgetting of faces in visual long-term memory. Atten Percept Psychophys 79:1878-1885 |
| Fukuda, Keisuke; Woodman, Geoffrey F (2017) Visual working memory buffers information retrieved from visual long-term memory. Proc Natl Acad Sci U S A 114:5306-5311 |
| Cosman, Joshua D; Arita, Jason T; Ianni, Julianna D et al. (2016) Electrophysiological measurement of information flow during visual search. Psychophysiology 53:535-43 |
| Reinhart, Robert M G; McClenahan, Laura J; Woodman, Geoffrey F (2016) Attention's Accelerator. Psychol Sci 27:790-8 |
| Fukuda, Keisuke; Kang, Min-Suk; Woodman, Geoffrey F (2016) Distinct neural mechanisms for spatially lateralized and spatially global visual working memory representations. J Neurophysiol 116:1715-1727 |
| Reinhart, Robert M G; Xiao, Wenxi; McClenahan, Laura J et al. (2016) Electrical Stimulation of Visual Cortex Can Immediately Improve Spatial Vision. Curr Biol 26:1867-72 |
| Reinhart, Robert M G; Zhu, Julia; Park, Sohee et al. (2015) Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling. J Neurosci 35:12232-40 |
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