The overarching goal of this research is to characterize how perception and memory interact, in terms of both the learning mechanisms that help transform visual experience into memory, and the intentional mechanisms that regulate this transformation. The specific goal of this proposal is to test the hypothesis that incidental learning about statistical regularities in vision (visual statistical learning) is limited to selectively attend visual information, and that this behavioral interaction arises because of how selective attention modulates neural interactions between human visual and memory systems. We propose a two-stage framework in which selective attention to a high-level visual feature/category increases neural interactions between regions of occipital cortex that represent low-level features and the region of inferior temporal cortex (IT) that represents the attended feature/category, and in turn between this IT region and medial temporal lobe (MTL) sub regions involved in visual learning and memory. In addition to assessing how feature-based selective attention influences learning at a behavioral level, we will use functional magnetic resonance imaging to assess how attention influences evoked neural responses in task-relevant brain regions, as well as neural interactions between these regions in the background of ongoing tasks. We will develop an innovative approach for studying neural interactions in which evoked responses and global noise sources are scrubbed from the data and regional correlations are assessed in the residuals. This background connectivity approach provides a new way to study how intentional goals affect perception and learning.
Aim 1 examines the first stage of our framework, testing: how selective attention modulates background connectivity between IT and occipital cortex, where in retinotopic visual cortex this modulation occurs, and how these changes are controlled by frontal and parietal cortex.
Aim 2 examines the second stage of our framework, first establishing the role of the MTL in visual statistical learning, and then testing: how selective attention modulates interactions between IT and the MTL, where in cortical and hippocampal sub regions of the MTL this modulation occurs, and how these changes facilitate incidental learning about statistical regularities and later retrieval of this knowledge. In sum, we relate behavioral interactions between selective attention and learning to neural interactions between the mechanisms that represent visual features and those that learn about their relations. This proposal addresses several key issues in the field, including: how attention modulates the MTL, how feature-based attention is controlled, whether different neural mechanisms support rapid versus long-term visual learning, how tasks and goals are represented, and how attention and memory retrieval are related. This research will improve our understanding of how humans learn from visual experience, and how visual processing is in turn influenced by learning. These advances will shed light on the plasticity that occurs during development and during the recovery and rehabilitation of visual function following eye disease, injury, or brain damage.

Public Health Relevance

This research will improve our understanding of how humans learn from visual experience, and how visual processing is in turn influenced by learning. These advances will shed light on the plasticity that occurs during development and during the recovery from visual impairment caused by eye disease, injury, or brain damage. The behavioral tasks that we develop to enhance learning with attention will inform practices for rehabilitating visual function, and the methods that we develop to study neural interactions during tasks will lead to new approaches for diagnosing disorders of visual processing.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY021755-02
Application #
8306867
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Steinmetz, Michael A
Project Start
2011-08-01
Project End
2016-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
2
Fiscal Year
2012
Total Cost
$355,624
Indirect Cost
$130,624
Name
Princeton University
Department
None
Type
Organized Research Units
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
Schapiro, Anna C; Turk-Browne, Nicholas B; Norman, Kenneth A et al. (2016) Statistical learning of temporal community structure in the hippocampus. Hippocampus 26:3-8
Aly, Mariam; Turk-Browne, Nicholas B (2016) Attention promotes episodic encoding by stabilizing hippocampal representations. Proc Natl Acad Sci U S A 113:E420-9
Fan, Judith E; Turk-Browne, Nicholas B; Taylor, Jordan A (2016) Error-driven learning in statistical summary perception. J Exp Psychol Hum Percept Perform 42:266-80
Hindy, Nicholas C; Ng, Felicia Y; Turk-Browne, Nicholas B (2016) Linking pattern completion in the hippocampus to predictive coding in visual cortex. Nat Neurosci 19:665-7
Hindy, Nicholas C; Turk-Browne, Nicholas B (2016) Action-Based Learning of Multistate Objects in the Medial Temporal Lobe. Cereb Cortex 26:1853-65
Aly, Mariam; Turk-Browne, Nicholas B (2016) Attention Stabilizes Representations in the Human Hippocampus. Cereb Cortex 26:783-96
Johnson, Matthew A; Turk-Browne, Nicholas B; Goldberg, Adele E (2016) Neural systems involved in processing novel linguistic constructions and their visual referents. Lang Cogn Neurosci 31:129-144
Córdova, Natalia I; Tompary, Alexa; Turk-Browne, Nicholas B (2016) Attentional modulation of background connectivity between ventral visual cortex and the medial temporal lobe. Neurobiol Learn Mem 134 Pt A:115-22
Fan, Judith E; Hutchinson, J Benjamin; Turk-Browne, Nicholas B (2016) When past is present: Substitutions of long-term memory for sensory evidence in perceptual judgments. J Vis 16:1
Fan, Judith E; Turk-Browne, Nicholas B (2016) Incidental biasing of attention from visual long-term memory. J Exp Psychol Learn Mem Cogn 42:970-7

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