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.
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.
| Aly, Mariam; Chen, Janice; Turk-Browne, Nicholas B et al. (2018) Learning Naturalistic Temporal Structure in the Posterior Medial Network. J Cogn Neurosci 30:1345-1365 |
| deBettencourt, Megan T; Norman, Kenneth A; Turk-Browne, Nicholas B (2018) Forgetting from lapses of sustained attention. Psychon Bull Rev 25:605-611 |
| Aly, Mariam; Turk-Browne, Nicholas B (2018) Flexible weighting of diverse inputs makes hippocampal function malleable. Neurosci Lett 680:13-22 |
| Ellis, Cameron T; Turk-Browne, Nicholas B (2018) Infant fMRI: A Model System for Cognitive Neuroscience. Trends Cogn Sci 22:375-387 |
| Kok, Peter; Turk-Browne, Nicholas B (2018) Associative prediction of visual shape in the hippocampus. J Neurosci : |
| Cohen, Jonathan D; Daw, Nathaniel; Engelhardt, Barbara et al. (2017) Computational approaches to fMRI analysis. Nat Neurosci 20:304-313 |
| Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M et al. (2017) Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci 372: |
| Schlichting, Margaret L; Guarino, Katharine F; Schapiro, Anna C et al. (2017) Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development. J Cogn Neurosci 29:37-51 |
| Bejjanki, Vikranth R; da Silveira, Rava Azeredo; Cohen, Jonathan D et al. (2017) Noise correlations in the human brain and their impact on pattern classification. PLoS Comput Biol 13:e1005674 |
| Kim, Ghootae; Norman, Kenneth A; Turk-Browne, Nicholas B (2017) Neural Differentiation of Incorrectly Predicted Memories. J Neurosci 37:2022-2031 |
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