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Larry Y. Cheng
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2019) 31 (11): 1658–1673.
Published: 01 November 2019
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Memory encoding for important information can be enhanced both by reward anticipation and by intentional strategies. These effects are hypothesized to depend on distinct neural mechanisms, yet prior work has provided only limited evidence for their separability. We aimed to determine whether reward-driven and strategic mechanisms for prioritizing important information are separable, even if they may also interact. We examined the joint operation of both mechanisms using fMRI measures of brain activity. Participants learned abstract visual images in a value-directed recognition paradigm. On each trial, two novel images were presented simultaneously in different screen quadrants, one arbitrarily designated as high point value and one as low value. Immediately after each block of 16 study trials, the corresponding point rewards could be obtained in a test of item recognition and spatial location memory. During encoding trials leading to successful subsequent memory, especially of high-value images, increased activity was observed in dorsal frontoparietal and lateral occipitotemporal cortex. Furthermore, activity in a network associated with reward was higher during encoding when any image, of high or low value, was subsequently remembered. Functional connectivity between right medial temporal lobe and right ventral tegmental area, measured via psychophysiological interaction, was also greater during successful encoding regardless of value. Strategic control of memory, as indexed by successful prioritization of the high-value image, affected activity in dorsal posterior parietal cortex as well as connectivity between this area and right lateral temporal cortex. These results demonstrate that memory can be strengthened by separate neurocognitive mechanisms for strategic control versus reward-based enhancement of processing.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2016) 28 (10): 1636–1649.
Published: 01 October 2016
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Language input is highly variable; phonological, lexical, and syntactic features vary systematically across different speakers, geographic regions, and social contexts. Previous evidence shows that language users are sensitive to these contextual changes and that they can rapidly adapt to local regularities. For example, listeners quickly adjust to accented speech, facilitating comprehension. It has been proposed that this type of adaptation is a form of implicit learning. This study examined a similar type of adaptation, syntactic adaptation, to address two issues: (1) whether language comprehenders are sensitive to a subtle probabilistic contingency between an extraneous feature (font color) and syntactic structure and (2) whether this sensitivity should be attributed to implicit learning. Participants read a large set of sentences, 40% of which were garden-path sentences containing temporary syntactic ambiguities. Critically, but unbeknownst to participants, font color probabilistically predicted the presence of a garden-path structure, with 75% of garden-path sentences (and 25% of normative sentences) appearing in a given font color. ERPs were recorded during sentence processing. Almost all participants indicated no conscious awareness of the relationship between font color and sentence structure. Nonetheless, after sufficient time to learn this relationship, ERPs time-locked to the point of syntactic ambiguity resolution in garden-path sentences differed significantly as a function of font color. End-of-sentence grammaticality judgments were also influenced by font color, suggesting that a match between font color and sentence structure increased processing fluency. Overall, these findings indicate that participants can implicitly detect subtle co-occurrences between physical features of sentences and abstract, syntactic properties, supporting the notion that implicit learning mechanisms are generally operative during online language processing.