Publications by authors named "Jenkin Mok"

Intertemporal choices require trade-offs between short-term and long-term outcomes. Ventromedial prefrontal cortex (vmPFC) damage causes steep discounting of future rewards (delay discounting [DD]) and impoverished episodic future thinking (EFT). The role of vmPFC in reward valuation, EFT, and their interaction during intertemporal choice is still unclear.

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If the tendency to discount rewards reflects individuals' general level of impulsiveness, then the discounting of delayed and probabilistic rewards should be negatively correlated: The less a person is able to wait for delayed rewards, the more they should take chances on receiving probabilistic rewards. It has been suggested that damage to the ventromedial prefrontal cortex (vMPFC) increases individuals' impulsiveness, but both intertemporal choice and risky choice have only recently been assayed in the same patients with vMPFC damage. Here, we assess both delay and probability discounting in individuals with vMPFC damage (n = 8) or with medial temporal lobe (MTL) damage (n = 10), and in age- and education-matched controls (n = 30).

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Remembering and imagining specific, personal experiences can help shape our decisions. For example, cues to imagine future events can reduce delay discounting (i.e.

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Objective: To design and preliminarily test a questionnaire intended to measure patient treatment burden resulting from participation in cognitive assessments and interventions.

Methods: An expert consensus process was used to develop the concept of patient treatment burden and to determine the first set of questionnaire items and administration protocol. The pilot questionnaire was administered to 20 patients with mild to severe acquired brain injuries on completion of a 2-h or longer neuropsychological assessment.

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An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data.

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