Recent work shows that vividly imagining oneself helping others in situations of need (episodic simulation) increases one's willingness to help. The mechanisms underlying this effect are unclear, though it is known that the medial temporal lobe (MTL) is critical for supporting episodic simulation in general. Therefore, individuals who have compromised MTL functioning, such as older adults and those who have undergone resection of medial temporal lobe tissue as treatment for epilepsy (mTLE patients), may not show the prosocial effects of episodic simulation. Our lab previously found that older adults and mTLE patients are impaired on a problem-solving task that requires the simulation of hypothetical scenarios. Using similar logic in the present study, we predicted that older adults and mTLE patients would show reduced effects of episodic simulation on their empathic concern for, and willingness to help, people in hypothetical situations of need, compared to young adults and age-matched healthy controls, respectively. We also predicted that the subjective vividness and the amount of context-specific detail in imagined helping events would correlate with willingness to help and empathic concern. Participants read brief stories describing individuals in situations of need, and after each story either imagined themselves helping the person or performed a filler task. We analyzed the details in participants' oral descriptions of their imagined helping events and also collected subjective ratings of vividness, willingness to help, and empathic concern. Episodic simulation significantly boosted willingness to help in all groups except for mTLE patients, and it increased empathic concern in young adults and healthy controls but not in older adults or mTLE patients. While the level of context-specific detail in participants' oral descriptions of imaged events was unrelated to willingness to help and empathic concern, the effects of episodic simulation on these measures was completely mediated by subjective vividness, though to a significantly lesser degree among mTLE patients. These results increase our understanding not only of how episodic simulation works in healthy people, but also of the social and emotional consequences of compromised MTL functioning.
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http://dx.doi.org/10.1016/j.neuropsychologia.2019.107243 | DOI Listing |
JMIR Med Inform
January 2025
Fundación INTRAS, Valladolid, Spain.
Background: This review explores the potential of virtual reality (VR) and artificial intelligence (AI) to identify preclinical cognitive markers of Alzheimer disease (AD). By synthesizing recent studies, it aims to advance early diagnostic methods to detect AD before significant symptoms occur.
Objective: Research emphasizes the significance of early detection in AD during the preclinical phase, which does not involve cognitive impairment but nevertheless requires reliable biomarkers.
Behav Res Methods
January 2025
Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, 999078, Macau, China.
The autobiographical implicit association test (aIAT) is an approach of memory detection that can be used to identify true autobiographical memories. This study incorporates mouse-tracking (MT) into aIAT, which offers a more robust technique of memory detection. Participants were assigned to mock crime and then performed the aIAT with MT.
View Article and Find Full Text PDFPsychol Rev
January 2025
Department of Cognitive Science, University of California, San Diego.
It has long been hypothesized that episodic memory supports adaptive decision making by enabling mental simulation of future events. Yet, attempts to characterize this process are surprisingly rare. On one hand, memory research is often carried out in settings that are far removed from ecological contexts of decision making.
View Article and Find Full Text PDFJ Surg Case Rep
January 2025
Department of General Surgery, Mohammed VI University Hospital, Oujda, Morocco.
We present a pioneering case of a duplication of the common bile duct associated with agenesis of the dorsal pancreas in a 66-year-old man. After an episode of cholestatic jaundice, radiological investigations revealed complex vascular and biliary anomalies, redefining the therapeutic strategy. Instead of risky surgery, endoscopic biliopancreatic drainage resolved the symptoms.
View Article and Find Full Text PDFStat Med
February 2025
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN.
The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task.
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