Ricardo Morales-Torres, Kaylee Miceli, Shenyang Huang, Karl Szpunar, Felipe De Brigard
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引用次数: 0
Abstract
People often engage in episodic counterfactual thinking, or mentally simulating how the experienced past might have been different from how it was. A commonly held view is that mentally simulating alternative event outcomes aids in managing future uncertainty and improving behavior, for which episodic counterfactual simulations need to be remembered. Yet the phenomenological factors influencing the memorability of counterfactual simulations remain unclear. To investigate this, we conducted two experiments using a paradigm where participants recalled autobiographical memories. After 1 week, they created counterfactual mental simulations of these memories, integrating a new object into each one and rating them on various phenomenological characteristics. Memory for these counterfactual mental simulations was tested the next day by recalling the new object. Across the two experiments we found that objects included in more plausible counterfactual simulations were better remembered compared with implausible counterfactual simulations. Our findings suggest that generating episodic counterfactual simulations perceived as plausible enhances their memorability, similar to other memory phenomena in which schematic knowledge improves subsequent episodic memory.
期刊介绍:
The journal provides coverage spanning a broad spectrum of topics in all areas of experimental psychology. The journal is primarily dedicated to the publication of theory and review articles and brief reports of outstanding experimental work. Areas of coverage include cognitive psychology broadly construed, including but not limited to action, perception, & attention, language, learning & memory, reasoning & decision making, and social cognition. We welcome submissions that approach these issues from a variety of perspectives such as behavioral measurements, comparative psychology, development, evolutionary psychology, genetics, neuroscience, and quantitative/computational modeling. We particularly encourage integrative research that crosses traditional content and methodological boundaries.