Abstract
Encountering events that are meaningfully related to prior episodes can prompt retrieval of those prior events. Reminders are events that prompt retrieval of prior learned information, while reminded materials are the events that are retrieved in response. Reminders are an important component of efficient and effective cognition because they partially automate the process of bringing relevant prior knowledge to bear on novel situations. Across four experiments, we investigated whether reminders boosts memory for the entire prior reminded episode or for only specific aspects of prior experiences that are relevant to the reminder. To do so, we combined a reminding procedure with a paradigm for measuring memory for the incidental context of encoding. Participants studied lists of words in which semantically related pairs (e.g., “volcano” and “erupt”) were presented across brief lags and in different color contexts. Memory for the content and color of the first-presented word in pairs (the reminded information) was measured. Recall of related word pairs was consistently greater than recall of unrelated pairs, in agreement with prior work on the reminding effect. However, when the color of related words differed across the pair, memory for the color of the reminded words was impaired compared to unrelated words. The results support a view of reminding as a leveling and sharpening process in which memory for the focal content of reminded episodes is enhanced, but differences in peripheral details are smoothed over. Such a process can lead to the acquisition and application of prototypical knowledge over experience.
Original language | English (US) |
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Article number | 104284 |
Journal | Journal of Memory and Language |
Volume | 121 |
DOIs | |
State | Published - Dec 2021 |
Keywords
- Context memory
- Reminder
- Reminding
- Study-phase retrieval
ASJC Scopus subject areas
- Neuropsychology and Physiological Psychology
- Language and Linguistics
- Experimental and Cognitive Psychology
- Linguistics and Language
- Artificial Intelligence