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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Does depth of processing affect temporal contiguity?
Abigail M D Mundorf1, Mitchell G Uitvlugt2, M Karl Healey2
1Department of Psychology, Michigan State University, 316 Physics Road, East Lansing, MI, USA. desterab@msu.edu.
Abstract:
Memory tends to be better when items are processed for their meaning (deep processing) rather than their perceptual features (shallow processing). This levels of processing (LOP) effect is well-replicated and has been applied in many settings, but the mechanisms involved are still not well understood. The temporal contiguity effect (TCE), the finding that recalling one event often triggers recall of another event experienced nearby in time, also predicts memory performance. This effect has given rise to several competing theories with specific contiguity-generating mechanisms related to how items are processed. Therefore, studying how LOP and the TCE interact may shed light on the mechanisms underlying both effects. However, it is unknown how LOP and the TCE interact-various theories make differing predictions. In this preregistered study, we tested predictions of three theoretical explanations: accounts which assume temporal information is automatically encoded, accounts based on a trade-off between item and order information, and accounts which emphasize the importance of strategic control processes. Participants completed an immediate free recall task where they either engaged in deep processing, shallow processing, or no additional task while studying each word. Recall and the TCE were highest for no-task lists and greater for deep than shallow processing. Our results support theories which assume temporal associations are automatically encoded and those which emphasize strategic control processes. Both perspectives should be considered in theory development. These findings also suggest temporal information may contribute to better recall under deeper processing with implications for determining which situations benefit from deep processing.
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