Academic journal article Memory & Cognition

Reinstating Higher Order Properties of a Study List by Retrieving a List Item

Academic journal article Memory & Cognition

Reinstating Higher Order Properties of a Study List by Retrieving a List Item

Article excerpt

Published online: 3 December 2013

© Psychonomic Society, Inc. 2013

Abstract In two experiments, we looked at the role of higher order list properties in episodic recall. A probabilistic paired-associate paradigm was used in which each cue was repeatedly paired with two different targets. This paradigm permitted us to cue for a target that had been studied with that cue in the last list, or to cue for either of the two targets that had been repeatedly paired with that cue, although neither the cue nor either of its two targets had been studied in the last list. In Experiment 1, the higher order property was whether all of the targets in a given list were animal names or vegetable names. In Experiment 2, the higher order property was whether all of the pairs in a list were associatively related or unrelated. The assumption was that if participants were using these higher order properties when they retrieved a target that had been studied in the last list, they would also use these properties when recalling in response to a cue that had been studied in other lists but not in the most recent list. The results supported the use of both kinds of higher order properties in episodic access. They also showed that these higher order properties were reinstated by retrieving a target, and were then used in the next memory access operation. The questions of why the retrieval of a target would reinstate a higher order list property and how these very different higher order list properties aid in episodic access were also discussed.

Keywords Episodic memory * Semantic memory * Context * Retrieval * Control processes

In memory research, context has been traditionally viewed as consisting of stimuli that are incidental to the focal task. Thus, context might include such aspects of the study environment as the study room, the experimenter, and the apparatus. It might also include temporary characteristics of the participant, such as posture, mood, or drug state (Hintzman, 2011). There is also a long history of assuming a rapidly changing temporal context (Dennis & Humphreys, 2001; Estes, 1955; Glenberg & Swanson, 1986; Howard & Kahana, 2002; Lehman & Malmberg, 2013; Mensink & Raaijmakers, 1988). Recently, support has emerged for expanding the definition of context to include the details about how the list items were processed (Annis, Malmberg, Criss, & Shiffrin, 2013; Dennis & Humphreys, 2001), the other words in the study list (Howard, Jing, Rao, Provyn, & Datey, 2009; Sederberg, Howard, & Kahana, 2008; Zaromb et al., 2006) and characteristics that the targets or pairs within a list have in common (higher order properties; Dennis & Chapman, 2010). In the present article, we seek to provide additional support for the use of higher order properties by showing that these properties are reinstated by the retrieval of an item from the study list(s) and are then used in the next retrieval attempt.

The primary motivation behind wanting to think about higher order properties as context has come from recent evidence suggesting that in many cases, explanations involving item similarity will not work. For example, list length effects in recognition (poorer performance with longer study lists) and category length effects (poorer performance when category length is manipulated within a list) have been widely assumed to be supportive of high levels of word similarity (Cary & Reder, 2003; Shiffrin, Ratcliff, & Clark, 1990). However, it is now known that list length effects in recognition are quite small (Dennis & Humphreys, 2001; Dennis, Lee, & Kinnell, 2008; Kinnell & Dennis, 2011, 2012). It is true that, with taxonomic categories when set size is manipulated within a list, the ability to discriminate old from new items often seems to be lost. However, this effect appears to go away, at least with set sizes of 8 or less, when an unequal- variance signal detection model (Neely & Tse, 2009) or a forced choice task (Maguire, Humphreys, Dennis, & Lee, 2010) is used. …

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