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People frequently continue to use inaccurate information in their reasoning even after a credible retraction has been presented. This phenomenon is often referred to as the continued influence effect of misinformation. The repetition of the original misconception within a retraction could contribute to this phenomenon, as it could inadvertently make the "myth" more familiar--and familiar information is more likely to be accepted as true. From a dual-process perspective, familiarity-based acceptance of myths is most likely to occur in the absence of strategic memory processes. Thus, we examined factors known to affect whether strategic memory processes can be utilized: age, detail, and time. Participants rated their belief in various statements of unclear veracity, and facts were subsequently affirmed and myths were retracted. Participants then rerated their belief either immediately or after a delay. We compared groups of young and older participants, and we manipulated the amount of detail presented in the affirmative or corrective explanations, as well as the retention interval between encoding and a retrieval attempt. We found that (a) older adults over the age of 65 were worse at sustaining their postcorrection belief that myths were inaccurate, (b) a greater level of explanatory detail promoted more sustained belief change, and (c) fact affirmations promoted more sustained belief change in comparison with myth retractions over the course of 1 week (but not over 3 weeks), This supports the notion that familiarity is indeed a driver of continued influence effects.

Working memory is crucial for many higher level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization. This article reports 2 studies that related people's working memory capacity (WMC) to their learning performance on multiple rule-based and information-integration perceptual categorization tasks. In both studies, structural equation modeling revealed a strong relationship between WMC and category learning irrespective of the requirement to integrate information across multiple perceptual dimensions. WMC was also uniformly related to people's ability to focus on the most task-appropriate strategy, regardless of whether or not that strategy involved information integration. Contrary to the predictions of the multiple systems view of categorization, working memory thus appears to underpin performance in both major classes of perceptual category-learning tasks. (Contains 8 tables, 12 figures and 3 footnotes.)

Working memory is crucial for many higher-level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization, and modeling in category learning has thus far been largely uninformed by knowledge about people's memory processes. This article reports a large study (N = 113) that related people's working memory capacity (WMC) to their category-learning performance using the 6 problem types of Shepard, Hovland, and Jenkins (1961). Structural equation modeling revealed a strong relationship between WMC and category learning, with a single latent variable accommodating performance on all 6 problems. A model of categorization (the Attention Learning COVEring map, ALCOVE; Kruschke, 1992) was fit to the individual data and a single latent variable was sufficient to capture the variation among associative learning parameters across all problems. The data and modeling suggest that working memory mediates category learning across a broad range of tasks. (Contains 7 figures, 5 tables and 8 footnotes.)

The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results were indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning. (Contains 5 figures and 2 tables.)

We investigated the effects of the duration and type of to-be-articulated distractors during encoding of a verbal list into short-term memory (STM). Distractors and to-be-remembered items alternated during list presentation, as in the complex-span task that underlies much of working-memory research. According to an interference model of STM, known as serial order in a box (SOB; Farrell & Lewandowsky, 2002), additional repeated articulations of the same word between list items should cause minimal further disruption of encoding into STM even though the retention interval for early list items is increased. SOB also predicts that the articulation of several different distractor items should lead to much enhanced disruption if the distractor interval is increased. Those predictions were qualitatively confirmed in 4 experiments that found that it is the type of distractors, not their total duration, that determines the success of encoding a list into STM. The results pose a challenge to temporal models of complex-span performance, such as the time-based resource sharing model (Barrouillet, Bernardin, & Camos, 2004). The results add to a growing body of evidence that memory for the short term is not exclusively governed by purely temporal processes. (Contains 6 footnotes, 3 tables, and 10 figures.)

Working memory updating (WMU) has been identified as a cognitive function of prime importance for everyday tasks and has also been found to be a significant predictor of higher mental abilities. Yet, little is known about the constituent processes of WMU. We suggest that operations required in a typical WMU task can be decomposed into 3 major component processes: retrieval, transformation, and substitution. We report a large-scale experiment that instantiated all possible combinations of those 3 component processes. Results show that the 3 components make independent contributions to updating performance. We additionally present structural equation models that link WMU task performance and working memory capacity (WMC) measures. These feature the methodological advancement of estimating interindividual covariation and experimental effects on mean updating measures simultaneously. The modeling results imply that WMC is a strong predictor of WMU skills in general, although some component processes--in particular, substitution skills--were independent of WMC. Hence, the reported predictive power of WMU measures may rely largely on common WM functions also measured in typical WMC tasks, although substitution skills may make an independent contribution to predicting higher mental abilities. (Contains 12 footnotes, 8 tables and 3 figures.)

What drives forgetting in working memory? Recent evidence suggests that in a complex-span task in which an irrelevant processing task alternates with presentation of the memoranda, recall declines when the time taken to complete the processing task is extended while holding the time for rehearsal in between processing steps constant (Portrat, Barrouillet, & Camos, 2008). This time-based forgetting was interpreted in support for the role of time-based decay in working memory. In this article, we argue the contrary position by (a) showing in an experiment that the processing task in Portrat et al.'s (2008) study gave rise to uncontrolled post-error processes that occupied the attentional bottleneck, thus preventing restorative rehearsal, and (b) showing that when those post-error processes are statistically controlled, there is no evidence for temporal decay in Portrat et al.'s study. We conclude that currently there exists no direct evidence for temporal decay in the complex-span paradigm. (Contains 2 tables, 1 figure and 1 footnote.)

Despite the fact that categories are often composed of correlated features, the evidence that people detect and use these correlations during intentional category learning has been overwhelmingly negative to date. Nonetheless, on other categorization tasks, such as feature prediction, people show evidence of correlational sensitivity. A conventional explanation holds that category learning tasks promote rule use, which discards the correlated-feature information, whereas other types of category learning tasks promote exemplar storage, which preserves correlated-feature information. Contrary to that common belief, the authors report 2 experiments that demonstrate that using probabilistic feedback in an intentional categorization task leads to sensitivity to correlations among nondiagnostic cues. Deterministic feedback eliminates correlational sensitivity by focusing attention on relevant cues. Computational modeling reveals that exemplar storage coupled with selective attention is necessary to explain this effect. (Contains 6 tables, 14 figures and 5 footnotes.)

Knowledge restructuring occurs when people shift to a new strategy or representation during learning. Although knowledge restructuring can frequently be experimentally encouraged, there are instances in which people resist restructuring and continue to use an expedient but imperfect initial strategy. The authors report 3 category learning experiments that reconciled those conflicting outcomes by postulating that, for restructuring to occur, learners must be dissatisfied with their knowledge and a usable alternative must be available. In line with expectation, restructuring was elicited only when an alternative strategy was pointed out and when people's initial expedient strategy entailed performance error. Neither error nor information about the alternative strategy by itself was sufficient to induce restructuring.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified.

This article investigated the role of the recognition criterion in the verbal overshadowing effect (VOE). In 3 experiments, people witnessed an event, verbally described a perpetrator, and then attempted identification. The authors found in Experiment 1, which included a "not present" response option and both perpetrator-present (PP) and perpetrator-absent (PA) lineups, an increased reluctance to identify a person from both lineup types after verbalization. Experiment 2 incorporated a forced-choice procedure, and the authors found no effect of verbalization on identification performance. Experiment 3 replicated the essential aspects of these results. Consequently, the VOE may reflect a change in recognition criterion rather than a changed processing style or alteration of the underlying memory trace. This conclusion was confirmed by computational modeling of the data.