Does {charm} need depth? Similarity and levels-of-processing effects in cued recall.

Eich (1985) recently presented a distributed memory model in which the pattern of results used to support the levels-of-processing view of Craik and Lockhart (1972) was modeled by different degrees of similarity between the encoding context and the to-be-recalled item. We report two experiments in which both phonemic and semantic similarity were varied between pairs of words and incidental acquisition (rhyme vs. category judgments) was varied across the same pairs of items. In both experiments the manipulation of the acquisition task produced a difference in cued-recall performance for positive and negative rhyme and category judgments. Recall was better following a category encoding decision than following a rhyme decision. This difference was independent of the effects of similarity, which demonstrated that Eich's (1985) assumptions regarding the effects of similarity are not sufficient to account for the differences resulting from the manner in which subjects encode information. An alternative method of modeling the levels-of-processing effect within the framework of distributed memory models is proposed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Context-gated knowledge partitioning in categorization.

According to the knowledge partitioning framework, people sometimes master complex tasks by creating multiple independent parcels of partial knowledge. Research has shown that knowledge parcels may contain mutually contradictory information, and that each parcel may be used without regard to knowledge that is demonstrably present in other parcels. This article reports 4 experiments that investigated knowledge partitioning in categorization. When component boundaries of a complex categorization were identified by a context cue, a significant proportion of participants learned partial and independent categorization strategies that were chosen on the basis of context. For those participants, a strategy used in one context was unaffected by knowledge demonstrably present in other contexts, suggesting that knowledge partitioning in categorization can be complete. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Priming in recognition memory for categorized lists.

Previous research on primed recognition of categorized lists has shown 2 discrepant patterns of results. The reasons for that discrepancy are investigated by focusing on the differences between the tasks used to effect priming in the various previous studies. The 1st 2 experiments, with a total of 48 university students, showed that lure processing was facilitated when priming was achieved through another recognition test item, whereas inhibition was obtained if a semantic category judgment task was performed on the priming items. Thus, both patterns could be reproduced under nearly identical circumstances, with the type of prime processing being the only difference. Two additional experiments, with a total of 42 university students, served to generalize the inhibition found in the 2nd experiment to other semantic priming tasks. The type of processing done on the prime determined whether inhibition or facilitation of lure rejection was obtained. Inhibition was obtained when a semantic task was used to prime a recognition judgment, whereas facilitation resulted from priming with an episodic task. The results are interpreted in the framework of the semantic/episodic distinction. (43 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Simplified learning in complex situations: Knowledge partitioning in function learning.

The authors explored the phenomenon that knowledge is not always integrated and consistent but may be partitioned into independent parcels that may contain mutually contradictory information. In 4 experiments, using a function learning paradigm, a binary context variable was paired with the continuous stimulus variable of a to-be-learned function. In the first 2 experiments, when context predicted the slope of a quadratic function, generalization was context specific. Because context did not predict function values, it is suggested that people use context to gate separate learning of simpler partial functions. The 3rd experiment showed that partitioning also occurs with a decreasing linear function, whereas the 4th study showed that partitioning is absent for a linearly increasing function. The results support the notion that people simplify complex learning tasks by acquiring independent parcels of knowledge. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Competing strategies in categorization: Expediency and resistance to knowledge restructuring.

The authors investigated people's ability to restructure their knowledge when additional information about a categorization task is revealed. In 2 experiments, people first learned to rely on a fairly accurate (but imperfect) predictor. At various points in training, a complex relationship between 2 other predictors was revealed in a schematic diagram that could support perfect performance. In Experiment 1, people adopted the complex strategy when it was revealed at the outset but were unable to restructure their knowledge after the expedient predictor had been learned. In Experiment 2, expedient knowledge persisted even with an adaptive display. The persistence of expedient knowledge is explained by associative blocking of potential alternative cues. A 3rd experiment analyzed the strategies people use with and without the diagram. The study confirmed that the diagram, when presented at the outset, significantly alters people's approach to the task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Base-rate neglect in ALCOVE: A critical reevaluation.

A recent hybrid model of categorization (Attention Learning Covering Map [ALCOVE]; J. K. Kruschke, 1992) has combined the most desirable properties of exemplar models with a connectionist architecture and learning rule. A critically important property of ALCOVE is its apparent ability to account for base-rate neglect, a phenomenon beyond the purview of previous exemplar models. This article reexamines ALCOVE's base-rate neglect predictions and shows that they are confined to a very limited set of circumstances. In most cases, ALCOVE is unable to produce base-rate neglect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The components of working memory updating: An experimental decomposition and individual differences.

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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Knowledge Partitioning in Categorization: Constraints on Exemplar Models.

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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Better learning with more error: Probabilistic feedback increases sensitivity to correlated cues in categorization.

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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Error discounting in probabilistic category learning.

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. (PsycINFO Database Record (c) 2011 APA, all rights reserved)