It is well known that continuous encoding and recall of similar semantic content leads to memory deficits caused by the build-up of proactive interference. This build-up can be released if the content is switched to a different semantic category. While release from proactive interference has been studied using non-semantic content, it has yet to be studied with relational categories (i.e., categories that cohere based on fulfilling a particular semantic relationship such as obstacles). Recent findings suggest that relational information may be stored as individual entities similar to traditional entity categories. To assess the extent to which entity and relational content are similarly stored and retrieved, participants repeatedly encode and recall lists of word singletons in either an entity or relational category condition. Recall performance is evaluated for each list to compare the build-up and release of proactive interference for participants exposed to a semantic content switch or corresponding no-switch controls.

Fuzzy Trace Theory and the Activation Monitoring Framework disagree on whether gist or backwards associative strength best describes false memories in DRM. Recent evidence suggests situational features best describe processes underlying DRM results. Thematic, goal-derived, and relational categories capture different aspects of situational features, but perform differently in memory and coherence. We constructed novel category-specific lists for use in the DRM paradigm to determine whether different aspects of situational features make different contributions to a successful DRM result, whether relational and goal-derived content can produce false recognition despite low gist and backwards associative strength, and whether DRM captures an aspect of category coherence that has been difficult to measure in relational and goal-derived content. Only relational and thematic content produced sufficient false recognition. This provides mixed support for situational features, evidence that relational content makes specific contributions to DRM success, and evidence of coherence in relational categories.

Key questions in the study of categorization are how individuals form categories from experience and extend that knowledge to assess membership of novel examples. Popular accounts predict generalization to be based on either similarity to reference points or the application of rules or bounds. However, recent data show that some categorization behavior defies the predictions of leading accounts. Expanding on these findings, in the present study participants learned a one or two dimensional alternating category structure and were then tested on near and far transfer tasks. Findings reveal that individuals can extend a learned alternating category structure across multidimensional spaces and increasingly distant generalization regions. Additionally, subjects readily invoke alternation during the far transfer task (a task which does not involve classification), providing critical evidence that learning of the alternating category structure was driven by relational rather than feature-based similarity.

Categorization research often employs traditional artificial classification learning (TACL) and a foundational category structure based on family resemblance (FR). In TACL trials, each training item is presented for classification with arbitrary labels followed by corrective feedback. In the FR structure, categories are organized around two opposite configurations (i.e., prototypes) of a handful of binary features such that each category consists of its prototype plus the off-by-one variations. Despite convention, there is reason to question whether these choices align with natural category processes and structures. We employ a richer instantiation of FR using prototypes in opposite corners of a 2D space with four levels of variation. In addition, we investigate variations on the TACL paradigm: 1) situating classification learning within an immersive, dynamic, goal-driven setting; and 2) altering the core task to predicting a graded level of category membership (akin to typicality). Results provide implications for theoretical accounts of categorization.