The relational match-to-sample (RMTS) task is used to gauge sensitivity and preference for relational content in the presence of compelling object-based alternatives. On each trial, participants see a triad of 3-element shape sequences: target item (YXY), object match (YVO), and relational match (TWT). Traditionally, human adults show a moderate relational preference — supporting the structural alignment account of similarity-based processing. In the present study, we replicate preliminary findings showing that relational responding is facilitated relative to baseline if the target item is initially presented in isolation with a query to generate a short written description. However, we also observed that initial isolated presentation of the target with no accompanying task led to a similarly elevated rate of relational responding. These findings suggest that a minimal manipulation of presenting the target by itself prior to revealing the full RMTS triad promotes relational responding. We discuss implications for underlying mechanisms driving RMTS performance.

Previous work has demonstrated that relational responding in the Relational Match-to-Sample (RMTS) task can be improved compared to baseline by providing people with the opportunity to consider individual problem elements in isolation prior to receiving the full problem. However, it remains unclear whether the benefits of this intervention persist in its absence, or can be observed when a prior strategy has already been established to complete the task. To this end, we deployed a 2x2 mixed design, where presentation type was the within-subjects factor and order of presentation type was the between-subjects factor. The aims of this work were to gain further insight into the utility of isolated-focus as a means of promoting relational responding, and better understanding how processing of problem elements changes as a consequence of isolated-focus. We discuss the implications for pedagogical practices and extensions of this work to other materials and tasks.

Previous work has demonstrated that relational preference in the Relational Match-to-Sample task can be improved compared to baseline by providing people with the opportunity to consider the target item in isolation prior to receiving the full triad. However, it remains unclear whether the benefits of these supports persist in their absence, or can be observed when a prior strategy has already been established to complete the task. To this end, we conducted two experiments using 2 (presentation-type) by 2 (order) mixed designs to examine the efficacy of two previously established task supports: isolated-focus and description. The aims of this work were to gain further insight into the utility of these supports as a means of promoting relational preference both when the supports are present and when they are absent. We discuss the implications for pedagogical practices and extensions of this work to other materials and tasks.

In this study we conducted further tests of the ability of a computational model to fit human category learning performance. The Divergent Autoencoder (DIVA) is a generative-connectionist model of human categorization that learns through error-driven adjustments to predict class membership on the basis of the fidelity of auto-associative item reconstructions with respect to each category (Kurtz 2007). DIVA was tested on the classic SHJ category structures resulting in new qualitative and quantitative fits to both the original and revised SHJ orderings that show the impact of proposed design modifications and parameters. Theoretical implications for psychological accounts of category learning are discussed.

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.