Spontaneous transfer of relational category structures between category learning tasks: A novel approach to measure analogical transfer.

The ability to spontaneously access knowledge of relational concepts acquired in one domain and apply it to a novel domain has traditionally been explored in the analogy literature via the problem-solving paradigm. In the present work, we propose a novel procedure based on categorisation as a complementary approach to assess spontaneous analogical transfer-using one category learning task to enhance learning of the same underlying category structures in another domain. In Experiment 1, we demonstrate larger improvements in classification performance across blocks of training in a target category learning task among participants that underwent a base category learning task relative to a separate group of participants learning the target category structures for the first time, thus providing evidence for spontaneous transfer of the category structures.

Object understanding: Investigating the path from percept to meaning.

Researchers tend to follow two paths when investigating categorization: 1) artificial classification learning tasks and 2) studies of natural conceptual organization involving reasoning from prior category knowledge. Largely separate, another body of research addresses the process of object recognition, i.e.

On the Generalization of Simple Alternating Category Structures.

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B).

Preventing inert knowledge: Category status promotes spontaneous structure-based retrieval of prior knowledge.

Analogical comparison of 2 provided cases promotes spontaneous analogical transfer by encouraging a more abstract representation of a target principle. This is widely understood as a process of schema abstraction that aids retrieval from memory in the absence of superficial similarity. The category status hypothesis states that if knowledge about a target principle is represented as a relational category, it is easier to activate as a result of categorizing (as opposed to cue-based reminding).

Similarity judgments predict N400 amplitude differences between taxonomic category members and thematic associates.

Human similarity judgments do not reliably conform to the predictions of leading theories of psychological similarity. Evidence from the triad similarity judgment task shows that people often identify thematic associates like dog and bone as more similar than taxonomic category members like dog and cat, even though thematic associates lack the type of featural or relational similarity that is foundational to theories of psychological similarity. This specific failure to predict human behavior has been addressed as a consequence of education and other individual differences, an artifact of the triad similarity judgment paradigm, or a shortcoming in psychological accounts of similarity.

Comparison-based learning of relational categories (you'll never guess).

In accord with structural alignment theory, same-category comparison opportunities within a classification learning task should promote relational category acquisition. However, a straightforward merging of the classification paradigm with copresentation of same-category item pairs does not yield an advantage relative to an equal number of single-item exposures. In 3 experiments, we explore the hypothesis that the traditional classification learning mode (guess-and-correct) and comparison have a previously unforeseen incompatibility.

Revisiting the linear separability constraint: New implications for theories of human category learning.

While the ability to acquire non-linearly separable (NLS) classifications is well documented in the study of human category learning, the relative ease of learning compared to a linear separable structure is difficult to evaluate without potential confounds. Medin and Schwanenflugel (Journal of Experimental Psychology: Human Learning and Memory, 7, 355-368, 1981) were the first to demonstrate that NLS classifications are not more difficult to acquire than linearly separable ones when structures are equated in terms of within- and between-category similarities. However, their evidence is less sturdy than might be expected due to non-standard methodology and low sample size.

Sorting out the problem of inert knowledge: Category construction to promote spontaneous transfer.

A fundamental goal in the study of human cognition is to understand the transfer of knowledge. This goes hand-in-hand with the translational goal of promoting such transfer via instructional techniques. Despite a rich history of research using the analogical problem-solving paradigm, no study activity has been found to produce a robust rate of successful spontaneous transfer-even when the test is immediate.

Evidence of analogical re-representation from a change detection task.

The flexibility to map similar, but non-identical relations, is a key characteristic of human analogical reasoning. Understanding how this flexibility is implemented is necessary for a complete accounting of analogical processes. The structure mapping theory of analogy addresses this issue by invoking re-representation-an online transformation of conceptually similar relational content that reveals potential partial identity matches between predicates.

Similarity is as similarity does? A critical inquiry into the effect of thematic association on similarity.

Leading theories of psychological similarity are based on the degree of match in semantic content between compared cases (i.e., shared features, low dimensional distance, alignable relations).

Analogies Without Commonalities? Evidence of Re-representation via Relational Category Activation.

Analogies between cases with matching sets of connected relational structure is well-explained by existing theory. Re-representation is posited as an important mechanism to increase the flexibility of analogical processing by allowing the alignment of non-identical predicates across compared cases. It has been proposed that certain kind of categories can be characterized in terms of the relational structure that its exemplars tend to satisfy.

Learning relational concepts from within- versus between-category comparisons.

This article examines relational category learning in light of 2 influential theories of concept acquisition: the structure-mapping theory of analogy and theories of feature-based category learning. According to current theories of analogy, comparing 2 instances of a relational concept enables alignment of their elements and reveals their shared relational structure. Therefore, learning relationally defined categories should be faster when comparing items of the same category than when comparing items of different categories.

Solving Nonlinearly Separable Classifications in a Single-Layer Neural Network.

Since the work of Minsky and Papert ( 1969 ), it has been understood that single-layer neural networks cannot solve nonlinearly separable classifications (i.e., XOR).

Similar to the category, but not the exemplars: A study of generalization.

Reference point approaches have dominated the study of categorization for decades by explaining classification learning in terms of similarity to stored exemplars or averages of exemplars. The most successful reference point models are firmly grounded in the associative learning tradition-treating categorization as a stimulus generalization process based on inverse exponential distance in psychological space augmented by a dimensional selective attention mechanism. We present experiments that pose a significant challenge to popular reference point accounts which explain categorization in terms of stimulus generalization from exemplars, prototypes, or adaptive clusters.

Observation versus classification in supervised category learning.

The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle.

Detecting anomalous features in complex stimuli: the role of structured comparison.

The ability to detect anomalies in perceived stimuli is critical to a broad range of cognitive tasks, yet acquiring this ability often requires lengthy practice. In this research, we asked whether findings from research on analogical comparison can be used to aid in the acquisition of perceptual expertise. Building on findings that comparison can facilitate the detection of differences, the present research addressed two questions: (1) Does having an alignable comparison standard improve performance on a difficult detection task? (2) Can such comparison experience improve subsequent detection performance on single anomalous targets? Across 3 experiments, university undergraduates were asked to find an anomalous bone in drawings of animal skeletons.

Comparison promotes learning and transfer of relational categories.

We investigated the effect of co-presenting training items during supervised classification learning of novel relational categories. Strong evidence exists that comparison induces a structural alignment process that renders common relational structure more salient. We hypothesized that comparisons between exemplars would facilitate learning and transfer of categories that cohere around a common relational property.

Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961).

The findings of Shepard, Hovland, and Jenkins (1961) on the relative ease of learning 6 elemental types of 2-way classifications have been deeply influential 2 times over: 1st, as a rebuke to pure stimulus generalization accounts, and again as the leading benchmark for evaluating formal models of human category learning. The litmus test for models is the ability to simulate an observed advantage in learning a category structure based on an exclusive-or (XOR) rule over 2 relevant dimensions (Type II) relative to category structures that have no perfectly predictive cue or cue combination (including the linearly-separable Type IV). However, a review of the literature reveals that a Type II advantage over Type IV is found only under highly specific experimental conditions.

Category learning in the context of co-presented items.

A series of four studies explore how the presentation of multiple items on each trial of a categorization task affects the course of category learning. In a three-category supervised classification task involving multi-dimensionally varying artificial organism-like stimuli, learners are shown a target plus two context items on every trial, with the context items' category membership explicitly identified. These triads vary in whether one, two, or all three categories are represented.

Relations, objects, and the composition of analogies.

This research addresses the kinds of matching elements that determine analogical relatedness and literal similarity. Despite theoretical agreement on the importance of relational match, the empirical evidence is neither systematic nor definitive. In 3 studies, participants performed online evaluations of relatedness of sentence pairs that varied in either the object or relational match.