Biomedical subjects

Bradley C Love

Publications and source records attributed to Bradley C Love.

9 recordsLinked to original sources

PubMed2006

Beyond common features: the role of roles in determining similarity.

Historically, accounts of object representation and perceived similarity have focused on intrinsic features. Although more recent accounts have explored how objects, scenes, and situations containing common relational structures come to be perceived as similar, less is known about how the perceived similarity of parts or objects embedded within these relational systems is affected. The current studies test the hypothesis that objects situated in common relational systems come to be perceived as more similar. Similarity increases most for objects playing the same role within a relation (e.g., predator), but also increases for objects playing different roles within the same relation (e.g., the predator or prey role in the hunts relation) regardless of whether the objects participate in the same instance of the relation. This pattern of results can be captured by extending existing models that extract meaning from text corpora so that they are sensitive to the verb-specific thematic roles that objects fill. Alternative explanations based on analogical and inferential processes are also considered, as well as the implications of the current findings to research in language processing, decision making, and category learning.

Child↗

PubMed2006

Recency effects as a window to generalization: separating decisional and perceptual sequential effects in category learning.

Accounts of learning and generalization typically focus on factors related to lasting changes in representation (i.e., long-term memory). The authors present evidence that shorter term effects also play a critical role in determining performance and that these recency effects can be subdivided into perceptual and decisional components. Experimental results based on a probabilistic category structure show that the previous stimulus exerts a contrastive effect on the current percept (perceptual recency) and that responses are biased toward or away from the previous feedback, depending on the similarity between successive stimuli (decisional recency). A method for assessing these recency effects is presented that clarifies open questions regarding stimulus generalization and perceptual contrast effects in categorization and in other domains.

Attention↗

PubMed2006

Vancouver, Toronto, Montreal, Austin: enhanced oddball memory through differentiation, not isolation.

What makes a person, event, or object memorable? Enhanced memory for oddball items is long established, but the basis for these effects is not well understood. The present work clarifies the roles of isolation and differentiation in establishing new memories. According to the isolation account, items that are highly dissimilar to other items are better remembered. In contrast, recent category learning studies suggest that oddball items are better remembered because they must be differentiated from similar items. The present work pits the differentiation and isolation accounts against each other. The results suggest that differentiation, not isolation, leads to more accurate memory for deviant items. In contrast, gains for isolated items are attributable to reduced confusion with other items, as opposed to preferential storage.

Affect↗

PubMed2004

SUSTAIN: a network model of category learning.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Humans↗

PubMed2004

Schematic influences on category learning and recognition memory.

The results from 3 category learning experiments suggest that items are better remembered when they violate a salient knowledge structure such as a rule. The more salient the knowledge structure, the stronger the memory for deviant items. The effect of learning errors on subsequent recognition appears to be mediated through the imposed knowledge structure. The recognition advantage for deviant items extends to unsupervised learning situations. Exemplar-based and hypothesis-testing models cannot account for these results. The authors propose a clustering account in which deviant items are better remembered because they are differentiated from clusters that capture regularities. The function of clusters is akin to that of schemas. Their results and analyses expose connections among research in category learning, schemas, stereotypes, and analogy.

Humans↗

PubMed2003

The nonindependence of stimulus properties in human category learning.

Typically, models of category learning are verified through behavioral experiments with stimuli consisting of putatively independent dimensions such as shape, size, and color. The assumption of independence is critical in both the design of behavioral experiments and the development of models and theories of learning. Using the standard classification learning paradigm and a common stimulus set, the present work demonstrates that the assumption of independence is unwarranted. Systematic relations span stimulus dimensions and govern learning performance. For example, shape is not independent of size and color, because humans quantify size and color over shape when shape is relevant to the categorization. This quantification is reflected in natural language use (e.g., "blue triangle" as opposed to "triangle and blue"). In this example, color and size are predicates and shape is the argument. Across four experiments, the difficulty of mastering a classification rule can be predicted by the number of predicates that must be unbound in order to free rule-relevant stimulus dimensions.

Color Perception↗

PubMed2003

The multifaceted nature of unsupervised category learning.

A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories (i.e., the co-occurrence patterns of feature values) affects acquisition. Recent work in supervised learning has shown that a learner's interactions with the stimulus set also plays an important role in acquisition. The present study extends this work to unsupervised learning situations involving simple one-dimensional stimuli. The results suggest that categorization performance is a function of both learning mode (i.e., study conditions) and learning problem (i.e., category structure). Unsupervised learning, like supervised learning, appears to be multifaceted, with different learning modes best paired with certain learning problems.

Adult↗

PubMed2002

Learning nonlinearly separable categories by inference and classification.

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.

Analysis of Variance↗

PubMed2002

Comparing supervised and unsupervised category learning.

Two unsupervised learning modes (incidental and intentional unsupervised learning) and their relation to supervised classification learning are examined. The approach allows for direct comparisons of unsupervised learning data with the Shepard, Hovland, and Jenkins (1961) seminal studies in supervised classification learning. Unlike supervised classification learning, unsupervised learning (especially under incidental conditions) favors linear category structures over compact nonlinear category structures. Unsupervised learning is shown to be multifaceted in that performance varies with task conditions. In comparison with incidental unsupervised learning, intentional unsupervised learning is more rule like, but is no more accurate. The acquisition and application of knowledge is also more laborious under intentional unsupervised learning.

Adult↗