Putting category learning in order: Category structure and temporal arrangement affect the benefit of interleaved over blocked study

2013 ◽  
Vol 42 (3) ◽  
pp. 481-495 ◽  
Author(s):  
Paulo F. Carvalho ◽  
Robert L. Goldstone
Keyword(s):  
Category Learning ◽  
Category Structure

scholarly journals The Influence of Presentation Order on Category Transfer

2016 ◽  
Vol 63 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Fabien Mathy ◽  
Jacob Feldman
Keyword(s):  
Associative Learning ◽  
Category Learning ◽  
Presentation Order ◽  
Category Structure ◽  
Training Condition ◽  
Rule Based ◽  
Category Knowledge ◽  
Training Examples ◽  
Logical Rules

Abstract. This study of supervised categorization shows how different kinds of category representations are influenced by the order in which training examples are presented. We used the well-studied 5-4 category structure of Medin and Schaffer (1978) , which allows transfer of category learning to new stimuli to be discriminated as a function of rule-based or similarity-based category knowledge. In the rule-based training condition (thought to facilitate the learning of abstract logical rules and hypothesized to produce rule-based classification), items were grouped by subcategories and randomized within each subcategory. In the similarity-based training condition (thought to facilitate associative learning and hypothesized to produce exemplar classification), transitions between items within the same category were determined by their featural similarity and subcategories were ignored. We found that transfer patterns depended on whether the presentation order was similarity-based, or rule-based, with the participants particularly capitalizing on the rule-based order.

scholarly journals The helpfulness of category labels in semi-supervised learning depends on category structure

2019 ◽  
Author(s):  
Wai Keen Vong ◽  
Danielle Navarro ◽  
Amy Perfors
Keyword(s):  
Supervised Learning ◽  
Category Learning ◽  
Category Structure ◽  
Rational Model ◽  
Category Labels

The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people’s responses are driven by the specific set of labels they see. We present an extension of Anderson’s Rational Model of Categorization that captures this effect.

Teaching by Examples: Implications for the Process of Category Acquisition

1997 ◽  
Vol 50 (3) ◽  
pp. 586-606 ◽  
Author(s):  
Judith Avrahami ◽  
Yaakov Kareev ◽  
Yonatan Bogot ◽  
Ruth Caspi ◽  
Salomka Dunaevsky ◽  
...  
Keyword(s):  
Category Learning ◽  
Category Structure ◽  
Fine Tuning ◽  
New Paradigm ◽  
Teaching Mode ◽  
High Agreement ◽  
Negative Case ◽  
Typical Sequence ◽  
Efficient Learning ◽  
Pattern Of Performance

A new paradigm, the “teaching-by-examples” paradigm, was used to shed new light on the process of category acquisition. In four experiments ( n = 90, 90, 115, 117), manipulating the variables of category structure, status of non-target category, learning mode, and teaching mode, participants first learned a category and then taught it to someone else. High agreement between participants on the teaching sequences was found across conditions, and a typical sequence was identified for each category structure. The typical participant-produced sequences started with several ideal positive cases, followed by an ideal negative case and then borderline cases. The efficiency of such sequences for teaching was tested in another experiment ( n = 60), in which they were compared with sequences emphasizing category borders and sequences emphasizing each dimension separately. The typical participant-produced sequences induced the most efficient learning. It is proposed that the pattern of performance may provide a rich source of data for testing and fine-tuning models of category acquisition.

scholarly journals Finding categories through words: More nameable features improve category learning

2018 ◽  
Author(s):  
Martin Zettersten ◽  
Gary Lupyan
Keyword(s):  
Category Learning ◽  
Conceptual Development ◽  
Structure Constant ◽  
Category Structure ◽  
Visual Features ◽  
The Novel ◽  
Linguistic Differences ◽  
Hypothesis Formation ◽  
Verbal Labels ◽  
Cognitive Consequences

What are the cognitive consequences of having a name for something? Having a word for a feature makes it easier to communicate about a set of exemplars belonging to the same category (e.g., “the red things”) - might it make it easier to learn the category itself? Here, we provide evidence that the ease of learning category distinctions based on simple visual features is predicted from the ease of naming those features. Across seven experiments, participants learned categories composed of colors or shapes that were either easy or more difficult to name in English. Holding the category structure constant, when the underlying features of the category were easy to name, participants were faster and more accurate in learning the novel category. These results suggest that compact verbal labels may facilitate hypothesis formation during learning: it is easier to pose the hypothesis “it is about redness” than “it is about that pinkish-purplish color”. Our results have consequences for understanding how developmental and cross-linguistic differences in a language’s vocabulary affect category learning and conceptual development.

scholarly journals Linguistic Overhypotheses in Category Learning: Explaining the Label Advantage Effect

2020 ◽  
Author(s):  
Anna Aleksandrovna Ivanova ◽  
Matthias Hofer
Keyword(s):  
Computational Model ◽  
Experimental Work ◽  
Category Learning ◽  
Verbal Label ◽  
Category Structure ◽  
Prior Beliefs ◽  
The World

When learning to partition the world into categories, people rely on a set of assumptions (overhypotheses) about possible category structures. Here we propose that the nature of these overhypotheses depends on the presence of a verbal label associated with a given category. We describe a computational model that demonstrates how labels can either accelerate or hinder category learning, depending on whether or not the prior beliefs imposed by their presence align with the true category structure. This account provides an explanation for the phenomena described in prior experimental work (Lupyan, Rakison, & McClelland, 2007; Brojde, Porter, & Colunga, 2011) that have remained unexplained by other models. Based on these results, we argue that the overhypothesis theory of label effects provides a way to formalize and quantify the effect of language on category learning and to develop a more precise delineation between linguistic and non-linguistic thought.

A Striatal-Based Model of Implicit Category Learning

2000 ◽  
Author(s):  
F. Gregory Ashby ◽  
Shawn W. Ell ◽  
Elliott M. Waldron
Keyword(s):  
Category Learning
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