6,246 research outputs found
Recommended from our members
Predicting Category Intuitiveness With the Rational Model, the Simplicity Model, and the Generalized Context Model
Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM—the authors refer to this way of applying the GCM as “unsupervised GCM.” The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness
Recommended from our members
Measuring category intuitiveness in unconstrained categorization tasks
What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models
Recommended from our members
On the adequacy of current empirical evaluations of formal models of categorization
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)
Unsupervised Learning of Individuals and Categories from Images
Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data
Recommended from our members
Supervised versus unsupervised categorization: Two sides of the same coin?
Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. (submitted; 2008) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets, and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions
Unsupervised learning of visual taxonomies
As more images and categories become available, organizing
them becomes crucial. We present a novel statistical
method for organizing a collection of images into a treeshaped
hierarchy. The method employs a non-parametric
Bayesian model and is completely unsupervised. Each image
is associated with a path through a tree. Similar images
share initial segments of their paths and therefore have a
smaller distance from each other. Each internal node in
the hierarchy represents information that is common to images
whose paths pass through that node, thus providing a
compact image representation. Our experiments show that
a disorganized collection of images will be organized into
an intuitive taxonomy. Furthermore, we find that the taxonomy
allows good image categorization and, in this respect,
is superior to the popular LDA model
Recommended from our members
Dyslexic participants show intact spontaneous categorization processes
We examine the performance of dyslexic participants on an unsupervised categorization task against that of matched non-dyslexic control participants. Unsupervised categorization is a cognitive process critical for conceptual development. Existing research in dyslexia has emphasized perceptual tasks and supervised categorization tasks (for which intact attentional processes are paramount), but there have been no studies on unsupervised categorization. Our investigation was based on Pothos and Chater's (Cognit. Sci., 2002; 26: 303–343) model of unsupervised categorization and the corresponding methodology for analysing results. Across all performance indices and various data-processing options, we could identify no difference between dyslexic and non-dyslexic participants
Recommended from our members
"Object Categorization: Reversals and Explanations of the Basic-Level Advantage" (Rogers & Patterson, 2007): A simplicity account
T. T. Rogers and K. Patterson (2007), in their article “Object Categorization: Reversals and Explanations of the Basic-Level Advantage” (Journal of Experimental Psychology: General, 136, 451–469), reported an impressive set of results demonstrating a reversal of the highly robust basic-level advantage both in patients with semantic dementia and in healthy individuals engaged in a speeded categorization task. To explain their results, as well as the usual basic-level advantage seen in healthy individuals, the authors employed a parallel distributed processing theory of conceptual knowledge. In this paper, we introduce an alternative way of explaining the results of Rogers and Patterson, which is premised on a more restricted set of assumptions born from standard categorization theory. Specifically, we provide evidence that their results can be accounted for based on the predictions of the simplicity model of unsupervised categorization
Unsupervised learning of clutter-resistant visual representations from natural videos
Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-association-based method e.g., Foldiak's trace rule [7].
Such methods exploit the temporal continuity of the visual world by assuming
that visual experience over short timescales will tend to have invariant
identity content. Thus, by associating representations of frames from nearby
times, a representation that tolerates whatever transformations occurred in the
video may be achieved. Many previous studies verified that such rules can work
in simple situations without background clutter, but the presence of visual
clutter has remained problematic for this approach. Here we show that temporal
association based on large class-specific filters (templates) avoids the
problem of clutter. Our system learns in an unsupervised way from natural
videos gathered from the internet, and is able to perform a difficult
unconstrained face recognition task on natural images: Labeled Faces in the
Wild [8]
- …