Feedback can be superior to observational training for both rule-based and information-integration category structures

The effects of two different types of training on rule-based and information-integration category learning were investigated in two experiments. In observational training, a category label is presented, followed by an example of that category and the participant's response. In feedback training, the stimulus is presented, the participant assigns it to a category and then receives feedback about the accuracy of that decision. Ashby, Maddox, and Bohil (2002) reported that feedback training was superior to observational training when learning information-integration category structures, but that training type had little effect on the acquisition of rule-based category structures. These results were argued to support the COVIS dual-process account of category learning. However, a number of non-essential differences between their rule-based and information-integration conditions complicate interpretation of these findings. Experiment 1 controlled, between category structures, for participant error rates, category separation, and the number of stimulus dimensions relevant to the categorization. Under these more controlled conditions, rule-based and information-integration category structures both benefitted from feedback training to a similar degree. Experiment 2 maintained this difference in training type when learning a rule-based category that had otherwise been matched, in terms of category overlap and overall performance, with the rule-based categories used in Ashby et al. These results indicate that differences in dimensionality between the category structures in Ashby et al. is a more likely explanation for the interaction between training type and category structure than the dual-system explanation they offered

Machine learning of visual object categorization: an application of the SUSTAIN model

Formal models of categorization are psychological theories that try to describe the process of categorization in a lawful way, using the language of mathematics. Their mathematical formulation makes it possible for the models to generate precise, quantitative predictions. SUSTAIN (Love, Medin & Gureckis, 2004) is a powerful formal model of categorization that has been used to model a range of human experimental data, describing the process of categorization in terms of an adaptive clustering principle. Love et al. (2004) suggested a possible application of the model in the field of object recognition and categorization. The present study explores this possibility, investigating at the same time the utility of using a formal model of categorization in a typical machine learning task. The image categorization performance of SUSTAIN on a well-known image set is compared with that of a linear Support Vector Machine, confirming the capability of SUSTAIN to perform image categorization with a reasonable accuracy, even if at a rather high computational cost

The effect of pre-exposure on family resemblance categorization for stimuli of varying levels of perceptual difficulty

This study investigated the effect that pre-exposure to a set of stimuli has on the prevalence of family resemblance categorization. 64 participants were tested to examine the effect that pre-exposure type (same-stimuli vs unrelated-stimuli) and the perceptual difficulty of the stimuli (perceptually similar vs perceptually different) has on categorization strategy. There was a significant effect of perceptual difficulty, indicating that perceptually different stimuli evoked a higher level of family resemblance sorting than perceptually similar stimuli. There was no significant main effect of pre-exposure type; however, there was a significant interaction between pre-exposure type and level of perceptual difficulty. Post-hoc tests revealed that this interaction was the result of an increase in family resemblance sorting for the perceptually different stimuli under relevant preexposure but no such effect for perceptually similar stimuli. The theoretical implications of these findings are discussed

Combination or Differentiation? Two theories of processing order in classification

ArticleCopyright © 2015 Elsevier Inc. All rights reserved.Does cognition begin with an undifferentiated stimulus whole, which can be divided into distinct attributes if time and cognitive resources allow (Differentiation Theory)? Or does it begin with the attributes, which are combined if time and cognitive resources allow (Combination Theory)? Across psychology, use of the terms analytic and non-analytic imply that Differentiation Theory is correct—if cognition begins with the attributes, then synthesis, rather than analysis, is the more appropriate chemical analogy. We re-examined four classic studies of the effects of time pressure, incidental training, and concurrent load on classification and category learning (Kemler Nelson, 1984; Smith & Kemler Nelson, 1984; Smith & Shapiro, 1989; Ward, 1983). These studies are typically interpreted as supporting Differentiation Theory over Combination Theory, while more recent work in classification (Milton et al., 2008, et seq.) supports the opposite conclusion. Across seven experiments, replication and re-analysis of the four classic studies revealed that they do not support Differentiation Theory over Combination Theory—two experiments support Combination Theory over Differentiation Theory, and the remainder are compatible with both accounts. We conclude that Combination Theory provides a parsimonious account of both classic and more recent work in this area. The presented data do not require Differentiation Theory, nor a Combination–Differentiation hybrid account

Does incidental training increase the prevalence of overall similarity classification? A re-examination of Kemler Nelson (1984)

Kemler Nelson (1984) reported that incidental training, relative to intentional training, increased the prevalence of overall similarity classification, supporting a non-deliberative account of overall similarity sorting. However, the analysis conducted by Kemler Nelson (1984) does not adequately distinguish between usage of an overall similarity classification strategy and single-attribute strategies. The current study replicates Kemler Nelson’s (1984) experiment, seeking to test the original conclusions using a more rigorous analysis. The current study approximates the original experimental procedure, using almost identical stimuli and a longer, modified test phase. Results replicate those found by Kemler Nelson (1984) when the original analysis is applied; however the model-based analysis suggest an overall similarity classification strategy is used rarely and that incidental training increases the prevalence of suboptimal single-attribute strategies. These results imply that overall similarity classification may be more deliberative than previously thought

Due process in dual process: Model-recovery simulations of decision-bound strategy analysis in category learning

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Behavioral evidence for the COVIS dual-process model of category learning has been widely reported in over a hundred publications (Ashby and Valentin, 2016). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants’ categorization strategies, a task that usually falls to Decision Bound analysis (Maddox and Ashby, 1993). Here, we examine the accuracy of this analysis in a series of model-recovery simulations. In Simulation 1, over a third of simulated participants using an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re-examined a recently-reported COVIS-supporting dissociation (Smith et al., 2014), and found that these misidentification errors permit an alternative, single-process, explanation of the results. Implications for due process in the future evaluation of dual-process theories, including recommendations for future practice, are discussed

The neural basis of overall similarity and single-dimension sorting

Copyright © 2009 Elsevier. NOTICE: This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NeuroImage, 2009, Vol. 46, pp. 319 – 326 DOI: http://dx.doi.org/10.1016/j.neuroimage.2009.01.043The ability to group stimuli into meaningful categories is fundamental to natural behavior. Raw perceptions would be useless without an ability to classify items as, for example, threat or food. Previous work suggests that people have a tendency to group stimuli either on the basis of a single dimension or by overall similarity (e.g., Milton, F.N., Longmore, C.A., and Wills, A.J. (2008). Processes of overall similarity sorting in free classification. J. Exp. Psychol. Hum. Percept. Perform, 34, 676-692.). It has recently been suggested that overall similarity sorting can engage similar rule-based processes to single-dimension sorting and, in addition, requires greater use of working memory (Milton, F.N., and Wills, A.J. (2004). The influences of stimulus properties on category construction. J. Exp. Psychol. Learn. Mem. Cogn, 30, 407-415.). These predictions were tested in an event-related fMRI study of spontaneous categorization. Results showed a striking overlap of activation between overall similarity and single-dimension sorting indicating engagement of common neural processes. Furthermore, overall similarity sorting recruited additional activity in bilateral precuneus, right cuneus, left cerebellum, left postcentral gyrus, right thalamus and right ventrolateral frontal cortex (VLFC). Our findings suggest that overall similarity sorting can be the result of rule-based processes and highlight a potential role for right VLFC in integrating multi-dimensional sensory information to form conceptual categories

The Neural Correlates of Similarity- and Rule-based Generalization

The idea that there are multiple learning systems has become increasingly influential in recent years with many studies providing evidence that there is both a quick, similarity, or feature-based, system, and a more effortful, rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby (1998). Unlike previous related studies employing other procedures, rule generalization in the Shanks-Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the prefrontal cortex and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed

Absence of cross-modality analogical transfer in perceptual categorization

This is the final version. Available on open access from Nurture Science Publishing Group via the DOI in this recordAnalogical transfer has been previously reported to occur between rule-based, but not information-integration, perceptual category structures (Casale, Roeder, & Ashby, 2012). The current study investigated whether a similar pattern of results would be observed in cross-modality transfer. Participants were trained on either a rule-based structure, or an information-integration structure, using visual stimuli. They were then tested on auditory stimuli that had the same underlying abstract category structure. Transfer performance was assessed relative to a control group who did not receive training on the visual stimuli. No cross-modality transfer was found, irrespective of the category structure employed