Andy J. Wills

Formal approaches in categorization

1. Introduction Emmanuel M. Pothos and Andy J. Wills 2. The generalized context model: an exemplar model of classification Robert M. Nosofsky 3. Prototype models of categorization: basic formulation, predictions, and limitations John Paul Minda and J. David Smith 4. COVIS F. Gregory Ashby, Erick J. Paul and W. Todd Maddox 5. Semantics without categorization Timothy T. Rogers and James L. McClelland 6. Models of attentional learning John K. Kruschke 7. An elemental model of associative learning and memory Evan Livesey and Ian McLaren 8. Nonparametric Bayesian models of categorization Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro and Joshua B. Tenenbaum 9. The simplicity model of unsupervised categorization Emmanuel M. Pothos, Nick Chater and Peter Hines 10. Adaptive clustering models of categorization John V. McDonnell and Todd M. Gureckis 11. COBWEB models of categorization and probabilistic concept formation Wayne Iba and Pat Langley 12. The knowledge and resonance (KRES) model of category learning Harlan D. Harris and Bob Rehder 13. The contribution (and drawbacks) of models to the study of concepts Gregory L. Murphy 14. Formal models of categorization: insights from cognitive neuroscience Lukas Strnad, Stefano Anzellotti and Alfonso Caramazza 15. Comments on models and categorization theories: the razors edge Douglas Medin.

Predictive Learning, Prediction Errors, and Attention: Evidence from Event-related Potentials and Eye Tracking

Prediction error (surprise) affects the rate of learning: We learn more rapidly about cues for which we initially make incorrect predictions than cues for which our initial predictions are correct. The current studies employ electrophysiological measures to reveal early attentional differentiation of events that differ in their previous involvement in errors of predictive judgment. Error-related events attract more attention, as evidenced by features of event-related scalp potentials previously implicated in selective visual attention (selection negativity, augmented anterior N1). The earliest differences detected occurred around 120 msec after stimulus onset, and distributed source localization (LORETA) indicated that the inferior temporal regions were one source of the earliest differences. In addition, stimuli associated with the production of prediction errors show higher dwell times in an eye-tracking procedure. Our data support the view that early attentional processes play a role in human associative learning.

Attention and associative learning in humans: An integrative review.

This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (learned predictiveness). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (learned value). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that uncertainty may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that learned predictiveness and learned value modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either goal-directed or stimulus-driven. Rather, it suggests a new kind of “derived” attention.

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).

Processes of overall similarity sorting in free classification.

The processes of overall similarity sorting were investigated in 5 free classification experiments. Experiments 1 and 2 demonstrated that increasing time pressure can reduce the likelihood of overall similarity categorization. Experiment 3 showed that a concurrent load also reduced overall similarity sorting. These findings suggest that overall similarity sorting can be a time-consuming analytic process. Such results appear contrary to the idea that overall similarity is a nonanalytic process (e.g., T. B. Ward, 1983) but are in line with F. N. Milton and A. J. Willss (2004) dimensional summation hypothesis and with the stochastic sampling assumptions of the extended generalized context model (K. Lamberts, 2000). Experiments 4 and 5 demonstrated that the relationship between stimulus presentation time and overall similarity sorting is nonmonotonic, and the shape of the function is consistent with the idea that the three aforementioned processes operate over different parts of the time course.

Global-feature classification can be acquired more rapidly than local-feature classification in both humans and pigeons

When humans process visual stimuli, global information often takes precedence over local information. In contrast, some recent studies have pointed to a local precedence effect in both pigeons and nonhuman primates. In the experiment reported here, we compared the speed of acquisition of two different categorizations of the same four geometric figures. One categorization was on the basis of a local feature, the other on the basis of a readily apparent global feature. For both humans and pigeons, the global-feature categorization was acquired more rapidly. This result reinforces the conclusion that local information does not always take precedence over global information in nonhuman animals.

Perceptual Learning and Free Classification

Two experiments are reported that investigate the effects of stimulus preexposure on discrimination performance in a free classification task, using adult humans as subjects. In free classification subjects are asked to put stimuli into gruops in any way that seems reasonable or sensible tothem. Experiment 1 shows that the effect of preexposure is contingent on stimulus structure. Experiment 1b is the first demonstration of a retardation in learning as a consequence of simple preexposure in adult human subjects (previous demonstrations have relied on incidental or masked preexposure). Experiment 2 further supports the conclusions of Experiment 1 and extends them with the demonstration that stimulus similarity is a crucial factor. Taken together, these experiments rule out a class of attention-based explanations of the phenomena reported here. The experiments also provide novel information about the effects of preexposure. Preexposure can change the actual classifications subjects form in addition to altering the rate at which they are formed. Implications of these results for current theories of category formation and perceptual learning are considered.

The Outcome Specificity of Learned Predictiveness Effects: Parallels Between Human Causal Learning and Animal Conditioning.

Two experiments examined the outcome specificity of a learned predictiveness effect in human causal learning. Experiment 1 indicated that prior experience of a cue-outcome relation modulates learning about that cue with respect to a different outcome from the same affective class but not with respect to an outcome from a different affective class. Experiment 2 ruled out an interpretation of this effect in terms of context specificity. These results indicate that learned predictiveness effects in human causal learning index an associability that is specific to a particular class of outcomes. Moreover, they mirror demonstrations of the reinforcer specificity of analogous effects in animal conditioning, supporting the suggestion that, under some circumstances, human causal learning and animal conditioning reflect the operation of common associative mechanisms.

The neural basis of overall similarity and single-dimension sorting

The 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.

Tests of the ratio rule in categorization

Many theories of learning and memory (e.g., connectionist, associative, rational, exemplar based) produce psychological magnitude terms as output (i.e., numbers representing the momentary level of some subjective property). Many theories assume that these numbers may be translated into choice probabilities via the ratio rule, also known as the choice axiom (Luce, 1959) or the constant-ratio rule (Clarke, 1957). We present two categorization experiments employing artificial, visual, prototype-structured stimuli constructed from 12 symbols positioned on a grid. The ratio rule is shown to be incorrect for these experiments, given the assumption that the magnitude terms for each category are univariate functions of the number of category-appropriate symbols contained in the presented stimulus. A connectionist winner-take-all model of categorical decision (Wills & McLaren, 1997) is shown to account for our data given the same assumption. The central feature underlying the success of this model is the assumption that categorical decisions are based on a Thurstonian choice process (Thurstone, 1927, Case V) whose noise distribution is not double exponential in form.