Premise typicality as feature inference decision-making in perceptual categories.

Making property inferences for category instances is important and has been studied in two largely separate areas-categorical induction and perceptual categorization. Categorical induction has a corpus of well-established effects using complex, real-world categories; however, the representational basis of these effects is unclear. In contrast, the perceptual categorization paradigm has fostered the assessment of well-specified representation models due to its controlled stimuli and categories. In categorical induction, evaluations of premise typicality effects, stronger attribute generalization from typical category instances than from atypical, have tried to control the similarity between instances to be distinct from premise-conclusion similarity effects, stronger generalization from greater similarity. However, the extent to which similarity has been controlled is unclear for these complex stimuli. Our research embedded analogues of categorical induction effects in perceptual categories, notably premise typicality and premise conclusion similarity, in an attempt to clarify the category representation underlying feature inference. These experiments controlled similarity between instances using overlap of a small number of constrained features. Participants made inferences for test cases using displayed sets of category instances. The results showed typicality effects, premise-conclusion similarity effects, but no evidence of premise typicality effects (i.e., no preference for generalizing features from typical over atypical category instances when similarity was controlled for), with significant Bayesian support for the null. As typicality effects occurred and occur widely in the perceptual categorization paradigm, why was premise typicality absent? We discuss possible reasons. For attribute inference, is premise typicality distinct from instance similarity? These initial results suggest not.

Coincidence judgment in causal reasoning: How coincidental is this?

Given the important conceptual connections between cause and coincidence as well as the extensive prior research on causality asking, "how causal is this?", the present research proposes and evaluated a psychological construction of coincidentality as the answer to the question, "how coincidental is this?" Four experiments measured the judgment properties of a reasonably large set of real coincidences from an initial diary study. These judgements included coincidentality and an array of other judgments about event uncertainty, hypothesis belief and surprise as predictors of coincidentality consistent with and supporting our prior definition of coincidence (Johansen & Osman, 2015): "coincidences are surprising pattern repetitions that are observed to be unlikely by chance but are nonetheless ascribed to chance since the search for causal mechanisms has not produced anything more plausible than mere chance." In particular, we evaluated formal models based on judgements of uncertainty, belief and surprise as predictors to develop a model of coincidentality. Ultimately, we argue that coincidentality is a marker for causal suspicion/discovery in terms of a flag that a new, unknown causal mechanism may be operating.

Comparing methods of category learning: Classification versus feature inference.

Categories have at least two main functions: classification of instances and feature inference. Classification involves assigning an instance to a category, and feature inference involves predicting a feature for a category instance. Correspondingly, categories can be learned in two distinct ways, by classification and feature inference. A typical difference between these in the perceptual category learning paradigm is the presence of the category label as part of the stimulus in feature inference learning and not in classification learning. So we hypothesized a label-induced rule-bias in feature inference learning compared to classification and evaluated it on an important starting point in the field for category learning - the category structures from Shepard, Hovland, and Jenkins (Psychological Monographs: General and Applied, 75(13), 1-42, 1961). They classically found that classification learning of structures consistent with more complex rules resulted in poorer learning. We compared feature inference learning of these structures with classification learning and found differences between the learning tasks supporting the label-bias hypothesis in terms of an emphasis on label-based rules in feature inference. Importantly, participants' self-reported rules were largely consistent with their task performance and indicated the preponderance of rule representation in both tasks. So, while the results do not support a difference in the kind of representation for the two learning tasks, the presence of category labels in feature inference tended to focus rule formation. The results also highlight the specialized nature of the classic Shepard et al. (1961) stimuli in terms of being especially conducive to the formation of compact verbal rules.

Causing time: Evaluating causal changes to the when rather than the whether of an outcome.

Studies of human causal learning typically conceptualize an effect as the presence or absence of an outcome or event in a given trial following a cause. However, causes may exert their influence in other ways, notably, by advancing or postponing the time at which an outcome occurs. Prior research has not examined how humans evaluate causal changes where the change in timing itself is the effect of interest. This research took a first step in this direction by investigating whether participants can accurately judge cause-effect contingencies when the effect is a change in outcome timing, as distinct from outcome occurrence: A change to the when of the outcome rather than to the whether. Three experiments presented scenarios where a candidate cause could either advance or postpone an inevitable outcome by a given amount of time and with a given probability. Consistent with previous research on judgments about event occurrence, participants gave higher ratings to scenarios with greater contingency. These effects were generally consistent for actions that advanced or postponed the outcome. Overall, our findings demonstrate that people are sensitive to probabilistic contrasts involving causal changes in event timing.

Seven-year-olds' aggressive choices in a computer game can be predicted in infancy.

Concerns about the relationship between computer games and children's aggression have been expressed for decades, but it is not yet clear whether the content of such games evokes aggression or a prior history of aggression promotes children's interest in aggressive games. Two hundred and sixty-six 7-year-old children from a nationally representative longitudinal sample in the UK played a novel computer game (CAMGAME) in which the child's avatar encountered a series of social challenges that might evoke aggressive, prosocial or neutral behaviour. Aggressive choices during the game were predicted by well-known risk factors for aggressive conduct problems and the children's own early angry aggressiveness as infants. These findings suggest that children who are predisposed to aggression bring those tendencies to virtual as well as real environments.

Salience Not Status: How Category Labels Influence Feature Inference.

Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special-mechanism hypothesis and the label super-salience hypothesis. The special-mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype-compatible inferences because the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super-salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super-salience hypothesis and in particular does not support the need for a special mechanism based on prototypes.

Structural awareness mitigates the effect of delay in human causal learning.

Many studies have demonstrated that reinforcement delays exert a detrimental influence on human judgments of causality. In a free-operant procedure, the trial structure is usually only implicit, and delays are typically manipulated via trial duration, with longer trials tending to produce both longer experienced delays and also lower objective contingencies. If, however, a learner can become aware of this trial structure, this may mitigate the effects of delay on causal judgments. Here we tested this "structural-awareness" hypothesis by manipulating whether response-outcome contingencies were clearly identifiable as such, providing structural information in real time using an auditory tone to delineate consecutive trials. A first experiment demonstrated that providing cues to indicate trial structure, but without an explicit indication of their meaning, significantly increased the accuracy of causal judgments in the presence of delays. This effect was not mediated by changes in response frequency or timing, and a second experiment demonstrated that it cannot be attributed to the alternative explanation of enhanced outcome salience. In a third experiment, making trial structure explicit and unambiguous, by telling participants that the tones indicated trial structure, completely abolished the effect of delays. We concluded that, with sufficient information, a continuous stream of causes and effects can be perceived as a series of discrete trials, the contingency nature of the input may be exploited, and the effects of delay may be eliminated. These results have important implications for human contingency learning and in the characterization of temporal influences on causal inference.

Instance memorization and category influence: challenging the evidence for multiple systems in category learning.

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential "5-4" structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.

Real behavior in virtual environments: psychology experiments in a simple virtual-reality paradigm using video games.

The purpose of this research was to illustrate the broad usefulness of simple video-game-based virtual environments (VEs) for psychological research on real-world behavior. To this end, this research explored several high-level social phenomena in a simple, inexpensive computer-game environment: the reduced likelihood of helping under time pressure and the bystander effect, which is reduced helping in the presence of bystanders. In the first experiment, participants had to find the exit in a virtual labyrinth under either high or low time pressure. They encountered rooms with and without virtual bystanders, and in each room, a virtual person requested assistance. Participants helped significantly less frequently under time pressure but the presence/absence of a small number of bystanders did not significantly moderate helping. The second experiment increased the number of virtual bystanders, and participants were instructed to imagine that these were real people. Participants helped significantly less in rooms with large numbers of bystanders compared to rooms with no bystanders, thus demonstrating a bystander effect. These results indicate that even sophisticated high-level social behaviors can be observed and experimentally manipulated in simple VEs, thus implying the broad usefulness of this paradigm in psychological research as a good compromise between experimental control and ecological validity.

Featural selective attention, exemplar representation, and the inverse base-rate effect.

Selective attention plays a central role in theories of category learning and representation. In exemplar theory, selective attention has typically been formalized as operating uniformly across entire stimulus dimensions. Selective featural attention operating within dimensions has been recognized as a conceptual possibility, but relatively little research has focused on evaluating it. In the present research, we explored the usefulness of selective featural attention in the context of exemplar representation. We report the results of embedding the feature-to-category relations typically associated with the inverse base-rate effect--a classic and paradoxical category-learning result--within a perceptual category-learning task using a category structure with three multivalued feature dimensions. An exemplar model incorporating featural selective attention accurately accounted for the inverse base-rate effect that occurred but failed to do so with only dimensional attention.

Paradoxical effects of base rates and representation in category learning.

The inverse base rate effect (IBRE) continues to be a puzzling case of decision making on the basis of conflicting information in human category learning. After being trained via feedback over trials to assign combinations of cues to high- and low-frequency categories, participants tend to respond with the low-frequency category to an otherwise perfectly conflicting pair of test cues, contrary to the category base rates. Our Experiment 1 demonstrated that decision making on the basis of an explicit summary of the cue-outcome and outcome base rate information from the standard learning task does not result in the effect. The remaining experimental conditions evaluated the necessary and sufficient conditions for the effect by systematically exploring experimental deviations between the standard learning task and the pure decision-making task. In partial disagreement with both recent theoretical accounts of the effect, these experiments indicate that asymmetric outcome representation and profound base rate neglect are individually necessary and jointly sufficient conditions. Broader theoretical implications are discussed.

Category representation for classification and feature inference.

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments.

Are there representational shifts during category learning?

Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusively used to mentally represent categories of objects. More recently, hybrid theories of categorization have been proposed that variously combine these different forms of category representation. Our research addressed the question of whether there are representational shifts during category learning. We report a series of experiments that tracked how individual subjects generalized their acquired category knowledge to classifying new critical transfer items as a function of learning. Individual differences were observed in the generalization patterns exhibited by subjects, and those generalizations changed systematically with experience. Early in learning, subjects generalized on the basis of single diagnostic dimensions, consistent with the use of simple categorization rules. Later in learning, subjects generalized in a manner consistent with the use of similarity-based exemplar retrieval, attending to multiple stimulus dimensions. Theoretical modeling was used to formally corroborate these empirical observations by comparing fits of rule, prototype, and exemplar models to the observed categorization data. Although we provide strong evidence for shifts in the kind of information used to classify objects as a function of categorization experience, interpreting these results in terms of shifts in representational systems underlying perceptual categorization is a far thornier issue. We provide a discussion of the challenges of making claims about category representation, making reference to a wide body of literature suggesting different kinds of representational systems in perceptual categorization and related domains of human cognition.