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

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

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 is 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

The Role of National Debts in the Determination of the Yen-Dollar Exchange Rate

An intertemporal optimization model is developed to examine the determinants of the long-run nominal yen-dollar exchange rate in the presence of national debts. The model is tested empirically using data from Japan and the USA. The proposed theoretical specification is well supported by the data and shows that relative national debts as well as monetary and financial factors may play a significant role in the determination of the long-run nominal exchange rate between the yen and the dollar

Engaged in play: Seven-year-olds’ engagement with the play frame when playing with toy figures and their engagement with the fictional world of a video game

Children’s engagement in fictional worlds created when playing with toys and video games has received little research attention. We explored whether children’s engagement with the play frame when playing with toy figures was associated with their engagement with the virtual world in a video game in a community sample of 251 seven-year-olds (M = 6.95 years, SD = 0.38, 44 % girls). Using observational coding, we found that children’s engagement with the play frame by enacting roles ‘within’ the fictional world was positively associated with engaging with the virtual world in the video game. We also found that child characteristics, particularly children’s sex and their propensity to talk during play, were associated with their engagement in the two forms of play, and explained the associations in engagement between play contexts. These findings are discussed in terms of the features of the two contexts of play and how they promote children’s engagement with the fictional worlds

Metabolomic Profiling in LRRK2-Related Parkinson's Disease

Mutations in LRRK2 gene represent the most common known genetic cause of Parkinson's disease (PD).We used metabolomic profiling to identify biomarkers that are associated with idiopathic and LRRK2 PD. We compared plasma metabolomic profiles of patients with PD due to the G2019S LRRK2 mutation, to asymptomatic family members of these patients either with or without G2019S LRRK2 mutations, and to patients with idiopathic PD, as well as non-related control subjects. We found that metabolomic profiles of both idiopathic PD and LRRK2 PD subjects were clearly separated from controls. LRRK2 PD patients had metabolomic profiles distinguishable from those with idiopathic PD, and the profiles could predict whether the PD was secondary to LRRK2 mutations or idiopathic. Metabolomic profiles of LRRK2 PD patients were well separated from their family members, but there was a slight overlap between family members with and without LRRK2 mutations. Both LRRK2 and idiopathic PD patients showed significantly reduced uric acid levels. We also found a significant decrease in levels of hypoxanthine and in the ratios of major metabolites of the purine pathway in plasma of PD patients.These findings show that LRRK2 patients with the G2019S mutation have unique metabolomic profiles that distinguish them from patients with idiopathic PD. Furthermore, asymptomatic LRRK2 carriers can be separated from gene negative family members, which raises the possibility that metabolomic profiles could be useful in predicting which LRRK2 carriers will eventually develop PD. The results also suggest that there are aberrations in the purine pathway in PD which may occur upstream from uric acid

Mental representations of values and behaviors

The present research provides the first direct assessment of the fit of diverse behaviors to putatively related personal and social values from Schwartz’s theory. Across three studies, we examined spatial representations of value-related behaviors that were explicitly derived from people’s mental representations of the values. Participants were asked how similar the behaviors were to each other and various values, and these judgments were used to specify multidimensional scaling solutions. The results indicated that the spatial representation of the behaviors was consistent with the two-dimensional space described in Schwartz’s (1992) model of values, although several deviations occurred. For example, self-enhancement behaviors were widely spread, indicating more variation in the way individuals interpret these behaviors, which are often associated with other value types. This data provides evidence that a range of behaviors can at least partly be reduced to underlying motivations expressed by values, while identifying cases of better or worse fit that can be utilized in future research. Furthermore, our findings indicate that behaviors are often expressed by several values, which might help to explain why value-behavior associations in previous studies were weak. Finally, they illustrate a new approach to learning which behaviors might relate to multiple values

XAI & I: Self-explanatory AI facilitating mutual understanding between AI and human experts

Traditionally, explainable artificial intelligence seeks to provide explanation and interpretability of high-performing black-box models such as deep neural networks. Interpretation of such models remains difficult, because of their high complexity. An alternative method is to instead force a deep-neural network to use human-intelligible features as the basis for its decisions. We tested this approach using the natural category domain of rock types. We compared the performance of a black-box implementation of transfer-learning using Resnet50 to that of a network first trained to predict expert-identified features and then forced to use these features to categorise rock images. The performance of this feature-constrained network was virtually identical to that of the unconstrained network. Further, a partially constrained network forced to condense down to a small number of features that was not trained with expert features did not result in these abstracted features being intelligible; nevertheless, an affine transformation of these features could be found that aligned well with expert-intelligible features. These findings show that making an AI intrinsically intelligible need not be at the cost of performance