Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

A probabilistic model of cross-categorization

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.     

Psychopathic traits from the perspective of self and informant reports: is there evidence for a lack of insight?

It has been suggested that psychopathic individuals are incapable of providing valid reports on their own personality functioning because they are either unwilling (i.e., pathologically lie) or unable (i.e. lack insight) to do so. Despite the long-standing nature of this suggestion, almost no empirical research exists on this topic. In the current study, the authors examined the issue of psychopathy and insight by testing self and informant convergence and mean level differences across 3 indices of psychopathy in a community sample (N=64). Self- and informant-report psychopathy scores were also examined in relation to self and informant reports on traits from the Five-Factor Model (FFM). Convergence was strong across the 3 psychopathy indices and their respective factors (i.e., median r=.64), and there was only modest evidence that individuals rated themselves as less psychopathic than did informants. In addition, the same FFM domains-low Agreeableness and Conscientiousness-characterized individuals with psychopathic traits regardless of reporter. Psychopathic individuals appear capable of reporting accurately on psychopathic traits when there are no direct consequences to accurate reporting (i.e., sentencing). It may be that the lack of concern for the consequences of these traits has been mistaken for a lack of insight into them.     

Learning to learn causal models

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.     

Rater Personality and Performance Dimension Weighting in Making Overall Performance Judgments

Purpose  

The present study examined the effects of rater personality on the performance appraisal process. Specifically, we determined the relative weights that raters place on different performance dimensions when making overall performance evaluations, and examined whether rater personality influenced this weighting process. The literatures on social/political values and mate/friend selection were used as guiding frameworks in developing specific hypotheses.     

Medroxyprogesterone acetate impairs memory and alters the GABAergic system in aged surgically menopausal rats

In women, medroxyprogesterone acetate (MPA) is the most commonly used progestin component of hormone therapy (HT). In vitro, MPA negatively impacts markers of neuronal health and exacerbates experimentally-induced neurotoxicity. There is in vitro evidence that these factors are driven by GABAergic and neurotrophic systems. Whether these effects translate to a negative impact on brain function has not been tested in vivo, clinically or preclinically. Here we evaluate the mnemonic and neurobiological effects of MPA in the surgically menopausal rat. Aged ovariectomized (OVX) rats were given subcutaneous vehicle, natural progesterone, low-dose MPA or high-dose MPA. Multiple cognitive domains were analyzed via the water radial-arm maze (WRAM) and Morris maze (MM). Cognitive brain regions were assayed for changes in the GABAergic system by evaluating GAD protein, the synthesizing enzyme for GABA, and neurotrophins. On the WRAM, both progestin types impaired learning. Further, high-dose MPA impaired delayed memory retention on the WRAM, and exacerbated overnight forgetting on the MM. While neurotrophins were not affected by progesterone or MPA treatment, both progestin types altered GAD levels. MPA significantly and progesterone marginally decreased GAD levels in the hippocampus, and both MPA and progesterone significantly increased GAD levels in the entorhinal cortex. These findings suggest that MPA, the most commonly used progestin in HT, is detrimental to learning and two types of memory, and modulates the GABAergic system in cognitive brain regions, in aged surgically menopausal rats. These findings, combined with in vitro evidence that MPA is detrimental to neuronal health, indicates that MPA has negative effects for brain health and function.     

Unraveling the race paradox of achievement and self-views

Considerable evidence indicates that African American students achieve less academically than European American students. Yet, African American students hold more positive self-views than their European American counterparts. Previous studies that address these seemingly paradoxical findings focus on students in a relatively narrow age range and/or convenience samples. Therefore, the current study examines two common explanations for these seemingly paradoxical findings, among a large and diverse sample of African American and European American students (N = 1, 493) from elementary to post-secondary school and across the socioeconomic spectrum. Results indicate that among a diverse group of students and conceptualized in two different ways, African American students do not devalue academics. However, African American students are more likely than European American students to discount academic feedback.     

Prototype abstraction by monkeys (Macaca mulatta)

The authors analyze the shape categorization of rhesus monkeys (Macaca mulatta) and the role of prototype- and exemplar-based comparison processes in monkeys' category learning. Prototype and exemplar theories make contrasting predictions regarding performance on the Posner-Homa dot-distortion categorization task. Prototype theory--which presumes that participants refer to-be-categorized items to a representation near the category's center (the prototype)--predicts steep typicality gradients and large prototype-enhancement effects. Exemplar theory--which presumes that participants refer to-be-categorized items to memorized training exemplars-predicts flat typicality gradients and small prototype-enhancement effects. Across many categorization tasks that, for the first time, assayed monkeys' dot-distortion categorization, monkeys showed steep typicality gradients and large prototype-enhancement effects. These results suggest that monkeys--like humans--refer to-be-categorized items to a prototype-like representation near the category's center rather than to a set of memorized training exemplars.