A conceptual and psychometric framework for distinguishing categories and dimensions

An important, sometimes controversial feature of all psychological phenomena is whether they are categorical or dimensional. A conceptual and psychometric framework is described for distinguishing whether the latent structure behind manifest categories (e.g., psychiatric diagnoses, attitude groups, or stages of development) is category-like or dimension-like. Being dimension-like requires (a) within-category heterogeneity and (b) between-category quantitative differences. Being category-like requires (a) within-category homogeneity and (b) between-category qualitative differences. The relation between this classification and abrupt versus smooth differences is discussed. Hybrid structures are possible. Being category-like is itself a matter of degree; the authors offer a formalized framework to determine this degree. Empirical applications to personality disorders, attitudes toward capital punishment, and stages of cognitive development illustrate the approach.     

How dogs navigate to catch frisbees

Using micro-video cameras attached to the heads of 2 dogs, we examined their optical behavior while catching Frisbees. Our findings reveal that dogs use the same viewer-based navigational heuristics previously found with baseball players (i.e., maintaining the target along a linear optical trajectory, LOT, with optical speed constancy). On trials in which the Frisbee dramatically changed direction, the dog maintained an LOT with speed constancy until it apparently could no longer do so and then simply established a new LOT and optical speed until interception. This work demonstrates the use of simple control mechanisms that utilize invariant geometric properties to accomplish interceptive tasks. It confirms a common interception strategy that extends both across species and to complex target trajectories.     

Asians on the edge: the reciprocity of allied behavior

The discussion of racial and ethnic minority issues has traditionally been a Black-White discussion. As such, American Indians, Latinos, and Asians have been left out of this discussion. Such exclusion does not make scientific sense, as it makes conclusions about race relations less generalizable. It also comes with a cost, as the excluded groups are often asked to give up their respective ethnicities to join the discussion. The present article, discusses some of these scientific and social losses and suggests that prominent African American and White leaders serve as allies for the inclusion of all groups in the discussion of race relations, focusing particularly on the Asian experience. Such allies must come not only from academicians but also from community activists and those in power positions in the media.     

Change in cognitive capabilities in the oldest old: the effects of proximity to death in genetically related individuals over a 6-year period

Change in cognitive abilities was assessed over a 6-year period in a sample of monozygotic and same-sex dizygotic twin pairs (N = 507 individuals), aged 80 and older (mean age = 83.3 years: SD = 3.1). who remained nondemented over the course of the study. Latent growth models (LGMs) show that chronological age and time to death are consistent predictors of decline in measures of memory, reasoning, speed, and verbal abilities. Multivariate LGM analysis resulted in weak and often negative correlations among rates of change between individuals within twin pairs, indicating greater differential change within twin pairs than occurs on average across twin pairs. These findings highlight several challenges for estimating genetic sources of variance in the context of compromised health and mortality-related change.     

The native mind: biological categorization and reasoning in development and across cultures

This article describes cross-cultural and developmental research on folk biology: that is, the study of how people conceptualize living kinds. The combination of a conceptual module for biology and cross-cultural comparison brings a new perspective to theories of categorization and reasoning. From the standpoint of cognitive psychology, the authors find that results gathered from standard populations in industrialized societies often fail to generalize to humanity at large. For example, similarity-driven typicality and diversity effects either are not found or pattern differently when one moves beyond undergraduates. From the perspective of folk biology, standard populations may yield misleading results because they represent examples of especially impoverished experience with nature. Certain phenomena are robust across populations, consistent with notions of a core module.     

Client-level predictors of adherence to MST in community service settings

This study examined the association of youth and family characteristics with therapist fidelity to an evidence-based treatment provided in real world practice settings. Participants were 233 families that reported on the 66 therapists organized into 16 teams in nine organizations providing multisystemic therapy (MST). Therapist adherence ratings were lower for youths referred for both criminal offenses and substance abuse than for youths referred either for substance abuse or status offenses, and was negatively associated with pretreatment arrests and school suspensions. Adherence ratings were positively associated with educational disadvantage and caregiver-therapist ethnic match and marginally positively associated with economic disadvantage. The findings suggest directions for future research on the implementation of evidence-based treatments in community settings.     

Bias in exponential and power function fits due to noise: Comment on Myung,Kim, and Pitt

Myung, Kim, and Pitt (2000) demonstrated that simple power functions almost always provide a better fit to purely random data than do simple exponential functions. This result has important implications, because it suggests that high noise levels, which are common in psychological experiments, may cause a bias favoring power functions. We replicate their result and extend it by showing strong bias for more realistic sample sizes. We also show that biases occur for data that contain both random and systematic components, as may be expected in real data. We then demonstrate that these biases disappear for two- or three-parameter functions that include linear parameters (in at least one parameterization). Our results suggest that one should exercise caution when proposing simple power and exponential functions as models of learning. More generally, our results suggest that linear parameters should be estimated rather than fixed when one is comparing the fit of nonlinear models to noisy data.