Alexandre Kojève and philosophical Stalinism

Alexandre Kojève not infrequently claimed that he was a Stalinist. While many have ignored his claim, this paper takes it seriously and outlines several aspects of Kojève’s thought that allow one to read Kojève as a philosopher of Stalinism, as one who articulates the self-consciousness of Stalinism. These aspects are three: (1) Kojève’s association of finality and freedom with the overcoming of individuality; (2) the attempt to achieve finality and freedom so defined in the universal homogeneous state, and (3) the structure of that state as an essentially juridical administrative system. The question remains, however, whether one can truly overcome individuality while one is still living, the conflict between individual material existence and the overarching state being irresolvable.     

Illicit Sexual Behavior: A Test of Self-Control Theory

Gottfredson and Hirschi (1990 Gottfredson , Michael and Travis Hirschi . 1990 . A General Theory of Crime . Standford , CA : Standford University Press . [Google Scholar]) contend that all deviance is subsumed under self-control theory and that individuals who commit any one deviant act will tend to commit other deviant acts as well. This research tests the correlation of illicit sexual behaviors with crime as they relate to measures of self-control. Secondly, this research examines the Gottfredson and Hirschi argument that their theory is a “general theory” and predicts all forms of deviance whereby low self-control is a predictor of both sexual deviance and crime. These analyses report illicit sexual behaviors are positively correlated with criminal behaviors providing support for Self-Control Theory. Furthermore, the analyses of this data support that low self-control is a predictor of illicit sexual behaviors and crime.     

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.     

Mands for information generalize across establishing operations

The current study extends the literature on teaching mands for information by assessing whether mands generalize across different establishing operations (EOs). Three children with autism were taught to perform multiple behavior chains, 3 of which included a common response (e.g., “Where is the spoon?”) used for different purposes. An interrupted-behavior-chain procedure was used to contrive the EO for each mand. After teaching a mand for information under 1 EO, the remaining chains were interrupted to determine whether the mand had generalized to different EOs. For all participants, mands for information generalized across EOs. For 2 participants, a new mand-for-information topography emerged after training.     

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predicts differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.     

SUSTAIN: a network model of category learning

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.     

"It's a hurricane! it's a hurricane!": can music facilitate social constructive and sociodramatic play in a preschool classroom?

Sustaining attention and successfully engaging with others in collaborative play are important accomplishments focused on in preschool classrooms and childcare centers. In addition, music is frequently used in early childhood classrooms, and even recommended as an environmental feature to motivate and regulate children's behavior. Although pretend play provides appealing opportunities for developing these social abilities, no studies to date have explored the use of music as a tool to motivate and sustain constructive and social pretend play. Results from the current study indicate that within 1 preschool classroom, more sustained play (with fewer interruptions) occurred when music played as compared to when no music played in the background. In addition, significantly more dyadic play occurred when slower music played in the background, than when no music played.     

Women's knowledge of prenatal ultrasound and informed choice

This study evaluated women's understanding of prenatal ultrasound in terms of meeting the requirements for informed choice. A cross-sectional survey was conducted to evaluate (1) how information is provided, (2) women's perceived value of the information received and, (3) their understanding of ultrasound in relation to the principles of informed choice. Women (n = 113) completed a questionnaire prior to their 18-week ultrasound. Fifty-five percent stated they received no information from their care provider. Only 31.9% considered health care providers as a very helpful source of information. Yet, 69.0% stated their care provider gave them information that facilitated their understanding. Gaps were identified in women's understanding of ultrasound. Specifically, 46.0% did not view ultrasound as a screen for anomalies; some were uncertain about the safety (18.6%), diagnostic capabilities (26.5%), and limitations of testing (37.2%). These results suggest that women's understanding of ultrasound does not meet the requirements of informed choice.     

The multifaceted nature of unsupervised category learning

A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories (i.e., the co-occurrence patterns of feature values) affects acquisition. Recent work in supervised learning has shown that a learner's interactions with the stimulus set also plays an important role in acquisition. The present study extends this work to unsupervised learning situations involving simple one-dimensional stimuli. The results suggest that categorization performance is a function of both learning mode (i.e., study conditions) and learning problem (i.e., category structure). Unsupervised learning, like supervised learning, appears to be multifaceted, with different learning modes best paired with certain learning problems.