Hoops and Barns: a new dilemma for Sosa

Synthese - This paper critically assesses Sosa’s normative framework for performances as well as its application to epistemology. We first develop a problem for one of Sosa’s central...     

Electrophysiological correlates of high-level perception during spatial navigation

We studied the electrophysiological basis of object recognition by recording scalp electroencephalograms while participants played a virtual-reality taxi driver game. Participants searched for passengers and stores during virtual navigation in simulated towns. We compared oscillatory brain activity in response to store views that were targets or nontargets (during store search) or neutral (during passenger search). Even though store category was solely defined by task context (rather than by sensory cues), frontal electrophysiological activity in the low frequency bands (primarily in the θ [4–8 Hz] band) reliably distinguished between the target, nontarget, and neutral store views. These results implicate low-frequency oscillatory brain activity in frontal regions as an important variable in the study of the cognitive processes involved in object recognition, categorization, and other forms of high-level perception.     

Tonic Activity Level in the Right Prefrontal Cortex Predicts Individuals' Risk Taking

ABSTRACT— Human risk taking is characterized by a large amount of individual heterogeneity. In this study, we applied resting-state electroencephalography, which captures stable individual differences in neural activity, before subjects performed a risk-taking task. Using a source-localization technique, we found that the baseline cortical activity in the right prefrontal cortex predicts individual risk-taking behavior. Individuals with higher baseline cortical activity in this brain area display more risk aversion than do other individuals. This finding demonstrates that neural characteristics that are stable over time can predict a highly complex behavior such as risk-taking behavior and furthermore suggests that hypoactivity in the right prefrontal cortex might serve as a dispositional indicator of lower regulatory abilities, which is expressed in greater risk-taking behavior.     

Stationäre Kinder- und Jugendpsychiatrie sowie Psychotherapie bei substanzabhängigen Jungen und Mädchen

This study examines post-treatment outcomes (6 and 12 months post-release) of a multimodal child and adolescent psychiatric and psychotherapeutic inpatient treatment program for boys and girls diagnosed with substance use disorders (n=71) in 2 centres. Up to now there has been a lack of outcome research in this field in Germany. Outcome measures included drug use patterns and psychopathology. The greatest reductions in prevalence at 1 year follow-up occurred for cannabis, methamphetamine, cocaine and heroin use. Additionally the patients and their parents also reported significant improvements in adolescent’s psychopathology at the 1 year follow-up. The results underline the importance of disorder-specific, multimodal inpatient programs for the treatment of children and adolescents with substance use disorders.     

Bias and regulation of bias in intergroup interactions: Implicit attitudes toward Muslims and interaction quality

Previous research suggests that automatically activated bias manifests itself in behavior that can jeopardize the quality of intergroup interactions. However, regulation of automatic associations has the potential to attenuate their influence on intergroup interaction. To test this possibility, 46 non-Muslim White participants interacted with a Muslim confederate and completed an implicit measure of attitudes toward Muslims. The Quadruple Process model [Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The self-regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314-335] was applied to the implicit measure to estimate participants’ strength of negative associations with Muslims and their ability to overcome those negative associations. The confederate’s ratings of how much he liked the participants were predicted by an interaction between automatic negative associations and the ability to overcome them. Specifically, when the strength of participants’ negative associations with Muslims was low, participants’ level of overcoming bias was unrelated to the confederate’s ratings. In contrast, the ability to regulate automatic negative associations predicted greater liking when those associations were strong.     

At your own peril: An ERP study of voluntary task set selection processes in the medial frontal cortex

A tool that is commonly used to investigate selection among different alternatives in a changing environment is the task-switching paradigm. Functional neuroimaging has pointed out a role for the posterior medial frontal cortex and the posterior parietal cortex in the voluntary selection of task sets. In the present study, we set out to investigate the temporal dynamics of these agency-related processes (in task choice vs. no-choice conditions) using event-related brain potentials (ERPs). The results revealed agency-related modulations of a series of ERP components, including (1) an early parieto-occipital activation, taken to reflect the evaluation of choice versus no choice; (2) a subsequent medial frontal expression of the voluntary selection between task sets; (3) a CNV-like sustained negativity in preparation for the target; (4) a target-induced N210—P210 complex, taken to reflect early sensory-perceptual processing; and (5) a target-induced P3, associated with the evaluation of the stimulus and its designated response vis-à-vis the chosen versus competing task sets. Together, these results indicate that the opportunity to choose between tasks invokes activity originating from the medial frontal cortex, associated with voluntary task set selection, but also activation at different time points in a number of other brain areas, not necessarily captured by functional neuroimaging.     

HMMTree: A computer program for latent-class hierarchical multinomial processing tree models

Latent-class hierarchical multinomial models are an important extension of the widely used family of multinomial processing tree models, in that they allow for testing the parameter homogeneity assumption and provide a framework for modeling parameter heterogeneity. In this article, the computer program HMMTree is introduced as a means of implementing latent-class hierarchical multinomial processing tree models. HMMTree computes parameter estimates, confidence intervals, and goodness-of-fit statistics for such models, as well as the Fisher information, expected category means and variances, and posterior probabilities for class membership. A brief guide to using the program is provided.