A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

Oxytocin effects on neural correlates of self-referential processing

Oxytocin (OT) influences how humans process information about others. Whether OT affects the processing of information about oneself remains unknown. Using a double-blind, placebo-controlled within-subject design, we recorded event-related potentials (ERPs) from adults during trait judgments about oneself and a celebrity and during judgments on word valence, after intranasal OT or placebo administration. We found that OT vs. placebo treatment reduced the differential amplitudes of a fronto-central positivity at 220-280 ms (P2) during self- vs. valence-judgments. OT vs. placebo treatment tended to reduce the differential amplitude of a late positive potential at 520-1000 ms (LPP) during self-judgments but to increase the differential LPP amplitude during other-judgments. OT effects on the differential P2 and LPP amplitudes to self- vs. celebrity-judgments were positively correlated with a measure of interdependence of self-construals. Thus OT modulates the neural correlates of self-referential processing and this effect varies as a function of interdependence.

Neural correlates of task switching in paternal 15q11-q13 deletion Prader-Willi syndrome

We report a first study of brain activity linked to task switching in individuals with Prader-Willi syndrome (PWS) PWS individuals show a specific cognitive deficit in task switching which may be associated with the display of temper outbursts and repetitive questioning The performance of participants with PWS and typically developing controls was matched in a cued task switching procedure and brain activity was contrasted on switching and non switching blocks using SARI Individuals with PWS did not show the typical frontal-parietal pattern of neural activity associated with switching blocks, with significantly reduced activation in regions of the posterior parietal and ventromedial prefrontal cortices We suggest that this is linked to a difficulty in PWS in setting appropriate attentional weights to enable task set reconfiguration In addition to this, PWS individuals did not show the typical pattern of deactivation, with significantly less deactivation in an anterior region of the ventromedial prefrontal cortex One plausible explanation for this is that individuals with PWS show dysfunction within the default mode network which has been linked to attentional control The data point to functional changes in the neural circuitry supporting task switching in PWS even when behavioural performance is matched to controls and thus highlight neural mechanisms that may be involved in a specific pathway between genes cognition and behaviour (C) 2010 Elsevier B V All rights reserved

The presence of a culturally similar or dissimilar social partner affects neural responses to emotional stimuli

Background: Emotional responding is sensitive to social context; however, little emphasis has been placed on the mechanisms by which social context effects changes in emotional responding.

Objective: We aimed to investigate the effects of social context on neural responses to emotional stimuli to inform on the mechanisms underpinning context-linked changes in emotional responding.

Design: We measured event-related potential (ERP) components known to index specific emotion processes and self-reports of explicit emotion regulation strategies and emotional arousal. Female Chinese university students observed positive, negative, and neutral photographs, whilst alone or accompanied by a culturally similar (Chinese) or dissimilar researcher (British).

Results: There was a reduction in the positive versus neutral differential N1 amplitude (indexing attentional capture by positive stimuli) in the dissimilar relative to alone context. In this context, there was also a corresponding increase in amplitude of a frontal late positive potential (LPP) component (indexing engagement of cognitive control resources). In the similar relative to alone context, these effects on differential N1 and frontal LPP amplitudes were less pronounced, but there was an additional decrease in the amplitude of a parietal LPP component (indexing motivational relevance) in response to positive stimuli. In response to negative stimuli, the differential N1 component was increased in the similar relative to dissimilar and alone (trend) context.

Conclusion: These data suggest that neural processes engaged in response to emotional stimuli are modulated by social context. Possible mechanisms for the social-context-linked changes in attentional capture by emotional stimuli include a context-directed modulation of the focus of attention, or an altered interpretation of the emotional stimuli based on additional information proportioned by the context.

Earthmoving trucks condition level prediction using neural networks

Purpose: The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model’s performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

Design/methodology/approach: An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Findings: Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Originality/value: Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.