Automated Author ProfileDiaconescu, A.O.
Diaconescu, A.O.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Individuals who are characterized by elevated levels of psychopathic traits tend to consistently violate social norms and seek personal gains. Previous studies in offender and non-offender samples have indicated that such behaviors could be caused by impairments in associative learning. In order for associative learning to be successful, we need to constantly monitor the relationship between events and their outcomes and adapt our behavior in response to changes in these event-outcome contingencies. Recent advances in computational modelling approaches offer insight into the unobservable computational processes that are thought to be required for associative learning. In the present study, we used such a model to investigate the associations between psychopathic traits in a non-offender sample and the cognitive computations underlying adaptive behavior during associative learning. We also investigated the potential engagement of adaptive control processes by measuring oscillatory theta activity in the prefrontal cortex. Participants performed a reinforcement learning task in which the trade-off between using social and non-social information affected task performance and the associated monetary reward for the participant. The findings indicated that increasing levels of psychopathic traits co-occurred with larger impairments in learning from social information, and suggested that antisocial traits were linked to a reduced ability to track changes in the trustworthiness of social advice over time. These impairments did not relate to a preference for one of the information sources, and the decreased task performance did not affect the risk that was taken in order to obtain a high reward. Furthermore, it was found that decreased theta power was linked to higher levels of psychopathic traits, which aligns with indications that theta is involved in tracking the volatility of social information. This is the first study that provides support for a relationship between associative learning, theta power, and psychopathic traits and contributes to our understanding of the underlying associative learning mechanisms that cause low responsivity towards current treatment interventions in those with psychopathy.
Authors
- Driessen, J.M.A. (Josi) ;
- Diaconescu, A.O. ;
- Buitelaar, J.K. ;
- Kessels, R.P.C. (Roy) ;
- Glennon, J.C. ;
- Brazil, I.A. (Inti)