Dissecting social interaction:Dual-fMRI reveals patterns of interpersonal brain-behavior relationships that dissociate among dimensions of social exchange

During social interactions, each individual’s actions are simultaneously a consequence of and an antecedent to their interaction partner’s behavior. Capturing online the brain processes underlying such mutual dependency requires simultaneous measurements of all interactants’ brains during real-world exchange (‘hyperscanning’). This demands a precise characterization of the type of interaction under investigation, however, and analytical techniques capable of capturing interpersonal dependencies. We adapted an interactive task capable of dissociating between two dimensions of interdependent social exchange: goal structure (cooperation vs competition) and interaction structure [concurrent (CN) vs turn-based]. Performing dual-functional magnetic resonance imaging hyperscanning on pairs of individuals interacting on this task, and modeling brain responses in both interactants as systematic reactions to their partner’s behavior, we investigated interpersonal brain-behavior dependencies (iBBDs) during each dimension. This revealed patterns of iBBDs that differentiated among exchanges; in players supporting the actions of another, greater brain responses to the co-player’s actions were expressed in regions implicated in social cognition, such as the medial prefrontal cortex, precuneus and temporal cortices. Stronger iBBD during CN competitive exchanges was observed in brain systems involved in movement planning and updating, however, such as the supplementary motor area. This demonstrates the potential for hyperscanning to elucidate neural processes underlying different forms of social exchange

Getting into sync:Data-driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by “performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion.” To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo-motor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to “dual-fMRI” data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction

Impaired Self-Other Distinction and Subcortical Gray-Matter Alterations Characterize Socio-Cognitive Disturbances in Multiple Sclerosis

Introduction: Recent studies of patients with multiple sclerosis (MS) have revealed disturbances in distinct components of social cognition, such as impaired mentalizing and empathy. The present study investigated this socio-cognitive profile in MS patients in more detail, by examining their performance on tasks measuring more fundamental components of social cognition and any associated disruptions to gray-matter volume (GMV). Methods: We compared 43 patients with relapse-remitting MS with 43 age- and sex-matched healthy controls (HCs) on clinical characteristics (depression, fatigue), cognitive processing speed, and three aspects of low-level social cognition; specifically, imitative tendencies, visual perspective taking, and emotion recognition. Using voxel-based morphometry, we then explored relationships between GMV and these clinical and behavioral measures. Results: Patients exhibited significantly slower processing speed, poorer perspective taking, and less imitation compared with HCs. These impairments were related to reduced GMV throughout the putamen, thalami, and anterior insula, predominantly in the left hemisphere. Surprisingly, differences between the groups in emotion recognition were not significant. Conclusion: Less imitation and poorer perspective taking indicate a cognitive self-bias when faced with conflicting self- and other-representations. This suggests that impaired self-other distinction, and an associated subcortical pattern of GM atrophy, might underlie the socio-cognitive disturbances observed in MS

You ≠ Me: Individual differences in the structure of social cognition

This study investigated the structure of social cognition, and how it is influenced by personality; specifically, how various socio-cognitive capabilities, and the pattern of inter-relationships and co-dependencies among them differ between divergent personality styles. To measure social cognition, a large non-clinical sample (n = 290) undertook an extensive battery of self-report and performance-based measures of visual perspective taking, imitative tendencies, affective empathy, interoceptive accuracy, emotion regulation, and state affectivity. These same individuals then completed the Personality Styles and Disorders Inventory. Latent Profile Analysis revealed two dissociable personality profiles that exhibited contrasting cognitive and affective dispositions, and multivariate analyses indicated further that these profiles differed on measures of social cognition; individuals characterised by a flexible and adaptive personality profile expressed higher action orientation (emotion regulation) compared to those showing more inflexible tendencies, along with better visual perspective taking, superior interoceptive accuracy, less imitative tendencies, and lower personal distress and negativity. These characteristics point towards more efficient self-other distinction, and to higher cognitive control more generally. Moreover, low-level cognitive mechanisms served to mediate other higher level socio-emotional capabilities. Together, these findings elucidate the cognitive and affective underpinnings of individual differences in social behaviour, providing a data-driven model that should guide future research in this area

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

PRESISTANT : data pre-processing assistant

A concrete classification algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. Typically, in order to improve the results, datasets need to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and non-experienced users become overwhelmed. Trial and error is not feasible in the presence of big amounts of data. We developed a method and tool—PRESISTANT, with the aim of answering the need for user assistance during data pre-processing. Leveraging ideas from meta-learning, PRESISTANT is capable of assisting the user by recommending pre-processing operators that ultimately improve the classification performance. The user selects a classification algorithm, from the ones considered, and then PRESISTANT proposes candidate transformations to improve the result of the analysis. In the demonstration, participants will experience, at first hand, how PRESISTANT easily and effectively ranks the pre-processing operators.Peer ReviewedPostprint (author's final draft

Simulation Model for Salmonella Typhimurium on a Farrow-to-Finish Herd

A stochastic model which simulates they dynamics of Salmonella Typhimurium in moderate to highly infeted farrow-to-finish farms in Portugal was developed. The model comprises six different stages: three at the reproductive phase (sows) and another three for pig growth

One-counter Markov decision processes

We study the computational complexity of central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. OC-MDPs are equivalent to a controlled extension of (discrete-time) Quasi-Birth-Death processes (QBDs), a stochastic model studied heavily in queueing theory and applied probability. They can thus be viewed as a natural ``adversarial'' version of a classic stochastic model. Alternatively, they can also be viewed as a natural probabilistic/controlled extension of classic one-counter automata. OC-MDPs also subsume (as a very restricted special case) a recently studied MDP model called ``solvency games'' that model a risk-averse gambling scenario. Basic computational questions about these models include ``termination'' questions and ``limit'' questions, such as the following: does the controller have a ``strategy'' (or ``policy'') to ensure that the counter (which may for example count the number of jobs in the queue) will hit value 0 (the empty queue) almost surely (a.s.)? Or that it will have infinite limsup value, a.s.? Or, that it will hit value 0 in selected terminal states, a.s.? Or, in case these are not satisfied a.s., compute the maximum (supremum) such probability over all strategies. We provide new upper and lower bounds on the complexity of such problems. For some of them we present a polynomial-time algorithm, whereas for others we show PSPACE- or BH-hardness and give an EXPTIME upper bound. Our upper bounds combine techniques from the theory of MDP reward models, the theory of random walks, and a variety of automata-theoretic methods

Mining association rules for label ranking

Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

Magnetic record associated with tree ring density: Possible climate proxy

A magnetic signature of tree rings was tested as a potential paleo-climatic indicator. We examined wood from sequoia tree, located in Mountain Home State Forest, California, whose tree ring record spans over the period 600 – 1700 A.D. We measured low and high-field magnetic susceptibility, the natural remanent magnetization (NRM), saturation isothermal remanent magnetization (SIRM), and stability against thermal and alternating field (AF) demagnetization. Magnetic investigation of the 200 mm long sequoia material suggests that magnetic efficiency of natural remanence may be a sensitive paleoclimate indicator because it is substantially higher (in average >1%) during the Medieval Warm Epoch (700–1300 A.D.) than during the Little Ice Age (1300–1850 A.D.) where it is <1%. Diamagnetic behavior has been noted to be prevalent in regions with higher tree ring density. The mineralogical nature of the remanence carrier was not directly detected but maghemite is suggested due to low coercivity and absence of Verwey transition. Tree ring density, along with the wood's magnetic remanence efficiency, records the Little Ice Age (LIA) well documented in Europe. Such a record suggests that the European LIA was a global phenomenon. Magnetic analysis of the thermal stability reveals the blocking temperatures near 200 degree C. This phenomenon suggests that the remanent component in this tree may be thermal in origin and was controlled by local thermal condition