The role of social cognition in decision making

Successful decision making in a social setting depends on our ability to understand the intentions, emotions and beliefs of others. The mirror system allows us to understand other people's motor actions and action intentions. ‘Empathy’ allows us to understand and share emotions and sensations with others. ‘Theory of mind’ allows us to understand more abstract concepts such as beliefs or wishes in others. In all these cases, evidence has accumulated that we use the specific neural networks engaged in processing mental states in ourselves to understand the same mental states in others. However, the magnitude of the brain activity in these shared networks is modulated by contextual appraisal of the situation or the other person. An important feature of decision making in a social setting concerns the interaction of reason and emotion. We consider four domains where such interactions occur: our sense of fairness, altruistic punishment, trust and framing effects. In these cases, social motivations and emotions compete with each other, while higher-level control processes modulate the interactions of these low-level biases

Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

Garbage collection auto-tuning for Java MapReduce on Multi-Cores

MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests

Relaxation of Spin Polarized 3^3He in Mixtures of 3^3He and 4^4He Below the 4^4He Lambda Point

We report the first study of the depolarization behavior of spin polarized 3He in a mixture of 3He-4He at a temperature below the 4He Lambda point in a deuterated TetraPhenyl Butadiene-doped deuterated PolyStyrene (dTPB-dPS) coated acrylic cell. In our experiment the measured 3He relaxation time is due to the convolution of the 3He longitudinal relaxation time, T1, and the diffusion time constant of 3He in superfluid 4He since depolarization takes place on the walls. We have obtained a 3He relaxation time ~3000 seconds at a temperature around 1.9K. We have shown that it's possible to achieve values of wall depolarization probability on the order of (1-2)x10^-7 for polarized 3He in the superfluid 4He from a dTPB-dPS coated acrylic surface.Comment: The Model used to interpret the data has been change