Abstract not found

The O.J. Simpson “trial of the century ” in the mid-1990s captured the attention of the American populace more than any other public spectacle since the kidnaping of the Lindberg baby in the 1920s. A prominent football player and popular sportscaster was charged with a gruesome double homicide. The attorneys for the prosecution and defense were of various races and genders. The evidence presented on each side was at times amazingly simple, visual, and emotional, and at times was verbal, abstract, and probably incomprehensible to jurors. The witnesses included individuals of diverse styles, demeanors, and credibility. The jurors, the recipients of the messages from these various sources, were themselves a mixed group of people of diverse backgrounds, beliefs, and personal experiences who had to sift through the trial material and arrive at a decision as to whether the defendant had been proven guilty or not. The context in which all of this took place was at times tense and sad, and at times filled with humor and positive feelings. Not surprisingly, no experiment has ever captured the extraordinary complexity inherent in this situation, yet almost all of the variables present in this trial (and many not present) have been examined in the social psychological literature on attitude formation and change. This chapter provides an overview of research on these diverse variables and addresses the processes by which these variables are thought to result in influence. Although it has become a cliché to say that the attitude construct is the most indispensable concept in

We are interested in quantitatively modeling the interactions between humans in conversational settings. While a variety of models are potentially appropriate, such as the coupled HMM, all require a very large number of parameters to describe the interactions between chains. We propose as an alternative the generative model developed in [1], the Influence Model, which parametrizes the hidden state transition probabilities by taking a convex combination of the pairwise transitions with constant "influence" parameters. We develop a learning algorithm for this model and show its abilities to model chain dependencies in comparison to other standard models using synthetic data. We also show early results of applying this model to human interaction data.

The authors propose that people adopt others ’ perspectives by serially adjusting from their own. As predicted, estimates of others ’ perceptions were consistent with one’s own but differed in a manner consistent with serial adjustment (Study 1). Participants were slower to indicate that another’s perception would be different from—rather than similar to—their own (Study 2). Egocentric biases increased under time pressure (Study 2) and decreased with accuracy incentives (Study 3). Egocentric biases also increased when participants were more inclined to accept plausible values encountered early in the adjustment process than when inclined to reject them (Study 4). Finally, adjustments tend to be insufficient, in part, because people stop adjusting once a plausible estimate is reached (Study 5). We have endeavored to show... that thought in the child is egocentric, i.e., that the child thinks for himself without troubling to make himself understood nor to place himself at the other person’s point of view.... If this be the case, we must expect childish reasoning to differ very considerably from ours, to be deductive and above all less rigorous. (Piaget, 1959, p. 1) Children view their perceptions of the world as accurate reflections

Increasing evidence suggests that mundane knowledge about objects, people, and events is grounded in the brain’s modality-specific systems. The modality-specific representations that become active to represent these entities in actual experience are later used to simulate them in their absence. In particular, simulations of perception, action, and mental states often appear to underlie the representation of knowledge, making it embodied and situated. Findings that support this conclusion are briefly reviewed from cognitive psychology, social psychology, and cognitive neuroscience. A similar representational process may underlie religious knowledge. In support of this conjecture, embodied knowledge appears central to three aspects of religious experience: religious visions, religious beliefs, and religious rituals. In religious visions, the process of simulation offers a natural account of how these experiences are produced. In religious beliefs, knowledge about the body and the environment are typically central in religious frameworks, and are likely to affect the perception of daily experience. In religious rituals, embodiments appear central to conveying religious ideas metaphorically and to establishing them in memory. To the extent that religious knowledge is like non-religious knowledge, embodiment is likely to play central roles. When most lay people hear the term, “knowledge, ” they think of material acquired explicitly in formal education, such as knowledge of history or algebra. They also think of products that result from academic inquiry, * Emory University.

We are interested in quantitatively modeling the interactions between humans in conversational settings. While a variety of models are potentially appropriate, such as the coupled HMM, all require a very large number of parameters to describe the interactions between chains. We propose as an alternative the generative model developed in [1], the Influence Model, which parametrizes the hidden state transition probabilities by taking a convex combination of the pairwise transitions with constant "influence" parameters. We develop a learning algorithm for this model and show its abilities to model chain dependencies in comparison to other standard models using synthetic data. We also show early results of applying this model to human interaction data.

and non-experiential routes of motor influences on affect and evaluation

"'9 ' UNIVERSITY PRESS

A simulation of new product market penetration in a social environment is performed, using a spintronic model, where each element of a 3D network interacts with its first neighbors. Agents are assumed to be rational, with a perfect market foresight. Unitary production cost decreases when consumption is increased. Simulations indicate that social interaction is the most important factor for new product market penetration as compared to the consumer readiness to pay a higher price. Besides the critical exponent of the nucleation of new consumers is computed, signing a phase transition characterized by the build up of new consumers intermediate clusters and this critical exponent is compared to others belonging to several critical phenomena. 1