The Experts below are selected from a list of 39909 Experts worldwide ranked by ideXlab platform
Pascal Vrticka - One of the best experts on this subject based on the ideXlab platform.
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conceptual analysis a social neuroscience approach to Interpersonal Interaction in the context of disruption and disorganization of attachment namda
Frontiers in Psychiatry, 2020Co-Authors: Lars O White, Charlotte Schulz, Margerete J S Schoett, Melanie T Kungl, Jan Keil, Jessica L Borelli, Pascal VrtickaAbstract:Humans are strongly dependent upon social resources for allostasis and emotion regulation. This applies especially to early childhood because humans – as an altricial species – have a prolonged period of dependency on support and input from caregivers who typically act as sources of co-regulation. Accordingly, attachment theory proposes that the history and quality of early Interactions with primary caregivers shape children’s internal working models of attachment. In turn, these attachment models guide behavior, initially with the set goal of maintaining proximity to caregivers, but eventually paving the way to more generalized mental representations of self and others. Mounting evidence in nonclinical populations suggests that these mental representations coincide with differential patterns of neural structure, function, and connectivity in a range of brain regions previously associated with emotional and cognitive capacities. What is currently lacking, however, is an evidence-based account of how early adverse attachment-related experiences and/or the emergence of attachment disorganization impact the developing brain. While work on early childhood adversities offers important indications, we propose that how these events become biologically embedded crucially hinges on the context of the child-caregiver attachment relationships in which the events take place. Our selective review distinguishes between direct social neuroscience research on disorganized attachment and indirect maltreatment-related research, converging on aberrant functioning in neurobiological systems subserving processing of aversion, approach, emotion regulation, and mental states in the wake of severe attachment disruption. To account for the heterogeneity of findings, we propose two distinct neurobiological phenotypes characterized by hyper- and hypo-arousal deriving, among others, from the caregiver serving either as a threatening or as an insufficient source of co-regulation, respectively.
Carter T Butts - One of the best experts on this subject based on the ideXlab platform.
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exponential family random graph models for rank order relational data
Sociological Methodology, 2017Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of Interpersonal Interaction among members of a technology education program.
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exponential family random graph models for rank order relational data
arXiv: Methodology, 2012Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks the others according to some criterion, often arise from sociometric measurements of judgment (e.g., self-reported Interpersonal Interaction) or preference (e.g., relative liking). We propose a class of exponential-family models for rank-order relational data and derive a new class of sufficient statistics for such data, which assume no more than within-subject ordinal properties. Application of MCMC MLE to this family allows us to estimate effects for a variety of plausible mechanisms governing rank structure in cross-sectional context, and to model the evolution of such structures over time. We apply this framework to model the evolution of relative liking judgments in an acquaintance process, and to model recall of relative volume of Interpersonal Interaction among members of a technology education program.
Huntong Tan - One of the best experts on this subject based on the ideXlab platform.
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judgment and decision making research in auditing a task person and Interpersonal Interaction perspective
Social Science Research Network, 2005Co-Authors: Mark W Nelson, Huntong TanAbstract:This paper discusses judgment and decision making research in auditing - i.e., research that uses a psychological lens to understand, evaluate, and improve judgments, decisions, or choices in an auditing setting. Much of this work uses the laboratory experiment approach, but we will also cover related studies that use survey and field study approaches. We classify extant auditing JDM literature as covering three broad areas: (a) the audit task, (b) the auditor and his/her attributes, and (c) Interaction between auditor and other stakeholders in task performance. We use this task, person, and Interaction categorization to assess the cumulative knowledge generated in the past 25 years, as well as to identify knowledge gaps and opportunities for future research.
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judgment and decision making research in auditing a task person and Interpersonal Interaction perspective
Ear and Hearing, 2005Co-Authors: Mark W Nelson, Huntong TanAbstract:This paper discusses judgment and decision making research in auditing—i.e., research that uses a psychological lens to understand, evaluate, and improve judgments, decisions, or choices in an auditing setting. Much of this work uses the laboratory experiment approach, but we also cover related studies that use survey and field study approaches. We classify extant auditing judgment and decision making (JDM) literature as covering three broad areas: (1) the audit task, (2) the auditor and his/her attributes, and (3) Interaction between auditor and other stakeholders in task performance. We use this task, person, and Interaction categorization to assess the cumulative knowledge generated in the past 25 years, as well as to identify knowledge gaps and opportunities for future research.
David Traum - One of the best experts on this subject based on the ideXlab platform.
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the effects of virtual agent humor and gaze behavior on human virtual agent proxemics
Intelligent Virtual Agents, 2011Co-Authors: Peter Khooshabeh, Cade Mccall, Sudeep Gandhe, Jonathan Gratch, Jim Blascovich, David TraumAbstract:We study whether a virtual agent that delivers humor through verbal behavior can affect an individual's proxemic behavior towards the agent. Participants interacted with a virtual agent through natural language and, in a separate task, performed an embodied Interpersonal Interaction task in a virtual environment. The study used minimum distance as the dependent measure. Humor generated by the virtual agent through a text chat did not have any significant effects on the proxemic task. This is likely due to the experimental constraint of only allowing participants to interact with a disembodied agent through a textual chat dialogue.
Pavel N. Krivitsky - One of the best experts on this subject based on the ideXlab platform.
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exponential family random graph models for rank order relational data
Sociological Methodology, 2017Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of Interpersonal Interaction among members of a technology education program.
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exponential family random graph models for rank order relational data
arXiv: Methodology, 2012Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks the others according to some criterion, often arise from sociometric measurements of judgment (e.g., self-reported Interpersonal Interaction) or preference (e.g., relative liking). We propose a class of exponential-family models for rank-order relational data and derive a new class of sufficient statistics for such data, which assume no more than within-subject ordinal properties. Application of MCMC MLE to this family allows us to estimate effects for a variety of plausible mechanisms governing rank structure in cross-sectional context, and to model the evolution of such structures over time. We apply this framework to model the evolution of relative liking judgments in an acquaintance process, and to model recall of relative volume of Interpersonal Interaction among members of a technology education program.
