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DTSTART;TZID=America/Los_Angeles:20130107T000000
DTEND;TZID=America/Los_Angeles:20130107T000000
DTSTAMP:20201013T004812Z
CREATED:20200922T215553Z
LAST-MODIFIED:20201013T004812Z
UID:4219-1357516800-1357516800@bec.ucla.edu
SUMMARY:Aaron Lukaszewski - The Origins of Heritable Personality Variation: An Integrative Evolutionary Approach
DESCRIPTION:Aaron Lukaszewski: Loyola Marymount UniversityTwo basic questions in the study of personality origins are (1) Why do people vary in their personality trait levels? and (2) Why do distinct trait dimensions covary in consistent patterns within individuals\, rather than varying independently? The current presentation describes an integrative evolutionary framework within which both of these questions can be addressed\, and highlights supportive empirical findings. For instance\, since physical strength and physical attractiveness likely predicted the reproductive payoffs of extraverted behavioral strategies across most of human history\, it was theorized that extraversion levels are facultatively calibrated to variations in these phenotypic features. Confirming these predicted patterns\, strength and attractiveness together explained a surprisingly large fraction of the variance in extraversion in Studies 1 and 2 – effects that were independent of variance explained by an androgen receptor gene polymorphism. Study 3 then provided evidence that the covariation among a wide array of interpersonal traits (e.g.\, extraversion\, emotionality\, attachment styles) is orchestrated by their facultative calibration in response to common input cues. Overall\, these findings suggest that multiple types of proximate mechanisms – facultative calibration and specific gene polymorphisms – operate in concert to determine adaptively-patterned personality (co)variation. http://bec.ucla.edu/papers/Lukaszewski_BEC.pdf
URL:https://bec.ucla.edu/event/aaron-lukaszewski-the-origins-of-heritable-personality-variation-an-integrative-evolutionary-approach/
CATEGORIES:Past Presentation,Presentation
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DTSTART;TZID=America/Los_Angeles:20130114T000000
DTEND;TZID=America/Los_Angeles:20130114T000000
DTSTAMP:20201013T004812Z
CREATED:20200922T215553Z
LAST-MODIFIED:20201013T004812Z
UID:4220-1358121600-1358121600@bec.ucla.edu
SUMMARY:James W. Pennebaker - Using function words to understand people\, groups\, and culture
DESCRIPTION:James W. Pennebaker: University of Texas at AustinThe smallest and most frequently used words in English are function words — pronouns\, prepositions\, articles\, auxiliary verbs\, etc.  These overlooked words are profoundly social and can signal the ways people think\, feel\, and relate to others.  Using a variety of text analysis methods\, it is possible to track function words to deduce an author’s age\, sex\, social class\, personality\, honesty\, status\, and emotional state.  By analyzing ongoing interactions\, the degree to which couples\, groups\, or larger entities are listening to and effectively communicating with others can be estimated. The function word analytic approach has interesting implications for research projects in the social sciences\, humanities\, education\, business\, medicine.
URL:https://bec.ucla.edu/event/james-w-pennebaker-using-function-words-to-understand-people-groups-and-culture/
CATEGORIES:Past Presentation,Presentation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20130128T000000
DTEND;TZID=America/Los_Angeles:20130128T000000
DTSTAMP:20201013T004812Z
CREATED:20200922T215554Z
LAST-MODIFIED:20201013T004812Z
UID:4221-1359331200-1359331200@bec.ucla.edu
SUMMARY:Mark Handcock - Statistical Modeling of Social Networks
DESCRIPTION:Mark Handcock: University of California\, Los AngelesIn this talk we give an overview of social network analysis from the perspective of a statistician.  The networks field is\, and has been\, broadly multidisciplinary with significant contributions from the social\, natural and mathematical sciences.  This has lead to a plethora of terminology\, and network conceptualizations commensurate with the varied objectives of network analysis.  As the primary focus of the social sciences has been the representation of social relations with the objective of understanding social structure\, social scientists have been central to this development. We illustrate these ideas with Exponential-family random graph models (ERGM) which attempt to represent the complex dependencies in networks in a parsimonious\, tractable and interpretable way. A major barrier to the application of such models has been lack of understanding of model behavior and a sound statistical theory to evaluate model fit.  This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the network structure produced by the models matches that of the data. \nWe will also consider  latent cluster random effects models and touch upon issues of the sampling of networks and partially-observed networks. \nWe illustrate these methods using the “statnet” open-source software suite (http://statnet.org).
URL:https://bec.ucla.edu/event/mark-handcock-statistical-modeling-of-social-networks/
CATEGORIES:Past Presentation,Presentation
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