ABSTRACT
This article presents a multimethod, multi-informant comparison of community samples of committed gay male (n=30) and lesbian (n=30) couples with both committed (n=50 young engaged and n=40 older married) and noncommitted (n=109 exclusively dating) heterosexual pairs. Specifically, in this study the quality of same- and opposite-sex relationships was examined at multiple levels of analysis via self-reports and partner reports, laboratory observations, and measures of physiological reactivity during dyadic interactions. Additionally, individuals in same-sex, engaged, and marital relationships were compared with one another on adult attachment security as assessed through the coherence of participants' narratives about their childhood experiences. Results indicated that individuals in committed same-sex relationships were generally not distinguishable from their committed heterosexual counterparts, with one exception--lesbians were especially effective at working together harmoniously in laboratory observations.
Subject(s)
Courtship/psychology , Heterosexuality/psychology , Homosexuality/psychology , Human Development , Interpersonal Relations , Marriage , Adolescent , Adult , Age Factors , Aged , Attitude , Emotions , Family Characteristics , Female , Homosexuality, Female/psychology , Humans , Male , Middle Aged , Models, Psychological , Quality of Life , Sex Factors , Sexual Partners , Surveys and Questionnaires , Videotape RecordingABSTRACT
The focus of this work was on the relation between grammatical gender and categorization. In one set of studies, monolingual English-, Spanish-, French-, and German-speaking children and adults assigned male and female voices to inanimate objects. Results from Spanish and French speakers indicated effects of grammatical gender on classification; results from German speakers did not. A connectionist model simulated the contradicting findings. The connectionist networks were also used to investigate which aspect of grammatical gender was responsible for the different pattern of findings. The predictions from the connectionist simulations were supported by the results from an artificial language-learning task. The results from this work demonstrate how connectionist networks can be used to identify the differences between languages that affect categorization.
