Date Published: April 1, 2016
Publisher: Public Library of Science
Author(s): Zita Oravecz, Chelsea Muth, Joachim Vandekerckhove, Xuchu Weng.
This pragmatic study examines love as a mode of communication. Our focus is on the receiver side: what makes an individual feel loved and how felt love is defined through daily interactions. Our aim is to explore everyday life scenarios in which people might experience love, and to consider people’s converging and diverging judgments about which scenarios indicate felt love. We apply a cognitive psychometric approach to quantify a receiver’s ability to detect, understand, and know that they are loved. Through crowd-sourcing, we surveyed lay participants about whether various scenarios were indicators of felt love. We thus quantify these responses to make inference about consensus judgments of felt love, measure individual levels of agreement with consensus, and assess individual response styles. More specifically, we (1) derive consensus judgments on felt love; (2) describe its characteristics in qualitative and quantitative terms, (3) explore individual differences in both (a) participant agreement with consensus, and (b) participant judgment when uncertain about shared knowledge, and (4) test whether individual differences can be meaningfully linked to explanatory variables. Results indicate that people converge towards a shared cognitive model of felt love. Conversely, respondents showed heterogeneity in knowledge of consensus, and in dealing with uncertainty. We found that, when facing uncertainty, female respondents and people in relationships more frequently judge scenarios as indicators of felt love. Moreover, respondents from smaller households tend to know more about consensus judgments of felt love, while respondents from larger households are more willing to guess when unsure of consensus.
By adulthood, people develop internal models of social context that consist of sets of cognitive schemata. Such schemata are generalized expectations and preferences regarding relationships that guide interpretation of interpersonal experiences . The research described below uses a novel methodological framework to disentangle the multiple pathways that people expect will elicit loving feelings in others. We provide a complete account of the methodology, including online supplements with computer scripts and the collected data, so that interested researchers can easily implement the proposed methodology for their own research questions or further explore our data.
The study presented below was approved by the University of California, Irvine, Office of Research, Institutional Review Board, under HS# 2013–9918. Data were collected via Amazon Mechanical Turk (MTurk). Participants indicated consent on the website.
We fitted the consensus model to the data on felt love in JAGS (“Just Another Gibbs Sampler” ) by running 8 chains, 1000 burn-in, and 1000 iterations. Convergence of the 8 chains was tested in both models by the R^ statistics, and confirmed using the standard criterion that the estimated potential scale reduction is R^<1.1. MATLAB and JAGS scripts to re-run the analysis are also included in the shared public GitHub folder indicated above. Source: http://doi.org/10.1371/journal.pone.0152803