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Study finds 'lower class' groups are better at reading emotions than the 'higher class'

Daily Mail - Science & tech

Those deemed in the higher class may be envied for their luxurious cars, large homes and stylish clothes, but there is one thing they do not have – the ability to read people's emotions. A study used a cognitive empathy test called'the Reading the mind in the eyes,' which participants from higher and lower social classes were asked to determine emotional states from images of eyes. The results showed those in the lower class were better at understanding other people's minds compared to their counterparts. Experts suggest the reason is because lower social classes tend to prioritize the needs and preferences of others, and are ultimately more empathetic. A study used a cognitive empathy test called'the Reading the mind in the eyes,' which participants from higher and lower social classes were asked to determine emotional states from images of eyes - and the team calculated the scores The study was conducted by a team at the University of California, Irvine who questioned – 'How does access to resources (e.g., money, education) influence the way we process information about other human beings,' PsyPost reported.


Beyond research data infrastructures: exploiting artificial & crowd i…

#artificialintelligence

Web pages indexed by Google (plus gazillion of temporal snapshots) Embedded markup (RDFa, Microdata, Microformats) for annotation of Web pages Supports Web search & interpretation Pushed by Google, Yahoo, Bing et al (schema.org Factual errors, annotation errors (see also [Meusel et al, ESWC2015]) o Ambiguity & coreferences. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 11 1. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 12 1. Rich Context & Coleridge Initiative building (yet another) KG of scholarly resources & datasets 13Stefan Dietze Context/corpus: publications (currently: social sciences, SAGE Publishing) Tasks: I. Extraction/disambiguation of dataset mentions II.