Adaptation to Changing Task Requirements Causes a Familiarity Bias

2014 ◽  
Author(s):  
Andre Assfalg ◽  
Devon Currie ◽  
Daniel Bernstein
2019 ◽  
Vol 40 (05) ◽  
pp. 1313-1329 ◽  
Author(s):  
Nora Kreyßig ◽  
Agnieszka Ewa Krautz

AbstractMultiple studies on bilingualism and emotions have demonstrated that a native language carries greater emotional valence than the second language. This distinction appears to have consequences for other types of behavior, including lying. As bilingual lying has not been explored extensively, the current study investigated the psychophysiological differences between German (native language) and English (second language) in the lying process as well as in the perception of lies. The skin conductance responses of 26 bilinguals were measured during reading aloud true and false statements and listening to recorded correct and wrong assertions. The analysis revealed a lie effect, that is, statistically significant differences between valid and fictitious sentences. In addition, the values in German were higher compared to those in English, in accordance with the blunted emotional response account (Caldwell-Harris & Aycicegi-Dinn, 2009). Finally, the skin conductance responses were lower in the listening condition in comparison to the reading aloud. The results, however, are treated with caution given the fact that skin conductance monitoring does not allow assigning heightened reactivity of the skin to one exclusive cause. The responses may have been equally induced by the content of the statements, which prompted positive or negative associations in the participants’ minds or by the specific task requirements.


2021 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Paul J. Silvia ◽  
Roger E. Beaty

The present research examined the varieties of poor metaphors to gain insight into the cognitive processes involved in generating creative ones. Drawing upon data from two published studies as well as a new sample, adults’ open-ended responses to different metaphor prompts were categorized. Poor metaphors fell into two broad clusters. Non-metaphors—responses that failed to meet the basic task requirements—consisted of “adjective slips” (describing the topic adjectivally instead of figuratively), “wayward attributes” (attributing the wrong property to the topic), and “off-topic idioms” (describing the wrong topic). Bad metaphors—real metaphors that were unanimously judged as uncreative—consisted of “exemplary exemplars” (vehicles that lacked semantic distance and thus seemed trite) and “retrieved clichés” (pulling a dead metaphor from memory). Overall, people higher in fluid intelligence (Gf) were more likely to generate a real metaphor, and their metaphor was less likely to be a bad one. People higher in Openness to Experience, in contrast, were more likely to generate real metaphors but not more or less likely to generate bad ones. Scraping the bottom of the response barrel suggests that creative metaphor production is a particularly complex form of creative thought.


Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.


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