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Luis-Alberto Casado-Aranda Vinod Venkatraman Juan Sánchez-Fernández Teodoro Luque-Martínez 《Political psychology》2020,41(1):145-164
Daily worldwide newspapers print articles exposing government corruption. Yet these messages do not lead to a loss of votes for the corrupt parties. Sympathizers’ partisan bias, which respectively minimizes and maximizes corruption and positive messages of their own parties respectively, is widely considered the main cause of the loss of effectiveness of political communications. Despite the well-established existence of such bias when processing political information, little is known as to its psychological origin. Through the use of neuroscience (fMRI), this study explores the underlying brain mechanisms of negative (corruption) and positive political messages related to a conservative and a socialist Spanish political party, as well as the differences between their sympathizers. The findings reveal that negative (vs. positive) political messages exert the greatest neuroimaging impact on the electorate, as shown in aversive, risk, and disappointment-related brain regions. Interestingly, we show that there exists a main partisan bias against opposite parties (and not a positive bias toward one’s own party) that stems from a higher risk, ambiguity, and disbelief provoked by both positive and negative information about rival parties. Furthermore, this bias was more pronounced among conservative supporters. The current findings provide valuable insights for political parties to improve their communication campaigns. 相似文献
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Topoi - Singular terms without referents are called empty or vacuous terms. But not all of them are equally empty. In particular, not all proper names that fail to name an existing object fail in... 相似文献
85.
Topoi - This article investigates the relationship between for-me-ness and sociality. I start by pointing out some ambiguities in claims pursued by critics that have recently pressed on the... 相似文献
86.
James S. Magnuson Heejo You Sahil Luthra Monica Li Hosung Nam Monty Escabí Kevin Brown Paul D. Allopenna Rachel M. Theodore Nicholas Monto Jay G. Rueckl 《Cognitive Science》2020,44(4):e12823
Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR. 相似文献
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We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age. 相似文献
88.
Jescheniak Jörg D. Wöhner Stefan Bethcke Hanna S. Beaupain Marie C. 《Psychonomic bulletin & review》2020,27(2):373-378
Psychonomic Bulletin & Review - Picture naming takes longer in the presence of a semantic-categorically related distractor word compared to an unrelated distractor word. This semantic... 相似文献
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Morales-Gualdrón Silvia Teresa La Rotta Forero Daniel Andrés Arias Vergara Juliana Andrea Montoya Ardila Juliana Herrera Bañol Carolina 《Science and engineering ethics》2020,26(6):3183-3215
Science and Engineering Ethics - This work describes the perceptions that Industrial Engineering students have regarding Colombian firms’ corporate social responsibility (CSR) practices. It... 相似文献
90.
Social Psychology of Education - In Chile, a vast and persistent gender gap in math performance at university admission has negative consequences for women’s opportunities. International... 相似文献