scholarly journals A Bayesian Approach to Block Structure Inference in AV1-Based Multi-Rate Video Encoding

Author(s):  
Bichuan Guo ◽  
Xinyao Chen ◽  
Jiawen Gu ◽  
Yuxing Han ◽  
Jiangtao Wen
2017 ◽  
Vol 33 (6) ◽  
pp. 445-452
Author(s):  
Monika Fleischhauer

Abstract. Accumulated evidence suggests that indirect measures such as the Implicit Association Test (IAT) provide an increment in personality assessment explaining behavioral variance over and above self-reports. Likewise, it has been shown that there are several unwanted sources of variance in personality IATs potentially reducing their psychometric quality. For example, there is evidence that individuals use imagery-based facilitation strategies while performing the IAT. That is, individuals actively create mental representations of their person that fit to the category combination in the respective block, but do not necessarily fit to their implicit personality self-concept. A single-block IAT variant proposed by attitude research, where compatible and incompatible trials are presented in one and the same block, may prevent individuals from using such facilitation strategies. Consequently, for the trait need for cognition (NFC), a new single-block IAT version was developed (called Moving-IAT) and tested against the standard IAT for differences in internal consistency and predictive validity in a sample of 126 participants. Although the Moving-IAT showed lower internal consistency, its predictive value for NFC-typical behavior was higher than that of the standard IAT. Given individual’s strategy reports, the single-block structure of the Moving-IAT indeed reduces the likelihood of imagery-based strategies.


CICTP 2020 ◽  
2020 ◽  
Author(s):  
Wanqing Zhang ◽  
Jinyan Zhu ◽  
Ying Cheng ◽  
Chen Liu ◽  
Rongchun Shi

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.


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