| 1. |
- Kerr, John R., et al.
(författare)
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Correlates of intended COVID-19 vaccine acceptance across time and countries : Results from a series of cross-sectional surveys
- 2021
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Ingår i: BMJ Open. - : BMJ. - 2044-6055. ; 11:8
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Tidskriftsartikel (refereegranskat)abstract
- Objective Describe demographical, social and psychological correlates of willingness to receive a COVID-19 vaccine. Setting Series of online surveys undertaken between March and October 2020. Participants A total of 25 separate national samples (matched to country population by age and sex) in 12 different countries were recruited through online panel providers (n=25 334). Primary outcome measures Reported willingness to receive a COVID-19 vaccination. Results Reported willingness to receive a vaccine varied widely across samples, ranging from 63% to 88%. Multivariate logistic regression analyses reveal sex (female OR=0.59, 95% CI 0.55 to 0.64), trust in medical and scientific experts (OR=1.28, 95% CI 1.22 to 1.34) and worry about the COVID-19 virus (OR=1.47, 95% CI 1.41 to 1.53) as the strongest correlates of stated vaccine acceptance considering pooled data and the most consistent correlates across countries. In a subset of UK samples, we show that these effects are robust after controlling for attitudes towards vaccination in general. Conclusions Our results indicate that the burden of trust largely rests on the shoulders of the scientific and medical community, with implications for how future COVID-19 vaccination information should be communicated to maximise uptake.
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| 2. |
- Recchia, Gabriel, et al.
(författare)
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Encoding Sequential Information in Vector Space Models of Semantics: Comparing Holographic Reduced Representation and Random Permutation
- 2010. - 11
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Konferensbidrag (refereegranskat)abstract
- Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random permutations have been enlisted for this purpose in semantic models, these operations have never been systematically compared. In Experiment 1 we compare their storage capacity and probability of correct retrieval; in Experiments 2 and 3 we compare their performance on semantic tasks when integrated into existing models. We conclude that random permutations are a scalable alternative to circular convolution with several desirable properties.
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| 3. |
- Recchia, Gabriel L., et al.
(författare)
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Encoding sequential information in semantic space models : Comparing holographic reduced representation and random permutation
- 2015
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Ingår i: Computational Intelligence and Neuroscience. - : Hindawi Limited. - 1687-5265 .- 1687-5273. ; 2015
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Tidskriftsartikel (refereegranskat)abstract
- Circular convolution and random permutation have each been proposed as neurally plausible binding operators capable of encoding sequential information in semantic memory. We perform several controlled comparisons of circular convolution and random permutation as means of encoding paired associates as well as encoding sequential information. Random permutations outperformed convolution with respect to the number of paired associates that can be reliably stored in a single memory trace. Performance was equal on semantic tasks when using a small corpus, but random permutations were ultimately capable of achieving superior performance due to their higher scalability to large corpora. Finally, "noisy" permutations in which units are mapped to other units arbitrarily (no one-to-one mapping) perform nearly as well as true permutations. These findings increase the neurological plausibility of random permutations and highlight their utility in vector space models of semantics.
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