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A model including only video type showed evidence in favour of the null hypothesis (BF10 = 0.194), indicating that video type did not affect interval production (Fig. 3).
A Bayesian two-factorial repeated measures ANOVA consisting of the factors interval production [1 s, 2 s, 4 s] and video type (control/Hallucination Machine) was used to investigate the effect of video type on interval production.
A two-factorial repeated measures ANOVA consisting of the factors interval production [1 s, 2 s, 4 s] and video type (control/Hallucination Machine) was used to investigate the effect of video type on interval production.
Bayes Factor analysis revealed strong evidence for no difference in subjective responses across video type (‡BF10 < 1/3).
Each block lasted 3 minutes, leading to a total exposure of 18 minutes for each video type.
In a similar fashion, for cases in which standard t-tests did not reveal significant differences in subjective ratings between video type we used additional Bayesian t-tests.
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Verbalizers and visualizers achieve the same learning performance with the three video types.
Produced time intervals are shown for both video types and target durations (1 second for low, 2 seconds for middle and 4 seconds for the high pitch tone).
The mechanism can support various video types with different characteristics in IEEE 802.11e HCCA mode.
These video types are quick and easy to create regularly and can drive real, measurable results for your business.
YouTube brings plenty of content in conventional and more immersive video types.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com