Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 2023/07/28 - 08:26 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? The main question is if children distinguish diverse types of awe-inspiring visual experiences (i.e., vast nature, natural disaster, and slow-motion images) from visual experiences that involve diverse everyday scenes, in terms of key emotional, motivational, and social effects.
3) Describe the key dependent variable(s) specifying how they will be measured. We will measure the following dependent variables in a forced choice manner:
1) Emotions: which group of pictures make participants feel awe, happiness, and fear.
2) The Small-Self: which group of pictures make participants feel diminished sense of self in comparison to the world.
3) Motivation to learn: which group of pictures make participants feel more excited to learn about new things in the world
4) Sense of Belonging: which group of pictures make participants feel they are being part of a big world.
5) Familiarity: which group of pictures show things that participants are more familiar with.
6) Preference: which group of pictures show things that participants like more.
4) How many and which conditions will participants be assigned to? Each participant will be randomly assigned to one of three conditions. In each condition, participants will view four awe-inspiring images and four everyday images. The conditions differ in terms of the content of the awe-inspiring images, being 1) vast nature, 2) natural disaster, or 3) slow-motion images. All participants will answer questions to the dependent variables after viewing both groups of images.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. 1) We will conduct a generalized linear mixed effects model to see if there is an interaction between condition and measure: glmer(response~measure*condition + (1|ID), family=binomial, data=data).
2) For each condition, we will conduct a generalized linear mixed effects model to see if there is an effect of measure: glmer(response~measure + (1|ID), family=binomial, data=data)
3) We will also conduct binomial tests to see if participants' choices significantly differ from chance level (.50) for each measure.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. No participants will be excluded for outliers. Exclusion criteria consist of removing consent or assent, not being in the designated age range, not being a typically developing child, does not complete study or responses being influenced by environmental influences.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We will recruit at least 60 children between the ages of 4 and 9 for each condition (180 in total).
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will also explore the effects of age for each measure. If there is a significant age effect, we will examine younger and older children's responses separately using median split of age, or examine for each measure whether there is an age effect.
