'Cognitive Load and Moral Dumbfounding' (AsPredicted #61188)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has one author.
Pre-registered on 03/17/2021 02:16 PM (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? We hypothesise that a cognitive load manipulation introduced as part of a moral dumbfounding paradigm will interfere with participants’ ability to provide reasons for their judgement. We expect that participants under cognitive load will be less likely to provide a reason and more likely to either present as dumbfounded or to select “there is nothing wrong”.
3) Describe the key dependent variable(s) specifying how they will be measured. The dependent variable is response to the critical slide. This is a standardised measure of dumbfounded responding. Following a moral judgement task, involving behaviours that are normally regarded as morally wrong, and a series of counter-arguments designed to challenge participants’ judgments, the critical slide presents participants with a statement defending the behaviour with a question asking how the behaviour could be wrong. There are three response options: (a) There is nothing wrong; (b) it’s wrong and I can provide a valid reason; and (c) It’s wrong but I can’t explain why. If participants select (b) they are subsequently required to provide a reason. Participants selecting (c) are identified as dumbfounded.
The independent variable is cognitive load manipulation with two levels: manipulated and control. In the manipulation condition participants will be required to engage with a secondary (attending to a video of numbers scrolling across their screen, and counting a specified number) task while making their judgements and while responding to the critical slide. In the control condition there will be no video present.
Participants will be presented with one of four scenarios (Incest, Cannibal, Trolley, Heinz). We do not anticipate an interaction between scenario and experimental condition, however we will test for this in our analyses.
Summary:
DV: Critical Slide: 3 levels; nothing wrong, reasons, dumbfounded
Primary IV: Cognitive Load: 2 levels, manipulated, control
Secondary IV: Scenario: 4 levels, Incest, Cannibal, Trolley, Heinz
4) How many and which conditions will participants be assigned to? Our independent variable of interest contains 2 levels, and as such, participants will be assigned to one of two conditions: cognitive load manipulation, and a control condition.
In addition, participants will also be randomly assigned one of four moral scenarios. These scenarios are not an experimental condition, however we will investigate possible variability between scenarios.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. The main hypothesis will be tested using a Pearson’s Chi-Squared test; 2x3 crosstabs to test if the independent variable (cognitive load / control) is associated with response to the critical slide (nothing wrong / reasons / dumbfounded).
This analysis will be conducted for the entire sample (across all scenarios) and for each scenario individually.
A follow-up generalised linear mixed model will be conducted to include experimental condition and scenario in the same analysis.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. Participants who failed the attention checks will be excluded from analysis.
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. Previous studies of moral dumbfounding have found that participants are overwhelmingly more likely to provide reasons than provide any other response. In order to have sufficient numbers of each response for each scenario to be able to conduct reliable analyses, we aim to collect 200 participants per condition for each scenario, this means the required sample size is N = 1600 (200x2x4).
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) Reaction time data will additionally be measured and we will conduct exploratory analyses on this.
