'Event summaries 01: Breakpoints and completion' (AsPredicted #96,900)
Author(s) Markus Huff (Eberhard-Karls-Universität Tübingen) - markus.huff@uni-tuebingen.de Tolgahan Aydın (Yaşar University) - t.aydin@iwm-tuebingen.de Ayşe Şimşek (Yaşar University) - a.simsek@iwm-tuebingen.de
Pre-registered on 2022/05/12 03:51 (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 purpose of the study is to investigate how coherent people's events models are when they are exposed to incomplete information. We examine event completion for everyday event summaries that contain event boundary or non-event boundary segments. We expect more event completion in causal activities in the form of more false alarms to missing segments. Also, we expect people to form more coherent models (more event completion) in breakpoint-keeping condition in the form of more false alarms to missing segments compared to non-breakpoint-keeping condition.
3) Describe the key dependent variable(s) specifying how they will be measured. The memory will be measured using d prime, of signal detection. D prime is calculated by finding the difference between standardized scores of hit and false alarm rates.
Additionally, response bias using criterion c will be measured to observe whether there is a response bias (e.g., liberal or conservative).
4) How many and which conditions will participants be assigned to? Experiment is implemented using a 2*2 mixed design analysis of variance. Participants will be assigned to one of four groups, based on two conditions. Videos are created either with breakpoints or non-breakpoints and they are either causal or non-causal. Causality will be treated as a between-subjects variable and event boundary as the within-subjects variable.
In the breakpoint-keeping condition, shots will be taken from event breakpoints (4 second segment coming before a coarse boundary and 4 second segment coming from after a coarse boundary). In the non-breakpoint-keeping condition, shots will be taken from the middle point of a coarse segment (4 second video coming before the middle point of a coarse segment and 4 second video coming from after the middle point of a coarse segment).
In the causal condition, the clip will end with a shot that shows the completion of the activity sequence (ex. in breakfast activity, the actor exits the scene carrying out the prepared items). In the non-causal condition, the clip will end without the last shot that shows the completion of the activity.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. Outlier analysis d prime scores, calculation of d prime scores and criterion c (response bias) scores will be executed before main test of hypotheses. Then two-way analysis of variance with one between-subjects factor of causality (causal vs non-causal) and within-subjects factor of segment type (breakpoints and non-breakpoints) will be utilized. Main effects and interactions that have smaller p values than 0.05 will imply significant effects.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. Exclusion criteria:
Exclusion of outliers with lower d prime than lower whisker according to the boxplot criterion – boxplot will be plotted using average d prime per person.
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. A power analysis using G*Power with a power of .80, alpha level probability of .01, effect size f .15 gave the number of 82. Considering the participants who might drop out, we planned to test our hypotheses upon reaching the number of 102.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) No.
