If not me, then who? Responsibility and replacement

This repository contains the experiments, data, analyses, and figures for the paper "If not me, then who? Responsibility and replacement" by Sarah A. Wu and Tobias Gerstenberg (2023, Cognition).

Contents:

• Introduction
• Preregistrations
• Repository structure
• CRediT author statement

Introduction

How do people hold others responsible? Responsibility judgments are affected not only by what actually happened, but also by what could have happened if things had turned out differently. Here, we look at how replaceability — the ease with which a person could have been replaced by someone else — affects responsibility. We develop the counterfactual replacement model, which runs simulations of alternative scenarios to determine the probability that the outcome would have differed if the person of interest had been replaced. The model predicts that a person is held more responsible, the more difficult it would have been to replace them. To test the model's predictions, we design a paradigm that quantitatively varies replaceability by manipulating the number of replacements and the probability with which each replacement would have been available. Across three experiments featuring increasingly complex scenarios, we show that the model explains participants' responsibility judgments well in both social and physical settings, and better than alternative models that rely only on features of what actually happened.

A short video on an earlier version of this work can be found here (from CogSci 2021).

Preregistrations

Preregistrations for all experiments are available on the Open Science Framework (OSF):

• Experiment 1 - Agent condition, Object condition
• Experiment 2 - Agent condition, Object condition
• Experiment 3 - Agent condition, Object condition

Repository structure

├── analysis
│   └── cache
├── data
│   ├── experiment1_agent
│   └── ...
├── docs
│   ├── experiment1_agent
│   └── ...
└── figures
    ├── experiment1
    └── ...

• analysis contains all the code for analyzing data and generating figures, written in R (view a rendered file here).
• data contains anonymized data from all experiments. For each experiment:
  • trial_info.csv is a description of the stimuli, also listed in the Appendices.
  • trials.csv contains the response data (i.e. responsibilty judgments).
  • participants.csv contains demographic information and post-experiment feedback/comments from participants.
  • model-fits.RData contains the best models fit to each individual participant.
• docs contains all the experiment code. You can preview the experiments below:
  • Experiment 1 - Agent condition, Object condition
  • Experiment 2 - Agent condition, Object condition
  • Experiment 3 - Agent condition, Object condition
• figures contains all the figures from the paper (generated using the script in analysis).

CRediT author statement

• Sarah A. Wu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition.
• Tobias Gerstenberg: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.