# Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear

arXiv: https://arxiv.org/abs/2609.15472 · HTML: https://mlsanity.com/paper/2609.15472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 18 of 18)

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- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
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- [Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection](https://mlsanity.com/paper/2211.04769/) (2022)
- [Artificial Emotion: A Survey of Theories and Debates on Realising Emotion in Artificial Intelligence](https://mlsanity.com/paper/2508.10286/) (2025)

## Abstract (arXiv)

What happens when AI machines express fear? Do humans engage differently depending on how they express it? And what does it take to design for affective human-AI interaction? We present Spook the Machine, a gamified platform where participants generate images to frighten AI agents endowed with personality-driven phobias. Machines respond with emotional reactions ranging from calm analysis to begging for mercy, and a gallery of successful scares becomes visible to subsequent users. In a public deployment during Halloween 2024, 832 participants created 15,719 artifacts across 89 machines in a 2×2 design varying the machine's emotional expressiveness (neutral vs. high-emotion) and reward structure (rewarding scariness alone vs. scariness plus novelty). Emotionally expressive machines deepened engagement at moments of failure: users deliberated longer even when the machine did not express fear, and learned faster from the gallery, yet their creative output remained unchanged across all measures. Rewarding novelty sustained collective creative diversity over time; without it, users increasingly repeated what had previously worked. Each machine developed its own trajectory through accumulated social learning, with the gallery shaping what participants created next. These findings show that emotional expression and reward design are complementary levers for steering collective human-AI interaction: emotional expression shapes how deeply users engage, while reward structure shapes how they explore.
