Emotional AI fear expressions shape human creativity and engagement
Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear
Human-Computer InteractionArtificial Intelligence
Summary
This paper studies how people interact with AI machines that can express fear and have their own fears. The authors created a game where players try to scare these AI machines, which react with different emotions. They found that when machines show more emotion, people pay more attention and learn faster, but they don't necessarily make scarier images. Designing rewards to encourage new ideas keeps creativity high, while plain scariness rewards lead to repeated ideas. This work shows that AI emotion and reward settings influence how people creatively engage with machines.
What this means in practice
- •For game developers: Create games with emotionally responsive AI that influence player creativity and engagement through fear expressions and reward designs.
- •For user experience designers: Design interactive AI systems whose emotional feedback and reward mechanisms shape user attention and promote exploration over repetition.
Authors
Levin Brinkmann, Hiromu Yakura, Sonia Nicoletti, Mar Canet Sola, Thomas F. Eisenmann, Ali Dasmeh, Omar Sherif, Bramantyo Ibrahim Supriyatno, Prateek Gupta, Ignacio Serna, Rodrigo Bermudez Schettino, Iyad Rahwan
Abstract
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 $2\times2$ 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.