Impact of Environmental Colors on Human Aggressiveness: Insights from a Minecraft-Based Behavioral Study

Austin Deng-Yao Yang; Shih-Jen Tsai; Hsin-Jung Tsai

Home/Archive/arXiv

arXiv · 2504.13857

Impact of Environmental Colors on Human Aggressiveness: Insights from a Minecraft-Based Behavioral Study

Austin Deng-Yao Yang·Shih-Jen Tsai·Hsin-Jung Tsai

View original source ↗Read source document ↗Export metadata ↓

Abstract

This study explores the influence of environmental colors on human behavior, specifically focusing on aggressiveness and passiveness. Color is widely regarded as an influential environmental factor shaping human behavior, yet existing studies present conflicting evidence regarding its impact on aggressiveness and passiveness. This study employed Minecraft as a controlled digital platform to investigate whether exposure to different colors influences both the frequency and nature of participant interactions (aggressive versus non-aggressive), and whether prolonged exposure amplifies these effects. Anonymous online participants were exposed to various colors before interacting with non-player characters simulating human-like encounters. Three key outcomes were measured: (1) total interactions per color, (2) ratios of aggressive to non-aggressive interactions per color, and (3) the effect of varying exposure durations on aggressiveness. While no significant overall differences in interaction frequency were observed among the colors, post-hoc analyses revealed that Red and Black elicited significantly more interactions compared to Green. Additionally, Red, Yellow, and Black were associated with higher ratios of aggressive behavior relative to Green or White. Prolonged exposure to Red also appeared to intensify aggressive responses. These findings underscore the potential role of environmental color in shaping online social behaviors and highlight the importance of environmental settings in areas ranging from online communication platforms to digital marketing strategies.

Explore related subjects

cs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Austin Deng-Yao Yang, Shih-Jen Tsai, Hsin-Jung Tsai. 2025-03-22. Impact of Environmental Colors on Human Aggressiveness: Insights from a Minecraft-Based Behavioral Study. https://arxiv.org/abs/2504.13857

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC↗

arXiv2026

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC↗

arXiv2026

VisCanvas: A Node-Based Interface for Exploratory Visualization Authoring with LLMs

Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.

cs.HC↗