Proof pending. Core topic summary fields are still materializing.
Human-Computer Interaction (HCI) is advancing through innovative approaches that enhance user experience by predicting actions, interpreting gaze data, and modeling interactions. Current research focuses on creating systems that anticipate user needs, streamline data analysis, and improve communication through natural language. For instance, next action prediction models leverage extensive user interaction data to foresee future actions, while eye-tracking technologies are becoming more accessible through code-free interfaces. Additionally, understanding user engagement and performance through physiological signals is paving the way for proactive interventions. These developments are crucial for builders aiming to create intuitive and responsive systems that cater to user behavior and preferences, ultimately enhancing the effectiveness of technology in everyday applications.
Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire ...
Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require specialized programming knowledge and careful handlin...
In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately respond to a speaker's ...
Natural language database interfaces broaden data access, yet they remain brittle under input ambiguity. Standard approaches often collapse uncertainty into a single query, offering little support for...
User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identification of users struggli...
As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecessary burden is a key challenge. While prior researc...
Inferring human engagement from gameplay video is important for game design and player-experience research, yet it remains unclear whether vision--language models (VLMs) can infer such latent psycholo...
Existing neural network calibration methods often treat calibration as a static, post-hoc optimization task. However, this neglects the dynamic and temporal nature of real-world inference. Moreover, e...
Expectations about the support of artificial intelligence (AI) may influence interaction outcomes similar to placebos. Such expectations may result from AI washing, a practice of overstating a system'...
Understanding how people allocate visual attention is central to Human-Computer Interaction (HCI), yet existing computational models of attention are often either descriptive, task-specific, or diffic...
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Canonical route: /topics
Agent Handoff
Canonical ID human-computer-interaction | Route /topic/human-computer-interaction
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/human-computer-interactionMCP example
{
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"arguments": {
"query": "Human-Computer Interaction",
"cluster": "Human-Computer Interaction"
}
}source_context
{
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"mode": "topic",
"query": "Human-Computer Interaction",
"normalized_query": "human-computer-interaction",
"route": "/topic/human-computer-interaction",
"paper_ref": null,
"topic_slug": "human-computer-interaction",
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}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.