Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction

[2502.05935] Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2502.05935 (cs) [Submitted on 9 Feb 2025 ( v1 ), last revised 25 Aug 2026 (this version, v10)] Title: Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction Authors: Roel Vertegaal , Timothy Merritt , Saul Greenberg , Aneesh P. Tarun , Zhen Li , Zafeirios Fountas View PDF HTML (experimental) Abstract: Neuromorphic Human-Computer Interaction (HCI) is a theoretical approach to designing better user experiences (UX) motivated by advances in the understanding of the neurophysiology of the brain. Inspired by the neuroscientific theory of Active Inference, Interactive Inference is a first example of such an approach. It offers a simplified interpretation of Active Inference that allows designers to more readily apply this theory to design and evaluation. The basic premise in Interactive Inference is that the user predicts a result prior to performing a task. User behaviour is modeled as Bayesian inference on progress and goal distributions that predicts the next action. The difference between the observed result and the prediction is what is processed by the brain. This discrepancy between goal and progress distributions, or Bayesian surprise, can be modeled as a simple mean square error of the signal-to-noise ratio (SNR) of a task. The problem is that the user's capacity to process Bayesian surprise follows the logarithm of this SNR. This means terminal execution errors rise quickly once average capacity is exceeded. Our model allows the quantitative analysis of performance and terminal error using one framework that can provide real-time estimates of the mental load of users that needs to be minimized by design. We show how three basic laws of HCI, Hick's Law, Fitts' Law and the Power Law can be expressed using our model. We then test the validity of the model by empirically measuring how well it predicts human performance and terminal error in a car following task. Results suggest that driver processing capacity is indeed a logarithmic function of the SNR of the distance to a lead car, and that terminal error follows the residual surprise predicted by a KL-divergence model. This result provides initial evidence that Interactive Inference can be useful as a new theoretical design tool. Comments: 19 pages, 7 figures, 1 table, 37 mathematical formulas, in press Subjects: Human-Computer Interaction (cs.HC) ; Information Theory (cs.IT) Cite as: arXiv:2502.05935 [cs.HC] (or arXiv:2502.05935v10 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2502.05935 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Timothy Merritt [ view email ] [v1] Sun, 9 Feb 2025 15:32:46 UTC (1,727 KB) [v2] Tue, 18 Feb 2025 18:07:42 UTC (1,727 KB) [v3] Thu, 20 Feb 2025 19:10:34 UTC (1,727 KB) [v4] Wed, 24 Sep 2025 17:46:21 UTC (2,652 KB) [v5] Thu, 25 Sep 2025 12:50:21 UTC (2,652 KB) [v6] Tue, 2 Dec 2025 20:57:00 UTC (2,652 KB) [v7] Mon, 4 May 2026 18:52:16 UTC (6,089 KB) [v8] Sun, 17 May 2026 18:56:46 UTC (6,089 KB) [v9] Tue, 18 Aug 2026 18:58:52 UTC (6,836 KB) [v10] Tue, 25 Aug 2026 09:05:58 UTC (6,836 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2025-02 Change to browse by: cs cs.IT math math.IT References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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