Theory in Human-Computer Interaction (HCI)

We suggest that HCI designs characteristically embody multiple, distinct psychological claims, that virtually every aspect of a system’s usability is overdetermined by independent psychological rationales inherent in its design. These myriad claims cohere in being implemented together in a running system. Thus, HCI artifacts themselves are perhaps the most effective medium for theory development in HCI. We advance a framework for articulating the psychological claims embodied by artifacts. This proposal reconciles the contrasting perspectives of theory-based design and hermeneutics, and clarifies the apparent paradox of HCI application leading HCI theory.

This chapter discusses the role of cognitive psychology in the design of humanly useful and usable systems. In this field, a useful theory is impossible, because the behavior of human–computer systems is chaotic or worse, highly complex, dependent on many unpredictable variables, or just too hard to understand. Where it is possible, the use of theory will be constrained and modest, because theories will be imprecise, will cover only limited aspects of behavior, will be applicable only to some parts of some systems, and will not necessarily generalize; as a result, they will yield little advantage over empirical methods. ...

Surveys rapid expansion of HCI and the import of theories from diverse disciplines, arguing that while many approaches yield insightful explication of HCI phenomena, their impact on interaction design practice has remained limited. Discusses why knowledge transfer from theory to practice has stalled and suggests mechanisms to help designers and researchers articulate and theoretically ground contemporary challenges.

In this essay, I explore several facets of research through design in order to contribute to discussions about how the approach should develop. The essay has three parts. In the first, I review two influential theories from the Philosophy of Science to help reflect on the nature of design theory, concluding that research through design is likely to produce theories that are provisional, contingent, and aspirational. ... In the final section, I suggest that, rather than aiming to develop increasingly comprehensive theories of design, practice-based research might better view theory as annotation of realised design examples, and particularly portfolios of related pieces. ...

This paper reports a study of the use of activity theory in human–computer interaction (HCI) research. We analyse activity theory in HCI since its first appearance about 25 years ago. Through an analysis and meta-synthesis of 109 selected HCI activity theory papers, we created a taxonomy of 5 different ways of using activity theory: (1) analysing unique features, principles, and problematic aspects of the theory; (2) identifying domain-specific requirements for new theoretical tools; (3) developing new conceptual accounts of issues in the field of HCI; (4) guiding and supporting empirical analyses of HCI phenomena; and (5) providing new design illustrations, claims, and guidelines. We conclude that HCI researchers are not only users of imported theory, but also theory-makers who adapt and develop theory for different purposes.

We present a case study of how Human-Computer Interaction (HCI) theory is reused within the field. We analyze the HCI literature in order to reveal the impact of one particular theory, the trajectories framework that has been cited as an example of both contemporary HCI theory and a strong concept that sits between theory and design practice. Our analysis of 60 papers that seriously engaged with trajectories reveals the purposes that the framework served and which parts of it they used. ...

“Usability” is a construct conceived by the human–computer interaction (HCI) community to denote a desired quality of interactive systems and products. Despite its prominence and intensive use in HCI research, the usefulness of the usability construct to HCI theories and to our understanding of HCI has been meager. In this article I propose and discuss two reasons for this state of affairs. ...

This article argues that our intimate entanglement with digital technologies is challenging the foundations of current HCI research and practice. Our relationships to virtual realities, artificial intelligence, neuro-implants or pervasive, cyberphysical systems generate ontological uncertainties, epistemological diffusion and ethical conundrums that require us to consider evolving the current research paradigm. ...

This essay addresses a foundational topic in applied sciences with interest in design: how do theories inform design? Previous work has attributed theory-use to abduction and deduction. However, design is about creating an intervention, a possible state that does not exist presently, and these accounts fail to explain how theories permit taking this leap. We argue that the practical value of a theory lies in counterfactual thinking. ...

Self-determination theory (SDT), a psychological theory of human motivation, is a prominent paradigm in human–computer interaction (HCI) research on games. However, our prior literature review observed a trend towards shallow applications of the theory. This follow-up work takes a broader view—examining SDT scholarship on games, a wider corpus of SDT-based HCI games research (N = 259), and perspectives from a games industry practitioner conference—to help explain current applications of SDT. Our findings suggest that perfunctory applications of the theory in HCI games research originate in part from within SDT scholarship on games, which itself exhibits limited engagement with theoretical tenets. Against this backdrop, we unpack the popularity of SDT in HCI games research and identify conditions underlying the theory’s current use as an oft-unquestioned paradigm. Finally, we outline avenues for more productive SDT-informed games research and consider ways towards more intentional practices of theory use in HCI.

Self-determination theory (SDT) has been widely successful in human–computer interaction (HCI). It offers ready concepts, measures, and theoretical propositions for third wave HCI topics such as user experience, fun, wellbeing, motivation, or user autonomy. Still, HCI applications of SDT have been partial, at times superficial, and disconnecting—leaving great unfulfilled potential which motivated the present special issue. In this introduction, we present SDT to interested scholars, chart its use across HCI to date, and outline six advances to move HCI toward more intentional applications of SDT. ...

This paper discusses the prospects of psychology playing a significant role in the progress of human–computer interaction. In any field, hard science (science that is mathematical or otherwise technical) has a tendency to drive out softer sciences, even if the softer sciences have important contributions to make. It is possible that, as computer science and artificial intelligence contributions to human–computer interaction mature, this could happen to psychology. ...

Introduces design claims as a way to connect evidence from social science and studies of online communities to concrete guidance for community design, with each claim grounded in theory, experiments, or observational work.

There is a growing interest in human-computer interaction (HCI) to envision, design, and evaluate technology-enabled interventions that support users’ emotion regulation. This interest stems in part from increased recognition that the ability to regulate emotions is critical to mental health, and that a lack of effective emotion regulation is a transdiagnostic factor for mental illness. However, the potential to combine innovative HCI designs with the theoretical grounding and state-of-the-art interventions from psychology has yet to be fully realised. In this article, we synthesise HCI work on emotion regulation interventions and propose a three-part framework to guide technology designers in making: (i) theory-informed decisions about intervention targets; (ii) strategic decisions regarding the technology-enabled intervention mechanisms to be included in the system; and (iii) practical decisions around previous implementations of the selected intervention components. ...

Many people prefer psychosocial interventions for mental health care or other concerns, but these interventions are often complex and unavailable in settings where people seek care. Intervention designers use technology to improve user experience or reach of interventions, and HCI researchers have made many contributions toward this goal. Both HCI and mental health researchers must navigate tensions between innovating on and adhering to the theories of change that guide intervention design. In this paper, we propose a framework that describes design briefs and evaluation approaches for HCI contributions at the scopes of capabilities, components, intervention systems, and intervention implementations. We show how theories of change (from mental health) can be translated into design briefs (in HCI), and that these translations can bridge and coordinate efforts across fields. ...

Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human–computer interaction loop. ...

This essay compares activity theory (AT) with distributed cognition theory (DCOG), asking what each can do for CSCW. It approaches this task by proposing that theories---when viewed as conceptual tools for making sense of a domain---have four important attributes: descriptive power; rhetorical power; inferential power; and application power. It observes that AT and DCOG are not so different: both emphasize cognition; both include the social and cultural context of cognition; both share a commitment to ethnographically collected data. ...

Design-oriented research practices create opportunities for constructing knowledge that is more abstracted than particular instances, without aspiring to be at the scope of generalized theories. We propose an intermediate design knowledge form that we name strong concepts ... Our aim is to foster an academic culture of discursive knowledge construction of intermediate-level knowledge and of how it can be produced and assessed in design-oriented HCI research.

Although Human–Computer Interaction research has developed various theories and frameworks for analyzing new and existing interactive systems, few address the generation of novel technological solutions, and new technologies often lack theoretical foundations. We introduce Generative Theories of Interaction, which draw insights from empirical theories about human behavior in order to define specific concepts and actionable principles, which, in turn, serve as guidelines for analyzing, critiquing, and constructing new technological artifacts. ...

Although the literature on Human-Computer Interaction (HCI) catalogues many theories, it offers surprisingly few tools for theorising. This paper critiques dominant approaches to engaging with theory and proposes a working model for theorising in HCI. We then present graphical causal modelling as an effective theorising tool. ...

Summary: A methods-oriented treatment of how to construct and evaluate theory in the social and behavioral sciences, emphasizing explicit units of analysis, lawful relationships among theoretical components, boundary conditions, and how empirical research can connect to cumulatively useful theoretical structure.

Summary: Clarifies the core ingredients of a theoretical contribution by focusing on the relationships among the key elements of a theory and on the conditions under which a manuscript advances understanding in ways that matter for cumulative knowledge.

Introductory treatment of how theories are constructed, stated, tested, and linked into a scientific body of knowledge, aimed at readers trained in the social, behavioral, or natural sciences but without a formal background in theory building; also discusses challenges of theorizing about social and human phenomena. This Allyn & Bacon Classics edition reproduces the 1971 text with updated design.

Correlation does not imply causation; but often, observational data are the only option, even though the research question at hand involves causality. This article discusses causal inference based on observational data, introducing readers to graphical causal models that can provide a powerful tool for thinking more clearly about the interrelations between variables. ...

Tutorial, presented as a dialogue, on formalizing verbal theories of psychological phenomena using concepts and tools from the mathematics of computation; includes links to supplementary interactive material.

Target article arguing that progress in psychological science depends on shifting epistemological perspective toward theory-centric practices, not only statistical or methodological reform.

Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned---that is, that the two must refer to roughly the same set of hypothetical observations. Here, I argue that many applications of statistical inference in psychology fail to meet this basic condition. ...

Editorial for a special issue arising from an interdisciplinary Lorentz Center workshop, arguing that improving psychological science requires improving theoretical practices---not only methodological reform---and posing three guiding questions: what criteria define good theories, how we can tell whether theories meet them, and what tools and practices help develop them.

Scientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, which in turn requires formal modeling. Yet, in psychological science, many theories are not precise and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. ...

Introduces theoretical modeling using dialogue between fictional characters and worked examples, aimed at cognitive science and psychology audiences learning to build and reason about computational theories.

We argue that psychology should aim its empirical research efforts at establishing phenomena. Psychology typically conceptualizes research in terms of testing theories against data. An alternative approach in philosophy of science views the scientific method in terms of a three-level relation between data, phenomena, and theory. In this conceptualization, phenomena are understood as robust features of the world, which are evidenced by patterns in data. Theories explain phenomena by positing the existence and causal operation of entities, processes, and structures that give rise to these phenomena. We argue that much psychological research should be understood as establishing phenomena rather than testing theories. Focussing on establishing phenomena will aid the robustness of psychological research, while simultaneously creating a better foundation for theory creation. ...

Open science innovations have focused on rigorous theory testing, yet methods for specifying, sharing, and iteratively improving theories remain underdeveloped. To address this limitation, we introduce FAIR theory, a standard for specifying theories as findable, accessible, interoperable, and reusable digital objects. FAIR theories are findable in well-established archives; accessible in terms of their availability and ability to be understood; interoperable for specific purposes, such as selecting control variables; and reusable in that they can be iteratively and collaboratively improved on. This article adapts the FAIR principles for theory; reflects on current FAIR practices in relation to psychological theory; and discusses FAIR theories’ potential impact in terms of reducing research waste, enabling metaresearch on theories’ structure and development, and incorporating theory into reproducible research workflows—from hypothesis generation to simulation studies. We present a conceptual workflow for FAIRifying theory that builds on existing open science principles and infrastructures. More detailed tutorials, worked examples, and convenience functions to automate this workflow are available in the theorytools R package. FAIR theory constitutes a structured protocol for archiving, communicating about, and iteratively improving theory, addressing a critical gap in open scholarly practices and potentially increasing the efficiency of cumulative knowledge acquisition in psychology and beyond.

In this commentary, we argue that the field of Ergonomics and Human Factors (EHF) has the tendency to present itself as a thriving and impactful science, while in reality, it is losing credibility. We assert that EHF science (1) has introduced terminology that is internally inconsistent and hardly predictive-valid ...

Psychology’s theory problem, long noted since Meehl, persists: most theories are verbal, underspecified, and make weak predictions, so hypotheses rarely follow from theory and findings seldom constrain it. Historical empiricism and today’s incentives (novelty, significance, volume) favor flexible, low-risk, strategically vague theorizing, while reforms and training emphasize methods over theory construction and formal/computational modeling. The result is fragmented subfields, drifting constructs, and “effects” that don’t cumulate—fueling replication failures, post-hoc rationalization, and shallow applied progress. Improving epistemic design requires educating and rewarding precise, falsifiable theories.

Many theories in psychology are formulated as verbal narratives. There are, however,no commonly accepted standards forhow suchdescriptions should be formulated. This lack of standardization can lead to inefficiencies in the scientific process. It may remain unclear what a theory predicts in a given situation,whether itapplies to that situation at all, which statements constitute the theory, or which version of the theory is current. We propose improved standards for verbal theory specification that address these problems. Specifically, we introduce a Proposition-Based Theory Specification (PBTS) methodology that adds structure and testability to verbal narrative theories while keeping technical requirements low. PBTS increases rigour by requiring researchers (a) to specify a theory as a set of verbal propositions, (b) to connect these propositions via explicit implications (i.e.,“IF propostion A,THEN proposition B”statements), (c) to define all concepts and operators contained in these propositions, and (d)to provide a broad range of valid operationalizations for each proposition. We provide step-by-step guidelinesfor applying PBTS to the specification and standardized analysis ofexisting theories, including indicators oftheory reproducibility, and we describe how PBTS can be used to specify new theories. Results from two validation studies demonstratethat the methodology is feasible for specifying verbal narrative theories and yields reasonable inter-specifier agreement. By enabling to systematic analysis of verbal theories and establishing fundamental prerequisites for multiple forms of theory specification, PBTS offersa potential foundation for addressing the theory crisis in psychology.

Although devices of all shapes and sizes currently dominate the technological landscape, human–computer interaction (HCI) as a field is not yet theoretically equipped to match this reality. In this article we develop the human–artifact model, which has its roots in activity theoretical HCI. ...

Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers ...

Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact—an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick’s sensemaking theory, which focuses on who the explanation is intended for. ...

Human–computer integration is an emerging area in which the boundary between humans and technology is blurred as users and computers work collaboratively and share agency to execute tasks. The sense of agency (SoA) is an experience that arises by a combination of a voluntary motor action and sensory evidence whether the corresponding body movements have somehow influenced the course of external events. ... In this review, we analyse and classify current integration technologies based on what we currently know about agency in the literature, and propose a distinction between body augmentation, action augmentation, and outcome augmentation. ...

An important challenge in interactive machine learning, particularly in subjective or ambiguous domains, is fostering bi-directional alignment between humans and models. Users teach models their concept definition through data labeling, while refining their own understandings throughout the process. To facilitate this, we introduce Mocha, an interactive machine learning tool informed by two theories of human concept learning and cognition. ...