People have different levels of affinity for technology, which impacts their attitudes and behavior when using novel technologies. Capturing this difference requires a validated multi-language instrument. Hence, we translated and validated English, Japanese, and Spanish versions of the Affinity for Technology questionnaire (TAEG), which has so far only been available in German. The TAEG consists of four scales assessing enthusiasm, perceived competence, and positive and negative consequences of technology. After systematic translation, we collected and analyzed age and gender-stratified samples from Germany, Mexico, Japan, and the US, with a total sample N=1206. All TAEG versions showed an excellent fit with the four-factor model and good criterion validity. We also introduced a short-scale (TAEG-S) that captures the global construct. We found significant cross-country variations, with Mexico reporting the highest TAEG scores on all scales. The validated versions of TAEG provide a robust tool to assess individuals’ affinity for technology internationally.
Quadratic Surveys (QSs) elicit more accurate preferences than traditional methods like Likert-scale surveys. However, the cognitive load associated with QSs has hindered their adoption in digital surveys for collective decision-making. We introduce a two-phase "organize-then-vote'' QS to reduce cognitive load. As interface design significantly impacts survey results and accuracy, our design scaffolds survey takers' decision-making while managing the cognitive load imposed by QS. In a 2x2 between-subject in-lab study on public resource allotment, we compared our interface with a traditional text interface across a QS with 6 (short) and 24 (long) options. Two-phase interface participants spent more time per option and exhibited shorter voting edit distances. We qualitatively observed shifts in cognitive effort from mechanical operations to constructing more comprehensive preferences. We conclude that this interface promoted deeper engagement, potentially reducing satisficing behaviors caused by cognitive overload in longer QSs. This research clarifies how human-centered design improves preference elicitation tools for collective decision-making.
This work introduces the concept of Embodied Measurement (EM), designed to improve the validity and inclusivity of cognitive load assessments by incorporating physical interactions that mirror mental effort. We implemented a haptic force-feedback turning knob as an alternative to traditional Likert-scale ratings and compared it with visual (mouse-based) and combined (haptic and visual) modalities. Participants completed a cognitive load task with varying difficulty levels using each modality, while biosignals such as heart rate variability, skin conductance, and pupil size were recorded to objectively assess cognitive load. In addition, qualitative feedback was gathered to explore participants' experiences with each input method. Our findings highlight the potential of EM to offer more tangible and intuitive ways of measuring cognitive load, with the combined modality providing the most comprehensive feedback. This study contributes to human-computer interaction (HCI) research by proposing new approaches for measuring cognitive and emotional effort through physical interaction.
Justice, epistemology, and marginalization are rich areas of study in HCI. And yet, we repeatedly find platforms and algorithms that push communities further into the margins. In this paper, we propose epistemic autonomy--—one’s ability to govern knowledge about themselves---as a necessary HCI paradigm for working with marginalized communities. We establish epistemic autonomy by applying the transfeminine principle of autonomy to the problem of epistemic injustice. To articulate the harm of violating one’s epistemic autonomy, we present six stories from two trans women: (1) a transfem online administrator and (2) a transfem researcher. We then synthesize our definition of epistemic autonomy in research into a research paradigm. Finally, we present two variants of common HCI methods, autoethnography and asynchronous remote communities, that stem from these beliefs. We discuss how CHI is uniquely situated to champion this paradigm and, thereby, the epistemic autonomy of our research participants.
Computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour.
Plausible findings about futures are inherently difficult to obtain as they require critical, well-informed speculations backed with data. HCI scholars tackle this challenge via user studies wherein futuristic prototypes and other props concretise possible futures for participants. By observing participants' actions, researchers then can 'time travel' to see that future as reality, in action. However, such studies may yield particularised findings, inherent to study’s intricacies, and lack broader plausibility. This paper suggests that triangulation of possible futures may help researchers disentangle particularities from more generalisable findings. We explored this approach by conducting a study on two alternative futures of AI-augmented knowledge work. Some findings emerged in both futures while others were particular to only one or the other. This approach enabled cross-checking of plausibility and simultaneously afforded deeper insight. The paper discusses how triangulating possible futures renders HCI studies more future-proof and provides means for reflective anticipation of possible futures.
Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits. Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities. In this work, we introduce ReverSim, a software environment that models key HRE subprocesses and integrates standardized cognitive tests. ReverSim enables quantitative studies with easier-to-recruit non-experts to uncover cognitive factors relevant to HRE. We empirically evaluated ReverSim in three studies. Semi-structured interviews with 14 HRE professionals confirmed its comparability to real-world HRE processes. Two online user studies with 170 novices and intermediates revealed effective differentiation of participant performance across a spectrum of difficulties, and correlations between participants’ cognitive processing speed and task performance. ReverSim is available as open-source software, providing a robust platform for controlled experiments to assess cognitive processes in HRE, potentially opening new avenues for hardware protection.