84 research outputs found
Computer-Cognition Interfaces: Sensing and influencing mental processes with computer interaction
No full textThe variety of information about users hidden in the details of interaction data is increasingly being utilized for recognizing complex mental processes. Digital systems can correspondingly influence mental processes of users, paving the way for new interactive systems that interface with the human mind. This thesis presents advances to such interfaces: through four papers I show how human affect and cognition can be sensed and influenced computationally.
Paper 1 presents two studies that together show that affect influences mobile interaction, which allows for binary discrimination between neutral and positive affect using sensor led machine learning classification. Paper 2 builds upon the methods presented in Paper 1 and extends the classification domain to dishonesty, also using mobile interaction data. The paper shows across three studies how dishonesty and honesty vary in interactional details, and how this difference can be utilized for estimating the veracity of user behavior based on features that are engineered by mobile interaction data.
Paper 3 presents a feasibility study of conducting virtual reality studies outside a laboratory, to increase heterogeneity and power. The paper shows through two studies how a range of VR tasks can be conducted without the use of an immediate experimenter, with participants carrying out experiments themselves. In Paper 4 I apply this methodology, and conduct a VR study with more than 200 participants to study how manipulations to avatars can influence affect responses. The paper presents evidence supporting the link between affect and avatars, and additionally discusses the interplay between positive affect and body ownership
Computer-Cognition Interfaces:Sensing and Influencing Mental Processes with Computer Interaction
No full textThe variety of information about users hidden in the details of interaction data is increasingly being utilized for recognizing complex mental processes. Digital systems can correspondingly influence mental processes of users, paving the way for new interactive systems that interface with the human mind. This thesis presents advances to such interfaces: through four papers I show how human affect and cognition can be sensed and influenced computationally.Paper 1 presents two studies that together show that affect influences mobile interaction, which allows for binary discrimination between neutral and positive affect using sensor led machine learning classification. Paper 2 builds upon the methods presented in Paper 1 and extends the classification domain to dishonesty, also using mobile interaction data. The paper shows across three studies how dishonesty and honesty vary in interactional details, and how this difference can be utilized for estimating the veracity of user behavior based on features that are engineered by mobile interaction data.Paper 3 presents a feasibility study of conducting virtual reality studies outside a laboratory, to increase heterogeneity and power. The paper shows through two studies how a range of VR tasks can be conducted without the use of an immediate experimenter, with participants carrying out experiments themselves. In Paper 4 I apply this methodology, and conduct a VR study with more than 200 participants to study how manipulations to avatars can influence affect responses. The paper presents evidence supporting the link between affect and avatars, and additionally discusses the interplay between positive affect and body ownership.<br/
Demand characteristics in human–computer experiments
No full textDemand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance. In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.</p
Hafnia Hands: A Multi-Skin Hand Texture Resource for Virtual Reality Research
Get PDFWe created a hand texture resource (with different skin tone versions as well as non-human hands) for use in virtual reality studies. This makes it easier to run lab and remote studies where the hand representation is matched to the participants’ own skin tone. We validate that the virtual hands with our textures align with participants’ view of their own real hands and allow to create VR applications where participants have an increased sense of body ownership. These properties are critical for a range of VR studies, such as of immersion.<br/
How we Guide, Write, and Cite at CHI
Get PDFThere are many opinions on how to write an influential CHI paper, ranging from writing in an active voice to including colons in the title. However, little is known about how we actually write, and how writing influences impact. We conducted quantitative analyses of the full text of all 6578 CHI papers published since 1982 to investigate. We looked at readability, titles, novelty, and name-dropping and related these measures to the papers' citation count; overall and for different subcommittees. We found that CHI papers are more readable than papers from other fields. Furthermore, readability, title length, and novelty markers all influence citation counts
Virtual reality studies outside the laboratory
No full textMany user studies are now conducted outside laboratories to increase the number and heterogeneity of participants. These studies are conducted in diverse settings, with the potential to give research greater external validity and statistical power at a lower cost. The feasibility of conducting virtual reality (VR) studies outside laboratories remains unclear because these studies often use expensive equipment, depend critically on the physical context, and sometimes study delicate phenomena concerning body awareness and immersion. To investigate, we explore pointing, 3D tracing, and body-illusions both in-lab and out-of-lab. The in-lab study was carried out as a traditional experiment with state-of-the-art VR equipment; 31 completed the study in our laboratory. The out-of-lab study was conducted by distributing commodity cardboard VR glasses to participants; 57 completed the study anywhere they saw fit. The effects found in-lab were comparable to those found out-of-lab, with much larger variations in the settings in the out-of-lab condition. A follow-up study showed that performance metrics are mostly governed by the technology used, where more complex VR phenomena depend more critically on the internal control of the study. We argue that conducting VR studies outside the laboratory is feasible, and that certain types of VR studies may advantageously be run this way. From the results, we discuss the implications and limitations of running VR studies outside the laboratory
Replicating replicability modeling of psychology papers
Get PDFYouyou et al. (1) estimated the replicability of more than 14,000 psychology papers using a machine learning model, trained on main texts of 388 replicated studies. The authors identified mean replicability scores of psychological subfields. They also verified the causality of the model predictions; correlations between model predictions and study details the model was not trained on (i.e., P value and sample size) were reported.In attempting replication, we identified important shortcomings of the approach and findings. First, the training data contain duplicated paper entries. Second, our analysis shows that the model predictions also correlate with variables that are not causal to replicability (e.g., language style). These issues impede the validity of the model output and thereby paint an erroneous picture of replication rates of the psychological science. In this letter, we attempt to mitigate these issues and nuance the findings of the original paper
- …
