The FVF framework and target prevalence effects

The Functional Visual Field (FVF) offers explanatory power. To us, it relates to existing literature on the flexibility of attentional focus in visual search and reading (Eriksen & St. James, 1986; McConkie & Rayner, 1975). The target article promotes reflection on existing findings. Here we consider the FVF as a mechanism in the Prevalence Effect in visual search (PE)

Dwelling on simple stimuli in visual search

Research and theories on visual search often focus on visual guidance to explain differences in search. Guidance is the tuning of attention to target features and facilitates search because distractors that do not show target features can be more effectively ignored (skipping). As a general rule, the better the guidance is, the more efficient search is. Correspondingly, behavioral experiments often interpreted differences in efficiency as reflecting varying degrees of attentional guidance. But other factors such as the time spent on processing a distractor (dwelling) or multiple visits to the same stimulus in a search display (revisiting) are also involved in determining search efficiency. While there is some research showing that dwelling and revisiting modulate search times in addition to skipping, the corresponding studies used complex naturalistic and category-defined stimuli. The present study tests whether results from prior research can be generalized to more simple stimuli, where target-distractor similarity, a strong factor influencing search performance, can be manipulated in a detailed fashion. Thus, in the present study, simple stimuli with varying degrees of target-distractor similarity were used to deliver conclusive evidence for the contribution of dwelling and revisiting to search performance. The results have theoretical and methodological implications: They imply that visual search models should not treat dwelling and revisiting as constants across varying levels of search efficiency and that behavioral search experiments are equivocal with respect to the responsible processing mechanisms underlying more versus less efficient search. We also suggest that eye-tracking methods may be used to disentangle different search components such as skipping, dwelling, and revisiting

Adding depth to overlapping displays can improve visual search performance

Standard models of visual search have focused upon asking participants to search for a single target in displays where the objects do not overlap one another, and where the objects are presented on a single depth plane. This stands in contrast to many everyday visual searches wherein variations in overlap and depth are the norm, rather than the exception. Here, we addressed whether presenting overlapping objects on different depths planes to one another can improve search performance. Across four different experiments using different stimulus types (opaque polygons, transparent polygons, opaque real-world objects, and transparent X-ray images), we found that depth was primarily beneficial when the displays were transparent, and this benefit arose in terms of an increase in response accuracy. Although the benefit to search performance only appeared in some cases, across all stimulus types, we found evidence of marked shifts in eye-movement behavior. Our results have important implications for current models and theories of visual search, which have not yet provided detailed accounts of the effects that overlap and depth have on guidance and object identification processes. Moreover, our results show that the presence of depth information could aid real-world searches of complex, overlapping displays

Modeling lag-2 revisits to understand trade-offs in mixed control of fixation termination during visual search

An important question about eye-movement behavior is when the decision is made to terminate a fixation and program the following saccade. Different approaches have found converging evidence in favor of a mixed-control account, in which there is some overlap between processing information at fixation and planning the following saccade. We examined one interesting instance of mixed control in visual search: lag-2 revisits, during which observers fixate a stimulus, move to a different stimulus, and then revisit the first stimulus on the next fixation. Results show that the probability of lag-2 revisits occurring increased with the number of target-similar stimuli, and revisits were preceded by a brief fixation on the intervening distractor-stimulus. We developed the Efficient Visual Sampling (EVS) computational model to simulate our findings (fixation durations and fixation locations), and to provide insight into mixed control of fixations and the perceptual, cognitive, and motor processes that produce lag-2 revisits

Probabilistic User Interface Design for Virtual and Augmented Reality Applications

The central hypothesis of this thesis is that probabilistic user interface design provides an effective methodology for delivering productive and enjoyable applications in virtual reality (VR) and augmented reality (AR). This investigation is timely given the recent emergence of mass-market virtual and augmented reality head-mounted displays and growing demand for tailored applications and content. The design guidance for building compelling and productive applications for these environments is, however, currently lagging the pace at which the underlying technology is maturing. This is problematic given important differences between designing conventional 2D interfaces and interactions and their embodied 3D counterparts. This dissertation investigates probabilistic user interface design as a method for solving many of the novel challenges encountered when developing applications for VR and AR. Probabilistic user interface design seeks to model the uncertain events in a system and identify, implement and validate strategies that drive improved system performance. This thesis addresses four research questions by applying a probabilistic treatment in four distinct but closely related case studies. These four case studies are selected to illustrate the flexibility and unique benefits offered by this method. Research Question 1 asks how the probabilistic qualities of an interface can be determined and how this can inform design. This question is investigated in the context of text entry in VR with a probabilistic characterisation performed on two fundamental design choices. Research Question 2 relates to the challenge of adapting AR applications to deployment contexts not knowable at design time. A study in which crowdworkers are employed to build a probabilistic understanding of the requirements for contextually adaptive AR answers this question. The text entry theme is revisited in answering Research Questions 3 which asks how high levels of input noise can be mitigated through inference. A probabilistic text entry method specifically tailored for use in AR is implemented and evaluated. Finally, Research Question 4 asks how the high dimensional design space in AR and VR applications can be efficiently explored to support ideal design choices. Interface refinement through probabilistic optimisation and crowdsourcing is shown to be highly efficient and effective for this purpose. A probabilistic treatment in the design process has many potential benefits, principle among which is increased robustness to circumstances unanticipated at design time. This thesis contributes to the toolset and guidance available to designers and supports the development of next generation user interfaces specifically tailored to virtual and augmented reality

Forecaster Warning Decision Making with Rapidly-Updating Radar Data

Phased-array radar is being considered as a potential future replacement technology for the current operational Weather Surveillance Radar 1988 Doppler system. One of the most notable differences in these weather radar systems is the temporal resolution. With phased-array radar collecting volumetric updates 4–6 times more frequently, the operational impacts of rapidly-updating radar data on forecasters’ warning decision processes must be assessed. The Phased Array Radar Innovative Sensing Experiment (PARISE) was therefore designed to examine forecasters’ warning performance and related warning decision processes during use of ~1-min radar updates in simulated real-time warning operation scenarios. While the 2010, 2012, and 2013 PARISE studies reported encouraging findings for forecasters’ use of these data, each of these studies were limited in terms of sample size and the chosen methods. Additionally, important research questions that had not yet been explored remained unanswered. To address these limitations and investigate new research questions, thirty National Weather Service forecasters were invited to the NOAA Hazardous Weather Testbed to participate in the 2015 PARISE. Participating forecasters completed three components of this study: 1) the traditional experiment, 2) an eye-tracking experiment, and 3) a focus group. The first component was designed to build on previous work by assessing and comparing forecasters’ warning performance and related cognitive workload when using 1-min, 2-min, and 5-min phased-array radar updates during simulated warning operations. This traditional experiment was comprised of nine weather events that varied in terms of weather threat. Next, forecasters’ eye movement data were observed as they each worked a single weather event with either 1-min or 5-min phased-array radar updates. This work was motivated by an eye-tracking pilot study, in which a forecaster’s eye movement data was found to correspond meaningfully to their retrospective recall data that described their warning decision process. The 2015 PARISE eye-tracking experiment allowed for an objective analysis of how forecasters interacted with a radar display and warning interface for a single weather event, and more specifically, supported an investigation of whether radar update speed impacts how forecasters distribute their attention. Lastly, six focus groups were conducted to enable forecasters to share their experiences on their use of rapidly-updating phased-array radar data during the experiment. The findings from the focus groups provide motivation for the integration of rapidly-updating radar data into the forecast office and highlight some important considerations for successful use of these data during warning operations. The work presented in this dissertation was approved by the University of Oklahoma’s Office of Human Research Participant Protection Institutional Review Board under projects #5226 and #5580

Interactive Machine Learning for User-Innovation Toolkits – An Action Design Research approach

Machine learning offers great potential to developers and end users in the creative industries. However, to better support creative software developers' needs and empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. This thesis asks the following research questions: How can we apply a user-centred approach to the design of developer tools for rapid prototyping with Interactive Machine Learning? In what ways can we design better developer tools to accelerate and broaden innovation with machine learning? This thesis presents a three-year longitudinal action research study that I undertook within a multi-institutional consortium leading the EU H2020 -funded Innovation Action RAPID-MIX. The scope of the research presented here was the application of a user-centred approach to the design and evaluation of developer tools for rapid prototyping and product development with machine learning. This thesis presents my work in collaboration with other members of RAPID-MIX, including design and deployment of a user-centred methodology for the project, interventions for gathering requirements with RAPID-MIX consortium stakeholders and end users, and prototyping, development and evaluation of a software development toolkit for interactive machine learning. This thesis contributes with new understanding about the consequences and implications of a user-centred approach to the design and evaluation of developer tools for rapid prototyping of interactive machine learning systems. This includes 1) new understanding about the goals, needs, expectations, and challenges facing creative machine-learning non-expert developers and 2) an evaluation of the usability and design trade-offs of a toolkit for rapid prototyping with interactive machine learning. This thesis also contributes with 3) a methods framework of User-Centred Design Actions for harmonising User-Centred Design with Action Research and supporting the collaboration between action researchers and practitioners working in rapid innovation actions, and 4) recommendations for applying Action Research and User-Centred Design in similar contexts and scale

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills