Andrew Howes

Model of visual search and selection time in linear menus

This paper presents a novel mathematical model for visual search and selection time in linear menus. Assuming two visual search strategies, serial and directed, and a pointing sub-task, it captures the change of performance with five fac- tors: 1) menu length, 2) menu organization, 3) target position, 4) absence/presence of target, and 5) practice. The novel aspect is that the model is expressed as probability density distribution of gaze, which allows for deriving total selection time. We present novel data that replicates and extends the Nielsen menu selection paradigm and uses eye-tracking and mouse tracking to confirm model predictions. The same parametrization yielded a high fit to both menu selection time and gaze distributions. The model has the potential to improve menu designs by helping designers identify more effective solutions without conducting empirical studies.

Scalable Proactive Event-Driven Decision Making

This paper proposes a methodology for proactive event-driven decision making. Proper decisions are made by forecasting events prior to their occurrence. Motivation for proactive decision making stems from social and economic factors, and is based on the fact that prevention is often more effective than the cure. The decisions are made in real time and require swift and immediate processing of Big Data, that is, extremely large amounts of noisy data flooding in from various locations, as well as historical data. The methodology will recognize and forecast opportunities and threats, making the decision to capitalize on the opportunities and mitigate the threats. This will be explained through user-interaction and the decisions of human operators, in order to ultimately facilitate proactive decision making.

Harmony and tension on social network sites: side effects of increasing online interconnectivity

The need to maintain harmony among ones social contacts is proposed in this paper to impose constraints on the interconnectivity between users of social network sites (SNS). A particular focus is on the connectivity between different social spheres. It is hypothesized that the type and number of social spheres and technological features of SNS interact such that increased levels of social tension result. These ideas are supported by the findings from a survey study among Facebook users. Social diversity of the Facebook network predicted online tension as did the number of family members on Facebook, in contrast to work and social contacts. Furthermore, evidence was found to support the idea that tension might impose an upper limit on network size. Follow-up interview data also showed that online tension was predominantly about unwanted connectivity between the spheres. All the technological features that users reported as problematic focused on the easy access to and broadcast of text and pictures. Findings are discussed in light of unintended, negative side-effects of SNS and social media in general.

The Emergence of Interactive Behavior: A Model of Rational Menu Search

One reason that human interaction with technology is difficult to understand is because the way in which people perform interactive tasks is highly adaptive. One such interactive task is menu search. In the current article we test the hypothesis that menu search is rationally adapted to (1) the ecological structure of interaction, (2) cognitive and perceptual limits, and (3) the goal to maximise the trade-off between speed and accuracy. Unlike in previous models, no assumptions are made about the strategies available to or adopted by users, rather the menu search problem is specified as a reinforcement learning problem and behaviour emerges by finding the optimal Markov Decision Process (MDP). The model is tested against existing empirical findings concerning the effect of menu organisation and menu length. The model predicts the effect of these variables on task completion time and eye movements. The discussion considers the pros and cons of the modelling approach relative to other well-known mod- elling approaches.

The adaptation of visual search to utility, ecology and design

An important question for Human-Computer Interaction is to understand the visual search strategies that people use to scan the results of a search engine and find the information relevant to their current task. Design proposals that support this task include space-filling thumbnails, faceted browsers, and textually enhanced thumbnails, amongst others. We argue that understanding the trade-offs in this space might be informed by a deep understanding of the visual search strategies that people choose given the constraints imposed by the natural ecology of images on the web, the human visual system, and the task demands. In the current paper we report, and empirically evaluate, a computational model of the strategies that people choose in response to these constraints. The model builds on previous insights concerning the human visual system and the adaptive nature of visual search. The results show that strategic parameters, including the number of features to look for, the evaluation-stopping rule, the gaze duration and the number of fixations are explained by the proposed computational model. Reports a computational model of eye movements over image search engine results.Reports an empirical study of visual attention for image search engine results.Shows that eye movements are rational given interface design and user priorities.Provides evidence that strategies are adapted to the ecology search engine results.

Why contextual preference reversals maximize expected value

Contextual preference reversals occur when a preference for one option over another is reversed by the addition of further options. It has been argued that the occurrence of preference reversals in human behavior shows that people violate the axioms of rational choice and that people are not, therefore, expected value maximizers. In contrast, we demonstrate that if a person is only able to make noisy calculations of expected value and noisy observations of the ordinal relations among option features, then the expected value maximizing choice is influenced by the addition of new options and does give rise to apparent preference reversals. We explore the implications of expected value maximizing choice, conditioned on noisy observations, for a range of contextual preference reversal types—including attraction, compromise, similarity, and phantom effects. These preference reversal types have played a key role in the development of models of human choice. We conclude that experiments demonstrating contextual preference reversals are not evidence for irrationality. They are, however, a consequence of expected value maximization given noisy observations.

Interaction science SIG: overcoming challenges

Over the past 30 years science has played a key role in shaping and advancing research in Human-Computer Interaction. Informed in part by methods, theories and findings from the behavioral sciences and from computer science, scientific contributions to HCI have provided explanations of how and why people interact through and with technology. We argue that the contribution of science to HCI could be enhanced if key challenges are met. During a SIG meeting we will discuss the challenges and potential responses and set an agenda for the coming years.

Inferring Cognitive Models from Data using Approximate Bayesian Computation

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

Utility Maximization and Bounds on Human Information Processing

Utility maximization is a key element of a number of theoretical approaches to explaining human behavior. Among these approaches are rational analysis, ideal observer theory, and signal detection theory. While some examples of these approaches define the utility maximization problem with little reference to the bounds imposed by the organism, others start with, and emphasize approaches in which bounds imposed by the information processing architecture are considered as an explicit part of the utility maximization problem. These latter approaches are the topic of this issue of the journal.

Adaptive Information Fusion for Situation Awareness in the Cockpit

Evidence is provided pointing to potential caveats associated with the use of information fusion techniques in the cockpit. Six pilots each with a minimum of ten years flight experience completed a series of missions using a simulated future jet cockpit. Each trial required a pilot to guide their aircraft towards a fixed location. The pilot was required to estimate the position of this location both during and five minutes after the flight. Different types of fusion were manipulated with regard to the information presented on a touchscreen display — Fused, Fused Drill-Down, and UnFused. Data suggested that information fusion alone can have negative consequences for both task performance and subsequent recollection of information. It is argued that reductions in system transparency and transfer-appropriate processing may account for these findings.