This special issue presents highlights from ECCE 2018, the 30th European Conference on Cognitive Ergonomics, which is the annual event of the European Association of Cognitive Ergonomics (EACE). EC...

Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the non-expert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.

In this study, we evaluated the ability of computational cognitive models of web‐navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.

This study investigated the change in the content of the queries when performing reformulations in relation to age and task difficulty. Results showed that both generalization and specialization strategies were applied significantly more often for difficult tasks compared to simple tasks. Young participants were found to use specialization strategy significantly more often than old participants. Generalization strategy was also used significantly more often by young participants, especially for difficult tasks. Young participants were found to reformulate much longer than old participants. The semantic relevance of queries with the target information was found to be significantly higher for difficult tasks compared to simple tasks. It showed a decreasing trend across reformulations for old participants and remained constant for young participants, indicating that as old participants reformulated, they produced queries that were further away from the target information. Implications of these findings for design of information search systems are discussed.

Current models of web navigation focus only on the influence of textual information and ignore the role of graphical information. We studied the differential role of text and graphics in identifying web page widgets classified into two kinds: textual and graphical. Four different versions of web pages were created by systematically removing textual and graphical information from each page. The task of the participants was to locate either textual or graphical widgets on the displayed web page. Results show that for any widget, the task-completion time and the number of clicks were significantly less in web pages with graphics than in those with no graphics. This demonstrates the importance of graphical information. However, textual information is also important because performance in locating graphical widgets under no-graphics conditions was better when text was present than with no text. Since, for identifying graphical widgets, text and graphics interact and complement each other, we conclude that cognitive models on web navigation should include the role of graphical information next to textual information.

The aim of this special issue is to contribute to the improvement of the existing cognitive models of web navigation like SNIF-ACT (Fu and Pirolli 2007), CoLiDeS (Blackmon et al. 2005, Kitajima et ...

CoLiDeS + Pic is a cognitive model of web-navigation that incorporates semantic information from pictures intoCoLiDeS. In our earlier research, we have demonstrated that by incorporating semantic information from pictures, CoLiDeS + Pic can predict the hyperlinks on the shortest path more frequently, and also with greater information scent, compared to earlier cognitive models of web-navigation like CoLiDeS that relied only on textualinformation from hyperlinks. In this article, we investigate the following research questions. First, would the increase in information scent have an impact on the actual user navigation behaviour? Second, do users actually follow the navigation path predicted by CoLiDeS + Pic? In other words, would CoLiDeS + Pic predict actual user navigation behaviour more accurately than CoLiDeS? We investigate these questions by varying the relevance of pictures on aweb page and studying the impact of varying relevance on the user navigation patterns. We found that under the highly relevant picture condition, users were more accurate and took less time to finish their tasks. Also, under thehighly relevant picture condition, CoLiDeS + Pic predicts significantly greater number of actual user clicks. There was no significant difference in model predictions between the lowly relevant picture condition and no-picture condition. These results validate the predictions made by CoLiDeS + Pic.

We studied the effects of menu type, navigation path complexity and spatial ability on information retrieval performance and web disorientation or lostness. Two innovative aspects were included: (a) navigation path relevance and (b) information gathering tasks. As expected we found that, when measuring aspects directly related to navigation performance such as time and lostness, path complexity plays an important role. During navigation, expandable menus are more beneficial than sequential menus, especially for people with low spatial skills and when the semantic similarity between the information goal and the navigation path is high. When measuring aspects only related to information retrieval, once navigation has occurred, the effects of menu type depend on the spatial skills and the difficulty of the task itself. Participants with high skills perform well in all conditions. Participants with low spatial skills benefit from the use of expandable menus showing only problems with the sequential menu and when the tasks demanded more than simply combining information from different locations on a web page. The opposite pattern found with participants with medium spatial skills needs further research.

Comprehension-based linked model of deliberate search (CoLiDeS) + Pic is a cognitive model of web-navigation that takes into account the semantic information from graphical elements present on a web-page to compute the information scent value of the hyperlinks. The model is based on CoLiDeS, which has a two-phase processing cycle: (a) attention phase, which first parses the web-page and focuses attention on the region of the web-page that is semantically most similar to the goal, and (b) action-selection phase, which evaluates the available actions in the focused region and selects a particular action such as clicking a link. The graphical elements are important both for attracting attention to a region of the web-page and for communicating semantic meaning that may alter or enhance the meaning of the hyperlink labels. In the first part of this article, we give a theoretical explanation of the CoLiDeS + Pic model and describe the methodology followed to implement it. In the second part, we run a simulation on a mock-up website and evaluate the effect of pictures on information scent of hyperlinks by means of the CoLiDeS + Pic model on basis of the simulation results. It was found that CoLiDeS + Pic with highly relevant pictures increases the values of information scent of task-relevant hyperlinks, and therefore it increases the probability of selecting those hyperlinks compared to CoLiDeS (without pictures) or CoLiDeS + Pic with lowly relevant pictures. These results confirm the importance of including information from pictures into the modelling of web-navigation.

The ever-increasing involvement of computer technology in work and living environments—for training and actual task performances—sets continuously new challenges for cognitive ergonomics in diverse domains like transport, crisis management and healthcare (Brinkman, 2011). A major challenge is to harmonize the technology development to the dynamics and complexity of the social, cognitive and affective processes in these environments, taking into account of the diversity and multiplicity of human needs (cf. Klein et al., 2004). Such a harmonization comprises effective and efficient human-automation collaboration that proves (1) to be resilient for critical situations and (2) to facilitate creative problem solving in such situations. Current research focuses on collaborative artefacts that help to establish these two effects by enhancing work team’s conditions, knowledge and capabilities for acting in a safe and healthy way. For example, studies on flying, driving, and sailing provide requirements for pilot-automation collaboration that brings about adequate recover piloting behaviour after a “failure” (cf. Woods and Hollnagel, 2006; Lenior et al., 2006). As a second example, recent research on shared situation awareness of distributed teams in crisis management provide support that may improve team coordination and corresponding performance (van der Kleij et al., 2009). For the development of such human-automation collaboration, cognitive ergonomics methods are needed for deriving and testing of the “situational” requirements systematically (cf. Neerincx and Lindenberg, 2008). For example, game-based evaluations with Virtual-Reality tools can help to train for new situations or to test specific artefacts (Smets et al., 2011). Furthermore, a combination of scenario-based investigation and controlled lab experiments can, for example, help to study automated assistant functions to support therapists in high demand situations when treating multiple patients simultaneously over the internet (Paping et al., 2011). As a final example, user experience sampling methods that apply advanced interaction events analysis (Brinkman et al., 2005; Brinkman et al., 2007) or sensor technology can improve the insight into situated activities over time (such as heart-rate, eye tracking), but it might prove to be difficult to apply in high-demand environments (e.g. Grootjen et al., 2007).

A challenge for serious games designers is to integrate learning with entertainment. For this purpose, the generation of curiosity using the foreshadowing/back story technique is promising. To implement this technique we propose the Game Discourse Analysis (GDA) which discerns between information flow (i.e. the sequence of information elements in a chronological order) and game discourse (i.e. the manipulation of the information flow to make the game more engaging and effective). We elaborate on the GDA and describe how two of the authors applied it in order to implement foreshadowing/back story in the game ReMission. The GDA was found to have potential as a communication tool for multidisciplinary design teams. Also, two problems were signaled: (1) creating an information flow is laborious and designers may benefit from automating parts of the GDA; (2) substantial deviations from the optimal information flow by players’ actions may interfere with the intention of the game discourse. Additionally, in an experiment we tested the impact of this GDA supported manipulation on engagement (curiosity) and learning. We found that the GDA-supported foreshadowing/back story yielded more curiosity, but that it did not yield learning.

In the AVERs sense-making tool for crime analysis different types of information are represented in different ways. More precisely, narrative knowledge is represented in an explanatory direction and testimonial knowledge in an indicative direction. This paper shows that this distinction agrees with the preference of potential users and reduces the number of interpretation errors made by them.

3D models of an urban area were manipulated to create an impression of neglect and dilapidation. In an experimental study the affective appraisals of the original and the manipulated models were compared. Results demonstrate that unfavourable elements in an urban environment, that are usually omitted from 3D models, influence the appraisal of the participants about the area, but only slightly and less than expected. Moreover, the judgment of the environment seems to be influenced by expectations of the representation. When 3D models are used for decision making in urban planning, this effect has to be taken into account. The experiment thus demonstrates that measuring instruments should be designed in such a way that the content (the environment) can be assessed separately from the chosen representation.

This paper investigates the influence of interface styles on problem solving performance. It is often assumed that performance on problem solving tasks improves when users are assisted by externalizing task-related information on the interface. Although externalization requires less recall and relieves working memory, it does not instigate planning, understanding and knowledge acquisition. Without this assistance, task-information must be internalized, stored in the user's memory, leading to more planning and thinking and perhaps to better performance and knowledge. Another variable that can influence behavior is "Need for Cognition" (NFC), the tendency to engage in effortful cognitive tasks. We investigated the effects of interface style and cognitive style on performance using a conference planning application. Interface style influenced behavior and performance, but NFC did not. The internalization interface led to more planful behavior and smarter solutions. When planning and learning are the aim, designers should thus beware of giving a user (too) much assistance. Understanding how people react to interface information can be crucial in designing effective software, especially important in the areas of education and learning.

This research investigates how externalizing information in program-interfaces influences problem solving performance. Externalization as screen-representation provides relevant interface-feedback, and remembering certain task-related knowledge becomes unnecessary, relieving working memory (WM). Examples are visual feedback aids such as "graying out" non-applicable menu-items. Differently, when certain task-related information is unavailable, it needs to be internalized, stored in WM. We examined the hypotheses that externalization yields better performance during initial learning, while later, internalization yields better performance and knowledge. Two versions (internalized & externalized) of an isomorph of the "missionaries and cannibals" problem were tested. 30 subjects solved 6 problems, then performed a distraction task erasing WM, and solved 3 more problems. Also knowledge of the problem's rules and states was tested. Internalization yielded better knowledge and reaching less dead-end problem-states. Time needed, steps and error-rates were in the expected direction, but not significantly. This research contributes to GUI design-guidelines for human-computer interaction of problem-solving tasks.

This study investigates the influence of structure and reading-manipulation, and more importantly, the interaction between these two variables on usability of hypertexts. Four types of hypertexts are distinguished, differing in structure (hierarchical, or hierarchical with partial linearity) and reading-manipulation (scrolling or paging). A fifth extra hypertext containing a hierarchical structure with partial linearity and both paging and scrolling was also investigated. The information itself, a city guide about Utrecht with cultural and tourist information, was exactly the same in all hypertexts. Three aspects of usability were examined: efficiency, ease of learning and user-satisfaction. These aspects are measured by performance on 24 search tasks and a task reflecting insight into the structure of the hypertext. Also, questions about the structure of the hypertext and satisfaction with the system had to be answered. The results indicated that structure and reading-manipulation did not interact. There were, however, significant main effects of structure and reading-manipulation. A purely hierarchical structure was frequently more usable than a hierarchical structure with partial linearity, and scrolling appeared to be more useful than paging. The fifth alternative hypertext seemed slightly more usable than the hypertexts with linearity, but less usable than purely hierarchical hypertexts. The findings combined suggest that a purely hierarchical hypertext with scrolling is most useful, probably because this structure and kind of reading-manipulation both provide a clear insight into the structure of the hypertext. We assume this insight to be necessary for adequate performance.

Abstract The central tenet of HCI is to ensure that software and hardware design supports users doing their tasks. Most of the papers in this Special Issue go one step beyond designing for usability — they also address design issues related to supporting human values. As computer usage becomes more diverse both in terms of the range of users and types of applications, human values such as democracy will become increasingly important and controversial, especially, as the number of people coming onto the Internet increases. For example, the issue of computer haves and have nots is well known, but governments are only just starting to think through its implications in terms of future policy. In addition, understanding users' affective responses to systems and how emotions are conveyed across networks is starting to gain designers' attention. Brenda Laurel's company, Purple Moon, is a clear example of the perceived need of designing to support users' emotional needs, in this case, teenage girls.

The characteristics of direct manipulation interfaces (DMIs) are examined. The main purpose of this examination is to provide ideas for future research on modelling exploratory learning in the context of using DMIs. Four topics are discussed: the perceptual characteristics of DMIs, exploratory learning and display-based problem-solving in general, modelling human-computer interaction in the context of DMIs, and the consequences of DMIs for modelling the interaction by means of a production system. Specifically, the questions that are discussed are: first, how do DMIs afford, encourage and support exploratory learning, and how can typical DMI characteristics such as the objects on the screen be included in models of user behaviour? Second, what are the characteristics of problem-solving and exploratory learning in the context of visual displays? Third, how is novice behaviour and, more generally, problem-solving modelled in the context of human-computer interaction? In the final section, suggestions are made based on the topics discussed, with the aim of presenting some steps towards developing a model consisting of production rules that can simulate human interaction with DMIs more adequately than has been the case thus far. Two important consequences of DMIs for modelling human interaction are discussed. First, the external display of DMIs allows recognition instead of recall. Consequently, production rules can be more recognition-based. Second, with regard to the structure of production systems, the mechanism of partial matching is proposed to account for errors during performance. Constraints and affordances can be accounted for by proposing production rules to fire contextdependently, and by assuming that production rules can be meaningfully grouped and actively scanned for a match.