HCI has a long history of accounting for individual differences in user analysis research [21]. In the field of visualisation the interest in individual differences is recent.
2.3.1 Individual Differences in Information Visualisation
Conati and Maclaren [70] in their paper explore the role of individual difference for the various cognitive abilities (visual memory, spatial visualisation, perceptual speed, disembodiment, need for cognition and learning style) with regards to performance in using target visualisations. In their experiment they designed the tasks according to the taxonomy by Amar et al. [51], also discussed in the insight section of this review. The results found only one significant effect attributed to a cognitive ability (perceptual speed) with regards to the visualisation effectiveness. When investigating further into the accuracy with target visualisations, they discovered that other cognitive abilities could be used as a predictor of performance (need for cognition, special visualisation and learning style).
Previous work by Allen [71] has shown similar results, associating user performance to design features on individual differences in the cognitive ability for search tasks, showing that both compensatory and capitalisation matching were present. A performance increase was observed for participants with lower cognitive ability. This increase was attributable to the augmentation benefits the system provided. Moreover users with higher cognitive ability got greater benefits with features of the system that demanded greater cognitive resources. Additionally, the results also suggest that users do not self- adapt to the systems features that best suited their cognitive capabilities, thus leading to the need to adapt the tools.
Ziemkiewicz and Kosara [72] investigated the individual differences compatibility with the preconceived visual metaphor preferences and associated performance. The individual differences studied were taken from the Mini-IPIP Big Five personality [73] covering Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism constructs. The results confirmed that compatibility had an influence on performance, but decreased with users with high scores in openness and spatial cognitive abilities, or user with no a
priori preference. A surprising result was that response time compatibility effect was only
true for women. A strong relationship of visual and verbal metaphors in participant’s comprehension of the task was present, but as in Allen [71], they did not find signs of adaptation. Ziemkiewicz and Kosara concluded that when evaluating new systems, gender, spatial ability and personality are important aspects to consider. Additionally, they recommend taking users preconceived preferences with care, as they do not show unambiguously true preferences.
Green and Fisher [9–11] undertook two studies comparing procedural learning with two interfaces, an information visualisation application and a web table. Their ongoing aim is to advance research towards a personal equation of interaction, which takes full account of individual differences in a predictive manner. In their studies, they investigated three main psychometric measures, Locus of control (LoC), IPIP 20-item Big Five Neuroticism and IPIP 20-item Big Five Extraversion [73], questioning whether these had a significant effect in performance and to what degree these relate to the number of insights reported. Green and Fisher define insight as knowledge gained from either the content or the ontological relationship. Table 2.4 outlines the key results on these studies. Additionally, in [9], they found that for inferential tasks, LoC was the best predictor of performance. Contrary to expectation, externally oriented LoC participants had better performance scores in inferential tasks and when the task became more complex made fewer mistakes with information visualisation tool than with the web table tool.
Completion Times Errors Insights
Interface Faster times in Web Table Fewer errors in Web Table
More Insight in Information visualisation Application Locus of Control Internal LoC faster times None External LoC more insights Extraversion More extraverted faster times None Less extraverted more insights Neuroticism More neurotic faster times None Less neurotic more insights
LoC: Locus of Control
Table 2.4. – Summary of Green and Fisher Results [9–11]
information usage, a dislike for ambiguity, low extraversion and low need for cognition scores. Additionally, these high performers were more prone to have their behaviour dictated by their emotions. These studies, lead the way to define user profiles using cognitive tasks instead of user group membership. In their discussion, they call attention to the need to further evaluate the individual differences with regards to insight as a measure. They highlight the lack of clear consensual definitions of insights is a key challenges in insight-based evaluations, where clear definition of insight is paramount to enable analytical comparison between studies. In their knowledge-insight perspective, they found significant differences in relation to the type of visualisation, but do not report any correlation with individual differences. Additionally, they mentioned that further research is required to understand the effects of learning style as a factor for visualisation.
Ziemkiewicz et al. [12], in their study, focus on the influence of LoC on performance according visualisation style building on previous research by Green et al. [9, 10]. They hypothesise that the differences found in Green et al. work regarding inferential tasks performance in the information visualisation tool, is due to layout rather than the task. In their study, they used, four layouts: basic tree view; bordered tree; indented boxes; and, nested boxes. These are used in search and inferential tasks, based on four data-sets as part of their experiment. The key finding supporting their hypothesis was that participants with an external LoC performed better in the nested box view which is the layout used by the information visualisation tool in Green et al. [9]. They conclude that LoC is a robust measure and can be directly related to the performance of data exploration tasks, where each group performed differently according to the visualisation layout. Although they cannot infer causality, they argue that external LoC users may be more willing to adapt to visualisation types, thus explaining these results. They link their findings to distributed cognition views discussed above in Liu et al. [61] and works by Cassidy and Eachus [74] where external LoC is linked to surface learning. Ziemkiewicz et
al. hypothesise that perhaps internally focused user ability to adapt is a benefit when
using visual analytics systems, where a reliance on external systems are required. Externally focused users, may find it difficult to use visual analytic tools, as they may need to align their internal views to the external representations of the VA system. Based on these results and hypothesis, they recommend that VA system designers should take into
consideration LoC relating to the explicit nature of the visualisation layout. More broadly, when the audience may have a more externally focused LoC, it might be useful to deviate from the classical Tufte’s ink-to-data ratio considerations [75], by making the layout hierarchical features more explicit to help the exploration process. On the other hand for more internally focused LoC user, they hypothesised that for users with pre-existing mental models such as experts, a stricter ink-to-data ratio design would be more beneficial.
Chen [76] investigated individual differences in spatial-semantic virtual environments finding that experience of the environment had the most significant effect on performance rather than the cognitive abilities studied (spatial ability and associative memory). The experiments compared spatial and textual settings, where the spatial user interface was developed as 3D visual representation of a network diagram. Chen concludes that more research is required to fully understand the interaction effect with the virtual worlds.
Chen and Toh [77] and more recently Hauptman and Cohen [78] investigated the learning style aspects of individual differences for virtual reality (VR) environments, using different learning style instruments. Whereas Hauptman and Cohen used the VARK multimodal learning preferences [79] and Chen and Toh the Kolb learning style inventory. Their findings differ, Chen and Toh adopted reported that learning style had no effect on the VR performance whereas Hauptman and Cohen in their more recent study, reported learning style effects on performance. These latter results promote the strength of VARK as a multi-dimensional and scaled instrument, as per Miller’s [80] recommendations when choosing a learning style instrument. Moreover, Hauptman and Cohen cannot fully explain their results and recommend further research to uncover the verbal and non- verbal impact on performance.
2.3.2 Individual differences : a summative overview
Research suggests that individual users do not adapt their cognitive abilities to the visualisation tool. Further, studies show the potential and need to adapt the visualisation tools according to the task and the individual differences to improve performance. Although the adaptation is limited at present, future research mapping further the
domain require a well-defined definition of insight, in order to enable inter-study comparisons and quantify contributions. Further research is needed to demonstrate the impact of individual differences such as learning styles and self-belief cognitive constructs on the outcomes of interaction. These advances will benefit the design of future VA tools, and interface individualisation.