Automated tools are increasingly being used to generate highly engaging concept maps as an aid to strategic planning and other decision-making tasks. Unless stakeholders can understand the principles of the underlying layout process, however, we have found that they lack confidence and are therefore reluctant to use these maps. In this paper, we present a qualitative study exploring the effect on users' confidence of using data-driven explanation mechanisms, by conducting in-depth scenario-based interviews with ten participants. To provide diversity in stimulus and approach we use two explanation mechanisms based on projection and agglomerative layout methods. The themes exposed in our results indicate that the data-driven explanations improved user confidence in several ways, and that process clarity and layout density also affected users' views of the credibility of the concept maps. We discuss how these factors can increase uptake of automated tools and affect user confidence.

In previous work we developed a method for interior designers to receive image-based feedback about a crowd's emotions when viewing their designs. Although the designers clearly desired a service which provided the new style of feedback, we wanted to find out if an internet crowd would enjoy, and become engaged in, giving emotion feedback this way. In this paper, through a mixed methods study, we expose whether and why internet users enjoy giving emotion feedback using images compared to responding with text. We measured the participants' cognitive styles and found that they correlate with the reported utility and engagement of using images. Those more visual than they are verbal were more engaged by using images to express emotion compared to text. Enlightening qualitative insights reveal, surprisingly, that half of our participants have an appetite for expressing emotions this way, value engagement over clarity, and would use images for emotion feedback in contexts other than design feedback.

Research into creating visualisations that organise ideas into concise concept maps often focuses on implicit mathematical and statistical theories which are built around algorithmic efficacy or visual complexity. Although there are multiple techniques which attempt to mathematically optimise this multi-dimensional problem, it is still unknown how to create concept maps that are immediately understandable to people. In this paper, we present an in-depth qualitative study observing the behaviour and discussing the strategy used by non-expert participants to create, interact, update and communicate a concept map that represents a collection of research ideas. Our results show non-expert individuals create concept maps differently to visualisation algorithms. We found that our participants prioritised narrative, landmarks, abstraction, clarity, and simplicity. Finally, we derive design recommendations from our results which we hope will inspire future algorithms that automatically create more usable and compelling concept maps better suited to the natural behaviours and needs of users.

Imagery and language are often seen as serving different aspects of cognition, with cognitive styles theories proposing that people can be visual or verbal thinkers. Most feedback systems, however, only cater to verbal thinkers. To help rectify this, we have developed a novel method of crowd communication which appeals to those more visual people. Designers can ask a crowd to feedback on their designs using specially constructed image banks to discover the perceptual and emotional theme perceived by possible future customers. A major component of the method is a summarization process in which the crowd's feedback, consisting of a mass of images, is presented to the designer as a digest of representative images. In this paper we describe an experiment showing that these image summaries are as effective as the full image selections at communicating terms. This means that designers can consume the new feedback confident that it represents a fair representation of the total image feedback from the crowd.

Inspired by studies [4, 23, 40] which compared rankings obtained by search engines and human observers, in this paper we compare texture rankings derived by 51 sets of computational features against perceptual texture rankings obtained from a free-grouping experiment with 30 human observers, using a unify evaluation framework. Experimental results show that the MRSAR [37], VZNEIGHBORHOOD [62], LBPHF [2] and LBPBASIC [3] feature sets perform better than their counterparts. However, none of those feature sets are ideal. The best average G and M measures (measures of ranking accuracy from 0 to 1) [15, 5] obtained are 0.36 and 0.25 respectively. We suggest that this poor performance may be due to the small local neighborhood used to calculate higher-order features which cannot capture the long-range interactions that humans have been shown to exploit [14, 16, 49, 56].

As part of the EPSRC funded 'Digital Sensoria' project a set of digital tools were utilised to better demonstrate the tactile qualities of textiles via the internet. Shoogleit [8], an online utility for the creation of interactive video was one such tool. A Shoogle player for iOS mobile devices was then created (Orzechowski) using an iterative process, during which experiments were carried out to determine if the added interactivity afforded by Shoogleit could more accurately describe textile qualities and thus aid in the creation of a next generation mobile browser for textiles.