Evidence from London taxi drivers of hierarchical route planning in a real-world environment

The ability to navigate a spatial environment strongly depends on how well individuals learn, represent and make use of their knowledge about the environment. In the past, research investigated these aspects separately and often in a virtual environment. The current work studied these three aspects of navigation in a real-real world setting to understand how humans navigate naturally in a complex, urban environment like London, UK. Of particular interest was to determine if there was evidence of hierarchical representations during route planning as found in previous behavioural, neuroscientific or computational studies. Most past studies have explored knowledge for simplistic environments or fragmented knowledge of real-world environments. By contrast, licensed London taxi drivers acquire a unique, almost perfect mental representation of the street network, the location of places and the traffic rules that apply to it. Here, the rare knowledge of these navigation experts was explored in three studies with novel approaches. First, to gain an understanding of the training process of unqualified taxi drivers, information from an interview with a teacher, training lessons and study material was collected, summarised and reported. A range of learning strategies was identified that was linked to theoretical, map-based learning and practical, in-situ experiences of London and pointed towards a segmented planning of routes through subgoal selection. Second, a potential mental segregation of London was studied with qualified taxi drivers through boundary drawings of specific London districts with a paper map to understand a potential hierarchical representation. Higher agreement was found for geographical structures and topically distinct districts surrounded by a linear, almost rectangular street network, whereas agreement was lowest for irregularly shaped districts with similarities to neighbouring areas. Finally, taxi drivers were asked to plan and then verbally recall each street they would take along routes between selected origin destination pairs. Audio recordings of these routes made it possible to relate the response times between individual streets to specific street network properties. The analysis using a linear mixed model indicated slower responses at upcoming turns and entering main roads, whereas boundary streets were recalled faster, as were finial streets when compared to initial street. No effects of Euclidean distance or detours were found. Observations from the training process indicate that a potential segregation of the environment, which might impact on later route planning, might be formed already through specific learning strategies. Faster response times for boundary streets support models in which planning is hierarchical. These findings extend past work on route planning in lab-based networks to real-world city street networks and highlight avenues for future research to explore and make use of real-world data

Cartographies of Heritage : Mapping the Interpretation of Landscape

Ph. D. Thesis.This thesis critically assesses the capabilities of data visualisation as a medium for effectively presenting and communicating fuzzy data, exploring cartographic methods as an effective form of knowledge communication. It considers how data visualisation can be used to explore landscape themes, specifically by integrating Geographic Information Systems (GIS) to represent and analyse qualitative data for understanding cultural heritage landscapes. Using heritage as a key geographical theme, the thesis considers how individuals place ‘value’ onto locations by reviewing theories of landscape aesthetics as a way of conceptualising landscape recognition. By utilising GIS, the thesis devises a mixed-methods approach to map interpretations and responses to heritage landscapes, developing techniques to enable the visualisation of landscape responses through forms of digital cartography. The methodology is designed to be completed in two separate stages. The first stage involves the collection of categorical and quantifiable data through identified research methods. The second stage requires utilising the empirical data collection to create data visualisation and subsequent maps as evidence of the possibility to deploy qualitative cartographies. Consequently, this thesis shows that cartographic representations can interrogate the relationships between people and place using mixed methods through a qualitative GIS approach. Whether these are part of a series of ongoing innovations, as unique stand-alone maps or as complimentary and supplementary methods of visualisation, data representations can effectively communicate people's experiences with or interpretations of landscapes. Data graphics and cartographic representations can be used alongside or in tandem to one another as part of the same visualisation, to create new innovative forms of data visualisation for interrogating and deciphering the complexities between people and place.Economic and Social Research Council (ESRC), North East Doctoral Training Centre (NEDTC

Biometric storyboards: a games user research approach for improving qualitative evaluations of player experience

Developing video games is an iterative and demanding process. It is difficult to achieve the goal of most video games — to be enjoyable, engaging and to create revenue for game developers — because of many hard-to-evaluate factors, such as the different ways players can interact with the game. Understanding how players behave during gameplay is of vital importance to developers and can be uncovered in user tests as part of game development. This can help developers to identify and resolve any potential problem areas before release, leading to a better player experience and possibly higher game review scores and sales. However, traditional user testing methods were developed for function and efficiency oriented applications. Hence, many traditional user testing methods cannot be applied in the same way for video game evaluation. This thesis presents an investigation into the contributions of physiological measurements in user testing within games user research (GUR). GUR specifically studies the interaction between a game and users (players) with the aim to provide feedback for developers to help them to optimise the game design of their title. An evaluation technique called Biometric Storyboards is developed, which visualises the relationships between game events, player feedback and changes in a player’s physiological state. Biometric Storyboards contributes to the field of human-computer interaction and GUR in three important areas: (1) visualising mixedmeasures of player experience, (2) deconstructing game design by analysing game events and pace, (3) incremental improvement of classic user research techniques (such as interviews and physiological measurements). These contributions are described in practical case studies, interviews with game developers and laboratory experiments. The results show this evaluation approach can enable games user researchers to increase the plausibility and persuasiveness of their reports and facilitate developers to better deliver their design goals. Biometric Storyboards is not aimed at replacing existing methods, but to extend them with mixed methods visualisations, to provide powerful tools for games user researchers and developers to better understand and communicate player needs, interactions and experiences. The contributions of this thesis are directly applicable for user researchers and game developers, as well as for researchers in user experience evaluation in entertainment systems

Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents

I describe the design and implementation of an integrated neural architecture, modelled on human executive attention, which is used to control both automatic (reactive) and willed action selection in a simulated robot. The model, based upon Norman and Shallice's supervisory attention system, incorporates important features of human attentional control: selection of an intended task over a more salient automatic task; priming of future tasks that are anticipated; and appropriate levels of persistence of focus of attention. Recognising that attention-based learning, mediated by the limbic system, and the hippocampus in particular, plays an important role in adaptive learning, I extend the Norman and Shallice model, introducing an intrinsic, attention-based learning mechanism that enhances the automaticity of willed actions and reduces future need for attentional effort required for dealing with distractions. These enhanced features support a new level of attentional autonomy in the operation of the simulated robot. Some properties of the model are explored using lesion studies, leading to the identification of a correspondence between the behavioural pathologies of the simulated robot and those seen in human patients suffering dysfunction of executive attention

Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes

Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees' behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees' behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012

Risk factors and indicators for engagement in violent extremism

Research on terrorism is increasingly empirical and a number of significant advancements have been made. One such evolution is the emergent understanding of risk factors and indicators for engagement in violent extremism. Beyond contributing to academic knowledge, this has important real-world implications. Notably, the development of terrorism risk assessment tools, as well as behavioural threat assessment in counterterrorism. This thesis makes a unique contribution to the literature in two key ways. First, there is a general consensus that no single, stable profile of a terrorist exists. Relying on profiles of static risk factors to inform judgements of risk and/or threat may therefore be problematic, particularly given the observed multi- and equi-finality. One way forward may be to identify configurations of risk factors and tie these to the theorised causal mechanisms they speak to. Second, there has been little attempt to measure the prevalence of potential risk factors for violent extremism in a general population, i.e. base rates. Establishing general population base rates will help develop more scientifically rigorous putative risk factors, increase transparency in the provision of evidence, minimise potential bias in decision-making, improve risk communication, and allow for risk assessments based on Bayesian principles. This thesis consists of four empirical chapters. First, I inductively disaggregate dynamic person-exposure patterns (PEPs) of risk factors in 125 cases of lone-actor terrorism. Further analysis articulates four configurations of individual-level susceptibilities which interact differentially with situational, and exposure factors. The PEP typology ties patterns of risk factors to theorised causal mechanisms specified by a previously designed Risk Analysis Framework (RAF). This may be more stable grounds for risk assessment however than relying on the presence or absence of single factors. However, with no knowledge of base rates, the relevance of seemingly pertinent risk factors remains unclear. However, how to develop base rates is of equal concern. Hence, second, I develop the Base Rate Survey and compare two survey questioning designs, direct questioning and the Unmatched Count Technique (UCT). Under the conditions described, direct questioning yields the most appropriate estimates. Third, I compare the base rates generated via direct questioning to those observed across a sample of lone-actor terrorists. Lone-actor terrorists demonstrated more propensity, situational, and exposure risk factors, suggesting these offenders may differ from the general population in measurable ways. Finally, moving beyond examining the prevalence rates of single factors, I collect a second sample in order to model the relations among these risk factors as a complex, dynamic system. To do so, the Base Rate Survey: UK is distributed to a representative sample of 1,500 participants from the UK. I introduce psychometric network modelling to terrorism studies which visualises the interactions among risk factors as a complex system via network graphs

Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes

Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees’ behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees’ behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012

Discourses, Modes, Media and Meaning in an Era of Pandemic

The COVID-19 pandemic has affected all aspects of our everyday lives – from the political to the economic to the social. Using a multimodal discourse analysis approach, this dynamic collection examines various discourses, modes and media in circulation during the early stages of the pandemic, and how these have impacted our daily lives in terms of the various meanings they express. Examples include how national and international news organisations communicate important information about the virus and the crisis, the public’s reactions to such communications, the resultant (counter-)discourses as manifested in social media posts and memes, as well as the impact social distancing policies and mobility restrictions have had on people’s communication and interaction practices. The book offers a synoptic view of how the pandemic was communicated, represented and (re-)contextualised across different spheres, and ultimately hopes to help account for the significant changes we are continuing to witness in our everyday lives as the pandemic unfolds. This volume will appeal primarily to scholars in the field of (multimodal) discourse analysis. It will also be of interest to researchers and graduate students in other fields whose work focuses on the use of multimodal artefacts for communication and meaning making