Multinomial Processing Models in Visual Cognitive Effort Diagnostics

The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to diagnose the cognitive effort involved with a task that was presented visually. A multinomial processing tree (MPT) was used as an analytical tool in order to disentangle and predict separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. An MPT model describes observed response frequencies from a set of response categories. The parameter values of an MPT model are the probabilities of moving from latent state to the next. An EM algorithm was used to estimate the parameter values based on the response frequency of each category. This results in a parsimonious, causal model that facilitates in the understanding the pupillary response to cognitive load. This model eventually could be instrumental in bridging the gap between human vision and computer vision

Using a Bayesian Framework to Develop 3D Gestural Input Systems Based on Expertise and Exposure in Anesthesia

Interactions with a keyboard and mouse fall short of human capabilities and what is lacking in the technological revolution is a surge of new and natural ways of interacting with computers. In-air gestures are a promising input modality as they are expressive, easy to use, quick to use, and natural for users. It is known that gestural systems should be developed within a particular context as gesture choice is dependent on the context; however, there is little research investigating other individual factors which may influence gesture choice such as expertise and exposure. Anesthesia providers’ hands have been linked to bacterial transmission; therefore, this research investigates the context of gestural technology for anesthetic task. The objective of this research is to understand how expertise and exposure influence gestural behavior and to develop Bayesian statistical models that can accurately predict how users would choose intuitive gestures in anesthesia based on expertise and exposure. Expertise and exposure may influence gesture responses for individuals; however, there is limited to no work investigating how these factors influence intuitive gesture choice and how to use this information to predict intuitive gestures to be used in system design. If researchers can capture users’ gesture variability within a particular context based on expertise and exposure, then statistical models can be developed to predict how users may gesturally respond to a computer system and use those predictions to design a gestural system which anticipates a user’s response and thus affords intuitiveness to multiple user groups. This allows designers to more completely understand the end user and implement intuitive gesture systems that are based on expected natural responses. Ultimately, this dissertation seeks to investigate the human factors challenges associated with gestural system development within a specific context and to offer statistical approaches to understanding and predicting human behavior in a gestural system. Two experimental studies and two Bayesian analyses were completed in this dissertation. The first experimental study investigated the effect of expertise within the context of anesthesiology. The main finding of this study was that domain expertise is influential when developing 3D gestural systems as novices and experts differ in terms of intuitive gesture-function mappings as well as reaction times to generate an intuitive mapping. The second study investigated the effect of exposure for controlling a computer-based presentation and found that there is a learning effect of gestural control in that participants were significantly faster at generating intuitive mappings as they gained exposure with the system. The two Bayesian analyses were in the form of Bayesian multinomial logistic regression models where intuitive gesture choice was predicted based on the contextual task and either expertise or exposure. The Bayesian analyses generated posterior predictive probabilities for all combinations of task, expertise level, and exposure level and showed that gesture choice can be predicted to some degree. This work provides further insights into how 3D gestural input systems should be designed and how Bayesian statistics can be used to model human behavior

Imeväisiän epilepsiaa sairastavien imeväisten katsekäyttäytyminen ja sen yhteys myöhempään neurokognitiiviseen kehitykseen

Katsekäyttäytymisen analysointia käytetään nykypäivänä yleisesti auttamaan erilaisten neurologisten sairauksien diagnosoinnissa ja poissulkemisessa sekä auttamaan tutkijoita ymmärtämään paremmin kognitiota aikaisissa elämänvaiheissa. Sen käyttöä imeväisiän epilepsiaa sairastavien lapsien kehityksen arvioinnissa ja seurannassa ei kuitenkaan ole vielä tutkittu perusteellisesti. Siksi tämän tutkimuksen tavoitteena oli tutkia imeväisiän epilepsiaa sairastavien imeväisten katsekäyttäytymisen yhteyttä heidän myöhemmin toteutuneeseen hermoston kehitykseen. Yhteyden ja sen ennustekyvyn tutkimiseksi luotiin kolme mallia. Kuusikymmentäkolme lasta, joiden epileptiset kohtaukset alkoivat ennen 12 kuukauden ikää, osallistuivat tutkimukseen vanhempien vapaaehtoisella suostumuksella. Imeväisten katsekäyttäyminen nauhoitettiin Tobii-Pro-X3-120:lla kahdessa mittauspisteessä. Tulokset osoittivat, että imeväisten alkuperäinen kyky kiinnittää katse, muutokset katseen siirtämisen todennäköisyydessä ensimmäisen 12 elinkuukauden aikana sekä rakenteellinen etiologia olivat merkittävästi yhteydessä imeväisten kehitystulokseen 24 kuukauden iässä. Siinä missä rakenteellinen etiologia liittyi merkitsevästi huonompaan kehitystulokseen, hyvä alkuperäinen katseenkiinnittämiskyky ja katseen siirron todennäköisyyden paraneminen ensimmäisen elinvuoden aikana olivat yhteydessä merkittävästi positiivisempaan tulokseen. Nämä havainnot viittaavat siihen, että katsekäyttäytyminen varhaisessa iässä on olennaista myöhemmän kehityksen kannalta imeväisille, jotka sairastavat imeväisiän epilepsiaa. Näin ollen katseenseuranta voisi tarjota keinoja arvioida imeväisiän epilepsiaa sairastavien imeväisten myöhemmin toteutuvia neurokognitiivisia tuloksia jo varhaisessa iässä.The analysis of gaze behaviour is nowadays commonly employed to help with the diagnosis and exclusion of differential neurological conditions as well as to help researchers better understand cognition in the early stages of life. However, its application in the developmental evaluation and follow-up of children with early-onset epilepsy has not been profoundly studied yet. Therefore, the current study aimed to investigate the association between the gaze behaviour of infants with early-onset epilepsy and their future neurodevelopmental outcome. To study the association and its predictive ability, three models were created. Sixty-three infants with epileptic seizure onset before 12 months of age participated in the study with the voluntary consent of their parents. Infants’ gaze behaviour was recorded with Tobii Pro-X3-120 at two measure points. The results showed infants’ initial ability to fixate their gaze, changes in their gaze shift probability in the first 12 months of life, and structural aetiology to be significantly associated with the infants' developmental outcome at 24 months of age. Where the structural aetiology was significantly associated with poorer developmental outcome, good initial fixation ability and improvements in the infants’ gaze shift probability during their first year of life were significantly associated with more positive outcome. These findings suggest that gaze behaviour at an early age is an essential predictor of later development in infants with early-onset epilepsy. Hence, eye-tracking could provide means to evaluate the later neurocognitive outcome of infants with early-onset epilepsy at an early age

Keeping an eye on cost : what can eye tracking tell us about attention to cost information in discrete choice experiments?

The University of Aberdeen and the Chief Scientist Office of the Scottish Government Health and Social Care Directorates fund the Health Economics Research Unit (HERU). We thank all participants who took part in the study, Alison Findlay for help with data collection, HESG participants, the editor, anonymous reviewers, and Dr Frouke Hermens for helpful comments and suggestions on the paper. The information and views set out in the article are those of the authors.Peer reviewedPublisher PD

Information Complexity in Material Culture

Humans invest a substantial amount of time in the creation of artworks. For generations, humans around the world have learned and shared their knowledge and skills on artistic traditions. Albeit large experimental settings or online databases have brought considerable insights on the evolutionary role and trajectory of art, why humans invest in art, what information artworks carry and how art functions within the community still remain elusive. To address these unresolved questions, this present thesis integrates ethnographic accounts with data governance and statistical approaches to systematically investigate a large corpus of art. This thesis specifically focuses on a large corpus of Tamil kolam art from South India to provide an exemplary case study of artistic traditions. The foundation for the projects presented in this thesis was the design and construction of a robust data infrastructure that enabled the synthesis of raw data from various sources into one database for systematic analyses. The data infrastructure on the kolam artistic system enabled the development of complex statistical methods to explore the substantial investments and information complexity in art. In the first chapter, I examine artists’ strategic decisions in the creation of kolam art and how they strive to optimize the complexity of their artworks under constraints using evolutionary signaling theory and theoretically guided statistical methods. Results revealed that artists strive to maintain a stable and invariant complexity measured as Shannon information entropy, regardless of the size of the artwork. In order to achieve an optimal artistic complexity “sweet spot”, artists trade-off two standard measures of biological diversity in ecology: evenness and richness. Additionally, results showed that although kolam art arises in a highly stratified and multi-ethnic society, artistic complexity is strategically optimized across the population and not constrained by group boundaries. Instead, the trade-off can most likely be explained by aesthetic preferences or cognitive limitations. While artistic complexity in kolam art can be strategically optimized across the population, distinct styles and patterns can still be employed by artists. Thus, in the second chapter, I focus on how artistic styles in kolam art covary along cultural boundaries. I employ a novel statistical method to measure the mapping between styles onto group boundaries on a large corpus of kolam art by decomposing the system into sequential drawing decisions. In line with Chapter 1, results demonstrate limited group-level variation. Distinct styles or patterns in kolam art can only be weakly mapped to caste boundaries, neighborhoods or previous migration. Both chapters strongly suggest that kolam art is primarily a sphere where artists differentiate themselves from others by displaying their unique skill set and knowledge. Thus, variability in kolam art is largely dominated by individual-level variation and not reflective of group boundaries or narrow socialization channels. This thesis contributes to an emergent understanding of how artists conceptualize what they are doing and how art functions within the community. Taken together, this thesis serves as an example approach that demonstrates an optimized workflow and novel approaches for the evolutionary study of a large corpus of artistic traditions

The College Shield: Examining the Role of Officer Education in Violent Police Encounters

Objectives: The latest spate of deadly police encounters across the U.S. sparked renewed calls for agencies to hire more college-educated police officers. But educational attainment’s impact on police–citizen altercations remains unclear. Using secondary data, this study examines the association between officer education level and three outcomes: police shootings, violent arrests, and physical altercations. Method: Using the Police Stress and Domestic Violence in Police Families in Baltimore, Maryland data, we employ a doubly robust propensity score design to compare outcomes among 1,104 Baltimore police officers. Results: We find that, on average, officers with some college experience or a completed bachelor’s degree are 8%–10% less likely, respectively, to be involved in a shooting compared to officers with a high school education only. Conversely, results show null effects of college-education on violent arrests and physical altercations. Conclusions: Prior research suggests that college-educated officers are more effective at deescalating potentially volatile situations. Our research suggests this may be the case only during the most dangerous and fraught encounters, such as those that often lead to shots fired

Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework

This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs

Consumer Reactions to Alcohol Advertising Health Warnings in Ireland: An Experimental Research Study

Introduction and Aims As part of several measures to inform consumers about the health risks of alcohol and reduce alcohol consumption, the Irish Government signed into law the Public Health (Alcohol) Act 2018, with Section 13 requiring the implementation of multiple health warnings in all alcohol ads. While health warnings on product labels have been subject to intensive political discussion and academic research, health warnings in alcohol ads have received little attention and empirical support. This doctoral dissertation investigates whether health warnings in alcohol ads can promote cognitive and affective reactions in consumers. Furthermore, this thesis also examines whether the exclusion of advertising social imagery makes health warnings more effective. Method A between-subject factorial survey experiment was conducted with a convenience sample of adults (n = 932) in Ireland to compare single-text, multiple-text, and shocking image-and-text health warning designs displayed on two types of alcohol ads (an ad with social imagery featuring people drinking alcohol in a social setting and an ad featuring only the alcohol product). Recall and believability of health warnings, negative emotions, perceived personal risks of alcohol use, knowledge of the health effects of alcohol and self-efficacy to drink less were measured after viewing each alcohol ad with and without health warnings. Results Factors yielding higher probabilities of recall include: health warning designs, gender, and drinking status. Significant differences were also found between health warning designs on negative emotions and believability, particularly that single-health warnings, with and without imagery, were more effective in increasing negative emotions than multiple health warnings, whereas multiple warnings were found more believable than single warnings. There were no significant direct effects between all three warning designs on perceived personal risks of alcohol use, knowledge of the health effects of alcohol and self-efficacy to drink less. The varied health warning designs did not differ across demographic groups, and there was no evidence to suggest that social imagery alcohol ads decrease the effectiveness of health warnings across the outcomes. Conclusions This research makes several theoretical and practical contributions, the most important of which is the examination of multiple-text health warnings and cancer warnings, with and without shocking imagery, in an entirely new context, which is that of alcohol advertising. Overall, this thesis demonstrates that alcohol ads with cancer health warnings were the most effective warning design, which is consistent with prominent fear appeal theories suggesting that an effort should be placed to design health warnings that lead to emotional effects as one powerful health message such as cancer can be more impactful than multiple-text health messages displayed simultaneously on alcohol ads