Peer recognition of prodromal signs of psychosis : a signal detection analysis : a thesis presented in partial fulfilment of the requirements for the degree in Master of Arts in Psychology at Massey University, Palmerston North, New Zealand

Using signal detection analysis, this study investigated young peoples' sensitivity to prodromal signs or psychotic symptoms compared to more everyday signs of distress in their friends. In a questionnaire format, 117 high school students (aged 13 to 16 years) were asked to report the level of concern they would have if one of their friends exhibited certain characteristics. Half of the latter were neutral, everyday phenomena (no signal), and the remainder were either DSM-IV symptoms of psychosis or empirically-derived prodromal signs of early onset psychosis (signal). Each possible sign was modified (made more serious) by descriptors used in psychological models to define pathology behaviorally: rare in youth, high in frequency, recent change, and lack of obvious (rational) environmental cause. High frequency was the modifier leading to the greatest degree of concern. Accurate and sensitive detection, based on d' values, was adequate for psychotic symptoms, especially by females rather than by males, although depressed mood (a prodromal sign in this context) was most readily detected as a worrisome feature. The study has implications for analyzing how youth judge indices of distress in their friends and for their general ability to recognize that certain characteristics are more troublesome than others. Telling a responsible adult of their concerns was the most frequently suggested response, followed by attempting to help and talking to the peer about their concerns. If rapid detection of early onset psychosis is to be a goal of preventative mental health services, youth who are sensitive to classic symptoms of psychosis may still need educating in recognizing the difference between behavioral characteristics that are part of everyday distress and those that are indicative of more serious adjustment difficulties that might be emerging

Separating haptic guidance from task dynamics: A practical solution via cutaneous devices

There is much interest in using haptic feedback for training new skills or guiding human movement. However, the results of studies that have incorporated haptic guidance to train new skills are mixed, depending on task complexity and the method by which the haptic guidance is implemented. Subjects show dependency on the guidance forces and difficulty in discerning which aspects of the haptic feedback are related to the task dynamics and which are meant to convey task completion strategies. For these reasons, new methods to separate haptic cues for guidance from haptic feedback of task dynamics are needed. In this experiment, 30 subjects completed a trajectory following task using a wrist exoskeleton which also rendered task forces. To assist subjects, guidance cues were provided in one of three forms: (1) cutaneous forces from a wearable skin-stretch device on the ipsilateral forearm and (2) contralateral forearm, and (3) kinesthetic forces from a kinematically similar wrist exoskeleton operated by the contralateral arm. The efficacies of each guidance condition are compared by examining subject performance and learning rates. The results indicate that cutaneous guidance is nearly as effective as kinesthetic guidance, making it a practical and cost-effective alternative for spatially separated assistance

Haptic-Enhanced Learning in Preclinical Operative Dentistry

Background: Virtual reality haptic simulators represent a new paradigm in dental education that may potentially impact the rate and efficiency of basic skill acquisition, as well as pedagogically influence the various aspects of students’ preclinical experience. However, the evidence to support their efficiency and inform their implementation is still limited. Objectives: This thesis set out to empirically examine how haptic VR simulator (Simodont®) can enhance the preclinical dental education experience particularly in the context of operative dentistry. We specify 4 distinct research themes to explore, namely: simulator validity (face, content and predictive), human factors in 3D stereoscopic display, motor skill acquisition, and curriculum integration. Methods: Chapter 3 explores the face and content validity of Simodont® haptic dental simulator among a group of postgraduate dental students. Chapter 4 examines the predictive utility of Simodont® in predicting subsequent preclinical and clinical performance. The results indicate the potential utility of the simulator in predicting future clinical dental performance among undergraduate students. Chapter 5 investigates the role of stereopsis in dentistry from two different perspectives via two studies. Chapter 6 explores the effect of qualitatively different types of pedagogical feedback on the training, transfer and retention of basic manual dexterity dental skills. The results indicate that the acquisition and retention of basic dental motor skills in novice trainees is best optimised through a combination of instructor and visualdisplay VR-driven feedback. A pedagogical model for integration of haptic dental simulator into the dental curriculum has been proposed in Chapter 7. Conclusion: The findings from this thesis provide new insights into the utility of the haptic virtual reality simulator in undergraduate preclinical dental education. Haptic simulators have promising potential as a pedagogical tool in undergraduate dentistry that complements the existing simulation methods. Integration of haptic VR simulators into the dental curriculum has to be informed by sound pedagogical principles and mapped into specific learning objectives

Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

Designing Affordances on Embedded Interfaces

The purpose of this study was to examine the effects of a user interface on users\u27 performance during an online shopping checkout task. Two interfaces were developed using the principles of Rasmussen\u27s SRK model: a high-affordance and a low-affordance interface. Seventy undergraduate and graduate students performed a simulated online shopping task with the two interfaces. It was hypothesized that the high-affordance interface would require less time and fewer clicks to conduct the shopping task when compared to a low-affordance interface. In addition, it was predicted that participants would prefer the high-affordance interface. The findings revealed participants spent more time on the task using the high-affordance interface, but the difference was not statistically significant. Participants made significantly fewer clicks using the high-affordance interface than they did using the low-affordance interface. Compared to the low-affordance interface, a significantly higher percentage of users reported that they would prefer the high-affordance interface. This is one of the first studies to examine the application of the SRK model to the design of consumer interfaces. Based on these results, the SRK model may be considered another conceptual tool to make interfaces easier to use and consumer experiences more satisfying and enjoyable

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping