1,721,007 research outputs found
Influencing the Investment Amount Decided Upon by Investors by Asking Reflective Questions
No full textMany investors make sub-optimal decisions regarding the height of their investments in the stock market. In order to help them make more optimal decisions, they can be asked questions through an application that are designed to make them think about the weak points in their thought process corresponding to their investment style. In this thesis it is attempted to make such an application. First, the participant has to respond to questions and statements presented by the application. Based on this an investment style is determined. Then he has to invest in three fictional companies while interacting with the application. During this interaction, the application asks both questions that are designed to influence the participant and questions that are designed to not influence him. After this has been done, it is determined if, during the time when the influenced questions were asked, the investors made more optimal investments. The results found in this pilot study suggest that most investors made more optimal investments; the height of their investments were lower and higher corresponding to the needs of their respective styles. Therefore, a follow-up study could be done using a large enough sample size to draw significant conclusions
Adapting and Employing Smart City Sensor Data for Strategic Planning
No full textToday, more and more cities are adopting the ‘smart city’ trend by deploying new smart devices, trackers and sensors in public spaces. They gather data about the city life dynamics in order to make city planning and maintenance more efficient and effective. However, currently, most smart systems are heavily data-driven and require vast amounts of information for training before they can deliver any useful insights. The focus of this study is one of the straight-forward applications for pedestrian traffic data – building a prediction model (by using pedestrian traffic data from the city of Nijmegen (the Netherlands)). The aim is to explore different prediction methods: multi-layer perceptron, Gaussian process and support vector regression models, compared to an averaging-based baseline model and find one that performs the best with only a year or less of training data. Then, attempt to improve the applicability of that model further with the conversion of single value prediction to a prediction range as well as applying spatial interpolation to gain insight about unobserved areas in the city. The results show that a simple averaging-based model performs the best, given a low complexity version of the problem (only 168 possible value combinations for the input variables), which highlights the importance of problem analysis, while a described attempt of radial basis function interpolation of spatially sparse observations (predictions), resulting in only very high-level insights, shows how impactful problem representation is for the results of the syste
Identifying traces of consciousness in the process of intending to act
No full textIn 2008, Matsuhashi & Hallett created a novel version of the experimental design of Libet et al. (1983) which made it possible to get real-time estimations of a participant’s awareness of consciously willing to act. Using this method, the onset of the intention to act was measured up to 1.42s prior to movement onset, whereas Libet et al. found it only about 0.2s prior to movement. If one takes intentions to be discrete mental states these results seem to be at odds with each other, yet they fit very well within a framework in which intentions are regarded as processes developing over time. While the later stages in this process of intending are available for self-initiated report (similar to the reported intention timings by Libet et al.), early stages appear to be reachable and reportable through external probing only (as used by Matsuhashi & Hallett). However, to the best of my knowledge, no one has conducted a within-subject comparison between the Libet- and Matsuhashi-task in order to investigate whether the measured onsets of intending indeed differ significantly between the two. In this thesis, I will propose a novel conceptual framework describing intending as a process consisting of multiple stages developing over time. With this framework in mind, I conducted a new experiment, incorporating adapted versions of both the Matsuhashi & Hallett experiment as well as that of Libet et al. into a within-subject design. The results indicate that the onsets of intending as measured using external probing indeed occur significantly earlier in time (and quite close to the onset of the neural preparation for action) compared to the onsets of intending as measured using self-initiated reports, thus providing credence to the interpretation of intending to act as a process developing over time
Emerging responsibility gaps in surgical robotics development
No full textSurgical robots have the capacity to assist with and improve surgical
procedures. Robotic systems are increasing in autonomy, such that hu-
mans are no longer part of the control loop, but assume a supervisory role.
This inherently leads to a decreased amount of control over the system.
Without meaningful control, traditional interpretations of responsibility
are prone to fail in new situations, giving rise to a responsibility gap.
In this thesis, we will explore possible emerging responsibility gaps, and
more concretely identify them for autonomy levels in surgical robotics.
In order to mitigate some of these responsibility gaps, it is proposed that
value sensitive design is required, taking into account human psychology
Do drivers take effort to learn about their car? The effect of knowledge on trust in level 2 automation vehicles.
No full textBehavioural and Social Influences on Human-Robot Proxemics
No full textKnowledge on how a robot's behaviour will in
uence human-robot proxemics
can help to optimize human-robot interaction. A successful interaction is
a requirement for the usefulness of social robots. This project investigated
whether di erent aspects of social robot behaviour had an in
uence on human
proxemics and perception of the robot. Several user studies were performed
to investigate whether a di erence in approach speed, experiencing how the
robot makes di erent types of mistakes or seeing di erent social behaviours
in
uenced proxemics. Results show that, even though there were no di er-
ences for approach distances within the three aspects, perceiving the robot
make a mistake and showing social behaviour have an in
uence on proxemics
and perception. Order of approach (human rst or robot rst) signi cantly
in
uences proxemics and perception as well. These results indicate that the
initial acquaintance between a robot and its user is essential for successful
human-robot interaction
An fMRI study on the effects of familiarity and context on moral stereotyping of people diagnosed with psychopathy
No full textIntroduction This study aims to gain insight into mental health stereotyping. Particularly we focus on stereotyp-ing of people diagnosed with psychopathic disorder. To examine behavioral effects of mental health stereotypes, psychopathy-labeling effects on subjects´ moral judgments were explored. Furthermore, we are interested in the underlying neural mechanisms that contribute to stereotyping of people labeled with a mental health diagnosis.
Methods A behavioral (n = 18) and fMRI (n = 18) were conducted. Subjects judged upon agents portrayed in 48 experimentally manipulated written moral scenarios. Moral scenarios experimentally varied in labeling of agents, and moral contexts. Repeated measures ANOVAs were used to analyze the behavioral data. The familiarity with psychopathy was established by a questionnaire. In the fMRI experiment, BOLD responses were measured when all information to make moral judgments was available.
Results Both experiments revealed a context dependent effect of familiarity on stereotyping towards psycho-paths. The effect of familiarity was apparent during judgments on inconclusive moral information. In these conditions, subjects unfamiliar with psychopathy judged psychopaths harsher compared to unlabeled agents, whereas subjects familiar with psychopathy judged psychopaths more lenient. Finally, the context-dependent effect of familiarity was concurrent with altered activity in the posterior and anterior insula.
Discussion In light of previous research, the behavioral and fMRI results suggest that familiar subjects judged psychopaths more lenient because they blocked the processing of emotionally aversive contextual information during judgments on psychopathy-labeled agents.
Conclusion These findings contribute to the understanding of intervention programs that reduce mental health stereotyping. Such programs diminish negative societal reactions towards mentally ill and thereby positively affect patient’s course of illness and prevent the adverse economic effects of mental health stigma
Mental health stereotyping an fMRI study on the effects of familiarity and context on mora/ stereotyping of people diagnosed with psychopathy
No full textlntroduction This study aims to gain insight into mental health stereotyping. Particularly we focus on stereotyping of people diagnosed with psychopathie disorder. To examine behavioral effects of mental health stereotypes, psychopathy-labeling effects on subjects' mora! judgments were explored. Furthermore, we are interested in the underlying neural mechanisms that contribute to stereotyping of people labeled with a mental health diagnosis. Methods A behavioral (n = 18) and fMRI (n = 18) were conducted. Subjects judged upon agents portrayed in 48 experimentally manipulated written moral scenarios. Moral scenarios experimentally varied in labeling of agents, and mora! contexts. Repeated measures ANOVAs were used to analyze the behavioral data. The familiarity with psychopathy was established by a questionnaire. In the fMRI experiment, BOLD responses were measured when all information to make moral judgments was available.
Results Both experiments revealed a context dependent effect of familiarity on stereotyping towa rds psychopaths. The effect of familiarity was apparent during judgments on inconclusive mora! information. In these conditions, subjects unfamiliar with psychopathy judged psychopaths harsher compared to unlabeled agents, whereas subjects familiar with psychopathy judged psychopaths more lenient. Finally, the context-dependent effect of familiarity was concurrent with altered activity in the posterior and anterior insula.
Discussion In light of previous research, the behavioral and fMRI results suggest that familiar subjects judged psychopaths more lenient because they blocked the processing of emotionally aversive contextual information du ring judgments on psychopathy-labeled agents.
Conclusion These findings contribute to the understanding of intervention programs that reduce mental health stereotyping. Such programs diminish negative societal reactions towards mentally ill and thereby positively affect patient's course of illness and prevent the adverse economie effects of mental health stigma
Identifying Patients at Risk for Suicidal Ideation and Key Factors Responsible by Means of a Self-Explaining Neural Network
No full textSuicide is a major cause of death in all of Europe, and it is on the rise.
Worldwide, suicide is the second most commom cause of death in the age group of 15
to 29 years (Bachman, 2018). Suicide experiences a treatment gap of around 50%,
meaning that only half the people that die by suicide receive treatment beforehand
(Bruffaerts et al., 2011). Thus, it is worthwhile to investigate whether an automated
solution could be used to identify people in the general population that may be at risk
for suicide. Not only being able to detect suicidal ideation, but also giving insight into
the underlying issues, is key to better treatment (WHO, 2019b). Therefore this should
be done using explainable methods. A self explaining neural network (SENN) was
trained to predict if a person suffers from suicidal ideation and state which factors
were important in that prediction. For this research data from the MIND-SET study
was used, which is a study by the Radboudumc. It includes 705 participants, of which
574 suffer from common psychiatric disorders and 131 are healthy controls. The best
performing model had an accuracy of 85.3% on the test set with a sensitivity of 79.1%
and a specificity of 89.1%; the positive predictive value was approximately 81.538%
and the negative predictive value was 87.5%. Most important risk factors are from
two questionnaires. One is the Outcome Questionnaire, designed to capture a low
quality of life. The second is the Inventory of Depressive Symptomatology, designed
to give insight into depression. Some factors seem to significantly reduce the risk, too,
such as a score describing a good mental health. Perhaps surprisingly, most other
relevant risk reducing factors stem from the measurement of autism characteristics
Using Artificial Intelligence To Support Students In Improving Their Mental Health
No full textThe number of young students struggling with mental health (MH) issues has reached alarming
levels. Carefully designed and implemented, Artificial Intelligence (AI) has the potential to help
diminish this problem. This paper explores the role AI can play in ethically and responsibly
supporting students in improving their mental health before human help is sought or available. We
aim to pave the way for systems that are not mere technofixes but empower users by giving them
autonomy in managing their mental health. Using a patient-forward approach, students’ needs and
wishes from MH-supporting systems were investigated, as well as their experiences with existing
technologies. Our findings indicate that successful AI implementation in mental health care must
meet the following requirements: It must be helpful, engaging, personalized to the user’s care
needs and background, trustworthy, technically robust, scientifically valid, fitting into the user’s
daily routine, accessible, and lawful. This paper also presents a concrete implementation plan,
based on state-of-the-art AI techniques and methods, considering their limitations, that could
function as a stepping-stone to practically implementing a minimum viable product. The proposed
system includes core functionalities like symptom tracking, educational content and exercise
recommendations, notifications, a chatbot, personalization options, and crisis-intervention
functionality. This thesis challenges the assumption that AI cannot have a place in mental health
care. Rather, it shows that to play a vital role in mental health care, implementing AI requires
thoughtful attention and should focus on empowering students to take control of their mental well bein
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