No description available.

No description available.

No description available.

No description available.

A proper calibration of trust in automation is imperative to achieve optimal overall performance in human-machine systems. Previous research has suggested that human operator trust could be influenced by various situational and dispositional factors, as well as operator self-confidence. It is critical to examine what traits and factors will influence how likely a person is to trust autonomous vehicles as they become more prevalent on today's roadways. The goal of this study was to further examine the relationship between individuals' level of self-confidence in their own driving abilities and their reported trust in automation when driving semi-autonomous cars. It was hypothesized that self-confidence and level of automation would be significant predictors of participants' trust. A total of 314 participants read through a series of vignettes describing several driving scenarios and completed an online assessment that measured both their trust and self-confidence in relation to autonomous driving functions. A series of multiple regression analyses showed that driving self-confidence was a significant predictor of operator trust when using level 1 automation. Results also indicated that gender was found to be a significant predictor across all levels of automation. These results suggest that self-confidence could be good a predictor of how individuals will respond to an automated system, which may have the potential to be generalized for implementation in training and selection environments. A series of repeated measures ANOVAs were conducted to determine the effect level of automation had on trust responses. Results indicated that trust levels significantly decreased as the automation levels increased. Theoretical and practical implications are discussed. These results can inform future research that aims to determine what makes an individual more likely to accept new technologies and help those creating autonomous vehicles design features and functionality that is more likely to be trusted and effectively utilized in on-road environments.

Image compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted game-play outcomes. Four experiments tested several hypotheses. The first hypothesis evaluated which of three magnitude estimation (ME) methods (absolute ME, absolute ME plus, or ME with a standard) for image quality assessment was the most reliable. The just noticeable difference (JND) point for textures compression against the Feature Similarity Index for color was determined The second hypothesis tested whether human participants perceived the same amount of distortion differently when textures were presented in three ways: when textures were displayed as flat images; when textures were wrapped around a model; and when textures were wrapped around models and in a virtual environment. The last set of hypotheses examined whether compression affected both subjective (immersion, technology acceptance, usability) and objective (performance) gameplay outcomes. The results were: the absolute magnitude estimation method was the most reliable; no difference was observed in the JND threshold between flat textures and textures placed on models, but textured embedded within the virtual environment were more noticeable than in the other two presentation formats. There were no differences in subjective gameplay outcomes when textures were compressed to below the JND thresholds; and those who played a game with uncompressed textures performed better on in-game tasks than those with the textures compressed, but only on the first in-game day. Practitioners and researchers can use these findings to guide their approaches to texture compression and experimental design.

There exists considerable research concerning how humans attribute fault to each other, both in cases of accidents and those instances of intentional harm. There also exist studies involving blame attribution towards robots, when such robots have caused harm through operational failure or lack of safety features. However, relatively little work has, to date, examined the ways in which fault is attributed to self-driving vehicles involved in collisions, despite many newspaper and popular articles which both report past incidents and warn of future risk. This dissertation examined fault attribution in collisions involving autonomous vehicles by conducting three separate experiments. The first experiment placed participants in the roles of witnesses to a collision, and compared fault attributed to an autonomous vehicle to fault attributed to a regular, manually-operated vehicle, when those cars were involved in identical collisions. The second, and third experiments explored the fault that operators attributed to both themselves and autonomous vehicles when involved in a collision, whether they were the operator of the autonomous vehicle or the operator of a regular car that shared the road with automated ones. Results showed that, across experiments, perceived avoidability of the collision was the largest predictor of fault regardless of whether the participant was a witness or a driver. Additionally, participants in all three experiments thought themselves in general to be better than average drivers.

This work is designed to address the questions as to what drives and collapses trust between a human and a robot. Such information is needed to properly design automated decision aids. Human-robot trust (HRT) has traditionally been measured by questionnaires, which can be subject to lack of participant understanding, disengagement, and dishonesty. Therefore, implicit measures of trust are needed to measure HRT. The goal here is to identify neuro-physiological underpinnings (implicit measures) for HRT to assist designers in the development of automated robotic aids. More specifically, experiment one, looked to determine the effects of witnessing robot error on skin conductance response (SCR) and heart rate variability (HrV). The second experiment complemented this first procedure by determining the effects of witnessing robot error on Event Related Potentials (ERPs). Each experiment employed situations which previously have been empirically demonstrated to elicit a trust change in human participants. Both studies included two different robot reliability rates in a within subject design. Reliability consisted of each robot identifying civilians at either 95% reliability or 75% reliability. Self-reported dependent measures were perceptional robot reliability, trust questionnaires, a stress measure and a cognitive workload measure. Neurological and physiological dependent variables included SC, HrV, and ERPs. Heart rate variability did not demonstrate any evident changes based on robot reliability. In addition, SC demonstrated mixed changes based on robot reliability. However, ERP measures showed predictable changes based on robot reliability. None of the measures significantly correlated to changes in trust.

Multiple stress pathways can impact economic decision making. Two of these stress systems are the SAM axis and HPA axis. Prior research suggested that these pathways have the potential to exert independent alterations to economic decision performance, each with its own distinctive time course. In addition, stress has been found in general to alter salience (how important information is), but how this effect impacts specific economic dimensions (e.g. magnitude, loss or gain) has yet to be tested. Finally, the literature does not use the temporal profiles of the SAM and HPA axes to determine economic performance differences related to specific stress pathway responses. To address how the salience of different dimensions of financial information changes under stress and the impact of the SAM and HPA axes on economic decision making, the current study aimed to: 1) Examine the effects of acute stress on economic decision making based on the specific temporal profiles of each stress pathway, and 2) Examine the qualitatively distinct effects of stress on decision making from each pathway. The established temporal pattern of the SAM axis first, HPA axis later determined neural responses to an acute stressor during a given time block. In Experiment 1, the salience of individual dimensions of financial information was directly tested (e.g., magnitude, domain) to determine if basic components lead to performance differences, and/or were stress pathway dependent. While economic dimensions impacted behavior, stress did not affect how economic dimensions are responded to. Experiment 2 investigated the impact of each pathway on more complex financial behaviors (e.g. loss aversion and risk aversion). Risk aversion was decreased under SAM axis activation only. Loss aversion increased as an effect of time but was not dependent on stress. The results indicated economic dimensions and timing, including immediate neural stress pathway activation, have significant impacts on financial behaviors.

The goal of this dissertation was to study the effects of state and trait anxiety on explicit and implicit category learning. It was hypothesized that participants with higher state anxiety scores would require more trials to learn the explicit rule learning task compared to participants with lower state anxiety scores. On the other hand, high state anxiety participants were expected to excel in the implicit rule learning task relative to participants with low state anxiety scores. The hypotheses were informed by two theories, COVIS and ACT. The ACT theory states that there are three major mechanisms of executive functions that worsen with increasing anxiety. The COVIS theory states that explicit and implicit category learning rely on separate structures of the brain and, therefore, differently affected by anxiety. In experiment 1, participants completed implicit and explicit category learning tasks in either the control condition or the pressure condition. In the pressure manipulation group, participants completed a mortality salience writing task and were told they had a partner relying on their success in learning the categorization rule for both to receive a reward to induce anxiety. While the control participants completed a neutral writing task and were offered a reward solely based on their performance. In experiment 2, the study design was same as experiment 1 except for the addition of neuroimaging during category learning. Manipulating pressure during category learning replicated earlier research showing worsened performance in explicit rule learning under pressure, but no effect for implicit rule learning. In general, there was evidence that category learning was better in participants with high state anxiety scores, contradicting predictions based on ACT theory.