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Effects of Individual Differences in Personality Traits and Self-Concept of Abilities on Willingness to Adopt AI Tools

(Georgia Institute of Technology, 2024-04-29) Provine, Lucas

Artificial intelligence (AI) is increasingly being used to automate and augment tasks in a variety of domains from the workplace to daily life. However, little is known about the influence that individual differences in personality and ability self-concept have on people’s attitudes and adoption of AI technology to assist with tasks. The objective of this study was to determine how select personality traits (e.g., extraversion, neuroticism, and propensity to trust) and ability self-concept (e.g., verbal, math, spatial, and organizational) contribute to one’s willingness to adopt AI for decision-making purposes in various contexts. I leveraged the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) to do so. To accomplish this, 231 working adults (126 females and 105 males) were recruited from Prolific to participate in a vignette study that involved assessment of attitudes and behavioral intentions to use AI in 22 scenarios. The results indicated that: (1) the personality and self-concept variables do not contribute additional meaningful variance in predicting behavioral intentions to use AI over and above UTAUT’s performance expectancy, effort expectancy, and social influence variables; (2) one’s general propensity to trust others is associated with more positive expectations of AI performance; (3) higher ability self-concept is positively associated with perceiving AI as requiring less effort to use; and (4) attitudes and intentions toward using AI are significantly lower when individuals perceive personal situational liability for the consequences of errors that might occur while using the AI. Future researchers are encouraged to further explore how salient situational factors and stable individual difference variables might interact to inform people’s attitudes and intentions toward using AI.
Assessment of coral ecosystem community calcifier composition using trace element cycling via ICP techniques

(Georgia Institute of Technology, 2024-04-29) Wyatt-Ngom, Sokhna Aminata

Coral reefs are an integral part of coastal ecosystems. They protect coastlines from storms, provide habitat for 25% of all marine life and contribute to local economies through tourism and fisheries. Unfortunately, climate change-related stressors (such as increased sea surface temperatures and acidification) associated with anthropogenic emissions of fossil fuel carbon dioxide (CO2) have contributed to declines in coverage and health of coral reefs throughout the world. Without healthy coral reefs, many coastal communities lose protection, significant amounts of marine life lose their homes and entire coastal ecosystems can collapse. Therefore, it is essential to quantify the rate of this global coral reef decline is of great concern and has been made possible through measurements of variability in seawater constituents (such as precipitation rates found via sedimentation analysis) that serve as metrics of metabolism, net ecosystem productivity (NEP) and net ecosystem calcification (NEC), on reefs. These traditional measurements are limited in their ability to measure precipitation, dissolution, and calcification rates. Studies have found precipitation rates in the same area are 40% higher than previously thought from sedimentary analysis (Steiner et al. 2014;2018). More nuanced indicators of calcification dynamic on reefs (such as trace element analysis) could be key in obtaining accurate calcification rates alongside precipitation and dissolution. Knowing this information can then greatly assist in creating adaptation and mitigation strategies for reefs under m these continued stressors. Here, I apply inductively coupled plasma¬-optical emission spectroscopy (ICP-OES) to measurements of reef seawater strontium-to-calcium (Sr/Ca) ratios from Tetiaroa Atoll, French Polynesia collected over two complimentary diel field campaigns in October 2015 and January 2016. In this study, we look at the differences and dominance of marine organisms made up of calcite or aragonite. Calcifiers made from calcite act as “glue” for coral skeletal structures, are more soluble in acidic conditions and have a partition coefficient (KD) at or around 0.35. Calcifiers made of aragonite become the foundation for habitats on a reef, are less soluble in acidic conditions and have a partition coefficient (KD) at or around 1.02. The next step is to apply a Rayleigh Mixing model to decompose the observed temporal variability in Sr/Ca ratios into net ecosystem partition coefficients (KD) that characterize the percent contributions of calcite and aragonite to hourly-to-daily gradients in calcification This measurement is of importance as it can give us a baseline for calcifier community dynamic within a reef, that assist in the monitoring of the reef in a time of warming and acidifying oceans. Additionally, establishing this technique in a relatively pristine reef (like Tetiaroa) allows for its calibration- for future applications in more degraded reefs- ultimately expanding our toolkit for conservation efforts. Primary results include: 1. ICP-OES captures reproducible variability in Sr/Ca seawater ratios on par with previously published mass spectrometry techniques having RSD values of at or below 0.1 mmol/mol. 2. The temporal variability in Tetiaroa seawater Sr/Ca ratios may be seasonally influenced. Diel (24 hour) variability in data from October 2015 have a broader a range of 0.155 mmol/mol when compared to January of 2016 with range of 0.031 mmol/mol. 3. KD values found based on observed temporal variability can give great insight on calcification dynamics on seasonal timescales and implies that while corals remain the dominant calcifier throughout the seasons, crustose coralline algae may play more of a role in NEC in January (winter) in Tetiaroa Atoll, French Polynesia. The overall results of this study suggest Sr/Ca in seawater is a promising proxy for monitoring reef calcification and community composition within rapidly warming and acidifying oceans. Further methodological advances in the development of this proxy may be made possible through the pursuit of high resolution and high precision mass spectrometry techniques.
The Role of Microbreaks in the Work Recovery Process

(Georgia Institute of Technology, 2024-04-15) Moran, Lauren H.

Job stress remains a threat to the health and productivity of workers nationwide, and in response, increased efforts have been made to understand how individuals recover from unavoidable stressors in the workplace. However, little research has been done on how at-work breaks such as microbreaks are related to off-work recovery experiences. This study sought to uncover when and why individuals use microbreaks as a part of the broader recovery process, as well as how family demands impact the relationship between fatigue and microbreaks. I test a serial mediation model at the daily level in which evening relaxation predicts next-day evening relaxation via morning fatigue and microbreak frequency. Specifically, I examine whether high evening relaxation predicts lower next-day morning fatigue, which in turn predicts lower at-work microbreak frequency, which then predicts higher evening relaxation. I also consider whether family role overload moderates the relationship between morning fatigue and microbreak frequency. Experience-sampling methodology was used to examine these relationships over a period of 4 weeks, with multilevel structural equation modeling used to examine the posited relationships. None of the hypothesized paths were significant. Limitations and implications of the study are discussed.
Feedback-Seeking Behavior in Synchronous Remote Learning of Juggling: Effects of Individual Motivational Traits and Self-Efficacy

(Georgia Institute of Technology, 2024-03-13) Qi, Ziyu

Existing literature examining remote learning has not sufficiently addressed the remote acquisition of procedural skills. Furthermore, the differences in teacher-learner interaction between remote and in-person environments have not been considered in a procedural skill learning context. In the current study, I examined the differences in feedback-seeking behavior between remote and in-person learning environments. I also examined whether predictors of feedback-seeking behavior, namely goal orientation and self-efficacy, continue to predict feedback-seeking in a remote setting. In the current study, undergraduate student participants’ goal orientation, self-efficacy in 3-ball cascade juggling, and self-concept in motor abilities were measured. Participants’ feedback-seeking behaviors were subsequently measured while learning a 3-ball cascade juggling task in either remote or in-person conditions. The study’s results showed that feedback-seeking behavior did not differ significantly between remote and in-person environments except for feedback-seeking via self-monitoring. No significant relationship was found between goal orientation, self-efficacy, and verbal feedback-seeking frequency, potentially due to insufficient power. Exploratory qualitative comparisons examining active feedback-seekers suggested that there were potential qualitative differences in the contents of feedback-seeking. Findings in existing literature considering remote classroom learning were largely not replicated, suggesting that patterns of feedback-seeking behaviors in complex classroom environments may not be applicable to lab learning. Similarly, the motivational model of feedback-seeking may not be suitable. Still, exploratory analyses provided preliminary evidence that a remote learning environment did impact feedback-seeking and general learning behaviors in procedural acquisition processes.
Conflict in Teams: An Episodic Approach to Assessing the Mediation of Conflict Behaviors on the Relationship Between Personality and Team-Level Outcomes

(Georgia Institute of Technology, 2024-03-12) Drose, Cooper

Conflict management behaviors have long been studied as critical components in successful teams because they may help to enhance positive and mitigate negative outcomes associated with conflict. Recent research has called for a more dynamic understanding of conflict; this study serves to answer this call by evaluating conflict as an emergent phenomenon using the IMOI model using an episodic methodological approach. Using a lab sample of 83 teams and 292 participants, this study looked at personality as a predictor of conflict behaviors and the subsequent impact these behaviors have on team performance and cohesion. Results from this study found that the Dark Triad was not a significant predictor of conflict behaviors in the first conflict episode. I then called upon the Conservation of Resources (COR) theory to predict conflict behaviors over time, finding that the use of individualistic and collectivistic conflict behaviors in the first episode significantly negatively predicted the continued use of these behaviors in subsequent episodes. Additionally, it was found that the use of individualistic and collectivistic conflict behaviors from the focal individual significantly negatively predicted the use of these behaviors in others within the team in subsequent episodes. While it was ultimately found that increased use of individualistic conflict behaviors negatively impacted the team-level group cohesion, collectivistic and individualistic conflict behaviors were not found to be a significant mediator between the Dark Triad and team level outcomes of performance and group cohesion. This study contributes to our understanding of conflict as a dynamic construct within teams, as well as providing further evidence in support of COR theory.
Great Expectations: The Consequences of Employee Caffeine Use to Meet Leader Performance Expectations

(Georgia Institute of Technology, 2024-02-12) Garcia, Spencer Christian

Leaders typically have expectations for their followers’ performance. These expectations can serve to improve follower performance. However, when leader performance expectations become sufficiently high, they may become demanding for followers. To meet these demands, individuals may use compensatory behaviors, including stimulant use (i.e., caffeine). However, these variables may relate to further negative well-being consequences (i.e., mental fatigue). Drawing from Conservation of Resources (COR), this study sought to elucidate the potential relationships between these variables by testing their interplay in a loss spiral. This study used an archival dataset that included 127 employees who completed 3 daily surveys across 10 working days. Results do not suggest that a loss spiral is occurring. Caffeine was not a significant predictor of performance or other next-day mental fatigue. Leader performance expectations positively predicted same-day caffeine use, same-day perceived job performance, and next-day mental fatigue. This highlights both positive and aversive consequences of leader performance expectations. This study contributes to the understanding of leadership theories and the effects of high leader performance expectations on employees. Notably, this study makes these contributions at the within-person level.
Testing the Useful Field: Perceptual Learning Is an Important Factor in UFOV Training Improvements

(Georgia Institute of Technology, 2023-08-09) Lloyd, Maugan

Computerized cognitive training on the Useful Field of View (UFOV) is associated with improved driving behavior in older adults, but the underlying reasons remain subject to debate. Some researchers think that UFOV training enhances fundamental cognitive skills such as selective attention or processing speed, while others remain unconvinced of this so-called process-based approach. Typically, UFOV training includes a briefly presented central discrimination task, coupled with a consistently mapped (CM) peripheral localization task. As the peripheral stimuli for both target and distractors remain constant, perceptual learning would be expected with extended practice on the peripheral task. This study compared training on variably mapped targets (VM), in which targets and distractors come from the same set, and consistently mapped versions of a UFOV task to isolate the component of perceptual learning. When comparing the transfer cost for participants trained on an adaptive UFOV paradigm when transferred to unfamiliar stimuli, VM - trained groups do not exhibit the same performance decrements as CM – trained groups due to the difference in target familiarity. Specifically, we observed that transfer to new CM stimuli following extensive practice was associated with a large performance cost for the CM-trained group due to the loss of the familiar stimulus advantage (d = -1.31, t = -7.91, pbonf < 0.001), while smaller changes in performance were noted for VM trained participants transferred to new VM stimuli (d = -0.86, t = -4.93, pbonf < 0.001). Our findings suggest that future research exploring the relationship between cognitive or everyday task performance and training improvements on the UFOV must take the effects of perceptual learning into account. Furthermore, the study challenges previous assertions that UFOV training improves processing speed, which in turn improves older adult driving.
Decreased Dissolved Oxygen Content of the Pacific Deep Water During the Last Glacial Maximum

(Georgia Institute of Technology, 2023-05-05) Kim, Grace

The mechanisms responsible for lowering atmospheric CO2 levels during glaciation have yet to be constrained, but the deep ocean is the most likely reservoir of CO2 drawdown. Deep ocean carbon export and storage are suggested to have increased cyclically during glacial periods due to greater biological pump efficiency and ocean stratification, and poor ventilation. Increased respired carbon in the Eastern Equatorial Pacific Ocean (EEP) would be evident with depleted dissolved oxygen content, but there is insufficient paleo-oxygen data in this region. This study uses a benthic foraminifera Δδ13C proxy to provide quantitative assessments of changes in oxygen concentration between the Holocene and LGM. The proxy relies on the empirical relationship between bottom water oxygen and the carbon isotope gradient between the sediment-water interface and oxic-anoxic interface preserved in benthic foraminifera. Cibicidoides wuellerstorfi and Globobulimina spp. are benthic foraminifera that preferentially reside at these interfaces and record δ13C at equilibrium with bottom water and pore water dissolved inorganic carbon, respectively. The findings of this paper provided oxygen concentrations in the Holocene and LGM that indicate a more depleted bottom water oxygen content and higher mid-depth oxygen concentrations during the last glacial period. This suggests increased carbon storage, poorer ventilation, and greater water mass stratification and supports the respired carbon deepening hypothesis and corresponds to oxygen trends of qualitative paleo-oxygen proxies in the EEP.
Deep Learning Enhanced Biofilm Topography through Convolutional Neural Network

(Georgia Institute of Technology, 2023-05-02) Zhao, Lin

Biofilms are surface attached communities microbes. One approach to study the formation and growth of biofilms is to observe its surface topography, such as with white light profilometry. However, this technique requires taking images of many fields of view and stitching them together, a time-consuming process. We thus sought to develop a convolutional neural network to that can convert low-resolution images to high-resolution images. Our results show that the technique succeeds with a low mean absolute error (~10^-4). We also found that the model prediction error is highly related to the biofilm's topographic roughness. As a result, highly rough surfaces are crucial resources for training deep learning super-resolution model. Roughness enriches the complexity of the biofilm surface, and a model trained on biofilm formed by strains with high roughness yield a lower error on other strains.
Assessment of High Resolution Numerical Weather Prediction (NWP) Parameters and their Contribution to Weather Integration Prototype (WIP) Performance to Aid High Energy Laser (HEL) Testing

(Georgia Institute of Technology, 2023-05-01) Murdock, Jordan N.

Since it was first discussed in 1960, testing and development of high energy lasers (HELs) has only continued to increase in interest for the U.S. military. To yield the most effective engagements with these HEL systems, there are many parameters that must be evaluated including atmospheric variables. The Weather Integration Prototype (WIP) is an instrument and software suite that measures and ingests various atmospheric data to produce a HEL performance assessment. In this work a study was conducted to determine whether mesoscale, high resolution numerical weather Prediction (NWP) model data can more accurately predict observational atmospheric data reported to the WIP from field atmospheric sensors, relative to lower resolution NWP models. Data from the WIP testing conducted during March 2022 are used for this study. The WIP-reported observed atmospheric conditions include temperature, pressure, humidity (expressed in terms of relative humidity or dewpoint), wind speed and direction, aerosol particle counts, and optical turbulence measurements and NWP model data provide similar outputs. Both measured and NWP atmospheric data are then used as inputs to both the Laser Environmental Effects Definition and Reference (LEEDR) and High Energy Laser End-to-End Operational Simulation (HELEEOS) models to determine the accuracy of NWP model data and ultimately the effectiveness in modeling HEL performance. The WIP takes in field sensor data and ingests it to LEEDR and HELEEOS and then the values output from the WIP are compared to the Weather Research Forecast (WRF) high resolution model data to determine mesoscale NWP model data accuracy and both are compared to the HEL diagnostic measurements. The WRF model is the selected high-resolution NWP used in this study. In addition to WRF model data, climatology data that are utilized in both HELEEOS and LEEDR are also examined to determine which atmospheric modeling method would best forecast the observed atmospheric conditions, with an emphasis on optical turbulence. The main goals of this study are to 1) determine whether using mesoscale NWP model data is a more or equally reliable method of forecasting observed atmospheric conditions needed for HEL operations as compared to the WIP; 2) prove that both the WIP and NWP model can accurately predict optical turbulence values; and 3) evaluate if mesoscale NWP forecasts and local measurements can provide the optimal HEL performance assessment as compared to laser diagnostic measurements. This study demonstrates that the WIP can evolve with the addition of higher resolution NWP model data, and become a reliable method to determine HEL performance measurements. The results of this study support the capability of the WIP to provide, with reasonable accuracy, forecasted HEL performance assessments well prior to HEL execution.