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School of Psychology

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College of Sciences

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Work Science Center

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Publication Search Results

Now showing 1 - 10 of 97

Demanding and Supportive Transformational Leadership Behaviors and Follower Sleep Outcomes: A Multilevel Moderated Serial Mediation Model

(Georgia Institute of Technology, 2022-12-23) Burnett, Claire Elyse

Transformational leadership behaviors in the workplace are commonly studied as a form of support and are associated with positive follower health outcomes. However, when parsed apart into its facets, transformational leadership may also act as a demand for followers that negatively impacts them daily. Drawing from the Job-Demands Resources (JD-R) Theory (Bakker & Demerouti, 2007), this study investigated the facets of transformational leadership (Bass, 1985) acting differentially to influence follower sleep outcomes—first through the mediation of fatigue and then through performance of sleep hygiene behaviors—all at the daily level. The supportive facets of transformational leadership were thought to increase sleep quality and quantity at the daily level, while the demanding facets were proposed to decrease them. Because of the heightened response to stressors that neurotic individuals exhibit, neuroticism was explored as a moderating mechanism on the relationship between leader demands and fatigue. This study used a sample of 127 full-time, working adults and experience sampling methods over a 10-day period in order to measure these variables at the daily level. Ultimately, the proposed facets of supportive and transformational leadership were supported, but the proposed direct, mediating, and moderating relationships were not. This study contributes to theory is in its expansion of transformational leadership theory—pointing to a demanding and a supportive factor. Further research is warranted to explore the timeframe during which relationships between leader behavior and follower health outcomes unfold.
Model Blindness: Investigating a model-based route-recommender system’s impact on decision making

(Georgia Institute of Technology, 2022-12-14) Parmar, Sweta

Model-Based Decision Support Systems (MDSS) are prominent in many professional domains of high consequence, such as aeronautics, emergency management, military command and control, healthcare, nuclear operations, intelligence analysis, and maritime operations. An MDSS generally uses a simplified model of the task and the operator to impose structure to the decision-making situation and provide information cues to the operator that is useful for the decision-making task. Models are simplifications, can be misspecified, and have errors. Adoption and use of these errorful models can lead to the impoverished decision-making of users. I term this impoverished state of the decision-maker model blindness. A series of two experiments were conducted to investigate the consequences of model blindness on human decision-making and performance and how those consequences can be mitigated via an explainable AI (XAI) intervention. The experiments implemented a simulated route recommender system as an MDSS with a true data-generating model (unobservable world model). In Experiment 1, the true model generating the recommended routes was misspecified to different levels to impose model blindness on users. In Experiment 2, the same route-recommender system was employed with a mitigation technique to overcome the impact of model-misspecifications on decision-making. Overall, the results of both experiments provide little support for performance degradation due to model blindness imposed by misspecified systems. The XAI intervention provided valuable insights into how participants adjusted their decision-making to account for bias in the system and deviated from choosing the model-recommended alternatives. The participants' decision strategies revealed that they could understand model limitations from feedback and explanations and could adapt their strategy to account for those misspecifications. The results provide strong support for evaluating the role of decision strategies in the model blindness confluence model. These results help establish a need for carefully evaluating model blindness during the development, implementation, and usage stages of MDSS.
Daily Influences on Everyday Memory, Well-Being, and Affect Among Dyadic Caregivers and Care Recipients With Mild Cognitive Impairment

(Georgia Institute of Technology, 2022-12-08) Giannotto, Emily L.

Multifaceted approaches to understanding daily fluctuations that affect memory and well-being among spousal dyads, where one member has diagnosed mild cognitive impairment (MCI) and the other serves as a care partner, is a relatively unexplored area of research. This study took a novel and exploratory approach to understanding the interconnectedness of different influences on spousal dyads’ daily fluctuations in memory, caregiver burden, stress, sleep, affect, relationship mutuality, and collaborative cognition from the perspective of the care partner and the care recipient. Using a nightly diary, 27 dyads (participants with MCI and their spousal care partners) filled out an online form for 14 consecutive nights. The diary forms included self-report and informant reports about daily stress, sleep quality, caregiver burden, depressive affect, memory, dyadic interactions, and collaboration. Using multilevel modeling, I investigated how daily fluctuations in these variables among both members of the dyad were associated with memory failures, depressive affect, and caregiver burden outcomes within days and from one day to the next. I anticipated higher reported daily stress, lower quality sleep, higher depressive affect, collaborative cognition, negative dyadic interactions, poorer sleep quality and lower daily memory ratings to negatively influence care partners’ daily caregiver burden, depressive affect, and reported memory failures within days and from one day to the next. Results were promising with respect to protective effects of mutuality and collaborative cognition whereas poorer-than-average sleep quality showed significant lagged sleep debt effects on aspects of daily cognition and depressive affect. Problematic behaviors related to cognitive impairment in the care recipients was also associated with poorer memory outcomes for caregivers. The present study was successful in implementing a novel study design and demonstrated the value of multidimensional investigations using repeated measures with both members of caring dyads dealing with MCI.
The nature and measurement of sustaining attention over time: The influence of cognitive ability, internal distraction, arousal, and motivation on sustained attention

(Georgia Institute of Technology, 2022-12-07) Tsukahara, Jason S.

It is evident that it takes a great deal of effort to sustain our attention on any one thing over a period of minutes or even seconds. This ability to sustain attention is critical for many everyday tasks and is often seen as a fundamental factor underlying differences in cognitive ability. Therefore, it is important to understand the factors that determine how long we can voluntarily sustain our attention. Across two studies I used a novel task, the sustained attention- to-cue task (SACT), to assess sustained attention. The critical element of the task is to sustain attention at a cued location for a variable amount of time (0 – 12 seconds). In Study 1, I investigated how individual differences in cognitive ability are related to sustained attention. I found that those higher on attention control showed less of a decline in performance the longer attention had to be sustained. However, sustained attention performance was not related to working memory capacity or fluid intelligence. In Study 2, I investigated how susceptibility to distraction, changes in arousal, and motivation are related to sustained attention performance on the SACT. Overall, there was a large decline in attention on a shorter timescale based on performance, eye gaze, pupil size, and mind wandering measures. There were no changes in attention at a longer timescale, however there was strong evidence that arousal declined over the course of the task. Reward and motivation lead to improvements in attention overall and motivation led to improvements in sustained attention at a shorter timescale. In general, these findings suggest that attention can fluctuate and wane over a relatively short time scale of around 10 seconds or less and that this is related to individual differences in attention control, distractibility, arousal, and motivation.
Examinee Variance in Strategy Shifts During Testing: An Explanatory IRT Approach

(Georgia Institute of Technology, 2022-12-06) Hauenstein, Clifford Erhardt

Considerable work (both within the psychometric and cognitive literature) has explored the tendency for response strategies to shift within the course of a single assessment. The current project proposes a novel, flexible item response model that intends to capture the pattern of these strategy shifts, as well as any latent classes defined by differences in these shifting patterns. The novel model represents an integration of hidden Markov techniques within the framework of Explanatory Item Response Modeling. The feasibility of such a modeling approach is evaluated via parameter recovery with a set of Monte Carlo simulations, and its practical utility demonstrated with an empirical example involving response data from a spatial reasoning task. While structural parameters are generally estimated well, much caution should be exercised when interpreting person level parameters.
Dysphoria, Depressive Rumination, and Working Memory

(Georgia Institute of Technology, 2022-09-16) Price, John Michael

Current research on depression and rumination has produced mixed and sometimes incongruent results. Some researchers have found evidence of general cognitive deficits, while others have found evidence of only mood-congruent cognitive deficits. Recent research on deficits in working memory (WM) has indicated that general WM deficits occurred in a reading span task after people suffering from depression were exposed to mood congruent stimuli in a modified reading span task (affective transfer, Hubbard et al. 2016). However, the precise nature of these WM deficits remains unclear. The present study examined these effects with the decomposition of a modified n-back task into its component parts: WM updating and focus switching. Whether depression, depressive rumination, and mood were predictive of updating and focus switching was assessed. This study employed 52 participants split into two groups: a control group who completed only non-emotional tasks over two sessions, and an experimental group, who completed first a set of emotional tasks, followed by a set of non-emotional tasks. In this way, performance in the set 2 tasks was compared based on whether the participants were in the emotional or non-emotional group in set 1. This, effectively, is an extension of the affective transfer effect of Hubbard et al. (2016) to see if updating costs or switch costs or both are the driving cause of affective transfer. Furthermore, this study examined whether there were general or mood congruent WM deficits in the emotional set 1 task for these updating and focus switch costs. Affective transfer should have occurred in at least one of WM updating or focus switching, for individuals with elevated depressive symptoms, especially those who concurrently tended to engage in depressive rumination. It did not. Furthermore, elevated depression and depressive rumination were not predictive of general nor of mood-congruent deficits in WM updating or focus switching.
The effects of 40 Hz gamma flicker stimulation on spatial memory, perceptual discrimination, and recall

(Georgia Institute of Technology, 2022-08) Salen, Ashley

The rising prevalence of Alzheimer's Disease (AD), which leads to progressively deteriorating memory and thinking skills is alarming. A preliminary data analysis was performed to predict potential behavioral changes that may occur in cognitively healthy older adults between conditions as a result of using the flicker for 8 weeks. Although the preliminary data analysis has not yet yielded any statistically significant effects induced by the 40 Hz gamma flicker on the memory of the flicker group compared to the control group, it may provide insight into what the results could look like further down the line. Based on graph analysis, it could be predicted that the flicker group may have fewer spatial memory deficits.
Communication Pattern Analysis in Human-Autonomy Teaming

(Georgia Institute of Technology, 2022-07-20) Zhou, Shiwen

Communication is critical to team coordination and interaction because it provides information flows allowing a team to build team cognition, which contributes to overall team performance. In recent years, autonomous (AI) team members are beginning to be considered as effective substitutes for human teammates. However, research has shown that AI team members may lack the communication skills that are required for effective team performance (McNeese et al., 2018). To better understand which aspects of communication an AI team member performs differently compared to a human team member, and how they impact team performance, the current study analyzes communication features of three-person teams that include all human teams and human-AI teams operating in a remotely piloted aircraft system (RPAS). The current study analyzed communication pattern predictability (communication determinism) and transition probabilities to measure communication flow and Latent Semantic Analysis (LSA) to measure communication content. The current study found that both communication flow and content distinguished communication in all-human teams from communication in human-AI teams and found that these communication flow and content features predicted team performance in all-human versus human-AI teams. In this way, the current study hopes these communication differences can provide feedback and suggestions to future adoption of AI as a teammate in team training and team operations.
EXPLAINING FEATURES OF SIMPLE HUMAN DECISIONS USING BAYESIAN NEURAL NETWORKS

(Georgia Institute of Technology, 2022-07-20) Rafiei, Farshad

Feedforward neural networks exhibit excellent object recognition performance and currently provide the best models of biological vision. However, despite their remarkable performance in recognizing unseen images, their decision behavior differs markedly from human decision-making. Standard feedforward neural networks perform an identical number of computations to process a given stimulus and always land on the same response for that stimulus. Human decisions, in contrast, take variable amount of time and are stochastic (i.e., the same stimulus elicits different reaction time, RT, and sometimes different responses on different trials). Here we develop a new neural network, RTNet, that closely approximates all basic features of perceptual decision making. RTNet has noisy weights and processes the same stimulus multiple times until the accumulated evidence reaches a threshold, thus producing both variable RT and stochastic decisions. In addition, RTNet exhibits several features of human perceptual decision-making including speed-accuracy tradeoff, right-skewed RT distributions, lower accuracy and confidence for harder decisions, etc. Finally, data from 60 human subjects on a digit discrimination task demonstrates that RT, accuracy, and confidence produced by RTNet for individual novel images correlate with the same quantities produced by human subjects. Overall, RTNet is the first neural network that exhibits all basic signatures of perceptual decision making.
Developing Objective Communication-based Measures of Trust for Human-Autonomy Teams

(Georgia Institute of Technology, 2022-07-19) Scalia, Matthew J.

As artificial intelligence capabilities have improved, humans have begun teaming with autonomous agents that have the capability to communicate and make intelligent decisions that are adaptable to task situations. Trust is a core component of human-human and human-autonomy team (HAT) interaction. As with all-human teams, the amount of trust held within a HAT will impact the team’s ability to perform effectively and achieve its goals. A recent theoretical framework, distributed dynamic team trust (D2T2; Huang et al., 2021), relates trust, team interaction measures, and team performance in HATs and has called for interaction-based measures of trust that go beyond traditional questionnaire-based approaches to measure the dynamics of trust in real-time. In this research, these relationships are examined by investigating HAT interaction communication-based measures (ICM; amount, frequency, affect, and “pushing” vs. “pulling” of information between team members) as a mechanism for D2T2 and tested for predictive validity using questionnaire-based trust measures as well as team performance in a three-team member remotely-piloted aerial system (RPAS) HAT synthetic task. Results suggest that ICM can be used as a measure for team performance in real-time. Specifically, ICM was a significant predictor of team performance and not trust, and trust was not a significant predictor of team performance. Exploratory factor analyses of the trust questionnaire items discovered clear differences in how human teammates characterize trust in all-human teams and HATs. Specifically for HATs, interpersonal and technical factors associated with trust in autonomous agents were found as a result of dynamic exposure to the autonomous agent by distinct stakeholders through communication. These findings confirmed the underlying theory behind D2T2 as a framework that includes both interpersonal and technical factors related to trust in HAT along a dynamic timeline among different types of stakeholders. The findings provide some insight into the dynamic nature of trust, but continued research to discover interactive measures of trust is necessary.