Doctor of Philosophy with a Major in Psychology

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Doctor of Philosophy with a Major in Psychology

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https://hdl.handle.net/1853/72216

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Degree Series

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School of Psychology

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Now showing 1 - 10 of 316

Musical Context Facilitates Event Segmentation and Sequential Learning Through Interconnected Neural Networks and Strengthened Hippocampal Encoding

(Georgia Institute of Technology, 2024-12-08) Ren, Yiren

This dissertation investigated how musical context influences temporal order memory and statistical learning using behavioral and fMRI methods. Participants engaged in a visual sequence learning task while undergoing fMRI scanning, with some sequences paired with familiar music and others learned in silence. Results demonstrated that musical context enhanced both sequence learning and event boundary detection. Neural imaging revealed that music modulated activity in key memory regions, with the medial temporal lobe showing enhanced boundary-related processing and more efficient within-sequence encoding. Functional connectivity analyses demonstrated that music facilitated a more consistent and integrated network among the medial temporal lobe, ventromedial prefrontal cortex, and striatum that supported learning. Furthermore, representational similarity analysis of hippocampal activity revealed that music enhanced both the binding of items within sequences and the separation between different sequences, while also providing more consistent positional coding for sequential learning. These findings suggest that music provides an effective temporal scaffolding for learning, with implications for educational and clinical applications. This research advances our understanding of how cross-modal context influences memory formation and provides insights into the neural mechanisms underlying music's facilitatory effects on learning.
A Modified SpAM Task for Investigating Typicality and Frequency Effects during Category Processing

(Georgia Institute of Technology, 2024-12-05) Koenen, Reba

This dissertation used three tasks to investigate categorization measures and the strength of category norms. Category norms are rank orderings of the most common exemplars (i.e., apple) which belong to a given category (i.e., Fruits). Over the past several decades, both cognitive psychologists and cognitive scientists have studied how people engage in category processing. The most common method used is the production task and its measure of frequency – the number of times an exemplar is produced across participants for a given category (e.g., Battig & Montague, 1969). An alternate, classic method is the rating task and its measure of typicality – the rating of how well an exemplar represents its category (e.g., Rosch, 1975). The third task used to inform studies of categorization is the spatial arrangement method (SpAM) and its similarity measure of position – the location of an exemplar, relative to other exemplars of that category, on a 2D plane (e.g., Goldstone, 1994a). In the current study, participants completed these three tasks (i.e., production, rating, a modified version of SpAM) to address three overarching research goals. The first goal was to investigate the relationship between the frequency and typicality effects. I asked whether both measures are comparable indices of the centrality of exemplars to categories, as the concepts and categorization literature suggests (e.g., Banks and Connell, 2022). I predicted that these measures, and their tasks (i.e., production, rating), are more distinct than previously assumed. There was mixed evidence for this hypothesis. Correlations between the frequency and typicality data from both the current and prior studies resulted in larger correlations (r  .40) than predicted. The second goal was to examine the usefulness of the modified SpAM task, its standard measure of position, and the novel measure introduced here of placement-order (i.e., the order in which participants place exemplars onto the 2D plane). I asked whether either of these measures, and the modified SPaM task more generally, could be used as an alternative method to the production task and its frequency data or the typicality task and its rating data. The modified SpAM task achieves the primary advantage of each of these classic tasks: it is short in duration, like the production task, and it provides information about a complete list of exemplars for a category, like the rating task. I predicted that correlations between both the position and placement-order data, with the frequency and typicality data from the current and prior studies, would be moderate or large (r  .30). There was weak evidence for this hypothesis. There were more small correlations (r < .30) than expected, especially with the placement-order data. The third goal was to test the novel hypothesis that the frequency and typicality effects emerge at different timepoints during category processing. To do so, I used the production-order variable (i.e., the order in which exemplars are produced during the production task). This measure was adopted from the category fluency task which is used to investigate the claim that more frequent exemplars are more accessible in semantic memory, and, thus, produced earlier in time (Hills et al., 2012). I predicted that in the modified SpAM task, the most frequent exemplars would be placed earlier in time and that more typical exemplars would be placed slightly later. There was mixed evidence for this hypothesis. Placement-order timing intervals correlated best with the frequency, and then the typicality data from the current study. This was not the case with prior data, where the correlations with placement-order timing and both frequency and typicality tended to be small (r < .30). This finding could have been a result of the within-subjects design of this study. All participants (N = 90) completed each of the tasks three times each, with the three experimental categories used in the current study (i.e., Birds, Fruits, Vegetables). Where the evidence did not support the hypotheses, there are several possible explanations. This gap may have been a consequence of the study’s novel design (completing all three categorization tasks within-subjects has not been done in prior studies). It may also have been a result of the small number of experimental categories (i.e., three) or participants’ unfamiliarity with some exemplars from the Birds category. Though these design decisions had their limitations, they also allowed a direct comparison of the tasks (i.e., production, rating, modified SpAM) and their measures (i.e., frequency, typicality, position). There was a significant difference in participants’ ratings of how well the different tasks captured their category structures, with participants ranking the rating task as more representative of their category structure than the production task. The modified SpAM task also had higher category structure representation ratings than the production task, though this difference did not reach statistical significance. These findings should guide future studies of categorization as they suggest there are limitations to the production task despite its efficiency (e.g., Banks & Connell, 2022).
Distal Trait and Proximal Strategic Predictors of Technological Fluency

(Georgia Institute of Technology, 2024-12-04) Lyndgaard, Sibley Frances

Modern workplaces are characterized by the necessity of just-in-time, non-routine problem-solving, often aided by technology. Although the Internet is an increasingly core resource for such problem-solving, the competencies which support leveraging technology for problem-solving are not well understood. Effective use of online resources depends upon a complex of individual differences including ability and non-ability traits, the accumulation of relevant knowledge and skills, and the use of adaptive strategic approaches during interactions with technological resources. I refer to this as technological fluency, a trait complex which describes individuals’ propensity (i.e., ability/willingness) to leverage technological resources to solve real-world problems. The present study examined the relative contributions of distal trait and proximal strategic variables to explaining individual differences on an assessment of technological fluency. While item-level floor effects limited the interpretability of aggregate performance data, there were several interesting findings related to individual differences in process variables. Cluster analyses, for example, showed that minimally satisficing participants (who had very low intrapersonal variability in item scores) tended to be more positively oriented toward technology, while the two clusters of participants with greater intrapersonal variability in item scores could be distinguished by patterns of differences in both ability and non-ability variables. Follow-up qualitative analyses identified several key process variables for which ability differences were most striking, including inference quality (including the ability to self-correct faulty inferences), the ability to leverage visual problem cues, and the nature of AI use during problem-solving. Finally, exploratory regression analyses suggest that proximal strategic variables do improve prediction of available process indicators above and beyond distal trait complexes, providing support for a key proposition of the study. In addition to providing methodological recommendations for the assessment of technological fluency (e.g., more direct assessment of metacognitive processes, inclusion of domain knowledge tests), the present study’s findings suggest that the quality of metacognitive processing in technology-supported problem-solving may be correlated with individual differences in ability – this is a critical direction for future research.
Learning, Efficacy, and Achievement Framework for Older Adults: A Technology Acceptance Model Approach

(Georgia Institute of Technology, 2024-08) Gleaton, Emily C.

Smart Assistive Technologies (SMATS) have the potential to improve the lives of older adults by helping them age in place. Many of these emerging technologies provide a range of benefits that assist older adults with activities of daily living, including improved health management, enhanced safety, and increased social connectivity. For instance, smartphone applications can assist elderly individuals with managing their medications independently, or conversational agent technologies can be used to contact emergency services if a telephone cannot be reached. By leveraging these technologies, older adults can enjoy greater autonomy, maintain their daily routines, and engage more actively in their communities. Despite the potential benefits of SMATS, many older adults struggle to adopt and use them. One potential method to improve the adoption and use of SMATS among older adults is improving the associated instruction and training. From a macroscopic perspective, effective instructional design and training methods could prompt a behavior change that leads to technology adoption, an idea that is supported by the Health Belief Model (HBM; Rosenstock, 1966). This model stands out as one of the foundational health psychology models because it was developed to understand practical problems, such as why people fail to adopt disease prevention measures like tuberculosis screenings (Rosenstock, 1974a). The systems approach to training (W. Rogers et al., 2001) can be applied to develop instructions and training to meet the needs of older adults. The systems approach to developing instructions and training involves identifying the problem domain, the specific tasks, the trainee population, the cost (in terms of time and money), and the resources available (S. Czaja & Sharit, 2013). The systems approach also involves identifying the best modality of instruction to be employed, given the details and context of an application. However, this approach does not integrate technology adoption literature, leaving a critical research gap. To address this gap constructs from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) and the Health Belief Model (HBM) were integrated into the Learning, Efficacy, and Achievement Framework (LEAF). The LEAF was then integrated within the systems approach to developing instructions and training, supplementing it to improve outcomes by addressing the unique challenges and barriers older adults face when adopting and using SMATS. Older adults often encounter specific barriers that younger adults do not experience. Therefore, incorporating insights from technology adoption literature, the LEAF provides a comprehensive framework for developing tailored instructions and training targeting these potential problems. This paper focuses on integrating technology acceptance literature into a framework that can be applied within the systems approach to developing training for older adults. The end goal of using the LEAF within the systems approach is to create an improved instruction and training program that can prompt behavior change, leading to the successful adoption and use of SMATS among older adults. The LEAF has three major components: 1) Learning, 2) Efficacy, and 3) Achievement. Learning incorporates two main principles: providing purpose and meeting learner needs. Providing purpose emphasizes the importance of users understanding their individual health-related needs and how adopting SMATS can address them. By offering clear, balanced information, users can develop outcome expectations crucial for the adoption and sustained use of SMATS. This involves understanding the health-related needs that SMATS can address and fostering a belief in users’ abilities to engage with new technology successfully. Meeting learner needs on reducing the effort required to adopt and use SMATS. It highlights the importance of integrating SMATS into daily life and addresses users’ abilities to engage effectively with new technology. Efficacy within the LEAF aims to increase older adults’ confidence in using technology by providing hands-on experience. These experiences demonstrate the specific benefits of SMATS and allow the learner to understand the amount of effort necessary to adopt and use the technology successfully. This approach ultimately enhances older adults’ readiness to adopt and use technology effectively. Additionally, it focuses on reducing anxiety associated with using a new technology by providing assistance and training appropriate for the user’s needs. Lastly, Efficacy in the LEAF emphasizes the role of social support networks. These networks are crucial in shaping attitudes toward technology adoption among older adults. Positive support from family and friends influences attitudes toward technology adoption, while negative influences can hinder adoption efforts. Lastly, Achievement in the LEAF underlines the importance of reducing cognitive load and breaking tasks into sub-goals to reduce barriers to adoption. Setting clear, achievable goals and celebrating accomplishments can enhance older adults’ self-efficacy, encouraging habitual use of SMATS. Moreover, breaking down larger learning goals into smaller, manageable tasks improves learning and self-efficacy by fostering problem-solving skills. Researchers and educators can utilize these strategies to foster greater confidence and proficiency in using technology. In conclusion, the LEAF represents a pivotal strategy for addressing some of the challenges older adults face that hinder their adoption and use of SMATS. By leveraging insights from technology adoption literature, the LEAF, within the systems approach, enables the development of tailored training programs that cater to older adults’ unique needs and circumstances. This, in turn, lowers barriers to adopting and using SMATS in their daily lives. Further research and application of the LEAF framework can continue to refine our understanding and implementation of effective technology training strategies for older adults. If applied, this framework, grounded in both theory and empirical research, has the potential to foster the ability of older adults to age in place using SMATS.
Explaining Individual Differences in the Rate of Focusing Attention: Dissociating Attention Control from Drift Rates

(Georgia Institute of Technology, 2024-07-22) Mashburn, Cody Athony

The top-down control of attention and the speed of information processing are two competing explanations of individual differences in higher-order cognitive abilities, such as working memory capacity and fluid intelligence. However, the relationship between attention control and processing speed remains unclear. While some studies show that the two constructs are related but distinct (e.g., Burgoyne et al., 2023), others contend that the two constructs are virtually indistinguishable (Löffler et al., 2024). The present study adds to this discussion by comparing attention control and processing speed as measured by drift rates from the Ratcliff (1978) drift-diffusion model for predicting working memory capacity, fluid intelligence, and selective attention dynamics in two flanker tasks. Results indicate that individual differences in attention control and drift rates are statistically distinct. Additionally, only individual differences in attention control uniquely predicts individual differences in working memory capacity and fluid intelligence. Finally, attention control and drift rates are sensitive to different processing stages underlying flanker performance, with attention control being more strongly related to the rate of selectively focusing attention and drift rate being more strongly related to perceptual decision making. These results affirm that attention control tasks measure processes related to the focusing of attention that are dissociable from individual differences in differences in drift rate. They also indicate that processes related to selective attention are more broadly related to individual differences in cognitive abilities than individual differences in perceptual decision making processes.
Socially Persuading Prosocial Behavior in Humans Using Automation: A Design Framework and Theoretical Model

(Georgia Institute of Technology, 2024) Scott-Sharoni, Sidney

Investigating methods to improve prosocial behavior has been a recent topic of interest for researchers studying human-automation and human-robot interaction. However, scientists have yet to uncover how and why certain features of technology enhance and encourage prosocial behavior in humans. By studying and facilitating prosocial actions between people, agents, and robots, not only will society benefit, but also personal factors such as psychological, social, and physical well-being will improve. The following preliminary examination paper reviews literature on automation, agent, and robot interaction with humans, focusing on their existence as social actors. Decades of work built from the media equation (Reeves & Nass, 1996) suggest that humans transfer social expectations, norms, and biases to non-human agents. This social existence affords non-human agents new possibilities to shape and persuade humans to increase prosocial behavior. To contextualize prosocial behavior in human-to-automation (H-A) interactions, definitions, motivations, and benefits of prosocial behavior within human-human (H-H) interactions and H-A contexts are discussed. The review highlights the lack of comparison researchers have made between the two domains. Additionally, it provides the first encompassing and comprehensive definition of H-A prosocial behavior, including core components necessary for its study. Throughout the review, there is an emphasis on understanding how social influences, that are paramount to H-H prosocial behavior, transfer to H-A contexts. While researchers assume, based on the media equation (Reeves & Nass, 1996), that social influences remain consistent across domains, the review discuses differences in H-A social influence. The theoretical role of social influence in prosocial behavior is detailed, as understanding conformity and persuasion is necessary to build agents and robots that encourage prosocial behavior in humans. Models of human to robot and agent social influence are examined with an exploration into how the Robot Social Influence model (Erel et al., 2024) can explain findings in prosocial behavior and persuasive social computing. The review presents and justifies a novel design framework and model that examines how and why specific characteristics in robots and virtual agents can promote prosocial behavior in human users. The framework, Robots and Agents as Persuasive Prosocial Actors (RAPPA), combines principles from persuasive social computing, social influence, and findings from the limited work within H-A prosocial behavior research. The theoretical model argues that anthropomorphism, social intelligence, and adaptiveness increase a human’s relatability, or sense of belonging, to the technology, which strengthens the automation’s influence on a human. This social influence can then persuade humans to behave prosocially. This is based on multiple theories that assert that close group identity increases social influence and prosocial behavior. Definitions and empirical evidence for each element of the RAPPA framework are provided, along with recommendations for its implementation into agent and robot design. The framework is then connected back to theories of human behavior such as the theory of planned behavior (Ajzen, 1991) and social learning theory (Bandura, 1971). RAPPA serves to enhance the understanding of how H-A prosocial behavior develops and provide scientists with a valuable reference for future work. The paper culminates in a series of applications and research topics that encourage researchers to include the framework in the study of in-vehicle agents.
A Conjoint Unfolding IRT Model Framework for the Analysis of Preference and Response Time Data

(Georgia Institute of Technology, 2023-11-20) Sparks, Jordan

The Generalized Graded Unfolding Model (GGUM) is an unfolding item response theory model that produces single-peaked, nonmonotonic item characteristic curves consistent with a proximity-based response process. The model can be applied to binary or graded item responses, or a mixture of the two. This paper proposes a modification to the GGUM estimation procedure, referred to as the Generalized Graded Unfolding Model with Response Times (GGUM-RT), which includes response time as collateral information in estimating model parameters. The inclusion of response time data illustrates how (a proxy for) cognitive processing relates to the latitude of acceptance construct from social judgment theory. It is also demonstrated to improve the precision of model parameter estimates obtained from the standard GGUM.
Mindfulness and its relationship to race-related stress, racial identity, age, gender, and ses across multiple racial minorities

(Georgia Institute of Technology, 2023-10-13) Mirabito, Grazia

Racism is still a very pervasive problem in our nation. The literature on racism and race-related stress has predominately focused on African American populations, which is not surprising since they experience a disproportional amount of discrimination compared to other ethnicities. Nevertheless, due to the different lived experiences with racism and discrimination for each minority, I believe it is important to assess multiple racial groups’ experiences with racism (e.g. African American, Asian American, and Latinx persons). Racism can lead to race-related stress and thus to significant detriments in mental and physical health outcomes in People of Color (POC). This study took a novel and exploratory approach to understanding whether mindfulness, coping, and ethnic identity can buffer against the effects of race-related stress. Using a single-point-in-time online survey amongst 676 Asian American, African American, and Latinx participants measuring trait mindfulness, coping, ethnic identity, frequency of exposure to racism, rumination, race-related stress, anxiety, well-being, depression, and demographic factors (i.e., age, gender, education, income, and personality). Using multigroup structural equation modeling, I investigated whether mindfulness, coping, and ethnic identity mitigated the effects of race-related stress on rumination and psychological outcomes amongst POC. I found that at high levels of ethnic identity and some mindfulness subscales, there was greater use of adaptive coping skills, reduced race-related stress and rumination, and improved psychological outcomes. Additionally, I found that at high levels of exposure to racism, the cascade from mindfulness to race-related stress to psychological outcomes was worsened. Results were promising concerning the protective effects of most mindfulness subscales and ethnic identity against race-related stress. These variables exerted their influence primarily through the mediator coping. There were also negative effects of exposure to racism on the psychological outcomes. The only mindfulness variables that had a negative impact were Nonjudgement (in the African American sample only) and Observing, where Nonjudgement’s effect is most likely caused by personality, age, or some unmeasured variable, while the effects of Observing are most likely caused by detrimental effects of monitoring without acceptance. Furthermore, many of these pathways (58 out of 64 pathways) do not vary by ethnicity suggesting a primarily universal relationship across groups. The present study was successful in collecting a large sample of POC to compare across group differences and demonstrated that many of these mindfulness, ethnic identity, coping, and race-related stress processes exist similarly across multiple ethnic groups.
A Meta-Analytic Investigation of Procedural Skill Retention and Decay

(Georgia Institute of Technology, 2023-09-26) Tatel, Corey E.

The extent to which procedural skills involving motor components decay over time is an issue that has significant ramifications for the safety and well-being of individuals and society. Prior researchers have concluded that there is a general pattern of skill decay as a function of the length of the retention interval. However, previous researchers relied primarily on studies that leveraged shorter retention intervals than are characteristic of real-world contexts (e.g., days or weeks) and included skills that require both declarative and procedural knowledge. This dissertation presents a new meta-analysis of skill retention that focuses specifically on procedural skills and leverages a recent influx of interdisciplinary literature (e.g., healthcare, sports psychology) consisting of longer retention intervals (e.g., months and years). A broad literature search led to the inclusion of 1,352 effect sizes from 457 sources. Random-effects meta-regression models were computed with retention interval as a predictor of standardized mean differences representing changes in performance between skill acquisition and skill retention for accuracy-based performance measures, speed-based performance measures, and performance measures that were a mix of accuracy and speed. Results indicated that standardized mean differences increased in magnitude by 0.08 per month for accuracy-based performance measures and 0.06 per month for speed-based and mixed performance measures. Initial skill acquisition performance gains were lost between one year and two and half years after they were acquired. Task type, task complexity, infrequent performance opportunities, and task instructions were identified as potentially meaningful moderators of skill decline rates. Findings provide applied audiences with an estimate of how much skill decay can be expected if skills are not frequently used and therefore, when refresher training should be considered. Important methodological considerations for skill retention research were also identified, including the need to isolate retention performance from relearning effects and the need to account for Speed-Accuracy-Tradeoff functions when interpreting changes in performance over time.
Career Calling in Older Adults: A Socioemotional Selectivity Perspective

(Georgia Institute of Technology, 2023-07-25) Kidwell, Kate E.

Over the past several decades, the meaning of work for employees has evolved beyond solely a means of financial support and toward a source of fulfillment and personal identity. Work that is purposeful, meaningful, and internally motivated can be considered a career calling. As the American workforce ages, fulfilling a career calling may be especially important for the longevity and well-being of older adult workers. Drawing on tenets of Socioemotional Selectivity Theory, the present study tested a model in which socioemotional motives and goal selection predict the attainment of calling, and occupational future time perspective was examined as a meaningful individual difference that may affect these relationships. I analyzed survey data collected from 267 working older adults over a two-week period using structural equation modeling. Support was found for the relationships between motives for meaning and positive emotions and calling. Emotional regulation goals were not found to mediate the relationships between motives and calling, and occupational future time perspective did not alter these relationships. By uniting Socioemotional Selectivity Theory with the calling literature, I further our understanding of antecedents of career calling in a priority working population. Theoretical implications for socioemotional selectivity theory and the calling literature, as well as practical implications for workers and organizations, are discussed.