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ItemSupporting Feedback Loop Reasoning in Simulated Systems with Computer-Based Scaffolding(Georgia Institute of Technology, 2023-04-12) Dunbar, TerriFeedback loops are a critical part of systems and a frequent source of misconceptions. These misconceptions are thought to occur because people inappropriately apply their everyday experiences of causality to the types of causal feedback loops present in systems. Feedback loop reasoning can improve with training; however, misconceptions such as failing to close the loop are particularly resistant to change. Two experiments investigated whether factors known to improve positive transfer with other cognitive skills could overcome learners’ misconceptions about feedback loops during simulation training, including learning from multiple examples, similarity to the training context, scaffolding, and desirable difficulties. Results revealed that similarity and potentially cognitive load had the largest impacts on transfer, and the type of scaffolding used or how it was sequenced over training had little effect. Near transfer only occurred for participants who learned from balance systems where the goal is to maintain system equilibrium by counterbalancing relationships, and not with pattern systems where the goal is to determine how spatial patterns emerge from local interactions. There was no evidence of far transfer. Across both experiments, participants also closed the loop more frequently when learning from balance systems. Overall, the current studies suggest that researchers need to carefully consider the type of system used during simulation training because subtle manipulations can lead to different learning experiences. Existing theories of system misconceptions are unable to satisfactorily explain why these performance differences occurred. Instead, the results and their implications are discussed using cognitive load theory.
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ItemSubgoal Level Feedback Benefits Novel Problem Solving(Georgia Institute of Technology, 2021-12-14) Schaeffer, Laura MayThe present study combined subgoal learning and feedback frameworks to further improve problem solving performance and demonstrated that subgoal level feedback resulted in better learning outcomes over step level feedback, first in a lab environment and then in an online-only environment. Feedback is an essential part of learning that tells the learners what they are doing correctly as well as where they can improve. Feedback can be provided at different levels such as the solution level, step level, and sub-step level. Previously, feedback at the step level had shown to be as effective as sub-step level feedback while requiring less time and fewer resources to create. Subgoals are components of a problem solution that transfer across problems in a given domain. Subgoal level feedback has several advantages over step level feedback that cause it to be more effective for learners. Subgoals help learners identify the structure of the problem, chunk steps together (thereby reducing extraneous load), and encourage self-explanations. The present study combined subgoal learning and feedback frameworks to further improve problem solving performance. Learners who received subgoal level feedback correctly completed more steps of novel problem solving tasks and were better able to explain problem solving solutions than learners who received step level feedback. The results suggest that subgoal level feedback leads to better transfer on novel tasks because the subgoal framework helps learners better understand and apply general procedures.
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ItemImpacts on performance effectiveness, processing efficiency, and subjective experience by music listening in extraverts and introverts(Georgia Institute of Technology, 2021-12-14) Levy, LauraThe present study evaluates the utility of a new model based on attentional control theory (ACT) in a music psychology study. This new model seeks to provide a mechanism to explain impacts of concurrent-task music listening on performance effectiveness, processing efficiency, and subjective experience of work by level of extraversion. After nearly 100 years of music psychology research, the literature is difficult to reconcile for whether listening to music while completing a cognitive task exerts a negative, positive, or null effect on performance. The Personality, Anxiety, and Musical Impacts (PAMI) model incorporates theories of arousal and anxiety as a mechanism that impinges on the cognitive functions of shifting and inhibition, as well as introduces a critical dependent variable of processing efficiency, and seeks to provide further understanding for the interaction of music listening, cognitive tasks, and individual differences. Two experiments were conducted in this study to assess the impacts on performance effectiveness and processing efficiency for inhibition and shifting tasks for extraverts and introverts in silence, low beats per minute (bpm), and high bpm conditions. Music exerted impacts on performance effectiveness for the Stroop task, on processing efficiency for both Stroop and the Wisconsin Card Sorting Task, as well as altered the subjective experience of tasks by level of extraversion making the tasks more enjoyable but seemingly more challenging and stressful. These findings suggest the PAMI model provides value in explaining the differing impacts concurrent task and music listening can have on individual differences, and move towards a prescriptive model of identifying the appropriate acoustic environments for certain kinds of people for specific kinds of work.
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ItemIndividual Differences in Deepfake Detection: Mindblindness and Political Orientation(Georgia Institute of Technology, 2021-01-14) Tidler, Zachary R.The proliferation of the capability for producing and distributing deepfake videos threatens the integrity of systems of justice, democratic processes, and the general ability to critically assess evidence. This study sought to identify individual differences that meaningfully predict one’s ability to detect these forgeries. It was hypothesized that measures of affect detection (theory of mind ability) and political orientation would correlate with performance on a deepfake detection task. Within a sample (N = 173) of college undergraduates and participants from Amazon’s Mechanical Turk platform, affect detection ability was shown to correlate with deepfake detection ability, r(171) = .73, p < .001, and general orientation to the political left was shown to correlate with deepfake detection ability, r(171) = .42, p < .001. Stronger correlations with deepfake detection ability were observed among specific facets of political orientation: economic liberalism, r(171) = .40, p < .001, and social progressivism, r(171) = .57, p < .001. Political orientation was shown to add incrementally predictivity in a model that included both, political orientation and affect detection as predictors of deepfake detection ability. The deepfake detection task was also assessed as a predictor of an autism spectrum disorder screening instrument, r(171) = -.23, p < .001. The results of this study serve to identify populations who are particularly susceptible to deception via deepfake video and to inform the development of interventions that may help defend the vulnerable from nefarious attempts to influence them.