Vagus Nerve Stimulation (VNS) research and clinical use demonstrate its efficacy in treating epilepsy, depression, migraines, and its potential to one day mitigate inflammation and improve working memory. However, variations in treatment outcomes indicate a need for further exploration into the individual factors that influence VNS sensitivity and responsiveness. The study aims to investigate how various individual factors like sex, body awareness, preterm birth, childhood socioeconomic status, childhood unpredictability, and adverse childhood experiences relate to sensitivity to VNS. A survey will administered to measure person-based variables of interest. Participants will undergo a VNS procedure involving a heart rate variability (HRV) recording during a baseline, stimulation, and recovery period. Some of our expected result are, individuals who were born prematurely will have higher sensitivity to VNS, and individuals who experienced more childhood stress will have lower sensitivity to VNS. The results of this study will improve future VNS clinical treatment and inform future VNS research pursuits.

It is commonly assumed that achieving financial success will remove many of the burdens associated with low socioeconomic status (SES). However, recent research suggests that individuals who transition from low to high SES environments are at even greater risk for poor health outcomes compared to those who remain in stably high SES environments and compared to those who remain in stably low SES environments. Although the underlying cause of these health outcomes are currently unknown, evidence from qualitative interviews of individuals experiencing upward mobility finds unique psychological stressors associated with transitioning from low to high SES. The current research was designed to experimentally examine the effect of upward mobility and childhood SES on psychological stress. Participants were randomly assigned to write about their life at a significantly higher SES than their childhood SES (upward mobility condition), or at the same SES as their childhood (no mobility condition) before reporting their psychological stress. Two studies were conducted in order to examine the effect of upward mobility in 1) a sample of undergraduate students and 2) to compare the effects of upward mobility in a sample of adults currently enrolled in college and never enrolled in college. Results revealed elevated psychological stress among participants in the upward mobility condition (vs. no mobility), but only among those who had low childhood SES. These findings suggest that upward mobility carries a psychological stress burden that uniquely impacts individuals from low SES environments.

Students wonder how to prepare for an upcoming exam, while instructors wonder how to best introduce materials to students. Research has found that practice testing after material is studied leads to better memory compared to other strategies like restudying (Rowland, 2014). Practice testing tends to be more effective when students recall material verbally or write it down (overt learning) compared to when they only mentally recall the material (covert learning; Tauber et al., 2018). Other research evaluates the value of pretesting individuals before they learn subject matter. Studies show that answering pretest questions before studying, even if people provide wrong answers, leads to beneficial learning outcomes compared to just studying material (Richland et al., 2009). We aimed to combine both research areas and investigate how covert and overt learning applies to pretesting: When taking a pretest, is it better to just think about the answer, or is it better to write it down? Undergraduate students at TCU were instructed to learn passages about the planet Saturn or Yellowstone National Park. For one of the passages, students simply read the passage (read-only condition). For the other passage, they were asked short-answer pretest questions about the passage prior to reading (pre-test condition). A random half of the participants complete their pretests overtly (typing their responses to the questions), whereas the other half of participants completed their pretests covertly (answering the questions in their mind). All participants completed a final multiple-choice test on the material they learned. Performance on this final test was higher for the pretest condition compared to the read-only condition, and this was true for both overt and covert pretesting. In strategy ratings made after the experiment, participants seemed to recognize that pretesting was more effective than learning because it helped them absorb relevant information while reading the passage. Our results suggest that pretesting is an effective learning strategy, even when learners do not provide articulated responses.

Bird song has been extensively investigated as a model for understanding the physiological basis for animal vocalization. Juvenile songbirds acquire their songs and perfect them as they transition into adulthood, just as we acquire our native language by exposure and imitation of adult tutors. Scientific investigation of bird song requires the collection of hundreds of hours of audio data containing songs, calls, and cage noise. These data must be sorted into categories of interest for specific research questions, with singing behavior being the dominant behavior of interest. Data categorization is a tedious and time-consuming process, and while current software hastens this process, substantial human effort is still required. This project investigates whether or not machine learning algorithms can be used to more efficiently categorize audio data collected in songbird research. Specifically, we developed a convolutional neural network (CNN) in PyTorch to classify whether or not 0.5 second sections of audio contain bird song. Using a supervised learning paradigm, we trained the CNN using labeled spectrograms (visual representations of audio frequencies across time) acquired from recordings of the zebra finch (Taeniopygia guttata). After training the CNN, we implemented it into an algorithm that identifies song within audio recordings. We then compared the CNN based software to a pre-existing, custom-written LabVIEW template-matching algorithm to determine the relative speed and accuracy of the software. Recordings were taken in both noisy and quiet recording environments to test the strengths and limitations of the two approaches. Our data indicate that the CNN based algorithm achieves comparable levels of accuracy to the pre-existing algorithm and accomplishes the categorization using a fraction of the time required by the template matching program. These results suggest that machine learning algorithms can effectively be used to automate and rapidly categorize stereotyped vocal patterns. Further development of this software may facilitate rapid analyses of data and be extended to categorization of a broader range of vocal patterns, including human speech.

Narcan is an over-the-counter nasal spray used to reverse the effect of opioid overdoses. In 2022, approximately 110,000 fatalities resulted from opioid overdoses in the United States. Considering the high rates of opioid overdose fatalities, it is important to employ a community-based approach to the distribution of Narcan. To address this, a survey was distributed to individuals in a local Fort Worth community center (N = 5) to evaluate the utilization of Narcan. Following the survey, each participant was given one box (2 doses) of Narcan to take home. One month after the initial survey, participants completed a follow-up survey , indicating if they had used or given away the Narcan. While we do not expect the majority of participants to have used the Narcan within this timeframe , we expect to find high willingness to use Narcan in the case of an emergency. The distribution of Narcan can provide a resource to families as a precaution to prevent potential opioid related deaths in this community. Study implications highlight a greater need for community education and increased distribution of Narcan to improve public perceptions and reduce instances of opioid overdose in the Fort Worth community.