Recent discussions surrounding law enforcement have highlighted varying opinions on the ability of police officers to respond effectively to mental health-related incidents. Given that 20% of police calls involve mental health or substance abuse issues, it is crucial that the general population is confident about the role of police as mental health interventionalists. The current study assessed DFW residents’ (N = 64) perceptions of the police’s ability to intervene in mental health crises utilizing in-person and online 7-point Likert scale surveys (where 1 = disagree strongly and 7 = agree strongly). Survey results showed that confidence in police’s ability to handle mental health crises increases with age, p = .04. Older participants expressed greater trust, while younger respondents were more skeptical. No significant differences were found in gender, race, ethnicity, or socioeconomic status, highlighting a generational divide in public trust. These results suggest that there is potential for improving younger individuals’ attitudes towards police intervention.

Rats use many cues when navigating to food, shelter, or a mate. The use of visual cues (e.g., landmarks) has been reported in many species. In rats, these cues include those around their start position, the experimenter, as well as landmarks located in (intramaze) or around (extramaze) the search space. In the current experiment, rats were placed into a start box with a transparent door and released onto an open field. We examined whether rats were able to discriminate between two different intramaze landmarks (wooden figurines; A and B) from the start box. Landmark A trials were reinforced with a Froot Loop© hidden in a cup behind the landmark (A+), but no Froot Loop was present on Landmark B (B-) trials or on C- trials with no landmark. Latency to the goal cup was measured and revealed no differences between the three trial types. The procedure was modified to include two response locations (to the left and right of the landmark). A+ and B+ trial types were reinforced at different cups. There was no difference in accuracy for searching the correct cup first. The use of non-visual cues, the discriminability of the landmarks, and the response cost of search will be discussed.

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 analysies of data and be extended to categorization of a broader range of vocal patterns, including human speech.

Hormonal contraceptives can have many negative side effects that deter women from using them. One example that many women are unaware of is alcohol craving. Preliminary studies in our lab have shown that women on hormonal contraceptives have greater alcohol cravings than women who are naturally cycling. Given that this data is mostly survey-based, we aim to add a research manipulation in the current study. We are testing this through a Qualtrics survey, distributed via Amazon’s Mechanical Turk (Mturk), that is designed to prime alcohol cravings. We will ask women to rate their alcohol cravings before and after they watch a video containing alcohol. We expect that when primed with an alcohol video, women’s cravings towards alcohol will increase to a level higher than before they watched the video. Further, we expect to find that women on hormonal contraceptives will have a higher increase in cravings than women who are regularly cycling. If we do find that women on hormonal contraceptives have a stronger reaction to an alcohol cue, women would benefit from being well informed about this effect. For instance, armed with the knowledge that hormones influence craving and behavior, women may be more mindful about their drinking habits. In addition, we eventually hope that this knowledge will influence those who are developing future contraceptives to take these side effects into account.

Existential isolation can be described as the experience of feeling alone in one’s experience of the world; that no one shares your experience or comes close to understanding it (Pinel et al., 2017). Research on existential isolation remains novel, however current findings among those chronically existentially isolated has found significant associations with a host of negative affective outcomes, including increased loneliness (Pinel et al., 2017), death thought accessibility (Helm et al., 2019), depression and anxiety (Constantino et al., 2019), and reduced self-esteem and self-worth (Helm et al., 2018). Finally, there is novel and consistent evidence that existential isolation is related to insecure attachment, specifically avoidant attachment (Helm et al., 2020).

Recent literature has found that adverse early life experiences are one of the factors that can proactively influence adult well-being, resulting in consequences such as anxiety and depressive disorders, and suicidal ideation (Hays-Grudo and Morris, 2020). Additional work has also shown that childhood abuse was uniquely associated with greater loneliness among young adults (Landry et al., 2022) but the impact of early life experiences on loneliness can be impacted by a multitude of factors including, but not limited to, age, gender, socioeconomic factors, and epigenetic factors (Southwick et al., 2014). While loneliness is inherently a part of the feeling of existentially isolated, existential isolation itself is distinct in theoretically meaningful ways that this study aims to investigate through the lenses of adverse childhood experiences and childhood uncertainty. This present study aims to qualitatively examine the relationship between adverse childhood experiences and existential isolation among adult individuals. Our findings across two studies established a positive association between individuals who have experienced adverse childhood experiences and existential isolation.