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.

Recent research has begun to explore the basic misperceptions that underly political divides. For instance, people tend to believe that their political opponents accept objective moral wrongs (e.g., homicide, watching child pornography). These misperceptions then motivate avoidance and dehumanization of political opponents. However, the socio-cognitive processes preceding the formation of these misperceptions are less understood. Across two studies, we examined existential isolation towards political opponents, or the belief that people with a different political orientation than you do not understand your perspective and worldview, as one such social determinant. Study 1 surveyed 194 undergraduate students, and Study 2 surveyed 250 adults via Amazon Mechanical Turk. Results provide consistent support to suggest that individuals feel more existentially isolated from political opponents (e.g., politically liberal individuals report feeling more existentially isolated from politically conservative others). The more existential isolation people felt towards liberal or conservative others, the more they believed that these individuals endorsed objective moral wrongs, the less willingness they were to engage in political discussions with these individuals, and the more they dehumanized them. These findings emphasize the important role of existential isolation in the formation and persistence of political divides, and highlight the need for interventions that target feelings of existential isolation towards one’s political opponents.