The use of computers equipped with Internet access by students during face-to-face (F2F) class sessions is perceived as academically beneficial by a growing number of students and faculty members in universities across the United States. Nevertheless, some researchers suggest unstructured computer use detached from the immediate class content may negatively influence student participation, increase distraction levels, minimize recall of recently presented information, and decrease student engagement. This study investigates graduate students' beliefs about computer use with Internet access during graduate face-to-face lecture classes in which computer use is neither mandated nor integrated in the class and the effect of such use on student recall. Methods include a 44-item questionnaire to investigate graduate students' beliefs about computers and two experiments to investigate the influence of computer use during a lecture on students' memory recall. One experimental group (open laptop) used computers during a lecture while the other (closed laptop) did not. Both groups were given the same memory recall test after the lectures, and the resulting scores were analyzed. Two weeks later, a second phase of the experiment was implemented in which laptop groups were reversed. Results from the first experiment indicated no statistically significant difference in recall scores between the open laptop group (M = 54.90, SD = 19.65) and the closed laptop group (M = 42.86, SD = 16.68); t (29) = -1.82, p = .08 (two tailed). Conversely, the second experiment revealed statistically significant differences in scores between the open laptop (M = 39.67, SD = 15.97) and the closed laptop group (M = 59.29, SD = 26.88); t (20.89) = 2.37, p = .03 (two tailed). The magnitude of the difference in mean scores (mean difference = 19.62, 95% CI: 2.39 to 36.85) was large (eta squared = 0.17). Multiple regression analysis suggests two factors accounted for 10% of the variance in recall scores: (1) students' beliefs about distractions from computer use, and (2) beliefs about the influence of computer use on memory recall. Based on survey findings, participants (N=116) viewed computers and Internet access in graduate classes as helpful academic tools, but distractions from computer use were major sources of concern for students who used computers in graduate classes and those who did not. Additionally, participants believed academic productivity would increase if instructors integrated computer use appropriately in the curricula. Results of the survey and experiments suggest unstructured computer use with Internet access in the graduate classroom is strongly correlated with increased student distractions and decreased memory recall. Thus, restricting unstructured computer use is likely to increase existing memory recall levels, and increasing unstructured computer use is likely to reduce memory recall. Recommendations include changes in the way students use computers, pedagogical shifts, computer integration strategies, modified seating arrangements, increased accountability, and improved interaction between instructors and students.

Despite a high proportion of the population experiencing traumatic events within their lifetime, the number of individuals who go on to develop posttraumatic stress disorder (PTSD) is comparatively small; herein highlighting the importance of individual differences in imparting risk and resilience towards the development and maintenance of PTSD. Existing literature illustrates that biological and ecological factors are important in predicting PTSD development, with pathological vulnerabilities excepting their effects at pre- peri- and post trauma stages. Whilst cognitive and emotion based models of PTSD account for the role of a minority of known pre-trauma risk factors, individual differences in peri- and post trauma processes are held as critical to the development of PTSD. The broad range of risk factors implicated in the empirical literature, and necessity of traumatic exposure to PTSD, implicates the utility of a diathesis-stress conceptualisation of PTSD development. The current thesis employed an analogue VR-trauma paradigm to investigate the respective importance of vulnerability factors at each stage, in the prediction of analogue PTSD symptoms (memory problems, startle responses, re-exposure fear habituation), whilst measuring affective and electrophysiological concomitance. Findings supported the importance of peri-traumatic responses in the prediction of PTSD, where present, showing increased predictive capacities over pre- and post-trauma factors. Biological and ecological factors also illustrated important predictive associations, with genetic SNPs implicated in reflex startle and cardiac responses towards intrusive memories. Moreover, peri-traumatic HR decelerations and accelerations mediated the association between pre-trauma factors and cued recall inaccuracy and intrusion severity respectively. Results support existing cognitive and emotional models in their emphasis on peri-traumatic processes but suggest the added utility of a diathesis stress conceptualisation of the development of PTSD, in highlighting the importance of pre-trauma biological and ecological risk and resilience factors.

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.