The D.O. Hebb Lecture Series was initiated in 1989 in memory of Hebb’s contribution to the science of behavior. Invited speakers of the D.O. Lecture series are scientists who have made distinguished empirical contributions to basic research in all areas of psychology. It is currently made possible by the generous support of the D.O. Hebb Endowment Memorial Fund.
Most speakers also deliver an informal seminar held in the morning.
All main lectures from 3.30 - 5:00 pm. Lectures are followed by a Wine and Cheese Reception in the Atrium of the Bellini Life Sciences Complex (3649 Promenade Sir William Osler). Admission is free.
2019 - 2020 Hebb Lectures
September 27, 2019 - Location: MCMED 504
Department of Psychology
Title: The Psychology of Pain and Disability: The long road from the laboratory to changing clinical practice.
Abstract: The role of psychological factors in the experience and expression of pain has been the topic of spirited debate for decades. Today, there is little room for debate; numerous research studies have provided convincing evidence that psychological factors play an important role in the experience and expression of pain. The questions of interest today no longer concern whether psychological factors play a role in pain experience, but rather, what psychological factors are implicated in pain experience, how psychological factors exert their influence on pain experience, and what the implications are for the treatment of pain. Our work has focused on the role of catastrophic thinking in the experience of chronic pain. This presentation will summarize what is currently known about the influence of catastrophic thinking on pain experience, and how this research contributed to the development of risk-targeted interventions aimed at reducing the magnitude of disability associated with chronic pain. This presentation will also highlight some of the challenges faced in efforts to change the clinical practice patterns of service providers who treat individuals with disabling chronic pain.
October 11, 2019 - Location: MCMED 522
Nilanjana (Buju) Dasgupta
Department of Psychological and Brain Sciences
University of Massachusetts at Amherst
Title: STEMing the Tide: How female scientists and peers act as ‘social vaccines’ to protect girls’ and women’s self-concept in STEM
Abstract: Individuals' choice to pursue one academic or professional path over another may feel like a free choice but it is often constrained by subtle cues in achievement environments that signal who naturally belong there and who don’t. What factors release these constraints and enhance individuals’ freedom to pursue academic and professional paths despite stereotypes to the contrary? I will present a decade-long program of research addressing this question in the context of young women and girls’ confidence, persistence, and career aspirations in science, technology, engineering, and mathematics in the face of societal stereotypes casting doubt on their ability. Our data identify people and environments that function as ‘social vaccines’ in high achievement learning environments by inoculating women and girls against negative stereotypes. Using these data, I will propose some research-driven remedies and interventions that promise to enhance the recruitment and retention of diverse groups in STEM classes, majors, and professions.
November 1, 2019 - Location: ENGTR 0100 (Trottier Building)
Wine and Cheese reception: lobby on the 4th floor of 2001 McGill College
Department of Educational Psychology
University of Wisconsin-Madison
Patricia Busk Professor of Quantitative Methods
Title: Recent Developments and Future Directions in Bayesian Model Averaging
Abstract: A key characteristic of Bayesian statistical inference that separates it from its frequentist counterpart is its focus on characterizing uncertainty in model parameters - encoding that uncertainty through the specification of prior probability distributions on all model parameters. From a Bayesian point of view however, parameters are not the only quantities that contain uncertainty. Specifically, the selection of a particular model from a universe of possible models can also be characterized as a problem of uncertainty (Raftery, et al., 1997). The method of Bayesian model averaging quantifies model uncertainty by recognizing that not all models are equally good from a predictive point of view. Rather than choosing one model and assuming that the chosen model is the one that generated the data Bayesian model averaging obtains a weighted combination of the parameters of a (smaller) subset of possible models, weighted by each models’ posterior model probability. Using the weighted parameters rather than the parameters of any particular sub-model is known to provide superior predictive performance according to a particular type of scoring rule. This address provides an overview of Bayesian model averaging with a focus on recent developments and applications to propensity score analysis, missing data, and probabilistic forecasting of relevance to educational research.
January 24, 2020 - Location: MCMED 504
Department of Psychological & Brain Sciences
Washington University in St-Louis
Title: Early Emergence of Depression: Understanding Risk Factors and Treatment
Abstract: This talk with overview research on the psychological and neurobiological risk factors and correlates of very early onset depression, with onset as early as preschool. These factors include reduced responses to rewarding outcomes associated with impaired activation of striatal and insular regions, increased responses to negative outcomes, also associated with disrupted amygdala, striatal and insular activation, impaired emotion regulation associated with decreased prefrontal activity, and disrupted connectivity between emotion reactivity and emotion regulation regions. I will also present results of a novel treatment for early onset depression and evidence for modulation of hypothesized neural targets as a function of treatment. Together, these data support the validity of early onset depression, and provide evidence for the psychological and neural factors that can be targeted by treatments and which may serve to identify children at risk for the development of early onset depression.
March 20, 2020 - Cancelled
Department of Psychology
Pershing Square Professor of Human Neuroscience