Thomas T Hills, Director, Bridges-Leverhulme Doctoral Training Centre in Mathematical and Social Sciences, University of Warwick, will be giving a talk on Wednesday, 4 December  (12:00-13:00, Seminar Room 00.008, ground floor, Missionsstrasse 64a) as part of the SWE colloquia.

The dark side of information proliferation

There are well-understood psychological limits on our capacity to process information. As information proliferation— the consumption and sharing of information—increases through social media and other communications technology, these limits create an attentional bottleneck, favoring information that is more likely to be searched for, attended to, comprehended, encoded, and later reproduced. In information-rich environments, this bottleneck influences the evolution of information via four forces of cognitive selection, selecting for information that is belief-consistent, negative, social, and predictive. Selection for belief-consistent information leads balanced information to support increasingly polarized views. Selection for negative information amplifies information about downside risks and crowds out potential benefits. Selection for social information drives herding, impairs objective assessments, and reduces exploration for solutions to hard problems. Selection for predictive patterns drives overfitting, the replication crisis, and risk-seeking. This talk will summarize the negative implications of these forces of cognitive selection representing severe pitfalls for the naive “informavore,” accelerating extremism, hysteria, herding, and the proliferation of misinformation.

On 28 November, our guest speaker in the SWE Colloquium is Professor June P. Tangney, Department of Psychology, George Mason University, Fairfax Virginia, USA.

Borrowing from social psychology: A clinical psychologist’s tale

In this talk, I emphasize the ways in which social and personality psychology can inform clinical practice in community and criminal justice settings. Trained as a clinical psychologist, I initially “defected” to social and personality psychology to examine basic research questions about shame and guilt. I’ll summarize key findings from this line of work, and then present a framework for thinking about the many different kinds of events that evoke these moral emotions. I’ll also describe our most recent TOSCA-4, with some findings that illustrate its increased flexibility and discuss of its limitations.

I then describe how I became interested in the plight of jail inmates, a large, high-risk, underserved population, in effect reclaiming my identity as a clinical psychologist. In our studies of inmates, we initially focused on shame and guilt as potential points of intervention to reduce recidivism, but have since looked more broadly at how social psychological theory and methods can inform the development of a range of brief novel interventions for incarcerated people soon to join the community.

On Thursday 21 November, Prof. Dr. Urte Scholz, Head, Applied Social and Health Psychology, University of Zurich, will give a presentation as part of the SWE colloquium series.

Benefits and pitfalls of social relationships for health-relevant behaviors

Health-related behaviors such as eating or smoking usually happen in a social context. Most of the standard theories of health-behavior change, however, strongly focus on individual self-regulation and neglect the health behavior’s social side. Recent theories highlight the importance of going beyond the individual and of focusing on social and dyadic influences on behavior. In this talk, I will emphasize the need for concise theories and systematic research of the role on social processes for health behavior change. I will present research from randomized controlled trials, and intensive-longitudinal studies highlighting the role of social support, social control and companionship for different health-behaviours and related outcomes. Moreover, I will address the benefits, but also the challenges and the complexity of social processes for health behavior change.

This week’s SWE Colloquium (14 November) features a presentation by Manuel Völkle, Humboldt University of Berlin

Timing is everything! An introduction to continuous time dynamic modeling

The primary goal of this presentation is to introduce participants to continuous time dynamic modeling. The secondary goal is to go beyond “yet another” statistical modeling approach and to discuss how better considering the role of time may advance our quest for understanding psychological processes. Continuous time dynamic models are models for the analysis of change that make optimal use of the time structure to infer the development and dynamic relationships among constructs of interest. After distinguishing between static and dynamic models for the analysis of change and a short discussion of their respective advantages and disadvantages, I will introduce the basics of continuous time dynamic modeling in a stepwise fashion. I will highlight the possibility to work with intensive longitudinal data, including the analysis of N = 1 time-series (e.g., dynamic factor models), as well as panel data (T small, N large). Apart from a general introduction, special emphasis will be put on the interpretation and practical implementation of these models. I will end with an overview of selected recent developments, current limitations, and future research directions.

This Thursday’s speaker in the SWE Colloquium is Adam Sanborn, professor at the University of Warwick, UK.

The Bayesian Sampler: Generic Bayesian inference causes incoherence in human probability judgments

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities, and we show in a new experiment that this model better captures these judgments both qualitatively and quantitatively.