Frank Van Overwalle
- More information on the Social Brain Project...
- More information on Breinwijzer...
- Internships at our lab...
- Social Psychology I: Introduction to Social Psychology
- Social Psychology II: Social relations
- Group dynamics
Research Interests: Social Neuroscience, Social Cerebellum, Causal and Trait Attribution, Social Connectionist Models, Social Cognition, Emotion and motivation.
|Watch short 15-minutes talks on:
|Watch longer talks on:
|- Hoe gedachten van anderen lezen?
|- The social cerebellum (research: 20 minutes)
|- Zijn we allemaal racisten?
|- Het sociale brein en corona (42 minuten)
My major research interest is currently on neuroimaging of social cognition processes including understanding another person's mental states, intentions and (causal) beliefs, as well as his or her traits. Most recently, my researched moved to the social cerebellum which seems to be critically important for understanding social action sequences.
Previously, I was involved in developing connectionist models of important domains in social cognition: causal attribution, group biases, person impression formation and attitude formation and change (including cognitive dissonance). I conducted simulations on representative findings from the literature in these domains using common network architectures and processing parameters in order to develop a general and unified process model of these judgments in social cognition. In addition, I also devised experiments to test some specific predictions that emerged from these network simulations and that sometimes contradict currently held beliefs on how these judgments are made.
During the previous ten years, I worked on several issues in the domain of causal and dispositional attributions. My earlier interests focused on attribution retraining programs with the aid of covariation information manipulation and the emotional and cognitive consequences of causal attributions in the achievement domain. Next, I moved to the question of how people make use of covariation information in order to make causal and dispositional inferences.
Social Brain Project
"Women like to shop" and "Americans are fat" are examples of stereotypes. "My friend is handsome" and "My mother is anxious" are judgments about people we know well, which often also are quite accurate. We use stereotypes judgments about people to navigate through the social world. How are stereotypes and judgments made in our brains? Where are the brain areas that make social judgments, and where are the groups and people we judge? How do we control our social behavior and the social context, and how do group norms have an impact on us? These are some of the questions that this project tries to answer.
The project is supported by researchers specialized in social neuroscience who study the mystery of the social brain. In social neuroscience, behavioral experiments and state-of-the art neuroimaging techniques like fMRI or TMS are used to explore which parts of our brains are active during certain social and cognitive processes. This tells us how we deal with other people, and the underlying mechanisms in our brains. For instance, what mechanisms ensure that we understand our own behavior and that of others in terms of their thoughts, intentions, interests, character traits.
Answers to these questions can provide information on a wide spectrum of topics such as mind reading (how spontaneous people infer goals and desires by observing them), autism (the lack of understanding of others) and paranoia (seeing too many hidden motives in others). Where we store information about traits and other people can tell us much about potential effects of brain damage by an accident or a stroke, and what impact this has on the social functioning of the patient.
The last decade has witnessed an upsurge of social neuroscientific approaches exploring the social and emotional aspects of the human mind and human behavior by applying novel neuroimaging techniques. This has resulted in several new areas of social psychological study including social (cognitive) neuroscience and affective neuroscience. Researchers from various areas including psychologists, neuroscientists, animal researchers and other sciences now explore this new area using neuroscience techniques to understand social cognition.
The first meeting on a Social Neuroscience approach to Social Cognition was held on May 22 – 25, 2008 in Ghent, Belgium
Since then many more meeting and workshops have been held.
Social Connectionism: A Reader and Handbook for Simulations
Author - Frank Van Overwalle
Hardcover, 456 pages. ISBN: 9781841696652, ISBN-10: 184169665X, Publisher: Psychology Press
For a for free download of the accompanying FIT Program or the FIT Exercises described in the book, contact Frank.VanOverwalle@vub.be.
About the Book
Many of our thoughts and decisions occur without us being conscious of them taking place; connectionism attempts to reveal the internal hidden dynamics that drive the thoughts and actions of both individuals and groups. Connectionist modeling is a radically innovative approach to theorizing in psychology, and more recently in the field of social psychology. The connectionist perspective interprets human cognition as a dynamic and adaptive system that learns from its own direct experiences or through indirect communication from others.
Social Connectionism offers an overview of the most recent theoretical developments of connectionist models in social psychology. The volume is divided into four sections, beginning with an introduction and overview of social connectionism. This is followed by chapters on causal attribution, person and group impression formation, and attitudes. Each chapter is followed by simulation exercises that can be carried out using the FIT simulation program; these guided exercises allow the reader to reproduce published results.
Social Connectionism will be invaluable to graduate students and researchers primarily in the field of social psychology, but also in cognitive psychology and connectionist modeling.
About the FIT program
"My students are absolute beginners with respect to running a simulation, but have mastered the FIT2 program pretty quickly. Okay, "mastered" may be a slight exaggeration, but they learned pretty quickly how to use it. The program is a very valuable tool!" (Frank Siebler)
You can directly compare the simulation output with real observed data from actual experiments (hence its name FIT). While it is of great importance to test whether a simulated theoretical model can reproduce actual data, this often is a tedious job in other programs. In this program, this is the basic of the program input (although it is possible also to specify no actual data). The program allows you to follow actual experimental procedures or imaged learning histories in detail, without complicated script writing.
In addition, you can automatically search for the parameter values of the simulated model that best fit with your actual data.
You specify the data input in a user’s friendly data grid, which is very similar to common spreadsheets like Excel. The simulated output is also given as grid data, and can be visually inspected by graphs, or can be exported to other programs.
The following models are currently available
- Feedforward Connectionist Models: Feedforward (McClelland and Rumelhart, 1988), Configural (Pearce, 1994), BackPropagation (McClelland & Rumelhart, 1988), Simple Recurrent Net (Elman, 1990)
- Recurrent Connectionist Models: Linear Auto-associator (McClelland and Rumelhart, 1988), BSB Linear Auto-associator (McClelland and Rumelhart, 1988), Non-Linear Auto-associator (McClelland and Rumelhart, 1988), DiscoNet with hidden layer (Labiouse, 1999)
- Algebraic Models: Modular Probabilistic (Cheng and Novick, 1990), Modular ANOVA (Försterling, 1989), Updating (Busemeyer, 1991; Hogarth & Einhorn, 1992), Evidential Evaluation (White, 1998)
- Associative Models (predecessors of connectionist models): Modular Associative (Rescorla and Wagner, 1972), Configural-Cue Associative (Gluck and Bower, 1988), Dimensionalized Configural-Cue (Gluck and Bower, 1988)
This is a view on the spreadsheet-like input from the program and some output
This is a view on the graphical interface to visualize the simulated data (broken lines) and the fit with the human data (full lines).