London Judgment & Decision Making Group
Summer term 2012 – 2013
Organizers
Emmanouil Konstantinidis
University College London
Contact details:
Department of Cognitive, Perceptual & Brain Sciences
Room 204c, 26 Bedford Way, London, WC1H 0AP
UK
Telephone: (+44) 020 7679 5364
E-mail: emmanouil.konstantinidis.09@ucl.ac.uk
Neil Bramley
University College London
Contact details:
Department of Cognitive, Perceptual & Brain Sciences
Room 201, 26 Bedford Way, London, WC1H 0AP
UK
E-mail: neil.bramley.10@ucl.ac.uk
LJDM website
http://www.ljdm.info
Web administrator:
Dr Stian Reimers (Stian.Reimers.1@city.ac.uk)
LJDM announcement emails
Contact: Dr Marianne Promberger (marianne.promberger@kcl.ac.uk)
Seminar Schedule
May-June 2013
5:00 pm in Room 313, 26 Bedford Way, UCL Psychology
1st May
Superstition as biased contingency detection
Miguel Vadillo
University College London
8th May
The intertemporal character of nations
Neil Stewart
University of Warwick
15th May
Use of external representations in reasoning about causality
David Mason
University College London
22nd May
TBC
29th May
TBC
05th June
Combining psychological and computational constraints
Adam Sanborn
University of Warwick
Abstracts
01.05.2013
Miguel Vadillo
University College London
Superstition as biased contingency detection
Most papers on causal learning begin with a couple of sentences remarking how important this
cognitive process is for our survival. Unfortunately, our ability to extract causal knowledge from
the environment and use that information does not make us immune to blatant causal illusions,
sometimes with far reaching consequences. Traditionally, these illusions have been explained in
motivational terms: Perceiving that the world is more controllable or predictable than it really is
can protect us from helplessness and depression. One the other hand, associative learning theory
offers a purely cognitive interpretation for these illusions. From this point of view, accidental
pairings of cues and outcomes can result in the perception of a causal link between them, even in
the absence of a real correlation. In the present series of experiments, I will show that the
cognitive explanation can account for many of the experimental findings traditionally attributed to
motivational processes. Moreover, in contrast with recent dual-process accounts, I will argue that
the illusory perception of causality is better explained as a learning effect than as a reasoning
effect.
08.05.2013
Neil Stewart
University of Warwick
The intertemporal character of nations
We used Google search trends to construct a measure of the intertemporal character of nations.
Motivated by research into the association between discounting, impulsive behaviour, and socioeconomic status at the level of individuals, we estimated discounting for whole nations. We find
that lower discount and a bias for the future are correlated with higher per-capita gross domestic
product, demonstrating the viability of a psychological characterisation of nations from nationlevel data.
15.05.2013
David Mason
University College London
Use of external representations in reasoning about causality
The visualization of causal models is a necessary component in their communication to others.
However, students often demonstrate difficulty when learning about causal models and path
analysis. Previous work on visualizing complex abstractions demonstrates how features of a
external representation can enhance or detract from the message that a designer is trying to
convey. Certain choices designers make (e.g. arrow placement) can significantly affect correct
inference. I will present a series of experiments that examined several features of causal model
representations and identified some which impaired deductive reasoning with the model.
22.05.2013
TBC
29.05.2013
TBC
1.06.2013
Adam Sanborn
University of Warwick
Combining psychological and computational constraints: A computational justification for
locally Bayesian learning
Different levels of analysis provide different insights into behavior: computational-level analyses
determine the problem an organism must solve and algorithmic-level analyses determine the
mechanisms that drive behavior. However, many attempts to model behavior are pitched at a
single level of analysis. Research into human and animal learning provides a prime example, with
some researchers using computational-level models to understand the sensitivity organisms
display to environmental statistics but other researchers using algorithmic-level models to
understand organisms' trial order effects, including effects of primacy and recency. Recently,
attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL)
creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed
of modules that are each individually Bayesian but communicate with restricted messages. A
different inspiration comes from computer science and statistics: Our brains are implementing the
algorithms developed for approximating complex probability distributions. I show that these
different inspirations for how to bridge levels of analysis are not necessarily in conflict by
developing a computational justification for LBL. I demonstrate that a scheme that maximizes
computational fidelity while using a restricted factorized representation produces the trial order
effects that motivated the development of LBL. This scheme uses the same modular motivation as
LBL, passing messages about the attended cues between modules, but does not use the rapid shifts
of attention considered key for the LBL approximation. This work illustrates a new way of tying
together psychological and computational constraints.