London Judgment & Decision Making Group
Summer term 2015 – 2016
Organizer
Neil Bramley
University College London
Contact details:
Experimental Psychology
Room 201, 26 Bedford Way,
London, WC1H 0AP,
UK
Telephone: +44 (0) 79 1441 9386
E-mail: neil.bramley@ucl.ac.uk
LJDM website
http://www.ljdm.info
LJDM members’ (Risk & Decision) list
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=RISK-AND-DECISION
Seminar Schedule
April - June 2016
Wednesdays, 5:00 pm in Room 313, 26 Bedford Way, UCL Psychology
27th April
Perception of time and judgment of causality
Marc Buehner
Cardiff University, Wales
4th May
TBC
Barbara Fasolo & Elena Reutskaja
LSE and IESE Business School, Spain
11th May
Learning optimal behaviour and discovering unforeseen possibilities
Alex Lascarides
University of Edinburgh
18th May
TBC (something on metacognition)
Benedetto de Martino
University of Cambridge/ Wellcome Trust
25th May
The compositional nature of inductive functions
Eric Schulz
UCL
1st June
Neural signals in human foraging and dynamic choice
Nils Kolling
University of Oxford
8th June
The erotetic theory as a unified approach to reasoning, judgment, and decisionmaking
Philipp Koralus
University of Oxford
Abstracts
27.04.2016
Perception of time and judgment of causality
Marc Buehner
Cardiff University
In this talk I will explore how perception of time influences causal judgment, and, how in turn
causal knowledge influences temporal experience. I will review previous research on the role of
temporal contiguity in causal judgments, and will outline that temporal regularity (or
predictability) is a further important cue to causal judgments. Most standard theories of causal
learning (whether based on associative or rule-based learning) cannot easily represent this role of
regularity, but prior-knowledge driven / evidence integration accounts (e.g. Bayes) can. Bayesian
accounts also fit well with the second aspect of the talk – systematic distortions of time perception
in the presence of causal knowledge. There is now a substantial body of research showing that
time perception is malleable by context, such that the same objective interval is perceived
differently when the events demarcating it are linked by a causal connection. Specifically, causal
intervals are perceived as shorter than non-causal intervals of identical length, and causes and
their effects mutually attract each other in subjective space-time. The overall pattern of evidence –
that perception of time and judgment of causality mutually constrain each other – fits well with
cognitive theories that assign a critical role to causality.
04.05.2016
Barbara Fasolo & Elena Reutskaja
LSE, London and IESE Business School, Spain
11.05.2016
Learning optimal behaviour and discovering unforeseen possibilities
Alex Lascarides
University of Edinburgh
Most models for learning optimal policies make it equivalent to learning the likely outcomes of
actions. The hypothesis space---that is, the set of chance and action variables (and their possible
values), the causal dependencies and the reward function---are all known in advance and don't
change during learning. But there are many scenarios where knowledge this precise and
exhaustive is unrealistic. We need methods for overcoming ignorance about unknown unknowns,
as well as known unknowns.
I will present some very preliminary work on modelling an agent that both discovers and learns to
exploit unforeseen possibilities. The agent learns through direct interaction with the world and
through interacting with a domain expert. We use a combination of probabilistic and symbolic
reasoning to compute posterior estimates of all components of the decision problem, including the
set of random variables. The hypothesis space is guaranteed to cover all observed evidence to
date, and defaults to simplicity and conservativity (i.e., it minimises changes to the prior
hypothesis space). Some very preliminary empirical results on toy examples show that the agent
converges on optimal polices even when she starts out unaware of factors that are critical to
success.
18.05.2016
Benedetto de Martino
University of Cambridge / Wellcome Trust
25.05.2016
The compositional nature of inductive functions
Eric Schulz
UCL / Harvard
How do we learn about functional structure? We propose compositionality, the ability to perform
computations over mental building blocks, as an explanation and operationalize this intuition as a
structural inductive bias induced by a grammar over Gaussian Process base kernels. It is assumed
that these kernels can be combined by simple arithmetic operations and that the resulting process
allows us to reason about more complex functions as made up of more simplistic structural
primitives.
Across a series of experiments, we show that participants prefer function extrapolations that are
generated by structured kernels over unstructured ones, manually extrapolate functions in line
with structured predictions, converge to a posteriori likely compositional predictions within a
Markov chain Monte Carlo with people approach, and perceive compositional functions as more
predictable than their similar but non-compositional counterparts. We argue that people's intuitive
functions are compositional by nature and that compositionality is a sensible way to simplify
structural inference.
01.06.2016
Neural signals in human foraging and dynamic choice
Nils Kolling
University of Oxford
During the past three decades we have learnt much about how the brain integrates evidence for
perceptual and simple reward-based decisions. Furthermore, there are increasingly sophisticated
biophysical models of reward based choice, i.e. of how, neurally, comparisons are made between
two or more valuable concrete options. We know however, surprisingly little about how other
kinds of ecologically relevant decisions are made, despite their great relevance to allow
appropriate behavioural flexibility. Whereas a lot of decision neuroscience has focused on using
simple economic models in order to understand evaluation between options, a large and rich
literature exists in ecology research, trying to understand how animals optimize their behaviour
within different environments. For this they have to track a variety of environmental parameters
such as average reward rates or risk pressure. In my talk I will discuss some of my recent
studies3,4 trying to understand different kinds of decision processes, inspired by distinctions seen
to be essential for ecological behaviours such as patch-leaving and risk sensitive foraging.
Neurally, I will highlight novel insights that can be gleaned from such an approach about the
potential functions of several prefrontal brain regions, particularly focused on dorsal and
perigenual anterior cingulate cortex, but also ventromedial and frontal polar cortex. Overall, mine
and other studies suggests a ubiquity of comparison processes in cortex, with key differences in
what is compared in a particular region and how the comparison is implemented.
I will furthermore discuss more broadly, how environmental changes can shape evaluative
contexts and lead to network changes that allow for multiple evaluative frameworks to co-exist
and interact with each other to enable flexible and adaptive behaviours in many different
environments.
08.06.2016
The erotetic theory as a unified approach to reasoning, judgment, and decision-making
Philipp Koralus
University of Oxford
Reasoning, judgment, and decision-making are still often treated as separate cognitive
phenomena. I will present a unified approach, the erotetic theory, based on the following key idea:
We treat reasoning and decision problems as questions, and we treat practical and epistemic
reasons as maximally strong answers to those questions. I discuss various empirical predictions of
this model and how it can be mathematically represented. I will gesture at how we can formally
derive classical models of rationality as a limiting case within the erotetic theory.