London Judgement and Decision Making seminars
The LJDM seminar series is supported by
University College London
City University London
Originally established at UCL in the early 1970’s as a weekly Cognition and Reasoning seminar, it later became an intercollegiate seminar on Language and Cognition in the early 1980’s.
The name LJDM was finally coined in 1990, and the group has been running seminars under this name ever since, with lecturers and researchers in and around the UK meeting on a regular basis to discuss judgment and decision making, judgments of likelihood, reasoning, thinking, problem solving, forecasting, risk perception and communication, and other related topics.
If you would like to present your research to the group or to suggest a speaker, please contact the organizer, Neil Bramley (email@example.com).
Unless specified otherwise, all seminars take place on Wednesdays at 5pm, in Room 313 at the Psychology Department, University College London (on the corner of Bedford Way, Gordon Square and Torrington Place, London WC1H 0AP). Map.
To get updates on the current schedule and weekly reminders of the seminars, please subscribe to the Risk and Decision mailing list.
All are welcome to attend.
Term 3 Seminar Schedule
April – June 2016
Perception of time and judgment of causality
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.
Defaults in digital choice architecture
Barbara Fasolo & Elena Reutskaja
LSE and IESE Business School, Spain
Previous research has found that a way to increase the choice of an option is to set this option as an opt-out default. However, these findings have been challenged recently and default effectiveness was shown to be attenuated under certain circumstances. In two artefactual field studies of online hospital and annuity choice we show that the effectiveness of defaults depends on i) popularity share of options, ii) the position of the defaulted alternative on the screen, and iii) numeracy of decision makers.
Learning optimal behaviour and discovering unforeseen possibilities
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.
The construction of confidence in value-based judgments
Benedetto de Martino
University of Cambridge / Wellcome Trust
Basic psychophysics tells us that decisions are rarely perfect: even with identical stimuli choice accuracy fluctuates and errors are often made. Metacognition allows appraisal of this uncertainty and correction of errors. For more complex value-based decisions (also known as economic decisions), however, metacognitive processes are poorly understood. In particular, how subjective confidence and valuation of choice options interact at the level of brain and behaviour is unknown. In this talk I will present recent work we conducted in my lab to investigate this relationship (combining psychophysics paradigms, computational modelling and neuroimaging tools). The aim of this approach is to provide new links between uncertainty in value computation and reports of confidence. I will also show how humans can use their metacognitive awareness to correct future decisions and how confidence evolves during value-based learning.
Neural signals in human foraging and dynamic choice
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.
The erotetic theory as a unified approach to reasoning, judgment, and decision-making
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.
The compositional nature of intuitive functions
Function learning lies at the core of everyday cognition. From learning which stimulus will lead to reward all the way to how other people's intentions influence their actions, almost any task requires the construction of mental representations that map inputs to outputs. Since the space of such mappings is infinite, inductive biases are necessary to constrain plausible inferences. What is the nature of the human inductive biases over functions? How do people deal with complex functions that are not easily captured by standard learning algorithms?
Insight into this question is provided by the observation that many complex functions encountered in the real world can be broken down into compositions of simpler form. We pursue this idea theoretically and experimentally, by first defining a hypothetical compositional grammar for intuitive functions and then investigating whether this grammar quantitatively predicts human learning. We show that participants utilise compositional pattern extrapolations in both forced choice and manual drawing tasks, that samples elicited from participants' priors over functions are more consistent with a compositional grammar, and that participants perceive compositional structure as more predictable and less complex. As compositionality leads to a rapid expansion of building blocks, we speculate that it is a necessary requirement for intelligent behaviour.