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, Eric Schulz (firstname.lastname@example.org).
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 2 Seminar Schedule
January – April 2017
Incorporating conflicting descriptions into decisions from experience
Most decision making research so far has looked either exclusively at decisions from experience or decisions from description, or compared the behavioural results from the two different paradigms performed separately. Very limited research has looked at decisions that rely on both descriptions and experience simultaneously, which we believe is a very important part of daily decision making, where we can rely on both descriptions and past experiences to make our decisions. A series of experiments has shown that while both sources of informations are taken into account, more relevance is given to experience, while descriptions appear to be discounted, but not completely ignored. Other influential factors are the plausibility of the descriptions, with plausible descriptions getting higher weights than implausible ones; and the complexity of the task, with descriptions having higher impact in medium-complexity tasks. This research is important for the understanding of the effectiveness of warning labels and signs, which can be seen as descriptions applied to our previously acquired experiences.
Trials-with-fewer-errors: Feature-based learning and exploration
UCL/UPF Barcelona, UK/Spain
Reinforcement learning algorithms have provided useful insights into human and animal decision making and learning. However, they perform poorly when faced with real world situations characterized by multi-featured alternatives and contextual cues. In this paper, we propose an approximate Bayesian optimization framework for tackling such problems. The framework relies on similarity-based learning of functional relationships between features and rewards, and choice rules that use uncertainty in balancing the exploration-exploitation trade-off. We tested our framework using a series of novel multi-armed bandit experiments in which alternative rewards are noisy functions of two features. The exploration behaviour of some participants showed signatures of Bayesian optimization, being guided by prior expectations along with the need for function learning, and taking uncertainty into account. However, a sizeable proportion of participants ignored the feature information; and barely any performed nearly as well as optimal Bayesian inference. We illustrate the fecundity of the paradigm and highlight several lines of future research.
The hippocampus as a predictive map
Google Deepmind, UK
A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation provides a useful basis for estimating expected future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.
What the Success of Brain Imaging Implies about the Neural Code
The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of neural code and ventral stream, as well as what can be successfully investigated with fMRI.
How formal models can illuminate mechanisms of moral judgment and decision-making
University of Oxford, UK
The cognitive and affective processes that give rise to moral judgments and decisions have long been the focus of intense study. Here, I review recent work that has used mathematical models to formally describe how features of moral dilemmas are transformed into decisions. Formal models have traditionally been used to study perceptual and value-based learning and decision making, but until recently they had not been applied to the study of moral psychology. Using examples from recent studies, I show how formal models can provide novel and counterintuitive insights into human morality by revealing latent subcomponents of moral decisions, improving prediction of moral behavior, and bridging moral psychology and moral neuroscience.
Hypothesis Testing in Intelligence Analysis
Middlesex University, UK
Changes of Mind
University of Leicester, UK
Will AI revolutionise the decision sciences? A medical perspective.
University of Oxford, UK