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London Judgement and Decision Making seminars

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.

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.

The LJDM seminar series is supported by

University College London
City, University of London

If you would like to present your research to the group or to suggest a speaker, please contact the organizers:
- Tamara Shengelia (tamara.shengelia.15@ucl.ac.uk)
- Sabine Topf (sabine.topf.14@ucl.ac.uk)
- Ayse Ozsari-Sahin (ayse.sahin.18@ucl.ac.uk)
 


Seminar Schedule Term 2 

January – March 2020

 

22 January 2020 | Alex Lloyd | Royal Holloway University

Risk, Ambiguity and Uncertainty in Adolescents’ Decision-Making

Adolescence is a period of increased exploratory and risk-taking behaviours. These behaviours have traditionally been considered maladaptive within the lifespan, having been associated with increased rates of dangerous driving, substance misuse and injury (e.g. Eaton et al. 2012). However, recent reviews have suggested that these behaviours have adaptive properties (Romer, Reyna & Satterthwaite, 2017). On this account, exploratory behaviours support information-gathering and allow adolescents to develop the independence necessary for adulthood. This talk will present evidence for an adaptive account of risk-taking in adolescence. First, findings will be presented demonstrating that adolescents are more likely to gamble in conditions of ambiguity compared to risk, suggesting this age group are more tolerant of variable outcomes when environmental information is limited. Next, evidence will be presented from a study examining adolescents’ performance in a decision-making task adapted from ecology: patch foraging. This paradigm measures the opportunity cost of choosing whether to exploit or explore a resource. Exploiting gradually yields fewer rewards over time, whilst exploration involves finding a new resource with a fresh distribution of rewards. Using computational and statistical modelling, it was found that adolescents’ predisposition for exploration led to more optimal outcomes, as they accrued greater points throughout the task compared to adult participants. Based on these findings, it will be suggested that risk-taking in adolescence is normative and accounts that characterise these behaviours as maladaptive are only applicable to a subsample of this population.

 

29 January 2020 | Astrid Kause | Leeds University Business School

A decision sciences approach for studying communications about uncertain climate projections to non-expert audiences

Policy makers and practitioners face decisions about climate change adaptation, which are often complex and long-term. At the same time, they often lack a background in climate science. Bodies such as the Intergovernmental Panel on Climate Change or The Met Office UK are tasked with communicating uncertain climate projections about the future to these audiences. In this seminar, I will present outcomes from an experiment, semi-structured interviews, as well as a systematic literature review on communications of such information. This will include the challenges these audiences face when trying to understand such information, as well as a set of recommendations on how findings from cognitive sciences and psychology can inform the design of such climate information, including different types of risk and uncertainty.



05 February 2020 | Marc J Bühner | Cardiff University

Temporal Binding of Cause and Effect

Temporal Binding refers to the mutual attraction in subjective time between a cause and its effect (Haggard et al., 2002, Buehner, 2012): When people observe a causal relation unfold and have to report either the elapsed time between cause and effect, or the point in time at which either the cause or the effect occurred, judgments are distorted. Specifically, subjective time appears shorter in causal compared to non-causal intervals, and causes appear to have happened later and effects earlier (i.e. their temporal distance contracts) when compared to two-event sequences that are not causally linked.

Even more striking than Temporal Binding is the related phenomenon of Causal Re-ordering (Bechlivanidis & Lagnado, 2013, 2016), which refers to a subjective reversal of temporal order in line with causal beliefs: When viewing a variation of Michotte-style collisions where object A moves towards object B, then object C moves, followed by B (ACB), people tend to erroneously report having seen ABC.  This effect cannot be reduced to post-perceptual response bias or lapses of attention and manifests as online shifts in points of subjective simultaneity (PSS).

Both effects suggest that the relationship between experienced time and causality is bi-directional: Not only does temporal evidence inform causal inference, but causal knowledge in turn impacts our experience of time.  In this talk I will present a series of studies examining the underpinning of these effects and will explore their implications of theories of time perception and our sense of agency.

 

12 February 2020 | Edmund Hunt | University of Bristol

The Bayesian Superorganism: understanding social insect collective decision-making via Bayesian statistics

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to their abundance, as they explore and exploit their environment collectively as if they were a single organism. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site. House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: essentially, we propose that they estimate the probability each choice is best, and then choose the highest probability. Although Bayesian models of decision-making have been developed for individual animals, or even social groups like fish schools, their ‘selfish’ behaviour prevents a truly group-level strategy from being enacted. The close-knit nature of social insect societies enables us to propose that their individual-level ‘behavioural algorithms’ can be understood – at a macroscopic level – in terms of statistical methods that have only recently been developed in mathematics. Our model of their nest finding incorporates insights from approximate Bayesian computation, as a model of collective estimation of alternative choices; and Thompson sampling, as an effective regret-minimising decision-making rule by viewing nest choice in terms of a multi-armed bandit problem. Coordination mechanisms like stigmergy – modifying the environment with e.g. pheromone markers – facilitate this process. Our Bayesian framework allows us to understand and quantify the effectiveness of individual and collective movement behaviours in informational terms. It points to the potential for further bio-inspired statistical techniques. Finally, it suggests simple, decentralised mechanisms for collective decision-making in engineered systems, such as robot swarms.

 

19 February 2020 | READING WEEK - no seminar

 

26 February 2020 | Maarten Speekenbrink | UCL

The role of uncertainty in exploration and learning

We often need to choose between alternative courses of action whilst we are uncertain about their outcomes. Examples include choosing which route to take to work and which song to play next at a party. In such decision problems, we don't precisely know what will occur when we make a choice, nor what would have occured if we chose differently. Normatively, uncertainty has two characteristic facets: it modulates the amount of learning after choice, and before choice the possibility of learning should increase the attractiveness of options which are otherwise not the most promising. In this talk, I will discuss our recent research investigating whether these are also descriptive facets of human learning and exploration.

 

4 March 2020 | Irene Scopelliti | City, University of London

Can Training Improve Decision Making?

Biases in judgment and decision making affect experts and novices alike, yet there is considerable variation in individual decision-making ability. To the extent that this variance reflects malleable differences, training could be an effective way to debias and improve human reasoning. I discuss the results of a program of research that includes laboratory, longitudinal, and field experiments in which one-shot debiasing training interventions are shown to substantively improve decision making. In these studies, different training interventions, including simple scripts, instructional videos, observation, and serious games, produced significant effects on measures of cognitive bias critical to intelligence analysis (i.e., anchoring, bias blind spot, confirmation bias, correspondence bias, representativeness, and social projection), on confirmatory hypothesis testing in a complex business decision, and on the frequency and quality of advice taking. The debiasing effects of training transferred across problems in different contexts and formats. These results provide new encouraging evidence that training can be an effective and scalable debiasing intervention to improve decision making.

 

11 March 2020 | Kathryn Francis | University of Bradford

Does mode of presentation influence moral decision-making? Investigating moral responses in virtual reality, audio-visual, and text-based dilemmas

Moral psychologists have investigated moral decision-making using hypothetical vignettes adopted from philosophy. Typically, these trolley-type problems are presented via text and participants are asked whether the action described in the scenario is morally appropriate. To examine what individuals might actually do in these up-close and personal moral dilemmas, we’ve incorporated Virtual Reality (VR) simulations of trolley-type problems and examined the influence of audio- visual and haptic features on moral responses. Across several studies, we find that utilitarian decision-making (sacrificing one person in order to save many more) is higher in VR moral dilemmas compared to text-based dilemmas (e.g., Francis et al., 2016; 2017; Patil et al., 2017). To develop a clearer picture of how these modes of presentation influence moral decision-making, we examine responses to trolley-type problems that are presented in different formats. We find that moral responses in text-based dilemmas do not differ to decisions in simple visual dilemmas (Experiment 1), complex visual dilemmas with audio (Experiment 2) or to 2D video sequences (Experiment 3). These findings might suggest that features specific to VR prompt differences in moral responses or that VR enables us to measure the construct of moral action as opposed to moral judgment.

 

18 March 2020 | Tim Mullett | Warwick Business School

Clairvoyant assumptions and omitted variable bias in attention based Drift Diffusion Models

Drift Diffusion Models have proved highly successful at predicting multiple properties of choice, such as choice proportions, reaction time distributions, fast errors, etc. Many such models include assumptions about attention. Some more explicitly than others. We show, across a range of eye tracking experiments and paradigms, that the most common mechanism/assumption by which attention is incorporated into such models is largely a false positive: The common approach for analysing and fitting such models includes an omitted variable bias. We show that when this variable is included – a main effect of attention bias – the results are instead better explained by a mere exposure mechanism. In more complex choice, we show that these models include assumptions that rely upon subjects having knowledge of information before it has been attended. When this assumption is removed, the models perform poorly.

 

25 March 2020 | Magda Osman | Queen Mary University

Guilting groups through nudge tactics (social comparisons) to behave cooperatively. Does it work?

Economic cooperation to tackle ongoing problems such as climate change requires social cooperation. This means going beyond spending effort for one’s own gain to spending effort for a collective goal (e.g. reducing carbon emissions through sourcing and implementing alternatives to fossil fuel). Psychology tells us that people look to others as a way to regulate how much effort to put into doing something, so much so, that simply knowing what others are doing can be a means of influencing one’s own effort exertion. For example, UK-listed companies are required to disclose their greenhouse gas emissions and account publicly for their contributions to climate change (2018). So, based on this, if social mechanisms are exploited (e.g., social comparison manipulation) in an adapted public goods game, can they reliably increase cooperation? In our version people spend effort (squeezing a handgrip device) for money that goes into a public pot, or to their personal pot. The experiment is conducted over two phases. Participants are required to return to repeat the main effort task after discussing with their group the feedback the have all received (either intended levels of cooperation [distribution of trials to the group pot], or actual cooperation [trials that entered into the group pot]). This project is funded by the Social Macroeconomic Hub of the Economic and Social Research Council’s (ESRC) Rebuilding Macroeconomics network.