Poster Presentation Abstracts

Poster Session: June 21, 12.50-14.50

Andriana Theodoropoulou, University of Essex

The effect of meta-cognitive fluency on cognitive reflection using Numerical and Verbal CRT

The Cognitive Reflection Test (CRT) measures the ability to suppress an initial intuitive incorrect answer and engage in cognitive reflection. Previous studies found that manipulating meta-cognitive fluency affected performance on the CRT by activating analytical processes involved in cognitive reflection. However, efforts to replicate these findings failed. This might be due to the numerical nature of the CRT, which confounds reflection ability with numerical ability. In a mixed-subjects design (n =201), we tested the effect of meta-cognitive fluency on cognitive reflection using a 7-item Numerical CRT and a 10-item Verbal CRT, which is free of the numeracy confound, followed by a measure of numerical ability. We found no evidence that disfluency affects performance on either the Numerical or the Verbal CRT through the activation of deeper forms of reasoning involved in cognitive reflection. We also found no evidence that numeracy is masking the effect of fluency on the Numerical CRT. We propose that this might be due to the CRT not actually requiring the activation of analytical thinking or due to other variables, apart from numeracy, masking the effect of fluency. We recommend further testing of these accounts by including a wider array of measures and a positive control condition. 

Monika Pompeo, University of Nottingham

Should I Stay or Should I Go? Self-Selection and Participation in Lab Experiments

Laboratory experiments are often used to study individual’s social and risk preferences and, more in general, to examine human behaviour. However, it remains a source of debate whether the results obtained in the lab, which often employ student samples, can be generalised to other groups of decision makers. In this work, I investigate whether there is self-selection into lab experiments and if it can be accounted for by differences in terms of preferences and socio-demographic information.  To do this, I employ a series of validated survey measures of risk aversion, discounting, trust, altruism, positive reciprocity, negative reciprocity and self-control. I then invite the subject who participate in the survey to an unrelated lab experiment. I compare the characteristics of those who sign-up to a lab experiment and participate, to those who sign up but don’t participate and to those who do not sign up at all. This study  contributes to the growing literature on the external validity of lab results. In particular, to the part which focuses on selection into experiments.

Milan Rybar, University of Essex

Towards intuitive brain-computer interface for communicating concepts

A brain-computer interface (BCI) provides an alternative pathway between a human brain and external devices. BCIs based on identifying activity in the brain related to semantic concepts have the potential to be highly intuitive and allow greater levels of accuracy and communication speed than possible with current BCIs. It would aid communication for people who experience difficulties communication via other means, for instance, patients with locked-in syndrome. It could also be used for healthy people in gaming and entertainment industry.

We are running an exploratory study to discriminate between concepts of animals and tools in four tasks using the joint recording of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). In the experiment, participants are shown images of animals and tools followed by four tasks: silent naming task (participants name the presented object in their minds) and three sensory-based tasks using visual, auditory, and tactile imagery perception (participants visualize / imagine sounds made by / imagine feel of the presented object). We will apply machine learning techniques to discriminate animals and tools from the simultaneous recording of EEG and fNIRS.

Jareth Wolfe, University of Essex

Using Explainable AI to Understand Gene Regulation

Enhancers are a regulatory element of DNA that control where and when genes are transcribed (activated) or repressed. They act as a platform for regulatory proteins to bind to increase the transcription of a target gene. The majority of mutations that cause genetic diseases are not located in genes, but in enhancers. The development of some cancers are due to disruptions in gene regulation, so understanding how enhancers function is crucial to understanding these conditions.

There are two key difficulties in studying enhancers: enhancers do not need to be located near the gene they target, and the mechanisms that cause these regions to be enhancers are not well understood.

Previous attempts to locate enhancers used machine learning methods to identify potential enhancers based on certain genetic markers. While these methods are capable of locating enhancers, they do not provide additional insight into the underlying mechanisms that govern them.

This project aims to apply a rules-based explainable AI approach to identifying enhancers. A series of natural language IF-THEN rules are generated to predict the location of enhancer regions. Then, the content of these rules can be studied to understand the underlying reasons why the model made the predictions that it did.

Elisabetta Leni, University of Essex

Affects, belief management, and choices

Affects are general feelings of pleasure or displeasure accompanied by arousing or quieting bodily activation. As neuroscience and cognitive psychology data suggest, affects are relevant for decision-making even when unrelated to the judgement at hand (i.e., they are incidental). Few researchers in economics have studied the influence of incidental affects on decision-making. Some studies found an impact on altruism, trust, generosity and performance. A fundamental question – not yet addressed in the literature – is whether incidental affects also perturb the way people manage their beliefs. This research explores this crucial yet unexplored idea using a lab experiment where participants receive an exogenous shock to their affective state after they enter the lab. Then, I measure how the shock influences choices and beliefs in a dictator game and in an effort task. I collected data for 313 participants. The analysis reveals a significant effect of incidental affects on choices and beliefs. Moreover, the data show that people with different ability to recognise their internal emotional states – as measured by the Trait Emotional Intelligence (TEI) (Petrides, 2009) – react differently to the exogenous shock in affects.

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