Cognitive and neural mechanisms underlying post-decision processing

Contested issues, such as climate change, can generate polarised and rigid views. A prominent source of entrenched beliefs is confirmation bias, where evidence against one’s position is selectively disregarded. Although an extensive literature has documented this altered processing of new information, the underlying cognitive, computational and neuronal mechanisms remain unknown. In this thesis, I explore the mechanisms underlying this altered processing of new information, its relation to broader societal attitudes, and finally I test an intervention to alleviate this cognitive bias. In a first set of studies, I combined human magnetoencephalography (MEG) with behavioural and neural modelling to identify the drivers of altered post-decision evidence integration. I show that high confidence in an initial decision leads to a striking modulation of post-decision neural processing, such that integration of confirmatory evidence is amplified while disconfirmatory evidence processing is abolished. This indicates that confidence shapes a selective neural gating for choice-consistent information, reducing the likelihood of changes of mind. Confirmation bias has received most attention for its potential contribution to societal polarization and entrenchment. Therefore, in a second set of studies, I tested whether cognitive alterations in post-decision evidence integration are related to broader societal attitudes, such as dogmatic and rigid political beliefs. I found that dogmatic participants showed a reduced sensitivity for disconfirming post-decision evidence (i.e. a stronger confirmation bias) and a reduced tendency to actively seek out corrective information. In a final study, I tested a metacognitive training procedure as a potential intervention to counteract confirmation bias. This training improved participants’ metacognitive ability and through this boosted their processing of post-decision evidence, both on a behavioural and neural level. These studies provide a novel mechanistic understanding of confirmation bias, exemplify the potential societal implications of altered post-decision processing and enabled an evidence-based intervention to counteract this cognitive bias

An Ecological and Longitudinal Perspective

Von der Entscheidung für ein Spiel bis zur Wahl einer Taktik, um die Schlafenszeit hinauszuzögern - wiederholte Entscheidungen sind für Kinder allgegenwärtig. Zwei paradigmatische Entscheidungsphänomene sind probability matching (dt. Angleichen der Wahrscheinlichkeit) und Maximieren. Um Belohnungen zu maximieren, sollte eine Person ausschließlich die Option auswählen, welche die höchste Wahrscheinlichkeit hat. Maximieren wird allgemein al ökonomisch rationales Verhalten angesehen. Probability matching beschreibt, dass eine Person jede Option mit der Wahrscheinlichkeit auswählt, wie deren zugrunde liegende Wahrscheinlichkeit einer Belohnung ist. Ob es sich bei probability matching um einen Fehlschluss oder einen adaptiven Mechanismus handelt, ist umstritten. Frühere Forschung zu probabilistischem Lernen zeigte das paradoxe Ergebnis, dass jüngere Kinder eher maximieren als ältere Kinder. Von älteren Kindern nimmt man hingegen an, dass sie probability matchen. Dabei wurde jedoch kaum berücksichtigt, dass Kinder die Struktur der Umwelt zu ihrem Vorteil nutzen können. Diese Dissertation untersucht die inter- und intraindividuelle Entwicklung des probabilistischen Lernens in der Kindheit unter ökologischen und kognitiven Aspekten. Vier empirischen Kapitel zeigen, dass die Interaktion zwischen heranreifenden kognitiven Funktionen, sowie Merkmalen der Lern- und Entscheidungsumgebung die Entwicklung des adaptiven Entscheidungsverhaltens prägt. Die Entwicklung des probabilistischen Lernens durchläuft in der Kindheit mehrere Phasen: von hoher Persistenz, aber auch hoher interindividueller Variabilität bei jüngeren Kindern zu wachsender Anpassungsfähigkeit durch zunehmende Diversifizierung und Exploration bei älteren Kindern. Die Ergebnisse dieser Dissertation unterstreichen insbesondere den Nutzen einer ökologischen Rationalitätsperspektive bei der Erforschung der Entwicklung des Entscheidungsvermögens.From choosing which game to play to deciding how to effectively delay bedtime—making repeated choices is a ubiquitous part of childhood. Two often contrasted paradigmatic choice behaviors are probability matching and maximizing. Maximizing, described as consistently choosing the option with the highest reward probability, has traditionally been considered economically rational. Probability matching, in contrast, described by proportionately matching choices to underlying reward probabilities, is debated whether it reflects a mistake or an adaptive mechanism. Previous research on the development of probability learning and repeated choice revealed considerable change across childhood and reported the paradoxical finding that younger children are more likely to maximize—outperforming older children who are thought to be more likely to probability match. However, this line of research largely disregarded the mind’s ability to capitalize on the structure of the environment. In this dissertation, I investigate the inter- and intra-individual development of probability learning and repeated choice behavior in childhood under consideration of ecological, cognitive, and methodological aspects. Four empirical chapters demonstrate that the interaction between the maturing mind and characteristics of the learning and choice environment shapes the development of adaptive choice behavior. The development of probability learning and repeated choice behavior in childhood progresses from high persistence but also high inter-individual variability to emerging adaptivity marked by increased diversification and exploration. The present research highlights the benefit of taking an ecological rationality view in research on the development of decision making abilities

Interactive animated visualizations of probabilistic models

Bayesian probabilistic models’ structure (determined by the mathematical relations of the model’s variables) and outputs (i.e., the posterior distributions inferred through Bayesian inference) are complex and difficult to grasp and interprete without specialized knowledge. Various visualizations of probabilistic models exist but it is very little known about whether and how they support users’ comprehension of the models. The aim of this thesis is to investigate whether adding interaction or animation to visual representations of probabilistic models help people better understand the structure of models and interprete the (causal and non-causal) relations of the variables. This research presents a generic pipeline to transform a probabilistic model expressed in a Probabilistic Programming Language (PPL) and associated inference results into a standardized format which can then be automatically translated into an interactive probabilistic models explorer (IPME). IPME provides at-a-glance communication of a model’s structure and uncertainty, and allows interactive exploration of the multi-dimensional prior or posterior MCMC sample space. A collapsible tree-like structure represents the structure of the model in IPME. Each variable is represented by a node that presents graphically the prior or posterior distribution of the variable. Slicing on indexing dimensions or forming conjunctive restrictions on variables by interacting with the distribution visualizations is supported. Each user interaction with the explorer triggers the reestimation and visualization of the model’s uncertainty. This closed-loop exchange of responses between the user and the explorer allows the user to gain a more intuitive comprehension of the model. IPME was designed to enhance informativeness, transparency and explainability and ultimately, the potential of increasing trust in models. This research investigates also whether adding interactive conditioning to classical scatter plot matrices that present samples from the prior distribution of probabilistic models helps users better understand the models, and if there are levels of structural detail and model designs for which it is beneficial. A user study was conducted. The analysis of the collected data showed that interactive conditioning is beneficial in cases of sophisticated model designs and the difference in response time between the interaction and static group becomes less important in higher levels of structural detail. Participants using interactive conditioning were more confident about their responses overall with the effect being stronger in tasks of lower level of detail. This research proposes a pipeline to generate simulated probabilistic data from interven tions applied on causal structures that are expressed in PPLs using probabilistic modeling and Bayesian inference. An automatic visualization tool for visualizing the simulated probabilistic data generated by this pipeline was developed. A user study to evaluate the proposed tool was conducted. How effectively and efficiently people identify the causal model of the presented data and make decisions on interventional experiments when the uncertainty in the simulated data of interventions was presented using static, animated, or interactive visualizations was investigated. The findings suggested that participants were able to identify the causal model of the presented data either given a single intervention or by exploring various interventions. Their performance in identifying sufficient interventions was poor. Participants did not rely on the sufficient interventions to identify the causal model in the case of multi-interventional tasks. They might have relied more on combining information from multiple interventions to draw their conclusions. There were three different visual exploration strategies of the information in the scatter plot matrices which participants followed; roughly 1/3 of them relied on both the scatter and KDE plots, another 1/3 of them relied more on the scatter plots, and the last 1/3 of them relied more on the KDE plots. Those who followed the last strategy had a better performance in identifying the causal model given a specific intervention. Most participants judged the design of the visualization positively with many having mentioned that “it was informative”

Approaches for the digital profiling of activities and their applications in design information push

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Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting valuable knowledge. Explainable AI (XAI) research aims to increase the transparency of DNN model behaviour to improve interpretability. Feature importance explanations are the most popular interpretability approaches. They show the importance of each input feature (e.g., pixel, patch, word vector) to the model’s prediction. However, we hypothesise that feature importance explanations have two main shortcomings concerning their inability to describe the complexity of a DNN behaviour with sufficient (1) fidelity and (2) richness. Fidelity and richness are essential because different tasks, users, and data types require specific levels of trust and understanding. The goal of this thesis is to showcase the shortcomings of feature importance explanations and to develop explanation techniques that describe the DNN behaviour with greater richness. We design an adversarial explanation attack to highlight the infidelity and inadequacy of feature importance explanations. Our attack modifies the parameters of a pre-trained model. It uses fairness as a proxy measure for the fidelity of an explanation method to demonstrate that the apparent importance of a feature does not reveal anything reliable about the fairness of a model. Hence, regulators or auditors should not rely on feature importance explanations to measure or enforce standards of fairness. As one solution, we formulate five different levels of the semantic richness of explanations to evaluate explanations and propose two function decomposition frameworks (DGINN and CME) to extract explanations from DNNs at a semantically higher level than feature importance explanations. Concept-based approaches provide explanations in terms of atomic human-understandable units (e.g., wheel or door) rather than individual raw features (e.g., pixels or characters). Our function decomposition frameworks can extract specific class representations from 5% of the network parameters and concept representations with an average-per-concept F1 score of 86%. Finally, the CME framework makes it possible to compare concept-based explanations, contributing to the scientific rigour of evaluating interpretability methods.The author would like to appreciate the generous sponsorship of the Engineering and Physical Sciences Research Council (EPSRC), The Department of Computer Science and Technology at the University of Cambridge, and Tenyks, Inc