Futurising science education: students' experiences from a course on futures thinking and quantum computing

To promote students' value-based agency, responsible science and sustainability, science education must address how students think about their personal and collective futures. However, research has shown that young people find it difficult to fully relate to the future and its possibilities, and few studies have focused on the potential of science education to foster futures thinking and agency. We report on a project that further explored this potential by developing future-oriented science courses drawing on the field of futures studies. Phenomenographic analysis was used on interview data to see what changes upper-secondary school students saw in their futures perceptions and agentic orientations after attending a course which adapted futures thinking skills in the context of quantum computing and technological approaches to global problems. The results show students perceiving the future and technological development as more positive but also more unpredictable, seeing their possibilities for agency as clearer and more promising (especially by identifying with their peers or aspired career paths), and feeling a deeper connection to the otherwise vague idea of futures. Students also felt they had learned to question deterministic thinking and to think more creatively about their own lives as well as technological and non-technological solutions to global problems. Both quantum physics and futures thinking opened new perspectives on uncertainty and probabilistic thinking. Our results provide further validation for a future-oriented approach to science education, and highlight essential synergies between futures thinking skills, agency, and authentic socio-scientific issues in developing science education for the current age.Peer reviewe

Opinions and Preferences as Socially Distributed Attitudes

The dissertation focuses on how to best represent the consensus and attitude dynamic of a group given the attitudes of its individuals. This is done in the Bayesian epistemology framework using pooling with imprecise probabilities, and in utility theory extending Harsanyi's aggregation theorem to characterize other directed attitudes like spite and altruism. The final part of the dissertation considers attitudes within social networks and provides explanations and simulation models for online segregation and tribalism as well as the spread of rumors through contagion. The dissertation hopes to contribute to foundational issues like that of epistemic consensus, but also to new emerging phenomena in social epistemology

Is the mind Bayesian? The case for agnosticism

This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: An epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that the adoption of this framework has two kinds of implications. The first one regards the methodology of the experimental study of probability judgment. The Bayesian framework creates pragmatic constraints on the methodology that are linked to the interpretation of, and the belief in, the information presented, or referred to, by an experimenter in order for it to be the basis of a probability judgment by individual participants. It is shown that these constraints have not been satisfied in the past, and the question of whether they can be satisfied in principle is raised and answered negatively. The second kind of implications consists of two limitations in the scope of the Bayesian model. They regard (i) the background of revision (the Bayesian model considers only revising situations but not updating situations), and (ii) the notorious case of the null priors. In both cases Lewis' rule is an appropriate alternative to Bayes' rule, but its use faces the same operational difficulties

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Generalized belief change with imprecise probabilities and graphical models

We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under extended conditions of uncertainty, inconsistency and imprecision. We motivate our kinematical approach by specializing our discussion to probabilistic reasoning with graphical models, whose modular representation allows for efficient inference. Most results in this direction are derived from the relevant work of Chan and Darwiche (2005), that first proved the inter-reducibility of virtual and probabilistic evidence. Such forms of information, deeply distinct in their meaning, are extended to the conditional and imprecise frameworks, allowing further generalizations, e.g. to experts' qualitative assessments. Belief aggregation and iterated revision of a rational agent's belief are also explored

Towards autonomous diagnostic systems with medical imaging

Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities. This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed. Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way. In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones. By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos. All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces

The cognitive basis of paranormal, superstitious, magical and supernatural beliefs : The roles of core knowledge, intuitive and reflective thinking, and cognitive inhibition

This series of studies addresses the question of why some people believe in phenomena such as horoscopes, telepathy, and omens, while others find them utterly unbelievable. The cognitive factors that explain belief in paranormal, superstitious, magical, and supernatural (PSMS) phenomena have been studied from a variety of perspectives, but consistent support for the theories and a deeper understanding of the nature of these beliefs have been missing. The present thesis argues that groundbreaking findings are unlikely as long as explanations are sought in domain-general cognitive deficits or in other domain-general factors. In the present thesis, a review of definitions and assessment methods of PSMS beliefs found that these beliefs can best be encompassed and distinguished from other unfounded beliefs by defining them as core knowledge confusions in which the basic attributes of mental phenomena, material objects, living, and animate organisms, and the processes these engage in, are applied outside their proper domains. Four empirical studies tested predictions derived from this definition and from dual-process theories of thinking. In support of the predictions, accepting core knowledge confusion statements was related to both traditional PSMS beliefs, such as beliefs in extra-sensory perception and witches, as well as to PSMS beliefs that are not typically included in assessments, such as the belief that random events happen for a purpose. Ontologically confused conceptions of energy were discovered to be present even in upper secondary school students and they were found seemingly resistant to an instructional intervention. In line with the notion that the basis of PSMS beliefs lies in biases in intuitive processing, the beliefs and core knowledge confusions were more common among those people who had an intuitive thinking style, and asking people to respond quickly increased their acceptance of the confusions. Given that theoretical arguments and previous findings indicate that analytical thinking restrains intuitive biases, it is surprising that previous studies have shown inconsistent findings regarding the relation of an analytical thinking style to PSMS beliefs. The present studies showed that such a relationship can indeed be found if the style is conceptualized as a striving for reflective thinking and measured accordingly. Finally, behavioral and brain imaging evidence converged to indicate that skepticism was related to stronger cognitive inhibition. By focusing on the interplay of intuitive and reflective processes and cognitive inhibition, the present approach makes it possible to better understand individual differences in the beliefs.Tässä tutkimussarjassa tutkittiin, miksi jotkut ihmiset uskovat esimerkiksi astrologiaan, henkiin ja enteisiin toisten pitäessä niitä täysin epäuskottavina. Paranormaaleja, taikauskoisia, maagisia ja yliluonnollisia (PTMY-) uskomuksia selittäviä ajatteluun liittyviä tekijöitä on tutkittu useasta näkökulmasta, mutta näitä uskomuksia koskevia teorioita ei ole kyetty todentamaan, eikä uskomusten syvempää olemusta ole kyetty selittämään. Väitöskirjassa ehdotetaan, ettei läpimurto ole todennäköinen niin kauan kuin selityksiä etsitään aihepiiristä riippumattomista ajattelun heikkouksista tai muista yleisistä tekijöistä. Ensimmäinen osatyö on katsaus PTMY-uskomusten määritelmiin ja arviointimenetelmiin. Katsauksen perusteella kaikki nämä uskomukset pystytään parhaiten kattamaan ja samalla erottamaan muista heikosti perustelluista uskomuksista määrittelemällä ne sekaannuksiksi, joissa psyykkisten ilmiöiden, aineellisten kappaleiden, elollisten ja ajattelevien olentojen sekä näitä koskevien prosessien ydinominaisuudet ulotetaan asianmukaisten kategorioidensa ulkopuolelle. Tästä määritelmästä sekä tiedon kaksoisprosessointiteorioista johdettuja hypoteeseja testattiin neljässä empiirisessä osatutkimuksessa. Tulosten mukaan sekaannusta sisältävien ydintietoväittämien hyväksyminen oli yhteydessä sekä perinteisiin paranormaaleihin uskomuksiin (kuten telepatiaan ja noitiin) että sellaisiin PTMY-uskomuksiin, jotka eivät yleensä ole sisältyneet uskomusten arviointimenetelmiin (kuten uskoon satunnaisten tapahtumien tarkoituksellisuudesta). Energiaan liittyviä ydintiedon sekaannuksia tutkittiin myös lukiolaisilla. Vaikutti siltä, ettei sekaannuksiin ole helppoa vaikuttaa opetuksen keinoin. PTMY-uskomukset ja ydintiedon sekaannukset olivat yleisempiä niillä, joilla on intuitiivisempi ajattelutyyli. Myös vastausajan rajoittaminen lisäsi ydintietoväittämien hyväksymistä. Tulokset tukevat ajatusta, että uskomukset kumpuavat intuitiivisen ajattelun vinoumista. Koska sekä teoria että aiempi tutkimus puoltavat käsitystä, että analyyttinen ajattelu hillitsee intuitiivisia vinoumia, on yllättävää, ettei analyyttinen ajattelutyyli ole aiemmissa tutkimuksissa ollut johdonmukaisesti yhteydessä PTMY-uskomuksiin. Väitöskirjassa osoitettiin, että yhteys löytyy, jos analyyttinen tyyli käsitteellistetään pyrkimykseksi reflektiiviseen ajatteluun, ja jos sen arvioimiseen käytetään asianmukaisia menetelmiä. Viimeinen löydös oli että sekä neuropsykologinen testi että aivokuvantamistulokset tukivat olettamusta skeptisyyden lisääntymisestä vahvan kognitiivisen inhibition myötä. Tällainen lähestymistapa, jossa tutkitaan intuitiivisten ja reflektiivisten prosessien sekä kognitiivisen inhibition välisiä suhteita, antaa aiempaa paremmat lähtökohdat ymmärtää yksilöllisiä eroja PTMY-uskomuksissa

A computational neuroscience perspective on subjective wellbeing within the active inference framework

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions to health and productivity. To date, the conceptualization of SWB has been grounded within social psychology and largely focused on self-report measures. In this paper, we explore the potentially complementary tools and theoretical perspectives offered by computational neuroscience, with a focus on the active inference (AI) framework. This framework is motivated by the fact that the brain does not have direct access to the world; to select actions, it must instead infer the most likely external causes of the sensory input it receives from both the body and the external world. Because sensory input is always consistent with multiple interpretations, the brain’s internal model must use background knowledge, in the form of prior expectations, to make a “best guess” about the situation it is in and how it will change by taking one action or another. This best guess arises by minimizing an error signal representing the deviation between predicted and observed sensations given a chosen action—quantified mathematically by a variable called free energy (FE). Crucially, recent proposals have illustrated how emotional experience may emerge within AI as a natural consequence of the brain keeping track of the success of its model in selecting actions to minimize FE. In this paper, we draw on the concepts and mathematics in AI to highlight how different computational strategies can be used to minimize FE—some more successfully than others. This affords a characterization of how diverse individuals may adopt unique strategies for achieving high SWB. It also highlights novel ways in which SWB could be effectively improved. These considerations lead us to propose a novel computational framework for understanding SWB. We highlight several parameters in these models that could explain individual and cultural differences in SWB, and how they might inspire novel interventions. We conclude by proposing a line of future empirical research based on computational modelling that could complement current approaches to the study of wellbeing and its improvement