Aesthetic Pleasure, Computational Models and Brain Activation. The mechanisms behind aesthetic appeal

Aesthetic appeal is a peculiarity of human behaviour that involves the integration of sensory information, individual experiences, cultural factors, emotional responses, and context, leading to unique and varied evaluation. Till today there is no consensus of what the main influence of aesthetic appeal is and if aesthetic value is truly a unique form of sensory valuation that is encoded separately in the brain from other forms of value, for example monetary value. In this thesis, we design several experiments to investigate these two issues. In chapter one, we explore the various psychological and neuroscience theories of aesthetic appeal to reveal the current status of the field. In chapter two, we designed a behavioural experiment including 1,190 artworks and 408 participants to determine if emotional instances, subjective factors or formal perceptual features have the most influence on aesthetic appeal. In chapter three, we conducted a behavioural and a fMRI experiment to investigate if there is a behavioural or neural dissociation between aesthetic value compared to incentive salience. Incentive salience was manipulated with a monetary reinforcement paradigm. The results, indicate that aesthetic value is primarily determined by participant-specific influences, but formal perceptual features have a significant but small effect. Also, aesthetic value can be dissociated from other forms of value (incentive salience due to monetary reinforcement) both at a neural and behavioural level. Even though incentive salience had effects on aesthetic value these effects were not robustly observed across experiments. Lastly, the brain region of the anterior medial prefrontal cortex is a potential candidate for context specific value encoding while the ventro medial prefrontal cortex is candidate for context general value encoding. Altogether, the evidence suggests that aesthetic value is not simply a conditioned response, instead aesthetic value is dependent on the insight we gain towards a stimuli based on highly individual experiences

Sensory Disconnection and Dreaming: The Functional and Phenomenological Impact of Sensory Stimulation During Sleep

Sleep is often perceived as a state of disconnection from the environment. Yet, accumulating evidence suggests that the brain can monitor and process external stimuli even while asleep. The accompanying subjective experiences, commonly referred to as dreams, are also thought to be influenced by sensory perceptions. However, the precise mechanisms through which sensory stimulation affects dreaming activity remain largely unknown. This work seeks to address this gap through a comprehensive, multi-faceted approach. It begins with a systematic review of the existing literature on the influence of sensory stimulation on dreams, uncovering key findings and identifying current limitations in the field. Following this, an experimental study investigates the use of multimodal sensory stimulation to enhance dream lucidity during REM sleep, highlighting the potential of sensory-based protocols for facilitating real-time communication with dreamers and objectively exploring perceptual awareness during sleep. Finally, the relationship between multimodal stimulation during NREM sleep and EEG aperiodic activity is empirically explored, indicating that aperiodic spectral slopes may serve as informative markers of subjective sleep experiences. By integrating theoretical, experimental, and analytical perspectives, this work aims to deepen the understanding of how external stimuli influence consciousness during sleep. The findings contribute to the growing body of knowledge on the dynamic interplay between the sleeping brain and sensory stimulation, offering valuable insights into how these interactions shape our dreams

Policy evaluation and machine learning in international economics

This thesis explores innovative empirical models in interna- tional economics, leveraging machine learning techniques and a dose-response method to address issues of multidimension- ality, heterogeneity, and nonlinearity, while exploiting detailed firm- and product-level microdata. Firstly, we investigate the capacity of machine learning tech- niques to forecast the firm’s exporting status. Analyzing com- prehensive financial accounts and firm-and industry-specific data from French manufacturing firms (2010-2018), we demon- strate that machine-learning methodologies can accurately fore- cast a firm’s exporting status with up to 90% accuracy. Unlike traditional econometrics, our method handles multidimen- sional data and exploits it to model non-linear relationships among endogenous predictors, thus proving a valuable tool for targeted trade promotion programs. Next, we assess the heterogeneous impacts of the EU-Canada Comprehensive Economic and Trade Agreement (CETA) on French trade using a causal machine learning approach. Em- ploying a non-parametric matrix completion algorithm rooted in potential outcome models, we predict multidimensional counterfactuals at the firm, product, and destination levels, capturing complex interactions without assuming functional forms. Using predicted potential outcomes allows us to un- cover significant heterogeneity in the trade agreement’s ef- fects, which conventional average effects models might over- look. Furthermore, our methodology is suitable to evaluate spillover effects. Within our framework, these manifest as classical Vinerian diversion effects, wherein trade to Canada partially substitutes for trade outside Canada, especially for products with a higher elasticity of substitution. Lastly, we examine the learning-by-exporting phenomenon by isolating the effect of export intensity on firm productivity from the endogenous selection into exporting status. Using a dose-response model that treats export intensity as a contin- uous treatment affecting firm productivity, we move beyond traditional binary treatment models to provide insights into how this relationship evolves across the full spectrum of ex- port intensity values. Our findings indicate that productiv- ity gains from exporting are non-linear, with firms needing to achieve a 60% export intensity threshold to fully capitalize on knowledge spillovers and effectively compete in interna- tional markets. Overall, this research expands the frontier of empirical re- search in international economics, revealing insights into the complex dynamics of trade through innovative methodolo- gies

Sharing is caring: Investigating accountability practices in Italian autonomous state museums

Since 2014, the Italian state museum system has been modified by — among other innovations — the introduction of the “autonomous museum” (d.p.c.m.August 29th 2014, n. 171 and d.m. December 23rd 2014, with subsequent amendments). Thanks to the reform, autonomous museums are accountable for their own management and for the fulfilment of their mission, are provided with specific management bodies and internal organization structures, are able to directly manage revenues and are required to prepare yearly accounting documents. Public museums operate as hybrid institutions at the intersection of public administration, cultural management, and heritage conservation, and they must address multiple accountability demands. However, empirical evidence on how museums engage with accountability remains limited, particularly concerning non-financial reporting and stakeholder engagement. Drawing from accountability theory, this thesis investigates the accountability practices of Italian autonomous state museums, examining how these institutions navigate the tensions between financial, managerial, and cultural responsibilities. The research employs a qualitative approach, using case studies of two autonomous museums to assess their accountability practices through semi- structured interviews and document analysis. Findings reveal that while autonomous museums comply with mandatory financial reporting, voluntary disclosure remains inconsistent and underdeveloped. The mainly hierarchical approach to accountability applied by the Ministry of Culture primarily emphasizes financial oversight and bureaucratic disclosure. The findings are also analyzed considering the broader constraints and limitations imposed on state museums by the current framework of autonomy. By addressing these gaps, this research contributes to the literature on museum accountability and cultural governance. It underscores the need for an integrated and multidimensional approach to accountability that can foster a more meaningful dialogue between museums, policymakers, and the public

Brexit, Trade Disruptions, and Export Recommendations

This doctoral dissertation is structured into three chapters, each ad- dressing aspects related to policy evaluation, with a focus on events car- rying substantial implications for the economy and international trade. These events include the Brexit referendum, trade disruptions, and the development of export recommendations. The Economic Cost of a Referendum. The Case of Brexit. This paper esti- mates how GDP would have behaved in the United Kingdom after the Brexit referendum in the absence of the mentioned poll using the Syn- thetic Control Method. We contribute to the research on the effects of Brexit by quantifying the macroeconomic cost of this referendum before the actual Brexit has taken place. We find a large and significant negative effect of the Brexit referendum on the GDP of UK. This loss is increasing in time representing, in 2017 Q4, 1.71% of the observed GDP of UK. Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms. This paper investigates the impact of COVID-19 on Colombian exports, revealing a substantial de- cline in survival probabilities during 2020. On average, we find that the COVID-19 shock decreased a firm’s probability of surviving in the export market by about 20 percentage points in April 2020. Importantly, ex- porters more integrated into Global Value Chains (GVCs) and importing higher value emerged as pivotal in bolstering exporter resilience during the crisis, emphasizing the need for policies supporting varied import networks, as well as international trade facilitation. Methodologically, this research innovates by utilizing causal Machine Learning (ML) tools in scenarios where the pervasive nature of the shock hinders the identi- fication of a control group unaffected by the shock, as well as the ex-ante definition of the intensity of the shock’s exposure of each unit, making a traditional control group identification unfeasible. This approach effec- tively predicts firms’ trade and uses these predictions to reconstruct the counterfactual distribution of firms’ trade under different scenarios and to study treatment effect heterogeneity. Exports’ Survival in New Markets: A firm-level export recommendation model. This paper investigates the factors that better predict a firm’s trade status of exporters after expanding to a new destination, specif- ically whether they continue exporting after two years. Using Colom- bian customs data, I show that market-level information is crucial for understanding export survival rates, beyond traditional firm-level char- acteristics like export experience. The paper introduces a novel Machine Learning-based export market entry recommendation tool, designed at the firm-product market level. While firms in the sample did not have access to this tool, the analysis observes which firms chose new destina- tions that align with the recommendations generated by the tool. Sim- ulated back-testing indicates that firms selecting destinations consistent with the tool’s guidance would have experienced a 5 percentage point higher survival rate compared to those choosing other destinations. Ad- ditionally, product growth would have been 34 percentage points higher for products where at least one firm followed the tool’s suggested market entry, compared to those that did not. The findings suggest that incom- plete market insights may lead to sub-optimal export decisions and that exporters incur temporary trade as a way of experimentation and to re- solve incomplete market information

Stratification of first episode psychosis based on clinical and neurobiological features: from single-center studies to big data

Psychosis is a common and functionally disruptive clinical syndrome that might be present in many psychiatric, neurodevelopmental, neurologic, and medical conditions. Rather than a nosological entity, psychosis is a syndrome characterized by different symptoms and domains. Therefore, an increasing amount of pointed out the importance of recognising and treating a first episode as soon as possible. For these reasons, first episode psychosis (FEP) rapidly became a very important population of study and assessment. More than just the first symptomatic presentation of a disease, FEP often shows already some of the features of the advanced psychiatric illnesses, although to a minor extent. On the other hand, great efforts are being made in order to establish an effective intervention, given the fact that early treatment has been proved to ameliorate the course of the disease, ranging from symptoms, relapse, and number of hospitalisations to quality-of-life measures such as involvement in school or work and global functioning. Given the multifactorial nature of FEP and the different trajectories it can follow (e.g., affective vs. non-affective psychosis), the possibility of predicting future trajectories and to obtain clear and valid biomarkers is becoming of paramount importance. Prediction modelling has the potential to revolutionize medicine by predicting individual patient outcome. Early identification of those with good and poor outcomes would allow for a more personalised approach to care, matching interventions and resources to those most at need. Through a series of studies, we explored: 1) the possibility to stratify FEP patients based on neuroimaging and biological measures; 2) the possibility of use cutting edge machine learning techniques to improve classification and cluster subtypes of FEP patients; 3) the presence of autoimmune features in FEP in a multi-site study I had the opportunity to coordinate as Co-PI (namely the PHLAMES study). Specifically, in single-site studies we showed that neuroimaging and biological variables can be predictive of the course of the disease. Moreover, in large multi-site bid data analyses we presented how machine learning can improve the prediction of the disorder and help in stratify the risk, using both clinical and neuroimaging data. Finally, in the first results of PHLAMES study emerged that a subsample of FEP with autoimmune characteristics might be defined. This subsample shows some unique features in terms of neurological symptoms, cognitive deficits, and brain imaging alterations. The studies presented in this dissertation point out that it is possible to dissect the clinical and biological heterogeneity of psychosis at the beginning of its disease course, by defining meaningful groups of patients and therefore tailor personalized management. In conclusion, these data foster the research for subtypes of FEP and the definition of disease trajectories. These advances might have a great impact on patients’ lives, by defining specific subgroups or progression that benefit of tailored interventions

Dynamical systems reduction through approximate lumping techniques

Model reduction is a fundamental technique utilized across various disciplines, such as engineering, physics, and compu- tational sciences, to simplify complex mathematical models while retaining essential dynamics. This thesis introduces two novel approaches for model reduc- tion, particularly focusing on dynamical systems described by polynomial ordinary differential equations (ODEs). The pro- posed techniques aim to reduce ODE systems while providing formal error bounds for the resultant reduced models. The first approach, based on backward and forward differen- tial equivalence (BDE/FDE), partitions the set of variables in an ODE system to construct a reduced model, incorporating a tolerance parameter ε to capture perturbations in polynomial coefficients. In the second approach, we present an algorithm to transform an ODE system into a so-called differential hull. This is a construction whereby variables with structurally sim- ilar dynamics but originally different parameters may be rep- resented by the same lower and upper bounds and reduced through the backward differential equivalence. Furthermore, the thesis explores the application of these tech- niques in discovering regular equivalences on networks. An iterative scheme, called iterative ε-BDE, is introduced to com- pute regular equivalences, allowing for the analysis of roles in networks. Experimental evaluations demonstrate the effectiveness and efficiency of the proposed approaches compared to existing methods in the literature

Numerical Modeling and Optimization of Fractured Structures via Machine Learning and Topology Optimization

During the continuous development of science and technol- ogy, optimization plays a tremendous role in improving our resources without compromising the quality of performance. This thesis work investigates the application of the phase- field method for fracture (PFF) in brittle materials, focusing on the understanding of the influence of the model parame- ters, both for the isotropic and the anisotropic cases, in cap- turing the mechanical response of experimental results. For the PFF isotropic case, an experimental investigation was car- ried out on an ABS co-polymers. A MATLAB-based algo- rithm combining particle swarm optimization (PSO) with PFF has been utilized to determine optimal values of Young’s mod- ulus (E), fracture toughness (Gc), and the PFF internal length scale (lc) through uni-axial tensile and three-point bending tests. To understand the potential of bio-polymers in vari- ous industrial applications, 3D printed PLA materials were fabricated via fusion deposition modeling, and due to their anisotropic behavior, an anisotropic PFF approach was ex- ploited. A metaheuristic machine learning algorithm coupled with PFF demonstrates robustness in estimating fracture pa- rameters (Gc, lc, β) and a strong influence of β the penalty parameter on the predicted force-displacement curves. The thesis examine also the critical issue of delamination at internal interfaces/adhesive joints and internal cracks in com- posite and multi-material components, which can lead to catas- trophic failures. Existing structural topology optimization (TO) methods typically assumes perfect bonding, which urges the development of approaches that explicitly optimize struc-tures against delamination. The proposed data-driven heuris- tic optimization strategy has been applied to identify optimal cohesive interface properties with linear grading, enhancing the composite structure’s resistance to peeling. Additionally, it explored the application of the Solid Isotropic Material with Penalty (SIMP) topology optimization approach to optimize substrate internal structures affected by interface delamina- tion. The integration of a phase-field for fracture (PFF) approach with TO has been highlighted as a robust mathematical frame- work to mitigate crack progression in structures compromised by initial damage under operational loads. Employing the SIMP technique and optimality criteria (OC) method, the re- search validated its effectiveness through numerical exam- ples, demonstrating potential improvements in fracture re- sistance for damaged structures crucial in aerospace, marine, automotive, and civil engineering industries

An Essay on the Emergence of Non-pathological Aggressive-like Behaviors in the Context of Social Interactions

Aggressivity is a type of widespread behavior in our society, yet its outcomes are everything but desirable. If evolutionary, being aggressive might have given some clear advantages to one to prevail (e.g., seizing resources, better mating options); nowadays, aggressive behavior held none of those advan- tages, being a form of prevarication where the ultimate goal is to harm another individual, either physically, emotionally, morally or materially. Why, then, is aggressive behavior still persistent despite the rise of cooperative societies? In this doctoral Thesis, with the aid of three controlled experiments and the expertise of Behavioral Economics and Neuroscience, I aim to shed more light on non-pathological aggressiveness, its genetic underpinnings, and cognitive mechanisms. Specif- ically, we found that some genetic variants of dopamine and serotonin are highly connected with actions and beliefs re- garding cooperation and punishment, where having a par- ticular variant makes one more prone to act and think pes- simistically toward the behaviors of others or to free-ride more. In another experiment, we demonstrate that extreme exertion of self-control makes it more probable to behave aggressively in a subsequent social situation. Frontal areas dedicated to impulse control regulation are, in fact, extremely vulnerable to functional fatigue, showing signs of local sleep. In this neu- ronal phenomenon, groups of neurons fire at frequencies typ- ical of sleep states instead of the ones of wake. Our exper- iment associated the prolonged exertion of self-control with the emergence of delta waves in frontal areas dedicated to impulse and emotion regulation and subsequent aggressive choices in a series of proxied social situations

A RIGHT TO DESTROY? THE LEGAL BOUNDARIES OF CULTURAL MEMORY. An Examination of the Role of the International Community in the Protection of National Heritage Sites against Deliberate Destruction.

The dissertation explores the deliberate destruction of cultural heritage under international law. The main thread of theresearch concerns the legal boundaries of cultural memory by examining when a duty to remember cultural heritage can translate into a legal obligation to preserve it - a right to remember- and when, at the same time, there may be a symmetrical legal duty to forget it. In other terms, the dissertation seeks to study not only the “pathological” dimension of destruction but also tracetheborders of destruction by verifying lawful situations in which is possible to recognize “a right to destroy”, b comparing different casesof destruction, or, as the case may be, removal of heritage. Thestudy explores cases of the destruction of heritage in national contexts authorized by domestic governments in light of applicable international norms in both peacetime and wartime contexts. The scope of the research includes mainly examples of tangible cultural heritage (more specifically, public monuments and buildings), which are characterized as contested heritage with accompanying issues of memory and divisive identity narratives. The research will focus on iconoclastic episodes driven by ideological reasons, rooted in three case-studies: Soviet monuments in Ukraine, Confederate iconography in the United States, and the situation of the Rohingya in Myanmar. It will exclude cases of peacetime threats to cultural heritage caused by economic development. The study seeks to enrich the interdisciplinary literature on memory and heritage studies in connection with law