Affordances and Language: How the Level of Object Familiarity modulates the Manipulation and Categorization of Objects across Development

According to much evidence, observing objects activates two types of information: structural properties, i.e., the visual information about the structural features of objects, and function knowledge, i.e., the conceptual information about their skilful use. Many studies so far have focused on the role played by these two kinds of information during object recognition and on their neural underpinnings. However, to the best of our knowledge no study so far has focused on the different activation of this information (structural vs. function) during object manipulation and conceptualization, depending on the age of participants and on the level of object familiarity (familiar vs. non-familiar). Therefore, the main aim of this dissertation was to investigate how actions and concepts related to familiar and non-familiar objects may vary across development. To pursue this aim, four studies were carried out. A first study led to the creation of the Familiar and Non-Familiar Stimuli Database, a set of everyday objects classified by Italian pre-schoolers, schoolers, and adults, useful to verify how object knowledge is modulated by age and frequency of use. A parallel study demonstrated that factors such as sociocultural dynamics may affect the perception of objects. Specifically, data for familiarity, naming, function, using and frequency of use of the objects used to create the Familiar And Non-Familiar Stimuli Database were collected with Dutch and Croatian children and adults. The last two studies on object interaction and language provide further evidence in support of the literature on affordances and on the link between affordances and the cognitive process of language from a developmental point of view, supporting the perspective of a situated cognition and emphasizing the crucial role of human experience.

"Ancestry, gender and language - The role of culture in economic development"

This thesis is a combination of research questions in development economics and economics of culture, with an emphasis on the role of ancestry, gender and language policies in shaping inequality of opportunities and socio-economic outcomes across different segments of a society. The first chapter shows both theoretically and empirically that heterogeneity in risk attitudes can be traced to the ethnic origins and ancestral way of living. In particular, I construct a measure of historical nomadism at the ethnicity level and link it to contemporary individual-level data on various proxies of risk attitudes. I exploit exogenous variation in biodiversity to build a novel instrument for nomadism: distance to domestication points. I find that descendants of ethnic groups that historically practiced nomadism (i) are more willing to take risks, (ii) value security less, and (iii) have riskier health behavior. The second chapter evaluates the nature of a trade-off between the advantages of female labor participation and the positive effects of female education. This work exploits a triple difference identification strategy relying on exogenous spike in cotton price and spatial variation in suitability for cotton, and split sample analyses based on the exogenous allocation of land contracts. Results show that gender differences in parental investments in patriarchal societies can be reinforced by the type of agricultural activity, while positive economic shocks may further exacerbate this bias, additionally crowding out higher possibilities to invest in female education. The third chapter brings novel evidence of the role of the language policy in building national sentiments, affecting educational and occupational choices. Here I focus on the case of Uzbekistan and estimate the effects of exposure to the Latin alphabet on informational literacy, education and career choices. I show that alphabet change affects people's informational literacy and the formation of certain educational and labour market trends.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Analysis of the Slow Food movement impact on the farmers and rural areas’ sustainable development

The evaluation of the farmers’ communities’ approach to the Slow Food vision, their perception of the Slow Food role in supporting their activity and their appreciation and expectations from participating in the event of Mother Earth were studied. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted in an agro-food sector context. A survey was conducted, 120 questionnaires from farmers attending the Mother Earth in Turin in 2010 were collected. The descriptive statistical analysis showed that both Slow Food membership and participation to Mother Earth Meeting were much appreciated for the support provided to their business and the contribution to a more sustainable and fair development. A positive social, environmental and psychological impact on farmers also resulted. Results showed also an interesting perspective on the possible universality of the Slow Food and Mother Earth values. Farmers declared that Slow Food is supporting them by preserving the biodiversity and orienting them to the use of local resources and reducing the chemical inputs. Many farmers mentioned the language/culture and administration/bureaucratic issues as an obstacle to be a member in the movement and to participate to the event. Participation to Mother Earth gives an opportunity to exchange information with other farmers’ communities and to participate to seminars and debates, helpful for their business development. The absolute majority of positive answers associated to the farmers’ willingness to relate to Slow Food and participate to the next Mother Earth editions negatively influenced the UTAUT model results. A factor analysis showed that the variables associated to the UTAUT model constructs Performance Expectancy and Effort Expectancy were consistent, able to explain the construct variability, and their measurement reliable. Their inclusion in a simplest Technology Acceptance Model could be considered in future researches.

Engineering Agent-Oriented Technologies and Programming Languages for Computer Programming and Software Development

Mainstream hardware is becoming parallel, heterogeneous, and distributed on every desk, every home and in every pocket. As a consequence, in the last years software is having an epochal turn toward concurrency, distribution, interaction which is pushed by the evolution of hardware architectures and the growing of network availability. This calls for introducing further abstraction layers on top of those provided by classical mainstream programming paradigms, to tackle more effectively the new complexities that developers have to face in everyday programming. A convergence it is recognizable in the mainstream toward the adoption of the actor paradigm as a mean to unite object-oriented programming and concurrency. Nevertheless, we argue that the actor paradigm can only be considered a good starting point to provide a more comprehensive response to such a fundamental and radical change in software development. Accordingly, the main objective of this thesis is to propose Agent-Oriented Programming (AOP) as a high-level general purpose programming paradigm, natural evolution of actors and objects, introducing a further level of human-inspired concepts for programming software systems, meant to simplify the design and programming of concurrent, distributed, reactive/interactive programs. To this end, in the dissertation first we construct the required background by studying the state-of-the-art of both actor-oriented and agent-oriented programming, and then we focus on the engineering of integrated programming technologies for developing agent-based systems in their classical application domains: artificial intelligence and distributed artificial intelligence. Then, we shift the perspective moving from the development of intelligent software systems, toward general purpose software development. Using the expertise maturated during the phase of background construction, we introduce a general-purpose programming language named simpAL, which founds its roots on general principles and practices of software development, and at the same time provides an agent-oriented level of abstraction for the engineering of general purpose software systems.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.