Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Integration of cognitive and affective processes in perception and decision-making

The relationship between emotion and cognition is a topic that raises great interest in research. Recently, a view of these two processes as interactive and mutually influencing each other has become predominant. This dissertation investigates the reciprocal influences of emotion and cognition, both at behavioral and neural level, in two specific fields, such as attention and decision-making. Experimental evidence on how emotional responses may affect perceptual and attentional processes has been reported. In addition, the impact of three factors, such as personality traits, motivational needs and social context, in modulating the influence that emotion exerts on perception and attention has been investigated. Moreover, the influence of cognition on emotional responses in decision-making has been demonstrated. The current experimental evidence showed that cognitive brain regions such as the dorsolateral prefrontal cortex are causally implicated in regulation of emotional responses and that this has an effect at both pre and post decisional stages. There are two main conclusions of this dissertation: firstly, emotion exerts a strong influence on perceptual and attentional processes but, at the same time, this influence may also be modulated by other factors internal and external to the individuals. Secondly, cognitive processes may modulate emotional prepotent responses, by serving a regulative function critical to driving and shaping human behavior in line with current goals.

Model reduction of stochastic groundwater flow and transport processes

This work presents a comprehensive methodology for the reduction of analytical or numerical stochastic models characterized by uncertain input parameters or boundary conditions. The technique, based on the Polynomial Chaos Expansion (PCE) theory, represents a versatile solution to solve direct or inverse problems related to propagation of uncertainty. The potentiality of the methodology is assessed investigating different applicative contexts related to groundwater flow and transport scenarios, such as global sensitivity analysis, risk analysis and model calibration. This is achieved by implementing a numerical code, developed in the MATLAB environment, presented here in its main features and tested with literature examples. The procedure has been conceived under flexibility and efficiency criteria in order to ensure its adaptability to different fields of engineering; it has been applied to different case studies related to flow and transport in porous media. Each application is associated with innovative elements such as (i) new analytical formulations describing motion and displacement of non-Newtonian fluids in porous media, (ii) application of global sensitivity analysis to a high-complexity numerical model inspired by a real case of risk of radionuclide migration in the subsurface environment, and (iii) development of a novel sensitivity-based strategy for parameter calibration and experiment design in laboratory scale tracer transport.

Untersuchung der Auswirkungen verschiedener Variationsbereiche des differenziellen Lernens und Lehrens im weiten Sinn auf ausgewählte technische Grundfertigkeiten im Hallenhockey

Die Frage wie großmotorische Bewegungen gelernt werden beschäftigt nicht nur Sportler, Trainer und Sportlehrer sondern auch Ärzte und Physiotherapeuten. Die sportwissenschaftlichen Teildisziplinen Bewegungs- und Trainingswissenschaft versuchen diese Frage sowohl im Sinne der Grundlagenforschung (Wie funktioniert Bewegungslernen?) als auch hinsichtlich der praktischen Konsequenzen (Wie lehrt man Bewegungen?) zu beantworten. Innerhalb dieser Themenfelder existieren Modelle, die Bewegungslernen als gezielte und extern unterstützte Ausbildung zentralnervöser Bewegungsprogramme verstehen und solche, die Lernen als Selbstorganisationsprozess interpretieren. Letzteren ist das Differenzielle Lernen und Lehren (Schöllhorn, 1999) zuzuordnen, das die Notwendigkeit betont, Bewegungen durch die Steigerung der Variationen während der Aneignungsphase zu lernen und zu lehren. Durch eine Vielzahl an Variationen, so die Modellannahme, findet der Lernende ohne externe Vorgaben selbstorganisiert ein individuelles situatives Optimum. Die vorliegende Arbeit untersucht, welchen Einfluss Variationen verschiedener Art und Größe auf die Lern- und Aneignungsleistung großmotorischer Bewegungen haben und in wie fern personenübergreifende Optima existieren. In zwei Experimenten wird der Einfluss von räumlichen (Bewegungsausführung, Bewegungsergebnis) und zeitlichen Variationen (zeitliche Verteilung der Trainingsreize) auf die Aneignungs- und Lernleistung großmotorischer sportlicher Bewegungen am Beispiel zweier technischer Grundfertigkeiten des Hallenhockeys untersucht. Die Ergebnisse der Experimente stützen die bisherige Befundlage zum Differenziellen Lernen und Lehren, wonach eine Zunahme an Variation in der Aneignungsphase zu größeren Aneignungs- und Lernleistungen führt. Zusätzlich wird die Annahme bestätigt, dass ein Zusammenhang von Variationsbereich und Lernrate in Form eines Optimaltrends vorliegt. Neu sind die Hinweise auf die Dynamik von motorischen Lernprozessen (Experiment 1). Hier scheinen individuelle Faktoren (z. B. die Lernbiografie) als auch die Phase im Lernprozess (Aneignung, Lernen) Einfluss zu haben auf den Umfang und die Struktur eines für die optimale Adaptation notwendigen Variationsbereichs. Darüber hinaus weisen die Befunde auf verschiedene Aneignungs- und Lerneffekte aufgrund alleiniger Variation der zeitlichen Verteilung bei ansonsten gleichen Trainingsreizen hin (Experiment 2). Für zukünftige Forschungsarbeiten zum Erlernen großmotorischer Bewegungen und für die sportliche Praxis dürfte es daher erkenntnisreich sein, die Historie der intrinsischen Dynamik der lernenden Systeme stärker zu berücksichtigen. Neben Fragestellungen für die Grundlagenforschung zum (Bewegungs-)Lernen ließen sich hieraus unmittelbar praxisrelevante Erkenntnisse darüber ableiten, wie Bewegungslernprozesse mittels verschiedener Variationsbereiche strukturiert und gesteuert werden könnten.

Dynamic computer simulations of electrophoresis: a versatile research and teaching tool

Software is available, which simulates all basic electrophoretic systems, including moving boundary electrophoresis, zone electrophoresis, ITP, IEF and EKC, and their combinations under almost exactly the same conditions used in the laboratory. These dynamic models are based upon equations derived from the transport concepts such as electromigration, diffusion, electroosmosis and imposed hydrodynamic buffer flow that are applied to user-specified initial distributions of analytes and electrolytes. They are able to predict the evolution of electrolyte systems together with associated properties such as pH and conductivity profiles and are as such the most versatile tool to explore the fundamentals of electrokinetic separations and analyses. In addition to revealing the detailed mechanisms of fundamental phenomena that occur in electrophoretic separations, dynamic simulations are useful for educational purposes. This review includes a list of current high-resolution simulators, information on how a simulation is performed, simulation examples for zone electrophoresis, ITP, IEF and EKC and a comprehensive discussion of the applications and achievements.