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My dissertation applies philosophical analysis to the problem of how we should cognitively characterize brain activity. Let us distinguish between high-level cognitive functions—e.g. decision-making, face recognition—and the lower-level computational operations that are carried out by discrete regions of the brain. One can assume that cognitive functions are assembled from interactions between relatively autonomous computational operations carried out by discrete brain regions. My thesis, stated very broadly, is that in order to be effective, the decomposition of a cognitive function into a set of interactions between localized computational operations may need to be specified domain-neutrally, and not in terms of a particular informational domain or stimulus class. Jerry Fodor’s influential work on modularity has sparked an industry of research that is based on the idea that the mind is, to a large extent, a configuration of domain-specific and relatively autonomous cognitive mechanisms, or modules. My treatment indicates how this modular approach must be modified in order successfully to decompose domain-specific cognitive functions into localizable computational operations. I proceed in two steps. First, I provide an analysis of the kinds of inferences that are used by cognitive scientists to postulate the existence of cognitive modules; I call these the modularity inferences. I offer a new characterization of these inferences, and argue that they can, and do, operate in three distinct modes in cognitive scientific research. Second, I present a general approach to the decomposition of a cognitive function into localizable computational operations. According to this approach, which I call the working zone approach, the contribution of a distinct brain region to a cognitive function is specified in terms of the type of operations that this region performs, and not in terms of a particular informational domain. I demonstrate the value of this approach in several research contexts within the cognitive sciences.

My dissertation applies philosophical analysis to the problem of how we should cognitively characterize brain activity. Let us distinguish between high-level cognitive functions—e.g. decision-making, face recognition—and the lower-level computational operations that are carried out by discrete regions of the brain. One can assume that cognitive functions are assembled from interactions between relatively autonomous computational operations carried out by discrete brain regions. My thesis, stated very broadly, is that in order to be effective, the decomposition of a cognitive function into a set of interactions between localized computational operations may need to be specified domain-neutrally, and not in terms of a particular informational domain or stimulus class. Jerry Fodor’s influential work on modularity has sparked an industry of research that is based on the idea that the mind is, to a large extent, a configuration of domain-specific and relatively autonomous cognitive mechanisms, or modules. My treatment indicates how this modular approach must be modified in order successfully to decompose domain-specific cognitive functions into localizable computational operations. I proceed in two steps. First, I provide an analysis of the kinds of inferences that are used by cognitive scientists to postulate the existence of cognitive modules; I call these the modularity inferences. I offer a new characterization of these inferences, and argue that they can, and do, operate in three distinct modes in cognitive scientific research. Second, I present a general approach to the decomposition of a cognitive function into localizable computational operations. According to this approach, which I call the working zone approach, the contribution of a distinct brain region to a cognitive function is specified in terms of the type of operations that this region performs, and not in terms of a particular informational domain. I demonstrate the value of this approach in several research contexts within the cognitive sciences.

My dissertation applies philosophical analysis to the problem of how we should cognitively characterize brain activity. Let us distinguish between high-level cognitive functions—e.g. decision-making, face recognition—and the lower-level computational operations that are carried out by discrete regions of the brain. One can assume that cognitive functions are assembled from interactions between relatively autonomous computational operations carried out by discrete brain regions. My thesis, stated very broadly, is that in order to be effective, the decomposition of a cognitive function into a set of interactions between localized computational operations may need to be specified domain-neutrally, and not in terms of a particular informational domain or stimulus class. Jerry Fodor’s influential work on modularity has sparked an industry of research that is based on the idea that the mind is, to a large extent, a configuration of domain-specific and relatively autonomous cognitive mechanisms, or modules. My treatment indicates how this modular approach must be modified in order successfully to decompose domain-specific cognitive functions into localizable computational operations. I proceed in two steps. First, I provide an analysis of the kinds of inferences that are used by cognitive scientists to postulate the existence of cognitive modules; I call these the modularity inferences. I offer a new characterization of these inferences, and argue that they can, and do, operate in three distinct modes in cognitive scientific research. Second, I present a general approach to the decomposition of a cognitive function into localizable computational operations. According to this approach, which I call the working zone approach, the contribution of a distinct brain region to a cognitive function is specified in terms of the type of operations that this region performs, and not in terms of a particular informational domain. I demonstrate the value of this approach in several research contexts within the cognitive sciences. / Arts, Faculty of / Philosophy, Department of / Graduate

The Same-Different task is an experimental paradigm in which a stimulus pair is presented in succession to a participant whose task is to determine if the stimuli are Same or Different. Typical results show that participants tend to be quicker to respond Same then they are to respond Different. Since the 1960s, many models were proposed to explain this effect, but none has yielded conclusive evidence. The objective of this thesis is to test these models with three experiments by focusing on three research questions: 1) what is the source of the effect, the participant or the stimuli?; 2) what is the organization of the cognitive mechanisms underlying the task?; and 3) what is the effect of the number of attributes on the processing capacity? Results show that the fast-same effect stems from the characteristics of the stimuli rather than an inherent preference for sameness. They also show that the cognitive architecture underlying the task is serial, but that it does not seem to explain solely the fast-same effect. Indeed, the fast-same effect seems to be rather caused by a more efficient processing of Same stimuli in the first 500 ms of the treatment compared to Different stimuli.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: A, page: . Adviser: Mohamed Boubekri. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.

Trust between humans and artificial systems has recently received increased attention due to the widespread use of autonomous systems in our society. In this context trust plays a dual role. On the one hand it is necessary to build robots that are perceived as trustworthy by humans. On the other hand we need to give to those robots the ability to discriminate between reliable and unreliable informants. This thesis focused on the second problem, presenting an interdisciplinary investigation of trust, in particular a computational model based on neuroscientific and psychological assumptions. First of all, the use of Bayesian networks for modelling causal relationships was investigated. This approach follows the well known theory-theory framework of the Theory of Mind (ToM) and an established line of research based on the Bayesian description of mental processes. Next, the role of gaze in human-robot interaction has been investigated. The results of this research were used to design a head pose estimation system based on Convolutional Neural Networks. The system can be used in robotic platforms to facilitate joint attention tasks and enhance trust. Finally, everything was integrated into a structured cognitive architecture. The architecture is based on an actor-critic reinforcement learning framework and an intrinsic motivation feedback given by a Bayesian network. In order to evaluate the model, the architecture was embodied in the iCub humanoid robot and used to replicate a developmental experiment. The model provides a plausible description of children's reasoning that sheds some light on the underlying mechanism involved in trust-based learning. In the last part of the thesis the contribution of human-robot interaction research is discussed, with the aim of understanding the factors that influence the establishment of trust during joint tasks. Overall, this thesis provides a computational model of trust that takes into account the development of cognitive abilities in children, with a particular emphasis on the ToM and the underlying neural dynamics.

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The dissertation is based on four papers that together offer a theory of General Situated Cognition. The project has two overarching goals: (1) to unify existing foundational approaches to cognition by investigating cognition within the framework of the philosophy of information; (2) to characterize the function of cognition and suggest a general (meta-)framework for cognitive architecture. Two of the papers, "Pre-cognitive Semantic Information" and "The Information Medium", deal primarily with the concept of information. They offer a pragmatic and structural account of information, as well as a novel and more general theory of meaning appropriate for simple, non-linguistic organisms - the interface theory of meaning. The papers lay the theoretical and conceptual machinery needed for the other two papers, "The Cognitive Agent: Overcoming Informational Limitations" and "Information Networks: A Meta-architecture for Situated Cognition", which investigate cognition as a general natural phenomenon. They specify the function of cognition as the mechanism in an organism that overcomes informational deficits. They also offer a broad architecture of cognitive systems based on networks of information media, which encompasses, and thus unifies existing approaches to cognition, such as the computational/symbolic approach, the connectionist approach, the dynamicist approach and the ecological embodied approach.

Works of fiction do things to us, and we do things because of works of fiction. When reading Hamlet, I mentally represent certain propositions about its characters and events, I want the story and its characters to go a certain way, and I emotionally respond to its goings-on. I might deem Hamlet a coward, I might wish that Hamlet stabbed Claudius when he had the chance, and I might feel sorrow at Ophelia’s senseless suicide. These fiction-directed mental states seem to resemble the propositional attitudes of belief, desire, and emotion, respectively — the everyday attitudes that represent and orient us toward the world. These mental states constitute our engagement with fiction, and the way in which they hang together is central to understanding our engagement with fiction. In that aim, this thesis hopes to provide an analysis of our belief-like attitudes about works of fiction. I argue that a folk psychological theory of fictional engagement should call upon belief, not imagination, to serve as the primary cognitive attitude with which we engage fictions.