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Thesis (Ph. D.)--City University of New York, 2005.

Among the figures of the Scientific Revolution, Galileo was the most influential in moving science away from Aristotle’s concept of scientific explanation to what became Modern science. My primary goal in this thesis is to explicate Galileo’s concept of scientific explanation, as well as the metaphysical and methodological underpinnings relied upon by Galileo, and to investigate where these depart from Aristotle as well as the Aristotelians of Galileo’s time. Galileo’s most revolutionary scientific achievement was to advance a new, more practical aim for scientific inquiry: he changed the focus of scientific investigations to the measuring, modeling, and predicting of phenomena. In order to increase the reliability of his hypotheses Galileo rejected those aspects of Aristotle’s account of scientific explanation that could not be rigorously empirically justified. The result was that empirical science no longer searched for the essential attributes of bodies or for Aristotle’s causes such as the “final” cause. The identified contributions and innovations promulgated by Galileo are significant because they dictated changes that became formative to contemporary models of scientific explanation. I argue that analyses such as the one given in this dissertation can provide a framework for better understanding twentieth-century criticisms that argue that Aristotle’s concept of scientific explanation contained elements that are indispensable to genuine scientific explanations but that are missing from standard contemporary accounts such as Hempel’s covering law models. Finally, I conclude that my analysis of Galileo’s contributions to scientific explanation suggests that contemporary claims that covering law models should be more receptive to Aristotle’s ideas of causation and essence are misguided. / text

<p> </p><p>The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature.</p><p>This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.</p><p> </p>

The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature. This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.

While the search for models and explanations of cognitive phenomena is a growing area of research, there is no consensus on what counts as a good explanation in cognitive science. This Ph.D. thesis offers a philosophical exploration of the different frameworks adopted to explain cognitive behaviour. It then builds on this systematic exploration to offer a new understanding of the explanatory standards employed in the construction and justification of models and modelling frameworks in cognitive science. Sub-goals of the project include a better understanding of some theoretical terms adopted in cognitive science and a deep analysis of the role of representation in explanations of cognitive phenomena. Results of this project can advance the debate on issues in general philosophy of cognitive science and be valuable for guiding future scientific and cognitive research. In particular, the goals of the thesis are twofold: (i) to provide some necessary desiderata that genuine explanations in cognitive science need to meet; (ii) to identify the framework that is most apt to generate such good explanations. With reference to the first goal, I claim that a good explanation needs to provide predictions and descriptions of mechanisms. With regards to the second goal, I argue that the neurocomputational framework can meet these two desiderata. In order to articulate the first claim, I discuss various possible desiderata of good explanations and I motivate why the ability to predict and to identify mechanisms are necessary features of good explanations in cognitive science. In particular, I claim that a good explanation should advance our understanding of the cognitive phenomenon under study, together with providing a clear specification of the components and their interactions that regularly bring the phenomenon about. I motivate the second claim by examining various frameworks employed to explain cognitive phenomena: the folk-psychological, the anti-representational, the solely subpersonal and the neurocomputational frameworks. I criticise the folk-psychological framework for meeting only the predictive criterion and I stress the inadequacy of its account of cause and causal explanation by engaging with James Woodward’s manipulationist theory of causation and causal explanation. By examining the anti-representational framework, I claim that the notion of representation is necessary to predict and to generalise cognitive phenomena. I reach the same conclusion by engaging with William Ramsey (2007) and Jose Luis Bermudez (2003). I then analyse the solely subpersonal framework and I argue that certain personal-level concepts are indeed required to successfully explain cognitive behaviour. Finally, I introduce the neurocomputational framework as more promising than the alternatives in explaining cognitive behaviour. I support this claim by assessing the framework’s ability to: (i) meet the two necessary criteria for good explanations; (ii) overcome some of the other frameworks’ explanatory limits. In particular, via an analysis of one of its family of models — Bayesian models — I argue that the neurocomputational framework can suggest a more adequate notion of representation, shed new light on the problem of how to bridge personal and subpersonal explanations, successfully meet the prediction criterion (it values predictions as a means to evaluate the goodness of an explanation) and can meet the mechanistic criterion (its model-based methodology opens up the possibility to study the nature of internal and unobservable components of cognitive phenomena).

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Proponents of mechanistic explanation have argued that dynamical models are mere phenomenal models, in that they describe rather than explain the scientific phenomena produced by complex systems. I argue instead that dynamical models can, in fact, be explanatory. Using an example from neuroscientific research on epilepsy, I show that dynamical models can meet the explanatory demands met by mechanistic models, and as such occupy their own unique place within the space of explanatory scientific models.

Recently, Gualtiero Piccinini and Carl Craver (2011) have argued that functional analyses in psychology lack explanatory autonomy from explanations in neuroscience. In this thesis I argue against this claim by motivating and defending a pragmatic-epistemic conception of autonomous psychological explanation. I argue that this conception of autonomy need not require that functional analyses be distinct in kind from neural-mechanistic explanations. I use the framework of Bas van Fraassen’s Pragmatic Theory of Explanation (van Fraassen 1980) to show that explanations in psychology and neuroscience can be seen as seeking understanding of autonomous levels of mechanistic phenomena.

Na presente pesquisa investigou-se de que forma o processo de modelagem contribui para o desenvolvimento da explicação e argumentação em sala de aula. Para isso, foram analisadas atividades de modelagem aplicadas em aulas de ciências para alunos do 8º ano do ensino fundamental de uma escola pública do interior paulista. A análise dos dados envolveu: i) mapeamento dos episódios; ii) identificação de etapas do processo de modelagem; iii) identificação das práticas discursivas dos sujeitos de pesquisa; e iv) categorização de situações explicativas e/ou argumentativas envolvendo estas práticas. Constatou-se um predomínio de explicações descritivas no início das discussões, mas estas ganharam maior complexidade à medida que relações causais foram traçadas em resposta à exigência de justificativas. Já as situações argumentativas não ocorreram de forma espontânea, sendo os professores os precursores da maioria dessas situações - os alunos demonstraram ganhar autonomia neste quesito à medida em que a discussão evoluía. Tendo isto em vista, pontua-se a importância de o professor não explicitar o modelo curricular aos alunos, mas, ao invés disto, estimular a análise do modelo expresso por eles, de forma a evidenciar suas incoerências, limitações e abrangências. Além do mais, o trabalho sugere que a análise gestual pode contribuir para a ampliação do entendimento das situações explicativas e argumentativas. / The present research aimed to investigate how the modeling process contributes to the development of explanation and argumentation in the classroom. We analyzed modeling activities applied in science classes for students of the 8th grade in a public school of São Paulo state. Data analysis involved: i) mapping of episodes; ii) identification of modeling process stages; iii) identification of discursive practices; and iv) categorization of explanatory/argumentative situations involving these practices. There was a predominance of descriptive explanations at the beginning of the discussions, but these became more complex as causal relations were drawn in response to the requirement of justification. Argumentative situations did not occur spontaneously, with teachers being the precursors of most of these situations - the students demonstrated to gain autonomy in this aspect as the discussion evolved. With this in mind, it is important that the teacher do not explain the curricular model to the students, but rather stimulate the analysis of their expressed model, in order to highlight their inconsistencies, limitations and scope. In addition, the work suggests that the gestural analysis can contribute to the understanding of explanatory and argumentative situations.