Fewer epistemological challenges for connectionism

Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field

Naturalizing ethics

In this essay we provide (1) an argument for why ethics should be naturalized, (2) an analysis of why it is not yet naturalized, (3) a defense of ethical naturalism against two fallacies—Hume’s and Moore’s—that ethical naturalism allegedly commits, and (4) a proposal that normative ethics is best conceived as part of human ecology committed to pluralistic relativism. We explain why naturalizing ethics both entails relativism and also constrains it, and why nihilism about value is not an especially worrisome for ethical naturalists. The substantive view we put forth constitutes the essence of Duke Naturalism. (NOTE: This is a slightly modified reprint of Flangan et al 2007 of the same title.

Curriculum development for an inquiry approach to construction education.

Doctoral Degree. University of KwaZulu-Natal, Durban.University graduates have been criticised for failing to make a meaningful contribution to professional practice in the construction industry in South Africa and across the world generally. Deficiencies have been reported in the ability of graduates of construction programmes to think critically, solve problems or apply theoretical knowledge in practical situations. Among other factors, the traditional didactic lecture approach to teaching and learning has been blamed for not providing students with an appropriate learning experience to adequately prepare them for professional practice. This is because the didactic lecture approach is characterised by attempts to transmit knowledge from the lecturer to the student which has been found to be inadequate in achieving effective learning. The traditional didactic approach to teaching is based on theories of learning which assumed that knowledge can be transmitted from the minds of lecturers to the minds of students. Contemporary theories of learning have rebuffed this assumption and demonstrated that knowledge and understanding are achieved by students actively engaging with the study material and constructing their own knowledge structures rather than passively receiving knowledge and understanding. Based on these contemporary theories of learning, several different pedagogy has been suggested and incorporated in educational practice. However, predominantly, contemporary pedagogy has been haphazardly applied within the traditional framework of segregated modules. Also, different pedagogy based on different contemporary theories has been researched and applied independent of each other. This has led to some contradictions in some pedagogy and a lack of synergistic collaboration among the contemporary pedagogy. Against this background, this thesis researched the problem that the traditional didactic lecture teaching approach to construction education at undergraduate level does not adequately prepare students for construction professional practice and therefore requires an alternative curriculum model which incorporates different contemporary theories of learning synergistically in a student centred inquiry based learning (IBL) pedagogical framework. To achieve this, the research established factors from the contemporary theories of learning which significantly contribute to the creation of knowledge structures in students studying construction programmes in South Africa. Subsequently the research proposed a curriculum model for construction programmes which incorporated the identified antecedents to effective learning underpinned in the contemporary pedagogical framework of IBL. The research followed a positivist epistemological philosophy and a subjective ontological philosophy, a deductive research approach, a survey research strategy, a cross sectional time horizon and a data collection technique and procedure of a questionnaire using the non-probability sampling technique of convenient sampling. The research procedure included an extensive literature review of three contemporary theories of learning namely, constructivism from philosophy, connectionism from behaviourism and cognitive load theory from cognitive science. Subsequently, an instrument measuring the concepts from the conceptual model was developed, pre-tested and then administered to undergraduate students studying construction programmes at a convenient sample of public universities in South Africa. The results show that the factors from the three contemporary theories of learning which directly influence the extent to which students studying construction programmes are able to create knowledge structures and achieve effective learning are individual learning, scaffolding, reflective thinking and group learning in that order. Repetition, reinforcement, readiness, self-directed learning and the use of worked examples have indirect relationships with the ability for students to create knowledge structures. Complex questions and authentic questions were also found to indirectly contribute to effective learning. Cognitive loading was found to interfere with learning and complex questions were found to induce cognitive loading while authentic questions did not. Subsequently, an IBL curriculum framework for construction programmes was proposed which integrated most of the topics which directly relate to construction practice. Based on the findings, the IBL class should involve students in both individual and group learning activities which should be appropriately scaffolded and students explicitly directed towards reflective thinking as they engage in the IBL projects. Complex questions and authentic questions should be used in collaboration with extra scaffolding in order to reduce the impact of the consequent cognitive loading induced by complex questions. The IBL projects should be simple initially and increase in complexity as the student’s advance

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning

Somatic computationalism: Damasio\u27s clever error

Neuroscientist Antonio Damasio wrote a book entitled DESCARTES’ ERROR (1994) in order to address popular misconceptions about the mind, particularly those which relate to Cartesian philosophy. One of the author’s major goals for the book is to argue that emotion contributes to reason, that emotion is in fact necessary for rational thought to occur. In order to link emotion to reason, Damasio proposes a theory of mind which explains several mental functions in terms of neurological representations. Consciousness, reason, instinct and emotion all occur because the brain forms representations of the subject’s body and of the world in which the body acts. Thought, in the broadest sense of the term, is the process in which the brain manipulates these representations and causes them to interact. This thesis will examine Damasio’s theory of mind in relation to two traditional topics in cognitive science: consciousness and intelligence. The first chapter simply explains the theory as given in DESCARTES’ ERROR. Chapter two argues that, like everyone before him, Damasio fails to explain how or why the brain generates consciousness. Although the theory fails in this regard, it is still useful as a description of the neurological processes which underlie consciousness, of the mechanics of mind. As such, this theory could serve as a conceptual complement to the traditional paradigms of cognitive science, “GOFAI” and Embodied Cognition. Chapter three will argue that Damasio’s theory is better suited to work with the latter paradigm than the former

Questioning the impact of AI and interdisciplinarity in science: Lessons from COVID-19

Artificial intelligence (AI) has emerged as one of the most promising technologies to support COVID-19 research, with interdisciplinary collaborations between medical professionals and AI specialists being actively encouraged since the early stages of the pandemic. Yet, our analysis of more than 10,000 papers at the intersection of COVID-19 and AI suggest that these collaborations have largely resulted in science of low visibility and impact. We show that scientific impact was not determined by the overall interdisciplinarity of author teams, but rather by the diversity of knowledge they actually harnessed in their research. Our results provide insights into the ways in which team and knowledge structure may influence the successful integration of new computational technologies in the sciences

Cognitive Mechanisms and Computational Models: Explanation in Cognitive Neuroscience

Cognitive Neuroscience seeks to integrate cognitive psychology and neuroscience. I critique existing analyses of this integration project, and offer my own account of how it ought to be understood given the practices of researchers in these fields. A recent proposal suggests that integration between cognitive psychology and neuroscience can be achieved `seamlessly' via mechanistic explanation. Cognitive models are elliptical mechanism sketches, according to this proposal. This proposal glosses over several difficulties concerning the practice of cognitive psychology and the nature of cognitive models, however. Although psychology's information-processing models superficially resemble mechanism sketches, they in fact systematically include and exclude different kinds of information. I distinguish two kinds of information-processing model, neither of which specifies the entities and activities characteristic of mechanistic models, even sketchily. Furthermore, theory development in psychology does not involve the filling in of these missing details, but rather refinement of the sorts of models they start out as. I contrast the development of psychology's attention filter models with the development of neurobiology's models of sodium channel filtering. I argue that extending the account of mechanisms to include what I define as generic mechanisms provides a more promising route towards integration. Generic mechanisms are the in-the-world counterparts to abstract types. They thus have causal-explanatory powers which are shared by all the tokens that instantiate that type. This not only provides a way for generalizations to factor into mechanistic explanations, which allows for the `upward-looking' explanations needed for integrating cognitive models, but also solves some internal problems in the mechanism literature concerning schemas and explanatory relevance. I illustrate how generic mechanisms are discovered and used with examples from computational cognitive neuroscience. I argue that connectionist models can be understood as approximations to generic brain mechanisms, which resolves a longstanding philosophical puzzle as to their role. Furthermore, I argue that understanding scientific models in general in terms of generic mechanisms allows for a unified account of the types of inferences made in modeling and in experiment

Towards explanatory pluralism in cognitive science

This thesis seeks to shed light on the intricate relationships holding between the various explanatory frameworks currently used within cognitive science. The driving question of this philosophical investigation concerns the nature and structure of cognitive explanation. More specifically, I attempt to clarify whether the sort of scientific explanations proposed for various cognitive phenomena at different levels of analysis or abstraction differ in significant ways from the explanations offered in other areas of scientific inquiry, such as biology, chemistry, or even physics. Thus, what I will call the problem of cognitive explanation, asks whether there is a distinctive feature that characterises cognitive explanations and distinguishes them from the explanatory schemas utilised in other scientific domains. I argue that the explanatory pluralism encountered within the daily practice of cognitive scientists has an essential normative dimension. The task of this thesis is to demonstrate that pluralism is an appropriate standard for the general explanatory project associated with cognitive science, which further implies defending and promoting the development of multiple explanatory schemas in the empirical study of cognitive phenomena

Critical for Pure Judgment: The \u27Socratic Method\u27 on Relativism

The following essay is an attempt to justify philosophically the possibility of a \u27natural law\u27 or prescriptive cross-experiential judgment. To accomplish this task, an examination of contemporary relativism and indication of what is wrong with that position is necessary at first. Why argue against something if its sound? The second stage is a survey of certain key thinkers on law, justice and judgment. Their thoughts will yield clues or suggestions about what is needed for a natural law. The third section lays out this author\u27s thoughts on how to solve the problem. The final section is objections and replies

A Defense of Pure Connectionism

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association