Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Material Symbols

What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). In the present treatment I explore a third option, which I shall call the “complementarity” view of language. According to this third view the actual symbol structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The “cognitive bonus” that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of “thinking in a given natural language” or via some process of exhaustive translation into another inner code. Instead, we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the “extended mind”) of understanding human cognition as essentially and multiply hybrid: as involving a complex interplay between internal biological resources and external non-biological resources

A Learning-Style Theory for Understanding Autistic Behaviors

Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name–number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities

Proverbial Economies: How an Understanding of Some Linguistic and Social Features of Common Sense Can Throw Light on More Prestigious Bodies of Knowledge, Science for Example

An evaluative contrast between learned expertise and lay knowledge is a pervasive and longstanding feature of modern culture. Occasionally, the learned have pointed to folkish proverbs to illustrate the inadequacies of common-sense reasoning and judgement. Proverbs are said perspicuously to display the superficiality, the imprecision, and even the logical contradictions of common-sense thinking. I offer an interpretation of proverbs in their naturally occurring settings as epistemically powerful, mnemonically robust, practically pertinent, and referentially flexible. My purpose is not just to recuperate the value of proverbial reasoning but, ultimately, to show the relevance of such reasoning to a revised appreciation of modern technical practices, including science, technology and medicine. To that end, the paper concludes with some speculative remarks about the linguistic forms in which the heuristics of present-day technical practices are expressed and transmitted.History of Scienc