The relationship between analogy and categorisation in cognition

This central topic of this thesis is the relationship between categorisation and analogy in cognition. Questions of what a straightforward representation of a concept or category is, and following from that how extra-categorical associations such as analogy and metaphor are possible are central to our understanding of human reasoning and comprehension. However, despite the intimate linkage between the two, the trend in cognitive science has been to treat analogy and categorisation as separable, distinctive phenomena that can be studied in isolation from one another. This strategy has proved remarkably effective when it comes to the cognitive modelling of extracategorical associations. A number of compelling and detailed models of analogy process exist, and there is widespread agreement amongst researchers studying analogy as to what the key cognitive processes that determine analogies are.However, these models of analogy tend to assume some kind of fully specified category processing module which governs and determines ordinary, straightforward conceptual mappings. Indeed, this assumption is required in order to talk about analogy and metaphor in the first place: few theorists actually define analogy and metaphor per se, but all agree that analogical and metaphoric judgements can be defined in contrast to ordinary categorisation judgements.This thesis reviews these models of analogy, and evidence for them, before conducting a detailed exploration of categorisation in relation to analogy. A theoretical and empirical review is presented in order to show that the straightforward notion of categorisation that underpins the distinctive phenomena approach to the study of analogy and categorisation is more apparent than real. Whilst intuitively, analogy and categorisation might feel like different things which can be contrasted with one another, from a cognitive processing point of view, this thesis argues that such a distinction may not survive a detailed scientific examination.A series of empirical studies are presented in order to further explore the 'no distinction' hypothesis. Following from these, further studies examine the question of whether models of analogical processing have progressed as far as they can in artificial isolation from categorisation, a process in which the processes that are normally deemed 'analogical' appear to play a vital role.The conclusion drawn in this thesis is that the analogy / categorisation division, as currently formulated, cannot survive detailed scientific examination. It is argued that despite the benefits that the previous study of these phenomena in isolation have brought in the past, future progress, especially in the development of cognitive models of analogy, is dependent on a more unified approach

Finding Structure in Silence: The Role of Pauses in Aligning Speaker Expectations

The intelligibility of speech relies on the ability of interlocutors to dynamically align their expectations about the rates at which informative changes in signals occur. Exactly how this is achieved remains an open question. We propose that speaker alignment is supported by the statistical structure of spoken signals and show how pauses offer a time-invariant template for structuring speech sequences. Consistent with this, we show that pause distributions in conversational English and Korean provide a memoryless information source. We describe how this can facilitate both the initial structuring and maintenance of predictability in spoken signals over time, and show how the properties of this signal change predictably with speaker experience. These results indicate that pauses provide a structuring signal that interacts with the morphological and rhythmical structure of languages, allowing speakers at all stages of lifespan development to distinguish signal from noise and maintain mutual predictability in time.Comment: 25 pages, 5 figure

The effects of linear order in category learning: some replications of Ramscar et al., (2010) and their implications for replicating training studies

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over-generalized high-frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature-label order predictions were derived from error-driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies

Learning is not decline: The mental lexicon as a window into cognition across the lifespan

As otherwise healthy adults age, their performance on cognitive tests tends to decline. This change is traditionally taken as evidence that cognitive processing is subject to significant declines in healthy aging. We examine this claim, showing current theories over-estimate the evidence in support of it, and demonstrating that when properly evaluated, the empirical record often indicates that the opposite is true. To explain the disparity between the evidence and current theories, we show how the models of learning assumed in aging research are incapable of capturing even the most basic of empirical facts of “associative” learning, and lend themselves to spurious discoveries of “cognitive decline.” Once a more accurate model of learning is introduced, we demonstrate that far from declining, the accuracy of older adults lexical processing appears to improve continuously across the lifespan. We further identify other measures on which performance does not decline with age, and show how these different patterns of performance fit within an overall framework of learning. Finally, we consider the implications of our demonstrations of continuous and consistent learning performance throughout adulthood for our understanding of the changes in underlying brain morphology that occur during the course of cognitive development across the lifespan

Investigation of the transfer of septum microbial contamination by hypodermic needles

The likelihood of the transfer of microbial contamination from the surface of a vial septum into the vial liquid, by penetration of a hypodermic syringe needle, has been investigated. Experimental work was carried out with vials containing sterile microbial growth media and the use of needles of three different diameters. Three different concentrations of microbes on the surface of the vial septum (10, 100, and 1000) were used. Microbial contamination that was transferred into the growth media was determined by incubation of the vials following penetration of the septum by the needles.Contamination was detected in 87% of all the vials tested, and was generally found to increase as the concentration of septum challenge organisms and needle diameter increased

The Enigma of Number: Why Children Find the Meanings of Even Small Number Words Hard to Learn and How We Can Help Them Do Better

Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions (“there are three balls”) structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions (“balls, there are three”) will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early numerical ability predicts later educational outcomes, this simple discovery may have far-reaching consequences

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning – focusing on its simplest form for clarity – and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial