- Cognitive Science, Evolutionary Psychology, Social cognition (Psychology), Language evolution and development, Origins of Language, Language Evolution, and 18 moreLanguages and Linguistics, Psychology, English, Linguistics, Cognitive Psychology, Sociolinguistics, Second Language Acquisition, Philosophy Of Language, Cognitive Neuroscience, Historical Linguistics, Morphology, Computer Science, Mathematics, Machine Learning, Embodiment, Gesture, Cultural Evolution, and Language Originsedit
ABSTRACT This paper explores the role of iconicity in spoken language and other human communication systems. First, we concentrate on graphical and gestural communication and show how semantically motivated iconic signs play an important... more
ABSTRACT This paper explores the role of iconicity in spoken language and other human communication systems. First, we concentrate on graphical and gestural communication and show how semantically motivated iconic signs play an important role in creating such communication systems from scratch. We then consider how iconic signs tend to become simplified and symbolic as the communication system matures and argue that this process is driven by repeated interactive use of the signs. We then consider evidence for iconicity at the level of the system in graphical communication and finally draw comparisons between iconicity in graphical and gestural communication systems and in spoken language.
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ABSTRACT this report I present an algorithm for finding planar segregations of phonemes for particular languages. This algorithm requires no domain-specific knowledge of phonology or phonetics. Despite this lack of knowledge, the... more
ABSTRACT this report I present an algorithm for finding planar segregations of phonemes for particular languages. This algorithm requires no domain-specific knowledge of phonology or phonetics. Despite this lack of knowledge, the implemented algorithm has identified the structurally significant segregations for thirty languages
In this paper, I propose a neurophysiologically plausible account for the evolution of arbitrary, categorical mental relationships. Topographic, or structure-preserving, mappings are widespread within animal brains. If they can be shown... more
In this paper, I propose a neurophysiologically plausible account for the evolution of arbitrary, categorical mental relationships. Topographic, or structure-preserving, mappings are widespread within animal brains. If they can be shown to generate behaviours in simulation, it is plausible that they are responsible for them in vivo. One behaviour has puzzled philosophers, psychologists and linguists alike: the categorical nature of language and its arbitrary associations between categories of form and meaning. I show here that arbitrary categorical relationships can arise when a topographic mapping is developed between continuous, but uncorrelated activation spaces. This is shown first by simulation, then identified in humans with synaesthesia. The independence of form and meaning as sensory or conceptual spaces automatically results in a categorial structure being imposed on each, as our brains attempt to link the spaces with topographic maps. This result suggests a neurophysiologically plausible explanation of categorisation in language.
Human communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that communication variants spread in a population following neutral evolutionary... more
Human communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of human communication systems must integrate selectionist models and (ii) human communication systems are functionally adaptive complex systems.
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This study examines the intergenerational transfer of human communication systems. It tests if human communication systems evolve to be easy to learn or easy to use (or both), and how population size affects learnability and usability.... more
This study examines the intergenerational transfer of human communication systems. It tests if human communication systems evolve to be easy to learn or easy to use (or both), and how population size affects learnability and usability. Using an experimental-semiotic task, we find that human communication systems evolve to be easier to use (production efficiency and reproduction fidelity), but harder to learn (identification accuracy) for a second generation of naïve participants. Thus, usability trumps learnability. In addition, the communication systems that evolve in larger populations exhibit distinct advantages over those that evolve in smaller populations: the learnability loss (from the Initial signs) is more muted and the usability benefits are more pronounced. The usability benefits for human communication systems that evolve in a small and large population is explained through guided variation reducing sign complexity. The enhanced performance of the communication systems that evolve in larger populations is explained by the operation of a content bias acting on the larger pool of competing signs. The content bias selects for information-efficient iconic signs that aid learnability and enhance usability.
Shieber (1987) describes a technique for limiting the number of active edges introduced into a chart by top-down prediction in chart parsers for PATR grammars, without affecting the correctness or completeness of the parser. That... more
Shieber (1987) describes a technique for limiting the number of active edges introduced into a chart by top-down prediction in chart parsers for PATR grammars, without affecting the correctness or completeness of the parser. That technique, termed restriction, is extendable to other parsing algorithms. It can be employed to increase parsing efficiency and to induce termination for some classes of grammars that would not otherwise terminate.
Here, we describe one class of grammars for which restriction, as described by Shieber, induces non-termination. We do not suggest that the concept of restriction is fatally flawed, however. On the contrary, relatively minor modifications to the implementation of restriction can make it a more flexible tool for fine-tuning PATR grammars.
Here, we describe one class of grammars for which restriction, as described by Shieber, induces non-termination. We do not suggest that the concept of restriction is fatally flawed, however. On the contrary, relatively minor modifications to the implementation of restriction can make it a more flexible tool for fine-tuning PATR grammars.
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Stabler (1984) attacks generative models of language acquisition for relying on the psychologically implau- sible basis of simplicity for grammar selection. In this paper I argue that simplicity judgements can be of- fered a... more
Stabler (1984) attacks generative models of language acquisition for relying on the psychologically implau- sible basis of simplicity for grammar selection. In this paper I argue that simplicity judgements can be of- fered a psychologically plausible basis. First, a ca- pacity for simplicity judgements is equated with the ability to model a distribution. This ability, in turn, is shown to be one of the capabilities of self-organising maps. These, I argue, are neurologically, and hence psychologically, plausible.
According to the front page, Archangeli & Langendoen's Optimality Theory : an overview (henceforth A&L) is 'the first in a series of volumes of essays which are designed to introduce and... more
According to the front page, Archangeli & Langendoen's Optimality Theory : an overview (henceforth A&L) is 'the first in a series of volumes of essays which are designed to introduce and explain major research areas in linguistic theory and practice'. On the back cover, we learn that it provides 'the first general introduction to optimality theory – arguably the linguistic theory of the s'. And the Foreword states that the intended audience is 'anyone with a serious interest in language who desires to understand [Optimality Theory], regardless of their ...
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ABSTRACT Our perceptions are often logically compatible with abstractions we would never imagine entertaining. The problem of induction is to account for this disparity: how does evidence confirm one generalisation to the exclusion of... more
ABSTRACT Our perceptions are often logically compatible with abstractions we would never imagine entertaining. The problem of induction is to account for this disparity: how does evidence confirm one generalisation to the exclusion of others with which it is also logically compatible? In particular, how do we justify uniformity, the claim that the future will be like the past? This paper introduces the problem of induction, and then proposes a solution based on similarity measures and topographic mapping. The premisses of this solution are the following. (i) All variation occurs in in a context of non-trivial similarity structures. (ii) Natural cognitive mappings between spaces of representation are topographic mappings, maximally preserving the similarity structures. The uniformity presumption can be justified by these two premisses. In this paper, I explore a new account for the difference between logical compatibility and inductive confirmation. It has been known for a long tim...
From the earliest days of generative grammar, developing a general method for grammar acquisition has been a major goal of linguistic theory. Chomsky (1975) proposed that grammars be selected for their simplicity: the simplest grammar... more
From the earliest days of generative grammar, developing a general method for grammar acquisition has been a major goal of linguistic theory. Chomsky (1975) proposed that grammars be selected for their simplicity: the simplest grammar which fits the data is the best one. At that time, no computationally useful definition of simplicity has been available. Recently, the method variously known as minimum message length or minimum description length has proved very successful in selecting between hypotheses.
This paper presents a simplicity measure for violable phonological constraints based on the minimum message length method. This measure captures the intuitive desiderata of conciseness, accuracy and precision. A family of constraints can be specified by parameterising a specific constraint, and so forming a template. The combination of this measure with a search algorithm is a powerful learning method for finding the best constraint matching a template and fitting a corpus. This method may be applied iteratively, using the same template, to learn a number of different constraints. Five applications of an implementation show some of the successes of this learning method: from learning consonant cluster constraints to vowel harmony.
(NB This pdf was formatted for submission to Computational Linguistics, so early versions had a (C) ACL notice. It was never published in that journal.)
This paper presents a simplicity measure for violable phonological constraints based on the minimum message length method. This measure captures the intuitive desiderata of conciseness, accuracy and precision. A family of constraints can be specified by parameterising a specific constraint, and so forming a template. The combination of this measure with a search algorithm is a powerful learning method for finding the best constraint matching a template and fitting a corpus. This method may be applied iteratively, using the same template, to learn a number of different constraints. Five applications of an implementation show some of the successes of this learning method: from learning consonant cluster constraints to vowel harmony.
(NB This pdf was formatted for submission to Computational Linguistics, so early versions had a (C) ACL notice. It was never published in that journal.)
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All languages make the same phonological generalisations. This is the remarkable claim of Optimality Theory (OT). In early generative phonology (Chomsky & Halle 1968) modelling languages with a xed same set of rewrite... more
All languages make the same phonological generalisations. This is the remarkable claim of Optimality Theory (OT). In early generative phonology (Chomsky & Halle 1968) modelling languages with a xed same set of rewrite rules was inconceivable. While not theoretically ...