Abstract: 1. Introduction PART I EVOLUTION OF SPEECH AND SPEECH SOUNDS: HOW DID SPOKEN LANGUAGE EMERGE? Introduction to Part I: How did links between perception and production emerge for spoken language? 2. The Mirror System Hypothesis: How did protolanguage evolve? 3. How Did Language go Discrete? 4. From Holistic to Discrete Speech Sounds: The blind snowflake maker hypothesis 5. Infant-Directed Speech and Evolution of Language PART II EVOLUTION OF GRAMMAR: HOW DID SYNTAX AND MORPHOLOGY EMERGE? Introduction to Part II: Protolanguage and the Development of Complexity 6. Initial Syntax and Modern Syntax: Did the clause evolve from the syllable? 7. The Potential Role of Production in the Evolution of Syntax 8. The Evolutionary Origin of Morphology 9. The Evolution of Grammatical Structures and 'Functional Need' Explanations 10. Deception and Mate Selection: Some implications for relevance and the evolution of language PART III ANALOGOUS AND HOMOLOGOUS TRAITS: WHAT CAN WE LEARN FROM OTHER SPECIES? Introductin to Part III: The Broadening Scope of Animal Communication Research 11. An Avian Perspective on Language Evolution: Implications of simultaneous development of vocal and physical object combinations by a Grey parrot (Psittacus erithacus) 12. Linguistic Prerequisites in the Primate Lineage PART IV LEARNABILITY AND DIVERSITY: HOW DID LANGUAGES EMERGE AND DIVERGE? Introduction to Part IV: Computer Modelling Widens the Focus of Language Study 13. Cultural Selection for Learnability: Three principles underlying the view that language adapts to be learnable 14. Coevolution of the Language Faculty and Language(s) With Decorrelated Encodings 15. Acquisition and Evolution of Quasi-regular Languages: Two puzzles for the price of one 16. Evolution of Language Diversity: Why fitness counts 17. Mutual Exclusivity: Communicative success despite conceptual divergence

Abstract: In order for the study of the human mind and brain to become a successful natural science, a sufficiently large number of researchers must organize their research on the basis of theoretical commitments and methodologies that reflect, in broad outline, the realities of their object of study. Yet there has been, for over a century, enormous resistance to incorporating into the human sciences the most fundamental truth about the species they study: our functional, species-typical design is the organized product of ancestral natural selection (for discussion, see Pinker, 2002; Tooby & Cosmides, 1992; for opposing views, see Fodor, 2000; Gould, 1997a, b). The brain came into existence and acquired a functional organization to the extent that its arrangements acted as a computational system whose operations regulated the organism’s behavior to promote propagation. Studying psychology and neuroscience without the analytical tools offered by evolutionary theory is like attempting to do physics without using mathematics. It may be possible, but the rationale for inflicting needless damage on our ability to understand the world is obscure.

Abstract: As language learners begin to analyze morphologically complex words, they face the problem of projecting underlying representations from the morphophonemic alternations that they observe. Research on learnability in Optimality Theory has started to address this problem, and this article deals with one aspect of it. When alternation data tell the learner that some surface [B]s are derived from underlying /A/s, the learner will under certain conditions generalize by deriving all [B]s, even nonalternating ones, from /A/s. An adequate learning theory must therefore incorporate a procedure that allows nonalternating [B]s to take a «free ride» on the /A/ → [B] unfaithful map.

Abstract: Here is a far-reaching and vitally important question for those seeking to understand the evolution of language: Given a thorough understanding of whatever cognitive processes are relevant to learning, understanding, and producing language, would such an understanding enable us to predict the universal features of language? This question is important because, if met with an affirmative answer, then an explanation for why language evolved to exhibit certain forms and not others must be understood in terms of the biological evolution of the cognitive basis for language. After all, such an account pivots on the assumption that properties of the cognitive mechanisms supporting language map directly onto the universal features of language we observe. We argue against this position, and note that the relation between language universals and any cognitive basis for language is opaque. Certain hallmarks of language are adaptive in the

Abstract: The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We define a model of learnability for ranking functions in a particular setting of the ranking problem known as the bipartite ranking problem, and derive a number of results in this model. Our first main result provides a sufficient condition for the learnability of a class of ranking functions ${\mathcal F}$: we show that ${\mathcal F}$ is learnable if its bipartite rank-shatter coefficients, which measure the richness of a ranking function class in the same way as do the standard VC-dimension related shatter coefficients (growth function) for classes of classification functions, do not grow too quickly. Our second main result gives a necessary condition for learnability: we define a new combinatorial parameter for a class of ranking functions ${\mathcal F}$ that we term the rank dimension of ${\mathcal F}$, and show that ${\mathcal F}$ is learnable only if its rank dimension is finite. Finally, we investigate questions of the computational complexity of learning ranking functions.

Abstract: We present a new model for configuring the connections between user input and system output in a physical interface with diverse sensor degrees of freedom across several input modalities. Our system allows a user to demonstrate input gestures and manipulations directly to the system, teaching it the desired mappings by example. We developed a musical control application in which userdefined gestures and user-assigned manipulations trigger and modify sounds. The effectiveness of our system was tested by experimentally comparing our user-definable system to a similar, pre-configured version. The results suggest that users prefer to actively configure a physical interface to having expertly-configured presets. In addition, we propose our model as a more general mapping discovery tool for physical interface designers.

Abstract: We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.

Abstract: Abstract Here is a far-reaching and vitally important question for those seeking to understand the evolution of language: given a thorough understanding of whatever cognitive processes are relevant to learning, understanding, and producing language, would such an understanding enable us to predict the universal features of language? This question is important because, if met with an affirmative answer, then an explanation for why language evolved to exhibit certain forms and not others must be understood in terms of the biological evolution of the cognitive basis for language. After all, such an account pivots on the assumption that properties of the cognitive mechanisms supporting language map directly onto the universal features of language that we observe. We argue against this position, and note that the relation between language universals and any cognitive basis for language is opaque.

Abstract: This paper aims to provide a new approach to the development of clause structure in L1 acquisition on the basis of the distinction between LF-interpretable and LF- uninterpretable features which is argued to have effects on learnability. The study concentrates on the acquisition of syntactic phenomena which are related to the syntax / pragmatics interface, namely focusing, dislocation and clitic-doubling in Greek. On the assumption that these syntactic phenomena involve a grammatical representation of certain functional features on 'peripheral' functional heads (CP / FP), the aim is to identify a developmental pattern that describes the sequence in which these structures emerge in Greek L1 acquisition. The early acquisition of the left-periphery is then juxtaposed to the relatively delayed acquisition of the inflectional domain.

Abstract: In recent articles Fodor and Lepore have argued that not only do considerations of learnability dictate that meaning must be compositional in the well-known sense that the meanings of all sentences are determined by the meanings of a finite number of primitive expressions and a finite number of operations on them, but also that meaning must be ‘reverse compositional’ as well, in the sense that the meanings of the primitive expressions of which a complex expression is composed must be determined by the meaning of that complex expression plus the manner of its composition. I argue against the requirement of reverse compositionality and against the claim that learnability requires it. I consider some objections and close the paper by arguing against the related claim that concepts are reverse compositional.

Abstract: A pattern is a finite string of constant and variable symbols. The non-erasing language generated by a pattern is the set of all strings of constant symbols that can be obtained by substituting non-empty strings for variables. In order to build the erasing language generated by a pattern, it is also admissible to substitute the empty string.The present paper deals with the problem of learning erasing pattern languages within Angluin's model of learning with queries. Moreover, the learnability of erasing pattern languages with queries is studied when additional information is available. The results obtained are compared with previously known results in case non-erasing pattern languages have to be learned.First, when regular pattern languages have to be learned, it is shown that the learnability results for the non-erasing case remain valid, if the proper superclass of all erasing regular pattern languages is the object of learning. Second, in the general case, serious differences have been observed. For instance, it turns out that arbitrary erasing pattern languages cannot be learned in settings in which, in the non-erasing case, even polynomially many queries will suffice.

Abstract: Background: There are a variety of graphic symbol sets/systems (GSSs) currently used in the field of augmentative and alternative communication (AAC). Various characteristics of these graphic symbol systems affect learnability and should be considered in order to make a meaningful match between the user of AAC and the system. Although a variety of studies on learnability of graphic systems have been conducted in the past, all studies conducted included participants from Western countries.Aims: To compare two symbol systems, namely Blissymbolics and CyberGlyphs in terms of learnability. To identify the overall performance between Blissymbolics and CyberGlyphs in terms of the percentage of symbols correctly identified at the various stages.Methods & Procedures: A quasi‐experimental crossover design between groups was carried out on two homogeneous groups of typically developing, Northern Sotho‐speaking children. Data were obtained by teaching 80 different referents (40 from each symbol system) to 50 Norther...

Abstract: Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In particular, we show learnability of a subclass which we call μ 2 -distinguishable Using an analog of the Myhill-Nerode theorem for probabilistic automata, we analyze μ-distinguishability and generalize it to μ p -distinguishability. By combining new results from property testing with the state merging algorithm we obtain KL-PAC learnability of the new automata class.

Abstract: We study a model of probably exactly correct (PExact) learning that can be viewed either as the Exact model (learning from equivalence queries only) relaxed so that counterexamples to equivalence queries are distributionally drawn rather than adversarially chosen or as the probably approximately correct (PAC) model strengthened to require a perfect hypothesis. We also introduce a model of probably almost exactly correct (PAExact) learning that requires a hypothesis with negligible error and thus lies between the PExact and PAC models. Unlike the Exact and PExact models, PAExact learning is applicable to classes of functions defined over infinite instance spaces. We obtain a number of separation results between these models. Of particular note are some positive results for efficient parallel learning in the PAExact model, which stand in stark contrast to earlier negative results for efficient parallel Exact learning.

Abstract: We argue that the field of neural-symbolic integration is in need of identifying application scenarios for guiding further research. We furthermore argue that ontology learning — as occuring in the context of semantic technologies — provides such an application scenario with potential for success and high impact for neural-symbolic integration. 1 Neural-Symbolic Integration Intelligent systems based on symbolic knowledge processing on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. They are both standard approaches to artificial intelligence and it would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. The importance of these efforts to bridge the gap between the connectionist and symbolic paradigms of Artificial Intelligence has been widely recognised. Since the amount of hybrid data which includes symbolic elements as well as statistical aspects and noise increases dramatically in diverse areas such as bioinformatics or text and web domains, this problem is of particular practical importance. The merging of theory (background knowledge) and data learning (learning from examples) in neural networks has been indicated to provide learning systems that are more effective than purely symbolic and purely connectionist systems, especially when data are noisy and described by real-valued as well as symbolic components. The above results, due also to the massively parallel architecture of neural networks, contributed decisively to the growing interest in developing neural-symbolic systems, i.e. hybrid systems based on neural networks that are capable of learning from examples and background knowledge, and of performing reasoning tasks in a massively parallel fashion. Typically, translation algorithms from a symbolic to a connectionist representation and vice-versa are employed to provide either (i) a neural implementation of a logic, (ii) a logical characterization of a neural system, or (iii) a hybrid system that brings together features from connectionism and symbolic Artificial Intelligence. However, while symbolic knowledge representation is highly recursive and well understood from a declarative point of view, neural networks encode knowledge implicitly in their weights as a result of learning and generalisation from raw data which is usually characterized by simple feature vectors. While significant theoretical progress has recently been made on knowledge representation and reasoning using neural networks on the one side and direct processing of symbolic and structured data with neural methods on the other side, the integration of neural computation and expressive logics such as first order logic is still in its early stages of methodological development. As for knowledge extraction, neural networks have been applied to a variety of real-world problems (e.g. in bioinformatics, engineering, robotics), having been particularly successful when data are noisy, but entirely satisfactory methods for extracting symbolic knowledge from such trained networks are still to be found, and principled problems to ensure the stability and learnability of recursive models currently impose severe restrictions on connectionist systems. In order to advance the state of the art, we believe that it is necessary to look at the biological inspiration for neural-symbolic integration, to use more formal approaches for translating between the connectionist and symbolic paradigms, and to pay more attention to potential application scenarios. We will argue in the following that ontology learning provides such an application scenario with potential for success and high impact. 2 The Need for Use Cases The general motivation for research in the field of neuralsymbolic integration just given arises from conceptual observations on the complementary nature of symbolic and neuralnetwork-based artificial intelligence which we described. This conceptual perspective is sufficient for justifying the mainly foundations-driven lines of research being undertaken in this area so far. However, it appears that this conceptual approach to the study of neural-symbolic integration has now reached an impasse which requires the identification of use cases and application scenarios in order to drive future research. Indeed, the theory of integrated neural-symbolic systems has reached a quite mature state but has not been tested so far on real application data. From the pioneering work by McCulloch and Pitts [22], a number of systems have been developed in the 80s and 90s, including Towell and Shavlik’s KBANN [28], Shastri’s SHRUTI [26], the work by Pinkas [24], Holldobler [17], and d’Avila Garcez et al. [11; 13], to mention a few, and we refer to [8; 12; 15] for comprehensive literature overviews. These systems, however, have been developed for the study of general principles, and are in general not suitable for real data or application scenarios. Nevertheless, these studies provide methods which can be exploited for the development of tools for use cases, and significant progress can now only be expected by developing practical tools out of the fundamental research undertaken in the past. The systems just mentioned — and most of the research on neural-symbolic integration to date — is based on propositional logic or similarly finitistic paradigms. Significantly large and expressible fragments of first order logic are rarely being used because the integration task becomes much harder due to the fact that the underlying language is infinite but shall be encoded using networks with a finite number of nodes [6]. The few approaches known to us for overcoming this problem are work on recursive autoassociative memory, RAAM, initiated by Pollack [25], which concerns the learning of recursive terms over a first-order language, and research based on a proposal by Holldobler et al. [19], spelled out first for the propositional case in [18], and reported also in [16]. It is based on the idea that logic programs can be represented — at least up to subsumption equivalence [21] — by their associated single-step or immediate consequence operators. Such an operator can then be mapped to a function on the real numbers, which can under certain conditions in turn be encoded or approximated e.g. by feedforward networks with sigmoidal activation functions using an approximation theorem due to Funahashi [10]. Despite a number of sophisticated theoretical results building on the latter approach — reported e.g. in [19; 4; 16; 6; 5] —, first-order neural-symbolic integration still appears to be a widely open issue, where advances are very difficult, and it is very hard to judge to date to what extent the theoretical approaches can work in practice. The development of use cases with varying levels of expressive complexity are therefore needed in order to drive the development of methods for neural-symbolic integration beyond propositional logic. 3 Semantic Technologies and Ontology

Abstract: Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In particular, we show learnability of a subclass which we call μ2-distinguishable. Using an analog of the Myhill-Nerode theorem for probabilistic automata, we analyze μ-distinguishability and generalize it to μp-distinguishability. By combining new results from property testing with the state merging algorithm we obtain KL-PAC learnability of the new automata class.

Abstract: Within learning theory teaching has been studied in various ways In a common variant the teacher has to teach all learners that are restricted to output only consistent hypotheses The complexity of teaching is then measured by the maximum number of mistakes a consistent learner can make until successful learning This is equivalent to the so-called teaching dimension However, many interesting concept classes have an exponential teaching dimension and it is only meaningful to consider the teachability of finite concept classes A refined approach of teaching is proposed by introducing a neighborhood relation over all possible hypotheses The learners are then restricted to choose a new hypothesis from the neighborhood of their current one Teachers are either required to teach finitely or in the limit Moreover, the variant that the teacher receives the current hypothesis of the learner as feedback is considered The new models are compared to existing ones and to one another in dependence of the neighborhood relations given In particular, it is shown that feedback can be very helpful Moreover, within the new model one can also study the teachability of infinite concept classes with potentially infinite concepts such as languages Finally, it is shown that in our model teachability and learnability can be rather different

Abstract: PARAMETER SETTING IN LANGUAGE ACQUISITION. Dalila Ayoun. New York: Continuum, 2003. Pp. x + 212. $110.00 cloth.This book presents an overview of the parameter setting theory of learnability in first (L1) and second (L2) language acquisition within the generative linguistic framework. It also attempts to challenge and refine common assumptions underlying the model. The book comprises five central chapters as well as short introductory and concluding chapters. The introductory chapter summarizes the general aim of the book and the specific aims of the chapters to follow. In chapter 2, Ayoun presents historical background on the concept of parameter throughout different versions of generative linguistics and distinguishes the standard notion of parameter from the notions of associated clusters of structures, microparameters (referring to structures), and macroparameters (which apply to a family of typologically different languages). This chapter also reviews the concept of parameter setting for language changes, creole formation, computational linguistics, and neurolinguistics, ending with a brief discussion of Universal Grammar and the Critical Period Hypothesis. In brief, this chapter argues that the parameter setting approach is a model worthy of further development and refinement, capable of explaining and predicting a wide range of phenomena in linguistic theory and its applications despite misunderstandings and lack of clarity in the field.

Abstract: Editors' Introduction.- Editors' Introduction.- Invited Papers.- Invention and Artificial Intelligence.- The Arrowsmith Project: 2005 Status Report.- The Robot Scientist Project.- Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources.- Training Support Vector Machines via SMO-Type Decomposition Methods.- Kernel-Based Learning.- Measuring Statistical Dependence with Hilbert-Schmidt Norms.- An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron.- Learning Causal Structures Based on Markov Equivalence Class.- Stochastic Complexity for Mixture of Exponential Families in Variational Bayes.- ACME: An Associative Classifier Based on Maximum Entropy Principle.- Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors.- On Computability of Pattern Recognition Problems.- PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance.- Learnability of Probabilistic Automata via Oracles.- Learning Attribute-Efficiently with Corrupt Oracles.- Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution.- Learning of Elementary Formal Systems with Two Clauses Using Queries.- Gold-Style and Query Learning Under Various Constraints on the Target Class.- Non U-Shaped Vacillatory and Team Learning.- Learning Multiple Languages in Groups.- Inferring Unions of the Pattern Languages by the Most Fitting Covers.- Identification in the Limit of Substitutable Context-Free Languages.- Algorithms for Learning Regular Expressions.- A Class of Prolog Programs with Non-linear Outputs Inferable from Positive Data.- Absolute Versus Probabilistic Classification in a Logical Setting.- Online Allocation with Risk Information.- Defensive Universal Learning with Experts.- On Following the Perturbed Leader in the Bandit Setting.- Mixture of Vector Experts.- On-line Learning with Delayed Label Feedback.- Monotone Conditional Complexity Bounds on Future Prediction Errors.- Non-asymptotic Calibration and Resolution.- Defensive Prediction with Expert Advice.- Defensive Forecasting for Linear Protocols.- Teaching Learners with Restricted Mind Changes.

Abstract: Although usability is a broad subject, the literature relating to in-car navigation devices tends to focus primarily on efficiency of use. In this paper, we explore the neglected issue of learnability, that is the ease with which users learn to use, as well as some justification of its importance as a distinct issue. We present an evaluation method designed to identify learnability related problems with in-car navigation devices, along with a complete set of basic tasks with which to apply it to. Our method is applied to a device, identifying various learnability problems. Recommendations for builders of in-car systems are made based on these observations.

Abstract: This paper examines the human computer interaction issue of learnability of interactive coordinated-view visualizations. We take the case of DataMaps, a census data visualization tool intended for a general audience with a huge percentage of novices. Usability tests conducted on DataMaps revealed three main kinds of problems that novices faced: they could not make strategic selections of coordinated visualizations according to a given task, they lacked familiarity with the nature of the attributes, and there were several misunderstandings of visual syntax and interaction widget usage. We outline design features which are desirable for novice-friendliness: Task based organization of coordinated views to enable strategic selection of views to suit the task, data centric approach to familiarize novices with data, self disclosure of visual syntax features and interaction mechanisms by the interface. The design should be such that they can smoothly transition from being a novice to expert. We examine how these principles may be applied to DataMaps to re-design it for "novice-friendliness".

Abstract: The analysis of theoretical learning models is basically concerned with the comparison of identification capabilities in different models. Modifications of the formal constraints affect the quality of the corresponding learners on the one hand and regulate the quantity of learnable classes on the other hand. For many inductive inference models-such as Gold's identification in the limit-the corresponding relationships of learning potential provided by the compatible learners are well-known. Recent work even corroborates the relevance of these relationships by revealing them still in the context of uniform Gold-style learning. Uniform learning is rather concerned with the synthesis of successful learners instead of their mere existence. The subsequent analysis further strengthens the results regarding uniform learning, particularly aiming at the design of methods for increasing the potential of the relevant learners. This demonstrates how to improve given learning strategies instead of just verifying the existence of more powerful uniform learners. For technical reasons these results are achieved using various formal conditions concerning the learnability of unions of uniformly learnable classes. Therefore numerous sufficient properties for the learnability of such unions are presented and illustrated with several examples.

Abstract: Within learning theory teaching has been studied in various ways. In a common variant the teacher has to teach all learners that are restricted to output only consistent hypotheses. The complexity of teaching is then measured by the maximum number of mistakes a consistent learner can make until successful learning. This is equivalent to the so-called teaching dimension. However, many interesting concept classes have an exponential teaching dimension and it is only meaningful to consider the teachability of finite concept classes. A refined approach of teaching is proposed by introducing a neighborhood relation over all possible hypotheses. The learners are then restricted to choose a new hypothesis from the neighborhood of their current one. Teachers are either required to teach finitely or in the limit. Moreover, the variant that the teacher receives the current hypothesis of the learner as feedback is considered. The new models are compared to existing ones and to one another in dependence of the neighborhood relations given. In particular, it is shown that feedback can be very helpful. Moreover, within the new model one can also study the teachability of infinite concept classes with potentially infinite concepts such as languages. Finally, it is shown that in our model teachability and learnability can be rather different.

Abstract: The most common methodology in symbolic learning consists in inducing, given a set of observations, a general concept definition. It is widely known that the choice of the proper description language for a learning problem can affect the efficacy and effectiveness of the learning task. Furthermore, most real-world domains are affected by various kinds of imperfections in data, such as inappropriateness of the description language which does not contain/facilitate an exact representation of the target concept. To deal with such kind of situations, Machine Learning approaches moved from a framework exploiting a single inference mechanism, such as induction, towards one integrating multiple inference strategies such as abstraction. The literature so far assumed that the information needed to the learning systems to apply additional inference strategies is provided by a domain expert. The goal of this work is the automatic inference of such information. The effectiveness of the proposed method was tested by providing the generated abstraction theories to the learning system INTHELEX as a background knowledge to exploit its abstraction capabilities. Various experiments were carried out on the real-world application domain of scientific paper documents, showing the validity of the approach.

Abstract: Hypermedia systems allow information to be created, stored, accessed, and manipulated in a variety of ways. One example of such a system is a digital library (DL). DLs are typically difficult to learn and to use. One aspect of learnability is that novice users should be able to learn how to search effectively; one approach to this is having the system provide context-relevant help. We report on two studies: the first identifies novices’ difficulties, which informed design changes to integrate adaptive help into a DL system; the second illustrates how interface design can influence users’ information seeking behaviour. It focuses on strategies developed and applied by users in response to two types of ‘tips’. This study provides an indication of how the interface can improve inexperienced users’ interactions with DLs and help them develop more sophisticated information seeking strategies, while also creating more adaptive DLs.

Abstract: Learnability is a key factor in the out-of-box (OOBE) experience. This paper is a conceptual analysis of learnability in the context of OOBE. We first analyse the concept of learnability in terms of different views of learning. Then we discuss how metaphors could be utilised as a way of making learnable products which provide a positive OOBE. We also present a method for analysing individual learning processes during the first few moments with a new product 7 and illustrate the use of the method with a description of the evaluation of a sample design. Finally, we derive some design guidelines relevant to OOBE.

Abstract: 本論文では, 日本語文章入力のための注視インタフェースを習熟していく過程, すなわち学習容易性について論じる. 著者らは, 注視により日本語文章を入力できる障害者向け注視インタフェースのプロトタイプを開発した. このプロトタイプでは, 注視のみならず, マウスおよび頭部運動によっても入力が可能となっている. 15名の被験者を用いた実験を実施し, 本プロトタイプを習熟していく際のタイピング速度, エラーの頻度, ならびに主観評価について, 注視, マウス, および頭部運動入力間で比較した. その結果, 注視による入力が, タイピング速度およびエラーの頻度について劣っており, タイピング速度, エラーの頻度および満足度に対する主観評価についても劣っていることが分かった. この結果に基づき, 注視を入力として用いる際に特有な現象について論じる. さらに, プロトタイプの改善に向けた示唆についても論じる.