Formal learning theory is an approach to the study of inductive inference that has been developed by computer scientists. In this paper, I discuss the relevance of formal learning theory to such standard topics in the philosophy of science as underdetermination, realism, scientific progress, methodology, bounded rationality, the problem of induction, the logic of discovery, the theory of knowledge, the philosophy of artificial intelligence, and the philosophy of psychology.

Formal learning theory is the mathematical embodiment of a normative epistemology. It deals with the question of how an agent should use observations about her environment to arrive at correct and informative conclusions. Philosophers such as Putnam, Glymour and Kelly have developed learning theory as a normative framework for scientific reasoning and inductive inference.

This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both (...) computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review. (shrink)

One version of the problem of induction is how to justify hypotheses in the face of data. Why advance hypothesis A rather than B — or in a probabilistic context, why attach greater probability to A than B? If the data arrive as a stream of observations (distributed through time) then the problem is to justify the associated stream of hypotheses. Several perspectives on this problem have been developed including Bayesianism (Howson and Urbach, 1993) and belief-updating (Hansson, 1999). These are (...) broad families of approaches; the citations are meant just as portals. (shrink)

This article argues that a successful answer to Hume's problem of induction can be developed from a sub-genre of philosophy of science known as formal learning theory. One of the central concepts of formal learning theory is logical reliability: roughly, a method is logically reliable when it is assured of eventually settling on the truth for every sequence of data that is possible given what we know. I show that the principle of induction (PI) (...) is necessary and sufficient for logical reliability in what I call simple enumerative induction. This answer to Hume's problem rests on interpreting PI as a normative claim justified by a non-empirical epistemic means-ends argument. In such an argument, a rule of inference is shown by mathematical or logical proof to promote a specified epistemic end. Since the proof concerning PI and logical reliability is not based on inductive reasoning, this argument avoids the circularity that Hume argued was inherent in any attempt to justify PI. (shrink)

This paper places formal learning theory in a broader philosophical context and provides a glimpse of what the philosophy of induction looks like from a learning-theoretic point of view. Formal learning theory is compared with other standard approaches to the philosophy of induction. Thereafter, we present some results and examples indicating its unique character and philosophical interest, with special attention to its unified perspective on inductive uncertainty and uncomputability.

While the traditional conception of inductive logic is Carnapian, I develop a Peircean alternative and use it to unify formal learning theory, statistics, and a significant part of machine learning: supervised learning. Some crucial standards for evaluating non-deductive inferences have been assumed separately in those areas, but can actually be justified by a unifying principle.

Nelson Goodman's new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper's notion of degrees of testability (...) is linked to minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham's razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered, of which Goodman's riddle is just one example. (shrink)

This essay demonstrates a previously unnoticed connection between formal and statistical learning theory with regard to Nelson Goodman’s new riddle of induction. Discussions of Goodman’s riddle in formal learning theory explain how conjecturing “all green” before “all grue” can enhance efficient convergence to the truth, where efficiency is understood in terms of minimizing the maximum number of retractions or “mind changes.” Vapnik-Chervonenkis (VC) dimension is a central concept in statistical learning theory and (...) is similar to Popper’s notion of degrees of testability. I show that for a class inductive problems of which Goodman’s riddle is one example, a reliable inductive method minimizes the maximum number of mind changes exactly if it always conjectures the hypothesis from the set with lowest VC dimension consistent with the data. I also discuss the relevance of these results to language invariance and curve fitting. (shrink)

Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the (...) extent that such assumptions accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through.. (shrink)

Numerous learning tasks can be described as the process of extrapolating patterns from observed data. One of the driving intuitions behind the theory of algorithmic randomness is that randomness amounts to the absence of any effectively detectable patterns: it is thus natural to regard randomness as antithetical to inductive learning. Osherson and Weinstein [11] draw upon the identification of randomness with unlearnability to introduce a learning-theoretic framework (in the spirit of formal learning theory) (...) for modelling algorithmic randomness. They define two success criteria—specifying under what conditions a pattern may be said to have been detected by a computable learning function—and prove that the collections of data sequences on which these criteria cannot be satisfied respectively correspond to the set of weak 1-randoms and the set of weak 2-randoms. This learning-theoretic approach affords an intuitive perspective on algorithmic randomness, and it invites the question of whether restricting attention to learning-theoretic success criteria comes at an expressivity cost. In other words, is the framework expressive enough to capture most core algorithmic randomness notions and, in particular, Martin-Löf randomness—arguably, the most prominent algorithmic randomness notion in the literature? In this paper, we answer the latter question in the affirmative by providing a learning-theoretic characterisation of Martin-Löf randomness. We then show that Schnorr randomness, another central algorithmic randomness notion, also admits a learning-theoretic characterisation in this setting. (shrink)

This book analyzes iconic British children's literature through the lens of formal educational theory, policy, and practice. Examining themes like growth mindset and project-based learning alongside educational philosophers like Plato, Rousseau, and Dewey, the author sheds new light on children’s classics from Alice in Wonderland to Harry Potter.

Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or “grammars”) according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turing-computable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles (...) differing in form and color. We constructed different sets of sequences, using artificial “grammars” (rule sets) at three key complexity levels. Because human linguistic syntax is classed as “mildly context-sensitive,” we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning. (shrink)

In "Learning at Not-School," Julian Sefton-Green explores studies and scholarly research on out-of-school learning, investigating just what it is that is distinctive about the quality of learning in these "not-school" settings.

In formal semantics, structure is treated as the essential ingredient in the creation of sentence meaning from individual word meaning. This book introduces some of the foundational concepts, principles and techniques in the formal semantics of natural language and outlines the mathematical principles that underlie linguistics meaning. Using English examples, Yoad Winter presents the most useful tools and concepts of formal semantics in an accessible style and includes a variety of practical exercises so that readers can learn (...) to utilize these tools effectively. For readers with an elementary background in set theory and linguistics or with an interest in mathematical modelling, this fascinating study is an ideal introduction to natural language semantics. Designed as a quick yet thorough introduction to one of the most vibrant areas of research in modern linguistics today this volume reveals the beauty and elegance of the mathematical study of meaning. (shrink)

Formal learning theory formalizes the process of inferring a general result from examples, as in the case of inferring grammars from sentences when learning a language. In this work, we develop a general framework—the supervised learning game—to investigate the interaction between Teacher and Learner. In particular, our proposal highlights several interesting features of the agents: on the one hand, Learner may make mistakes in the learning process, and she may also ignore the potential relation (...) between different hypotheses; on the other hand, Teacher is able to correct Learner’s mistakes, eliminate potential mistakes and point out the facts ignored by Learner. To reason about strategies in this game, we develop a modal logic of supervised learning and study its properties. Broadly, this work takes a small step towards studying the interaction between graph games, logics and formal learning theory. (shrink)

Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the “laws” of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can (...) be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget’s theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget’s theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level. (shrink)

Youth is a historical construction and an answer to a specific challenge of individualisation in biography. And, as a historical and social construction, youth has to be learned. This article focuses on youth development from an action or activity theory perspective and as a learning process. It demonstrates how different youth problems and forms of youth differentiation follow forms of youth learning. Moreover, it shows how late modern development creates the demand for a new non-formal (...) class='Hi'>learning perspective to secure the development of new forms of competence. Based on Danish research concerning peer learning as a non-formal learning context, some perspectives of peer-learning competence are discussed. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players (...) to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

This Special Issue addresses a range of educational issues linked to main themes from our 2019 book The Emergence of Complexity: Rethinking Education as a Social Science. This book elaborated two major theses that raise fundamental questions for philosophy of education. First, that learning by groups is typically a distinctive kind of learning that is not reducible to learning by individuals. Second, that a degree of holism, as against a focus on individuals, is essential for achieving a (...) convincing understanding of this distinctive type of group learning. These two theses are of direct interest to philosophy of education since they challenges the resources of received theories of learning. Yet this kind of group learning characterises the vast bulk of human learning situations that occur outside of formal education systems. In this Special Issue introductory article we, firstly, introduce some basic principles of complexity theory, and demonstrate that, together with the concept of a ‘co-present group’ (between 2 and about 12 individuals), these ideas offer develop novel understandings of the distinctive learning that occurs within such groups. Secondly, we outline and illustrate with further examples the main features of co-present groups. Thirdly, we consolidate our conceptual work by showing five ways in which our account of group learning serves to refurbish the concept of ‘learning’. (shrink)

The present study used social learning theory and situated learning theory as a way to examine secondary social studies teacher participants' formal and informal professional learning. Existing literature is just beginning to attend to the potential of informal professional learning and to distinguish between formal and informal professional learning, so this exploratory study used observations of scheduled and spontaneous professional learning experiences, semi-structured interviews with 12 secondary social studies teachers, and (...) relevant documents to consider the following research question: In what ways do high school social studies teacher participants engage in formal and informal professional learning? Results indicate that while secondary social studies teachers engage in a variety of formal and informal professional learning experiences, their informal learning experiences are the most valuable and significant for their classroom practice. While informal professional learning is not a replacement for traditional formal professional learning, lessons learned here can inform ways to improve formal professional learning and to capitalize upon teachers’ existing informal professional learning experiences. This study provides insight into the ways in which social studies teachers support their growth outside of formal professional development. (shrink)

This paper starts by summarizing work that philosophers have done in the fields of inductive logic since 1950s and truth approximation since 1970s. It then proceeds to interpret and critically evaluate the studies on machine learning within artificial intelligence since 1980s. Parallels are drawn between identifiability results within formal learning theory and convergence results within Hintikka’s inductive logic. Another comparison is made between the PAC-learning of concepts and the notion of probable approximate truth.

The impetus for this volume developed from the 1982 University of Western Ontario Learnability Workshop, which was organized by the editors and sponsored by that University's Department of Philosophy and the Centre for Cognitive Science. The volume e~plores the import of learnability theory for contemporary linguistic theory, focusing on foundational learning-theoretic issues associated with the parametrized Government-Binding framework. Written by prominent re searchers in the field, all but two of the eight contributions are pre viously unpublished. The (...) editor's introduction provides an overview that interrelates the separate papers and elucidates the foundational issues addressed by the volume. Osherson, Stob, and Weinstein's "Learning Theory and Natural Language" first appeared in Cognition ; Matthews's "The Plausi bility of Rationalism" was published in the Journal of Philosophy. The editors would like to thank the publishers for permission to reprint these papers. Mr. Marin Marinov assisted with the preparation of the indices for the volume. VB ROBERT 1. MATTHEWS INTRODUCTION: LEARNABILITY AND LINGUISTIC THEORY 1. INTRODUCTION Formal learning theory, as the name suggests, studies the learnability of different classes of formal objects under different formal models of learning. The specification of such a model, which specifies a learning environment, a learn ing strategy, and a criterion for successful learning, determines a class of formal objects, namely, the class that can be acquired to the level of the specified success criterion by a learner implementing the specified strategy in the specified enviroment. (shrink)

We examine the prospects for finding “best possible” or “ideal” computing machines for various learning tasks. For this purpose, several precise senses of “ideal machine” are considered within the context of formal learning theory. Generally negative results are provided concerning the existence of ideal learning‐machines in the senses considered.

Formal learning theory constitutes an attempt to describe and explain the phenomenon of learning, in particular of language acquisition. The considerations in this domain are also applicable in philosophy of science, where it can be interpreted as a description of the process of scientific inquiry. The theory focuses on various properties of the process of hypothesis change over time. Treating conjectures as informational states, we link the process of conjecture-change to epistemic update. We reconstruct and (...) analyze the temporal aspect of learning in the context of dynamic and temporal logics of epistemic change. We first introduce the basic formal notions of learning theory and basic epistemic logic. We provide a translation of the components of learning scenarios into the domain of epistemic logic. Then, we propose a characterization of finite identifiability in an epistemic temporal language. In the end we discuss consequences and possible extensions of our work. (shrink)

Formal epistemology is the study of crucial concepts in general or main- stream epistemology including knowledge, belief , certainty, ra- tionality, reasoning, decision, justi cation, learning, agent interaction and information processing using a spread of di¤erent formal tools. These formal tools may be drawn from elds such as logic, probability theory, game theory, decision theory, formal learning theory, and distributed com- puting –such variety is typical in formal epistemology, a (...) eld in which interaction with topics outside of philosophy proper is the rule rather than the exception. Practitioners of formal epistemology include philosophers, computer scientists, social scientists, cognitive psychologists, theoretical economists, mathematicians, and theoretical linguists. The interdiscipli- nary nature of formal epistemology can make it di¢ cult for those new to the eld to have a sense of some of its basic agendas, actors, and issues. What follows is a breezy overview of formal epistemology as organized around notions of agency and interaction. (shrink)

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal (...) class='Hi'>learning theory.In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

Linguists intuitions about language change can be captured by adynamical systems model derived from the dynamics of language acquisition.Rather than having to posit a separate model for diachronic change, as hassometimes been done by drawing on assumptions from population biology (cf.Cavalli-Sforza and Feldman, 1973; 1981; Kroch, 1990), this new modeldispenses with these independent assumptions by showing how the behavior ofindividual language learners leads to emergent, global populationcharacteristics of linguistic communities over several generations. As thesimplest case, we formalize the example of (...) two grammars and show that eventhis situation leads directly to a nonlinear (quadratic) dynamical system.We study this one parameter model in a variety of situations for differentkinds of acquisition algorithms and maturational times, showing howdifferent learning theories can have very different evolutionaryconsequences. This allows us to formulate an evolutionary criterion for theadequacy of grammatical and learning theories. An application of thecomputational model to the historical loss of Verb Second from Old French toModern French is described showing how otherwise adequate grammaticaltheories might fail the evolutionary criterion. (shrink)

An interpretation of memory from the perspective of logical learning theory is presented. In contrast to traditional associationistic theories of learning and memory, which rest on mediation modeling, LLT rests on a predication model. Predication draws on formal and final causation whereas mediation is limited to material and efficient causation. It is held in LLT that memory begins in predicate organization, where framing meanings are logically extended to targets. Passage of time is irrelevant in this meaning (...) extension. The effectiveness of memory depends on the cohesiveness or "tightness" of meaningful organization as framed by a relevant context. Well-organized contexts facilitate memory and poorly organized contexts are detrimental to memory. Possible reasons for good or poor organization in memory are discussed, and relevant research findings cited. The paper closes with a separate definition of memory as a content and as a process. (shrink)

Like other health care practices, the increasing complexity in dentistry signals the need for a reconceptualisation of dentist professional learning. Professional dental bodies, at large, still privilege formal continuing professional development (CPD) provisions focusing on off-the-job activities despite growing evidence that much invaluable learning occurs through and at work. In exploring the two common dentist CPD approaches, this article critiques the narrow conceptions of learning inscribed in these frameworks, which are individualistic and acquisition oriented. Drawing on (...) a vignette of dentists’ professional practice at work, this paper argues for a shift in discussion from an emphasis on which CPD models work best to what counts as professional learning for dentists. To flesh out these arguments, the paper proposes using an innovative conceptual approach through the lens of ‘complexity thinking’ and the concept of ‘co-present group’. Through this lens, the reframing of thinking brings out two key features of learning: Emergent learning cannot be specified in advance and much significant learning is typically beyond an individual’s learning. Given the learning potentials of group practice and group learning, the paper concludes with suggestions to support dentists’ lifelong learning at work. (shrink)

[full article, abstract in English; abstract in Lithuanian] Many countries are adopting computing in schools, for pupils from 5 years of age. Educational philosophies that such curricula might be based on are not clear in curriculum documentation. Many Western countries’ curricula are based on developmental concepts of cognitive constructivism, with activities progressing through sensorimotor, preoperational, concrete operational, and formal operational stages. Social constructivism and constructionism add new dimensions to this learning framework, both fundamentally important for developing computing practices. (...) We review selected learning theories, and investigate features that should underpin computing curricula if practices and outcomes are to develop computing practitioner competencies of a software developer. (shrink)

Convergent realists desire scientific methods that converge reliably to informative, true theories over a wide range of theoretical possibilities. Much attention has been paid to the problem of induction from quantifier-free data. In this paper, we employ the techniques of formal learning theory and model theory to explore the reliable inference of theories from data containing alternating quantifiers. We obtain a hierarchy of inductive problems depending on the quantifier prefix complexity of the formulas that constitute the (...) data, and we provide bounds relating the quantifier prefix complexity of the data to the quantifier prefix complexity of the theories that can be reliably inferred from such data without background knowledge. We also examine the question whether there are theories with mixed quantifiers that can be reliably inferred with closed, universal formulas in the data, but not without. (shrink)

We owe most scientific knowledge to methods of inquiry that are never formally analyzed. The analysis of behavior does not call for hypothetico-deductive methods. Statistics, taught in lieu of scientific method, is incompatible with major features of much laboratory research. Squeezing significance out of ambiguous data discourages the more promising step of scrapping the experiment and starting again. As a consequence, psychologists have taken flight from the laboratory. They have fled to Real People and the human interest of “real life,” (...) to Mathematical Models and the elegance of symbolic treatments, to the Inner Man and the explanatory preoccupation with inferred internal mechanisms, and to Laymanship and its appeal to “common sense.” An experimental analysis provides an alternative to these divertissements.The “theories” to which objection is raised here are not the basic assumptions essential to any scientific activity or statements that are not yet facts, but rather explanations which appeal to events taking place somewhere else, at some other level of observation, described in different terms, and measured, if at all, in different dimensions. Three types of learning theories satisfy this definition: physiological theories attempting to reduce behavior to events in the nervous system; mentalistic theories appealing to inferred inner events; and theories of the Conceptual Nervous System offered as explanatory models of behavior. It would be foolhardy to deny the achievements of such theories in the history of science. The question of whether they are necessary, however, has other implications.Experimental material in three areas illustrates the function of theory more concretely. Alternatives to behavior ratios, excitatory potentials, and so on demonstrate the utility of rate or probability of response as the basic datum in learning. Functional relations between behavior and environmental variables provide an account of why learning occurs. Activities such as preferring, choosing, discriminating, and matching can be dealt with solely in terms of behavior, without referring to processes in another dimensional system. The experiments are not offered as demonstrating that theories are not necessary but to suggest an alternative. Theory is possible in another sense. Beyond the collection of uniform relationships lies the need for a formal representation of the data reduced to a minimal number of terms. A theoretical construction may yield greater generality than any assemblage of facts; such a construction will not refer to another dimensional system. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent (...) manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

It is argued that current attempts to model human learning behavior commonly fail on one of two counts: either the model assumptions are artificially restricted so as to permit the application of mathematical techniques in deriving their consequences, or else the required complex assumptions are imbedded in computer programs whose technical details obscure the theoretical content of the model. The first failing is characteristic of so-called mathematical models of learning, while the second is characteristic of computer simulation models. (...) An approach to model building which avoids both these failings is presented under the title of a black-box theory of learning. This method permits the statement of assumptions of any desired complexity in a language which clearly exhibits their theoretical content.Section II of the paper is devoted to the problem of testing and comparing alternative learning theories. The policy advocated is to abandon attempts at hypothesis testing. It is argued that, in general, we not only lack sufficient data and sufficiently powerful techniques to test hypotheses, but that the truth of a model is not really the issue of basic interest. A given model may be true in the sense that on the basis of available evidence we cannot statistically reject it, but not interesting in the sense that it provides little information about the processes underlying behavior. Rather, we should accept or reject models on the basis of how much information they provide about the way in which subjects respond to environmental structure. This attitude toward model testing is made precise by introducing a formal measure of the information content of a model. Finally, it is argued that the statistical concept of degrees-of-freedom is misleading when used in the context of model testing and should be replaced by a measure of the information absorbed from the data in estimating parameters. (shrink)

This paper pursues a thorough-going instrumentalist, or means-ends, approach to the theory of inductive inference. I consider three epistemic aims: convergence to a correct theory, fast convergence to a correct theory and steady convergence to a correct theory (avoiding retractions). For each of these, two questions arise: (1) What is the structure of inductive problems in which these aims are feasible? (2) When feasible, what are the inference methods that attain them? Formal learning (...) class='Hi'>theory provides the tools for a complete set of answers to these questions. As an illustration of the results, I apply means-ends analysis to various versions of Goodman's Riddle of Induction. (shrink)

In this article we discuss what are the implications for improving the design of corporate ethics programs, if we focus on the moral motivation accounts offered by main ethical theories. Virtue ethics, deontological ethics and utilitarianism offer different criteria of judgment to face moral dilemmas: Aristotle's virtues of character, Kant's categorical imperative, and Mill's greatest happiness principle are, respectively, their criteria to answer the question "What is the right thing to do?" We look at ethical theories from a different perspective: (...) the question we ask is "Why should I do the right thing?" In other words, we deal with the problem of moral motivation, and we examine the different rationale the main ethical theories provide. We then point out the relation between moral motivation and the concept of rationality in the different approaches - is acting morally seen as an expression of rational behavior? Our analysis of moral motivation provides a useful framework to improve the understanding of the relationships between formal and informal elements of corporate ethics programs, emphasizing the importance of the latter, often overlooked in compliance-focused programs. We conclude by suggesting that the concept of moral imagination can provide a unifying approach to enhance the effectiveness of corporate ethics programs, by providing an intangible asset that supports the implementation of their formal components into management decision making. (shrink)

This volume features work on learning by researchers in various disciplines who share an interest in the systematic study of cognition and in the study of the formal and semantic aspects of language acquisition. A recurring theme is that language learning involves the acquisition of certain competencies and the formation of a system of beliefs which are significantly underdetermined by the linguistic and nonlinguistic inputs available to the learner. Theories of language learning must confront the epistemological (...) problem of how it is possible to induce and fixate a belief-system on the basis of exposure to limited data. A typical strategy in dealing with this problem has been to specify various types of formal and empirical constraints on linguistic and conceptual development in terms of specific hypotheses about the character of what is learned and about the kinds of resources and strategies available to the learner. Most of the contributions in this volume are concerned with the specification and evaluation of such constraints. (shrink)

This three-part paper presents Gordon Pask’s conversation theory (CT) and interaction of actors theory (IA) and outlines ways to apply these cybernetic approaches to designing technologies and scenarios for both formal and informal learning. The first part of the paper covers concepts central to CT and IA, explaining the relationship between conceptual and mechanical operators, and machines mediating informal and formal learning. The second part of the paper applies visual representations of CT and IA (...) to understanding the use of Pask’s course assembly systems (CASTE and THOUGHTSTICKER), created between the late 1960s and the 1980s, and the ThoughtShuffler search interface, created in the 2010s. The third part proposes two pathways for design based on CT and IA: firstly, the potential for designers and observers to reformulate the qualities of tools and learning scenarios to augment human thinking and collective action; and secondly, the possibility to create tools with malleable interfaces that allow users to become intermediaries rather than consumers of knowledge. (shrink)

There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. (...) Participants were found to generalize velar palatalization (e.g., the change from [k] as in keep to [t⌢∫S] as in cheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e] as in cape) to the high front vowel context (i.e., before [i] as in keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed. (shrink)

Online informal learning spreads quickly in the COVID-19 Pandemic. Studies have predicted that both online and workplace IL have potential value to individual and organization development, whereas the study on its link with innovation remains scarce. IL is an individualized learning pattern different from formal learning, and its functioning mechanism on innovation will deepen our understanding of the relationship between learning and innovation. Self-efficacy and autonomous motivation are considered as two streams of motivational mediating mechanisms (...) to innovation. However, previous studies have proceeded largely in separation from each other. Researchers highlight the need to develop a more fine-grained theory of motivation and innovation. In addressing these literature gaps, this paper takes college teachers as the sample and focuses on the motivational mediating mechanism between online IL and innovation. The results showed that teachers IL could positively predict innovative teaching performance. Personal teaching efficacy and autonomous motivation played as sequential mediators on the link between IL and innovative teaching performance. This study extends the literature of IL–innovation relationship and enriches understanding of cognition-oriented motivation theory, highlighting one's internal autonomous construction is the key to innovation. Theoretical and practical implications for psychological empowerment are discussed. (shrink)

Developmental psychologists have long recognized the extraordinary influence of action on learning (Held & Hein, 1963; Piaget, 1952). Action experiences begin to shape our perception of the world during infancy (e.g., as infants gain an understanding of others’ goal-directed actions; Woodward, 2009) and these effects persist into adulthood (e.g., as adults learn about complex concepts in the physical sciences; Kontra, Lyons, Fischer, & Beilock, 2012). Theories of embodied cognition provide a structure within which we can investigate the mechanisms underlying (...) action’s impact on thinking and reasoning. We argue that theories of embodiment can shed light on the role of action experience in early learning contexts, and further that these theories hold promise for using action to scaffold learning in more formal educational settings later in development. (shrink)

Abstract: Pluralistic ignorance is a nasty informational phenomenon widely studied in social psychology and theoretical economics. It revolves around conditions under which it is "legitimate" for everyone to remain ignorant. In formal epistemology there is enough machinery to model and resolve situations in which pluralistic ignorance may arise. Here is a simple first stab at recovering from pluralistic ignorance by means of knowledge transmissibility.

Holistic theories of meaning have, at least since Dummett’s Frege: The Philosophy of language, been assumed to be problematic from the perspective of the incremental nature of natural language learning. In this essay I argue that the general relationship between holism and language learning is in fact the opposite of that claimed by Dummett. It is only given a particular form of language learning, and a particular form of holism, that there is a problem at all; in (...) general, for all forms of holism, and irrespective of how language learning is understood, semantic holism is conducive to language learning. The paper has three main parts. In the first, I demonstrate with the use of a simple formal system, that the form of holism that generates the problem that Dummett draws attention to is really decomposable into three distinct components, each of which is necessary for the problem to arise. In the second part, I demonstrate that even Dummett’s strong form of holism is compatible with one natural way in which to understand the incremental nature of language learning. In the third part, I outline the reasons why all forms of holism are conducive to language learning and offer two ways in which this general fact can be spelled out precisely. I end the paper by addressing some possible objections, and in doing so I draw attention to some affinities between semantic holism and the principle of compositionality, a semantic principle which has long been assumed to be conducive to language learning. (shrink)

This paper investigates the role of the lifelong learning discourse in actual governmentality. Starting with a description of the origins of lifelong learning in the discussions about alternative education in the 1960s and 1970s, the current adoption of lifelong learning by the European Union is used to show its critical components. Along with the distinction between formal and informal learning it is demonstrated how lifelong learning attempts to change the field of learning from (...) enclosed environments to a totality of learning events, while at the same time attempting to change the individuals into self‐organizing learners. We show that lifelong learning has a crucial role within the strategies of subjectivation, since its mandate is to provide individuals with the necessary skill‐sets. Finally the methodological prerequisites of the administration of Lifelong Learning are investigated, showing the corresponding developments in the European Union and their contribution to generating a European population. (shrink)

What makes our utterances mean what they do? In this work I formulate and justify a structural constraint on possible answers to this key question in the philosophy of language, and I show that accepting this constraint leads naturally to the adoption of an algebraic formalization of truth-theoretic semantics. I develop such a formalization, and show that applying algebraic methodology to the theory of meaning yields important insights into the nature of language. ;The constraint I propose is, roughly, this: (...) the meaning of an utterance of a natural language sentence is derived from the place of that sentence within a system that includes other sentences, and is structured in virtue of primitive semantic facts about complete sentences. This constraint calls for a formal semantics program where the meaning of natural language sentences is represented by an appropriate assignment of the elements of an algebraic structure to sentences, an assignment that represents facts concerning complete sentences. I call the above constraint 'The Algebraic Thesis', and the corresponding formal semantics program 'Algebraic Semantics.' ;I introduce AT by carving it out of the holistic views of language of Quine and Davidson, and I show that it is independent of other important features of these philosophers' views. I then argue for AT, taking into account recent work by Brandom, Dummett, Fodor and Lepore, Gaifman and Perry. In developing Algebraic Semantics I present structures of two basic types: Boolean algebras and cylindric algebras; these structures are construed in the context of AT in a novel way: they are treated as applying to classes of sentences as numbers apply to physical objects in measurement. Applying algebraic methodology to formal semantics and to the theory of meaning, I develop these results: ; An account of the semantics of necessity and possibility that avoids both Quine's dismissal of these notions and their reduction by David Lewis to quantification over possible worlds; ; The application of the model-theoretic notion of expansion to capture the process of our learning progressively more of the meaning of expressions in our first languages. (shrink)

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not (...) eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work. (shrink)