As the field of AI and its capabilities begin to enhance the abilities of the various aspects of work and work outcomes, a significant area that is also getting influenced by AI is the Higher Education domain. The ability of AI tools to enhance the methods and mechanics of provisioning better growth, pedagogy and thereby ensuring a greater or higher outcomes in terms of learnability of the students is still a growing space and this paper attempts to explore AI (...) as a tool to upgrade the skills and knowledge of the Higher Education teachers towards reaping the full benefit of AI.In the process of developing this paper, the research methodology would include, among other items, the evaluation of the effectiveness of AI tools as a facilitator to professional development and competency enhancement through custom/tailored approaches for learning programs. A survey would be the preferred tool to gather data for this study, targeting a sample of the population of higher education faculty. The research design will also include a comprehensive literature review to synthesize the existing knowledge available on this subject.One of the key outcomes intended out of this research and the eventual full paper is to determine and document the effects of a customized learning approach utilizing AI in the upskilling process aimed at the Higher education faculty. (shrink)

It is now a common belief that the truths of our lives are hidden in the databases streamed from our interactions in smart environments. In this current hype of big data, the Internet of Things has been suggested as the idea to embed small sensors and actuators everywhere to unfold the truths beneath the surfaces of everything. However, remaining the technology that promises more than it can provide thus far, more important for the IoT’s actual expansion to various social domains (...) than the actual discovery of hidden truths has been people’s speculations about the unknown problems, such as hidden security issues or lifestyle concerns, beyond the narrow human knowability but assumed to leave their traces in the IoT-collected big data. This paper discuss this speculation as the concealed cognitive labor of IoT users that projects some fictitious values to the big data IoT companies accumulate. By the term pan-kinetics, the systemic operation of smart actuators is analyzed as the process through which fictitious values of data are converted to the real values as these actuators draw some profitable correlations from physical domains of the IoT. Analyzing smart electroencephalogram headsets as the unique IoT devices operating on human brains, it argues how the IoT translates this speculative realism of unknown problems into its big data, which the IoT developers believe to be full of machine-learnable correlations that would lead to the smart solutions of the problems. (shrink)

Mary learns all the physical facts that one can learn by watching lectures on black-on-white television. According to Jackson, Mary learns new facts when she leaves the room and has color experiences, and that this undermines physicalism. Physicalists have responded by denying the new facts thesis; they argue, she acquires abilities, acquaintance knowledge, or new guises. I argue that the NFT is more plausible than any of the proposed alternatives. I also argue that the NFT does not undermine physicalism unless (...) physicalism entails that all physical facts are discursively learnable, and that the latter entailment is questionable. (shrink)

In his pioneering work in the field of inductive inference, Gold (Inf Control 10:447–474, 1967) proved that a set containing all finite languages and at least one infinite language over the same fixed alphabet is not identifiable in the limit (learnable in the exact sense) from complete texts. Gold’s work paved the way for computational learning theories of language and has implications for two linguistically relevant classes in the Chomsky hierarchy (cf. Chomsky in Inf Control 2:137–167, 1959, Chomsky in Knowledge (...) of language: its nature, origin, and use, Praeger, 1986; Shieber in Linguist Philos 8:333–343, 1985; Heinz in Linguist Inq 41:623–661, 2010). Within that same framework, Angluin (Inf Control 45:117–135, 1980) provided a complete characterization for the learnability of language families. Mathematically, the concept of identification in the limit from that classical setting can be seen as the use of a particular type of metric for learning in the limit (Wharton in Inf Control 26:236–255, 1974). In this research note, I use Niyogi’s extended version of a theorem by Blum and Blum (Inf Control 28:125–155, 1975) on the existence of locking data sets to prove a necessary condition for learnability in the limit of any family of languages with the Gold property in any given metric. This recovers the most general version of Gold’s Theorem as a particular case. Moreover, when the language family is further assumed to contain all finite languages, the same condition also becomes sufficient for learnability in the limit. Finally, we discuss questions that are left open and outline a research program regarding language learnability in the limit from other input types and cognitive feasibility. (shrink)

An agent must act on the situation before it, learn what it cannot yet represent, and model other agents well enough to coordinate. These faculties are usually realized by separate mechanisms, yet they share a failure mode: the situation can exceed what the agent can currently represent, and the honest response is then a principled refusal that says what was missing. -/- We develop a small cognitive architecture in which these limits arise from a single quantity. An Interpretation-Decision Unit (IDU) (...) interprets a content vector through a family of regimes, local representational frames with private bases, and decides which actions the interpretation licenses. A scalar residual of the content against the active regimes' representational scope drives the unit. Low residual with a clean licensing emits an action. Otherwise, the unit re-interprets, attempts a description-length-justified expansion of its representation, or halts with a typed, witnessed terminal. -/- We prove the unit is total and deterministic: for any content and fixed configuration, it halts in finitely many bounded-cost steps with a unique terminal witness, so abstention carries its cause by construction. We then show, by binding the architecture's open parameters without changing its mechanics, that the same residual-against-scope constraint recovers three independently documented phenomena at three scopes: the typology of not-knowing (typed abstention), a misunderstanding between agents that is forced, localized to one shared concept, and structurally invisible to the agent committing it (bounded empathy), and prerequisite dependence in learning derived from a bounded focus window rather than posited (developmental prerequisites). -/- Each instantiation is worked for both a natural and an artificial agent and states a falsifiable prediction, showing that the same constraint can model limits in both human and machine cognition. The account contributes a unification: three faculties as one constraint, and a notion of accountable abstention that is typed and witnessed by construction. (shrink)

A recent theory provides a unified cross-linguistic analysis of the interpretations that are assigned to expressions for disjunction, Negative Polarity Items, Free Choice Items, and the non-interrogative uses of wh-phrases in languages such as Mandarin Chinese. If this approach is on the right track, children should be expected to demonstrate similar patterns in the acquisition of these linguistic expressions. Previous research has found that, by age four, children have acquired the knowledge that both the existential indefinite renhe “any” and wh-words (...) in Mandarin Chinese are interpreted as Negative Polarity Items when they are bound by downward entailing operators, but the same expressions are interpreted as Free Choice Items when they are bound by deontic modals or by the Mandarin adverbial quantifier dou “all”. The present study extends this line of research to the Mandarin disjunction word huozhe. A Truth Value Judgment Task was used to investigate the possibility that disjunction phrases that are bound by the adverbial quantifier dou generate a conjunctive interpretation in the grammars of Mandarin-speaking 4-year-old children. The findings confirmed this prediction. We discuss the implications of the findings for linguistic theory and for language learnability. (shrink)

It is increasingly argued that polymathy—vocational and avocational pursuits in multiple domains—is deeply associated with creativity and innovation, and that its development enables the creation of important bridges between otherwise fragmented, dispersed sets of knowledge. Nevertheless, the dominant culture in both industry and academia is still that of narrow specialization. In this paper, we argue that in the context of COVID-19 crisis, with its wicked and transdisciplinary nature, the disciplinary approach of specialization is ill-suited to solve our increasingly complex problems, (...) and that polymathic thinking can be a crucial asset in this regard. Drawing on different literature strands, we first examine the interplay between polymathy and other well-developed constructs in personality and temperament research. We then advance theoretical predictions regarding the relationship between trait polymathy and resilience in the COVID-19 crisis. After that, we discuss learnable strategies that can be used in complex, uncertain and adverse situations, which are associated with development of a more polymathic set of knowledge. Later, we discuss how it may be possible to better capitalize on the key features of polymathic thinking at the societal level. Finally, we conclude with a reflection on the adequacy of our current institutions for dealing with complex problems, and we underscore the crucial role of polymathic thinking in an increasingly complex and interrelated world. (shrink)

What role, if any, should centered possible worlds play in characterizing the attitudes? Lewis :513–543, 1979) argued that, in order to account for the phenomena of self-location :474–497, 1977, Noûs 13:3–21, 1979), the contents of the attitudes should be taken to be centered propositions. Stalnaker Assertion: New philosophical essays, Oxford University Press, Oxford, 2011, Context, Oxford University Press, Oxford, 2014), however, has argued that while centered worlds are needed to characterize e.g. belief states, the contents of such states should be (...) understood as ordinary, uncentered propositions. But Stalnaker does not, as is common, provide a semantics of attitude ascriptions based on the models he develops of the attitudinal states themselves. This paper begins to explore the prospects for doing so. It argues that a simple but well-motivated approach does not yield the principles of knowledge and belief Stalnaker endorses; and that a modification which does brings with it worries of its own surrounding communication and learnability. A technical appendix contains novel and pertinent results in doxastic/epistemic logic. (shrink)

We study the learning power of iterated belief revision methods. Successful learning is understood as convergence to correct, i.e., true, beliefs. We focus on the issue of universality: whether or not a particular belief revision method is able to learn everything that in principle is learnable. We provide a general framework for interpreting belief revision policies as learning methods. We focus on three popular cases: conditioning, lexicographic revision, and minimal revision. Our main result is that conditioning and lexicographic revision can (...) drive a universal learning mechanism, provided that the observations include only and all true data, and provided that a non-standard, i.e., non-well-founded prior plausibility relation is allowed. We show that a standard, i.e., well-founded belief revision setting is in general too narrow to guarantee universality of any learning method based on belief revision. We also show that minimal revision is not universal. Finally, we consider situations in which observational errors may occur. Given a fairness condition, which says that only finitely many errors occur, and that every error is eventually corrected, we show that lexicographic revision is still universal in this setting, while the other two methods are not. (shrink)

Commitment to a pattern of altruism or self-control may indeed be learnable and sometimes rational. Commitment may also result from illusions. In one illusion, people think that their present behavior causes their future behavior, or causes the behavior of others, when really only correlation is present. Another happy illusion is that morality and self-interest coincide, so that altruism appears self-interested.

The nature–nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. (...) Second, the controversy persists because we lack a precise, testable theory of the process by which genes and the environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (outcome X is so peculiar that it must be innate), (2) species specificity (we are the only species who do X, so X must lie in the human genome), (3) localization (outcome X is mediated by a particular part of the brain, so X must be innate), and (4) learnability (we cannot figure out how X could be learned, so X must be innate). We believe that an explicit, plausible theory of interaction is now around the corner, and that many of the classic maneuvers to defend or attack innateness will soon disappear. In the interim, some serious errors can be avoided if we keep these confounded issues apart. That is the major goal of this chapter: not to attack innateness but to clarify what claims about innateness are (and are not) about. (shrink)

This paper investigates the learnability by positive examples in the sense of Gold of Pregroup Grammars. In a first part, Pregroup Grammars are presented and a new parsing strategy is proposed. Then, theoretical learnability and non-learnability results for subclasses of Pregroup Grammars are proved. In the last two parts, we focus on learning Pregroup Grammars from a special kind of input called feature-tagged examples. A learning algorithm based on the parsing strategy presented in the first part is (...) given. Its validity is proved and its properties are examplified. (shrink)

Revised and reprinted in Handbook of Philosophical Logic, volume 10, Dov Gabbay and Frans Guenthner (eds.), Dordrecht: Kluwer, (2003). -- Two sorts of property theory are distinguished, those dealing with intensional contexts property abstracts (infinitive and gerundive phrases) and proposition abstracts (‘that’-clauses) and those dealing with predication (or instantiation) relations. The first is deemed to be epistemologically more primary, for “the argument from intensional logic” is perhaps the best argument for the existence of properties. This argument is presented in the (...) course of discussing generality, quantifying-in, learnability, referential semantics, nominalism, conceptualism, realism, type-freedom, the first-order/higher-order controversy, names, indexicals, descriptions, Mates’ puzzle, and the paradox of analysis. Two first-order intensional logics are then formulated. Finally, fixed-point type-free theories of predication are discussed, especially their relation to the question whether properties may be identified with propositional functions. (shrink)

Metaphor, I argue, is a type of expression that is used to communicate information beyond that communicated by its literal meaning. I argue that the literal meaning of metaphors are essential. I attempt to account for metaphor in such a way as to retain the literal meaning, while also accounting for what is called the "metaphorical meaning" of metaphors. Secondly, I am concerned with the mechanisms in virtue of which we are able to distinguish the metaphorical from the literal. ;Chapter (...) I is a discussion of the problems that I will consider in the dissertation. These include distinguishing metaphorical from literal expressions, Donald Davidson's learnability and scrutability criteria, the exclusionary criteria, conversion and constancy theories of metaphor, and truth-values of metaphors. ;Chapter II critically examines Aristotle's, Max Black's, and Monroe C. Beardsley's intensional conversion theories. Here I argue that conversion theories are inadequate and unnecessary to account for metaphorical attributions. ;Chapter III critically examines extensional conversion theories of metaphor including Nelson Goodman's version. I discuss the difficulties that arise in postulating such reference conversions. I argue that this type of conversion theory is also unsuccessful and unnecessary. ;Chapter IV is a critical examination of four types of emotive theory of metaphor, one of which is Max Rieser's. These theories maintain, and I deny, that metaphors have an "emotive meaning" in addition to or instead of their literal meaning. I consider the shortcomings and consequences of emotive views, and argue that the postulation of emotive meanings is also misguided and unnecessary. ;Chapter V offers an alternative to conversion theories. Here I argue for a theory requiring no meaning or reference conversions. My view involves H. P. Grice's notions of conversational maxims and implicatures. I conclude that metaphors retain their literal meaning, partly in virtue of which they generate implicatures. I also conclude that our recognition of, and ability to distinguish, metaphors from literal expressions involves the violations of conversational maxims. (shrink)

We consider the arithmetic complexity of index sets of uniformly computably enumerable families learnable under different learning criteria. We determine the exact complexity of these sets for the standard notions of finite learning, learning in the limit, behaviorally correct learning and anomalous learning in the limit. In proving the${\rm{\Sigma }}_5^0$-completeness result for behaviorally correct learning we prove a result of independent interest; if a uniformly computably enumerable family is not learnable, then for any computable learner there is a${\rm{\Delta }}_2^0$enumeration witnessing (...) failure. (shrink)

We argue that the problem of ideographic codes stems from neither learnability nor standardization, but from a general issue of pragmatic interpretation. As ideographic codes increase in expressive power, in order to reduce ambiguity, they must become more detailed – such that production becomes more cumbersome, and requires greater artistry on the part of users, limiting their capacity for growth.

In the paper, I propose a novel approach to Frege’s view on the principle of compositionality, its relation to the propositional holism and the formation of concepts. The main idea is to distinguish three stages of constructing a logically perfect language. At the first stage, only a sentence as a whole expresses a Thought. It is impossible to assign meaning to less complex units. This is the stage of an ordinary language. The second phase concerns the proper level of construction (...) of a logically perfect language. We are forced to discriminate syntactic and semantic parts of sentences to account for the inference relations. We can distinguish senses and references of parts of sentences. Furthermore, it is possible here to choose between different ways of analysing the given Thought. Finally, at the third stage, every expression of the language has an unambiguous sense and this sense determines a unique reference. The logically perfect language is ready. We may view Thoughts as composed from primitive elements. Moreover, the senses of parts of a sentence correspond to the parts of a Thought, so that the structure of the sentence serves as the image of the structure of the Thought. The principle of compositionality is met and we can discern how understanding of the infinite numbers of Thoughts is possible and how languages are learnable. The main advantage of the presented view is that it allows accommodating some aspects of Frege’s philosophy that are often seen as mutually incompatible. Furthermore, I submit extensive textual data in favour of the discussed views and conceptions. (shrink)

This chapter uses a normative pragmatic metavocabulary to introduce a notion of the _incompatibility_ of claims, which then serves as a semantic primitive that is shown to suffice to introduce classical logical connectives, including S5 modal operators. A meaning-use analysis of this construction is offered. The semantics is recursive, but not compositional, showing that compositionality is not the only way to secure learnability and projectibility. The notion of the logic that is _intrinsic_ to a consequence relation (whether material or (...) logical) is introduced and applied. (shrink)

We investigate how Reinforcement Learning methods can be used to solve the production selection and production ordering problem in ACT‐R. We focus on four algorithms from the Q learning family, tabular Q and three versions of Deep Q Networks, as well as the ACT‐R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision tasks framed as sequential decision problems.

There is no doubt that emotions have an important effect on practices of moral reasoning such as clinical ethics consultation. Empathy is not only a basic human emotion but also an important and learnable skill for health care professionals. A basic amount of empathy is essential both in patient care and in clinical ethics consultation. This article debates the “adequate dose” of empathy in ethics consultations in clinical settings and tries to identify possible situations within the process of consultation in (...) which this crucial feeling is at risk. (shrink)

We compute that the index set of PAC-learnable concept classes is m-complete Σ30\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\Sigma^{0}_{3}}$$\end{document} within the set of indices for all concept classes of a reasonable form. All concept classes considered are computable enumerations of computable Π10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\Pi^{0}_{1}}$$\end{document} classes, in a sense made precise here. This family of concept classes is sufficient to cover all standard examples, and also has the property that PAC (...) class='Hi'>learnability is equivalent to finite VC dimension. (shrink)

Visual phenomena such as shadows and reflections are lexicalized in idiosyncratic ways. In particular, we appear to master notions of certain types of shadows, at least notions that \are learnable and communicable, for which we do not have names. We also have many imprecise lexicalizations; so much so that in certain conditions notions that appear to be antonyms, such as that of shadows and of light, for example, even get applied to the same visual phenomena. Reflections offer another field of (...) study. I shall investigate whether constraints on lexicalization depend on particular cognitive advantages conferred upon objects or object-like entities. (shrink)

This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics (John Goldsmith), psychology (Nick Chater), computer science (Alex Clark), and cognitive science (Amy Perfors), the book sheds light on the central (...) problems of learnability and language, and traces their implications for key questions of theoretical linguistics and the study of language acquisition. (shrink)

About 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory and machine learning. The following years saw a fruitful exchange of ideas between PAC-learning and the model theory of NIP structures. In this article, we point out a new and similar connection between model theory and machine learning, this time developing a correspondence between stability and learnability in various settings of online learning. In particular, this gives many (...) new examples of mathematically interesting classes which are learnable in the online setting. (shrink)

Clark & Thornton's type-1/-2 distinction is not well-defined. The classes of type-1 and type-2 problems are too broad: many nocomputable functions are type-1 and type-2 learnable. They are also too narrow: trivial functions, such as identity, are neither type-1 nor type-2 learnable. Moreover, the scope of type-1 and type-2 problems appears to be equivalent. Overall, this distinction does not appear useful for machine learning or cognitive science.

We develop a bounded version of the finite injury priority method in recursion theory. We use this to study the learnability of unions of rectangles over the domain {0, …, n − 1}d with only equivalence queries. Applying this method, we show three main results:1. The class of unions of rectangles is polynomial time learnable for constant dimension d.2. The class of unions of rectangles whose projections at some unknown dimension are pairwise-disjoint is polynomial time learnable.3. The class of (...) unions of two disjoint rectangles is polynomial time learnable with unions of two rectangles as hypotheses. (shrink)

We present a framework that provides a logic for science by generalizing the notion of logical (Tarskian) consequence. This framework will introduce hierarchies of logical consequences, the first level of each of which is identified with deduction. We argue for identification of the second level of the hierarchies with inductive inference. The notion of induction presented here has some resonance with Popper's notion of scientific discovery by refutation. Our framework rests on the assumption of a restricted class of structures in (...) contrast to the permissibility of classical first-order logic. We make a distinction between deductive and inductive inference via the notions of compactness and weak compactness. Connections with the arithmetical hierarchy and formal learning theory are explored. For the latter, we argue against the identification of inductive inference with the notion of learnable in the limit. Several results highlighting desirable properties of these hierarchies of generalized logical consequence are also presented. (shrink)

The aim of this paper is to introduce Robert Brandom’s Inferentialism (Inferential theory of meaning) and Fodor and Lepore’ compositionality objection, and to protect Inferentialism from the objection based on compositionality. According to Inferentialism, To grasp or understand a concept is to have practical mastery over the inferences in which it is involved. However, Fodor and Lepore oppose Inferentialism by offering the compositionality objection. They argue that compositionality is needed to explain productivity, systematicity and learnability of language, meaning is (...) compositional. Since inferential role is not compositional, however, meaning is not an inferential role. Against Fodor and Lepore’s objection, I present Brandom’s responses and develop my own views. (shrink)

In this thesis, we study the complexity of some mathematical problems: in particular, those arising in computable analysis and algorithmic learning theory for algebraic structures. Our study is not limited to these two areas: indeed, in both cases, the results we obtain are tightly connected to ideas and tools coming from different areas of mathematical logic, including for example descriptive set theory and reverse mathematics.After giving the necessary preliminaries, we first study the uniform computational strength of the Cantor–Bendixson theorem in (...) the Weihrauch lattice. This work falls into the program connecting reverse mathematics and computable analysis via the framework of Weihrauch reducibility. We concentrate on problems related to perfect subsets of Polish spaces, studying the perfect set theorem, the Cantor–Bendixson theorem, and various problems arising from them. As far as we know, this is the first systematic study of problems at the level of $\mathbf {\Pi }^1_1\mathsf {-CA}_0$ in the Weihrauch lattice. We show that the strength of some of the problems we study depends on the topological properties of the Polish space under consideration, while others have the same strength once the space is rich enough.We continue considering problems related to (induced) subgraphs. We provide results on the (effective) Wadge complexity of sets of graphs, that are also used to determine the Weihrauch degree of certain decision problems. The decision problems we consider are defined for a fixed graph G, and they take as input a graph H, answering whether G is an (induced) subgraph of H: we also consider the opposite problem (i.e., answering whether H is an induced subgraph of G). We conclude this part on (induced) subgraphs considering the Weihrauch degree of “search problems.”These problems are defined for a fixed graph G, and they take as input a graph H such that G is an (induced) subgraph H: the output is a copy of G in H. In both cases, we highlight differences and analogies between the subgraph and the induced subgraph relation.We then move our attention to algorithmic learning theory, and we present the framework we use to study the learnability of families of algebraic structures: here, given a countable family of pairwise nonisomorphic structures $\mathfrak {K}$, a learner receives larger and larger pieces of an arbitrary copy of a structure in $\mathfrak {K}$ and, at each stage, is required to output a conjecture about the isomorphism type of such a structure. We say that $\mathfrak {K}$ is learnable if there exists a learner which eventually stabilizes to a correct guess. The framework was lacking a method for comparing the complexity of nonlearnable families, and so we propose a solution to this problem using tools coming from invariant descriptive set theory. To do so, we first prove that a family of structures is learnable if and only if its learning domain is continuously reducible to the relation $E_0$ of eventual agreement on infinite binary sequences and then, replacing $E_0$ with Borel equivalence relations of higher complexity, we obtain a new hierarchy of learning problems. This leads to the notion of E-learnability, where a family of structures $\mathfrak {K}$ is E-learnable, for a Borel equivalence relation E, if there is a continuous reduction from the isomorphism relation associated with $\mathfrak {K}$ to E. It is then natural to ask how the notion of E-learnability interacts with “classical” learning paradigms.Finally, we study the number of mind changes that a learner needs to learn a given family, both from a topological and a combinatorial point of view, and we consider how the complexity of a learner (in terms of Turing reducibility) affects the number of mind changes for learning a given family.Abstract prepared by Vittorio Cipriani.E-mail: [email protected]: https://air.uniud.it/handle/11390/1252226. (shrink)

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...) Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study—the language acquisition device—rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. (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)

Regularities in natural language systems, despite their cognitive advantages in terms of storage and learnability, often coexist with exceptions, raising the question of whether and why irregularities survive. We offer a complex system perspective on this issue, focusing on the irregular past tense forms in English. Two separate processes affect the overall regularity: new verbs constantly entering the vocabulary in the regular form at low frequency, and transitions in both directions (from irregular to regular and vice-versa) occurring in a (...) narrow frequency range. The introduction of new verbs leads to an increase in regular types, that, entering at low frequencies, have a small impact on the perceived irregularity in terms of tokens. The frequency of usage acts as a control parameter, the majority of verbs types being fully-regular(irregular) at low(high) frequencies, with no evidence of irregularity facing extinction. Very few verbs types in an intermediate frequency region exhibit both regular and irregular forms at the same time, suggesting that the coexistence is unstable. The observed pattern of usage showing an abrupt change in response to small variations of the control parameter only appears in agent-based models provided that the word state is non-binary. By introducing this key ingredient, high-frequency irregular past-tense can survive the tendency to regularize over time, as observed in natural languages. (shrink)

Mindfulness for the High Performance World provides a unique approach to mindfulness training, built upon the principles of Buddhist philosophy written in line with the Dalai Lama’s description of meditation and mindfulness as “Science of the Mind”. This unique volume explores mindfulness as a learnable skill in context with the underpinnings of the teachings of Eastern psychology. The authors, Norm, a physician, cancer researcher and triathlete and Karolynn, a psychotherapist, mindfulness meditation teacher and marathoner, live and work in a high-stress, (...) high-expectation world. Their approach is rooted in an understanding that thoughts produce biochemical and physiological changes and provides a strategic framework to instruct an individual on how to categorize types of thoughts. After harnessing this ability, one is positioned to become both more aware of his or her thoughts as well as the specific patterns of sensations they produce, or Sentinel SitesSM. The awareness of what the mind is doing and the ability to interrupt a thought pattern and/or control the response almost instantly leads one to having a healthier life, improved relationships with others and better adaptability to one’s environment. Emphasizing the importance of physical activity and nutrition, the authors present a systematic approach for people who want to learn and incorporate mindfulness and transform how they live without having to divert their lives and careers. Offering itself as an accessible and skill-based introduction to the principles, practices, and benefits of mindfulness, Mindfulness for the High Performance World is a useful resource for students, athletes and professionals living and working in high-performance, high-stress environments and also for mindfulness practitioners seeking to deepen their skill level. (shrink)

In his recent book, Concepts: Where Cognitive Science Went Wrong, Jerry Fodor retracts the radical concept-nativism he once defended. Yet that postion stood, virtually unchallenged, for more than twenty years. This neglect is puzzling, as Fodor's arguments against concepts being learnable from experience remain unanswered, and nativism has historically been taken very seriously as a response to empiricism's perceived shortcomings. In this paper, I urge that Fodorean nativism should indeed be rejected. I argue, however, that its deficiencies are not so (...) obvious that they can simply be taken for granted. Fodor can counter extant objections by stressing two distinctions: between historicist and counterfactual semantic theories and between explaining reference and explaining concept-acquisition. But, I argue, this victory is pyrrhic. Reformulated as objections to his account qua theory of concept-acquisition, and not qua theory of reference, analogous difficulties are fatal to the Fodorean position. (shrink)

A fundamental goal of linguistic theory is to explain how natural languages are acquired. This paper describes some recent findings on how learners acquire syntactic knowledge for which there is little, if any, decisive evidence from the environment. The first section presents several general observations about language acquisition that linguistic theory has tried to explain and discusses the thesis that certain linguistic properties are innate because they appear universally and in the absence of corresponding experience. A third diagnostic for innateness, (...) early emergence, is the focus of the second section of the paper, in which linguistic theory is tested against recent experimental evidence on children's acquisition of syntax. (shrink)

The deep formal and conceptual link existing between artificial life and artificial intelligence can be highlighted using conceptual tools derived by Karl Popper's evolutionary epistemology.Starting from the observation that the structure itself of an organism embodies knowledge about the environment which it is adapted to, it is possible to regard evolution as a learning process. This process is subject to the same rules indicated by Popper for the growth of scientific knowledge: causal conjectures (mutations) and successive refutations (extinction). In the (...) field of machine learning such a paradigm is represented by genetic algorithms that, simulating biological processes, emulate cognitive processes. From a practical viewpoint, that perspective allows to identify the two different kinds of learning considered by artificial intelligence, knowledge acquisition and skill improvement, and to get a different view of the problem of heuristic knowledge in learning systems. From a theoretical point of view, these considerations can shade a new light on an old epistemological problem: why do we live in a learnable world? (shrink)

D O N A L D D AV I D S O N’S “ Meaning and Truth,” re vo l u t i o n i zed our conception of how truth and meaning are related (Davidson    ). In that famous art i c l e, Davidson put forw a rd the bold conjecture that meanings are satisfaction conditions, and that a Tarskian theory of truth for a language is a theory of meaning for that language. In (...) “Meaning and Truth,” Davidson proposed only that a Tarskian truth theory is a theory of meaning. But in “Theories of Me a n i n g and Learnable Languages,” he argued that the finite base of a Tarskian theory, together with the now familiar combinatorics, would explain how a language with unbounded expre s s i ve capacity could be learned with finite means ( Davidson    ). This certainly seems to imply that learning a language is, in p a rt at least, learning a Tarskian truth theory for it, or, at least, learning what is specified by such a theory. Davisdon was cagey about committing to the view that meanings actually a re satisfaction conditions, but subsequent followers had no such scru p l e s. We can sum this up in a trio of claims: Davidson’s Conjecture () A theory of meaning for L is a truth-conditional semantics for L. () To know the meaning of an expression in L is to know a satisfaction condition for that expression. () Meanings are satisfaction conditions. For the most part, it will not matter in what follows which of these claims is at stake. I will simply take the three to be different ways of formulating what I will call Davidson’s Conjecture (or sometimes just The Conjecture). Davidson’s Conjecture was a very bold conjecture. I think we are now in a.. (shrink)

Davidson replies to critics (in particular Hartry Field and Gilbert Harman) who have objected that truth theories cannot do the duty of meaning theories if they fail to elucidate the concept of reference on which they rely (Hartry Field, Gilbert Harman). In response, Davidson explains precisely why truth theories do not and need not explain or analyse the concepts of satisfaction and denotation (cf Essay 14) once these are characterized recursively (which requires that the basic vocabulary is finite, as Essay (...) 1 had demanded on grounds of the language's learnability). He further shows how taking the relation of reference as being central when explaining the relation of language to world collapses into a discredited ‘building‐block theory’ of language (see Essay 1) and neglects the semantic primacy of the sentence. This primacy, argues Davidson, can only be acknowledged by making truth and sentential structure one's central semantic concepts; further, truth theories are testable only at the sentential level, not at that of subsentential reference. (shrink)

Develops the criterion of learnability proposed in Essay 1 into a constraint for a satisfactory theory of meaning: such a theory must show how, on the basis of a finite stock of semantic primitives, speakers come to understand a potentially infinite number of sentences; more precisely, how the meaning of each and every sentence in the language is owed to the sentence's structural composition. Davidson notes that introducing distinct entities as meanings for each expression (‘intensions’, ‘senses’) does not illuminate (...) these structural workings of the language (this complaint, he says, operates quite independently of ontological reservations about intensions being ‘abstract’ or lacking clear individuation conditions). If, on the other hand, we dispense with intensions, we must define ‘meaning’ by mere appeal to reference; but this clearly won’t work since, if we concede that sentences ‘refer’ to their truth‐values, all sentences alike in truth value would be alike in meaning. Davidson therefore individuates a sentence's meaning by its ‘logical place’ in the language to which it belongs, i.e. by its entailment relations to other sentences in the language; the meaning of a subsentential expression is likewise individuated holistically, namely in terms of the systematic effect the expression has on the entailment relations of the sentence in which it occurs. Since a Tarskian truth theory captures these entailment relations for each sentence (in a specified language) by associating it with its ‘truth‐condition’, the truth theory can be said to give the ‘meaning’ thus construed for each sentence (_a fortiori_ for their subsentential expressions), while meeting both the constraint for finiteness and the restriction to purely referential means. Davidson discusses various objections to this proposal (since called ‘Davidson's programme’), e.g. that it cannot accomodate sentences containing indexicals or contexts in which extensionality breaks down; and he shows its merit in its varied applicability to questions of logical form, the success of which is evaluated by whether the aforementioned entailments are preserved and explained for a variety of sentences (see Essays 6–8, and Essays 6–10 of the companion volume _Essays on Actions and Events_). (shrink)

Davidson asks what properties a language must have to be learnable. He criticizes a (then) popular response that models the order of language acquisition on the epistemological priority of the types of expressions learnt; he labels this position the ‘building‐block theory’ (see further Essay 16). He discusses Strawson's critique of Quine's elimination of singular terms and shows how it is likewise premissed on the questionable derivation of claims about language learning from purely _a priori_ considerations. On the positive side, Davidson (...) proposes that a language is learnable by a creature with finite means if the language's number of semantic primitives or undefinables is finite. Using this criterion, he demonstrates that various theories in the philosophy of language introduce an infinite number of semantic primitives into the language and thus make it unlearnable; theories he alleges of this error (1) model quotations on names of expressions (Tarski, Quine; cf Essay 6), (2) analyse belief attributions in terms of linguistic marks (Scheffler, Carnap) or distinct one‐place predicates for each attributed belief (Quine; cf Essay 7), or (3) postulate intensional entities into their overall semantic framework (Frege, Church). (shrink)

Selection through iterated learning explains no more than other non-functional accounts, such as Universal Grammar (UG), why language is so well designed for communicative efficiency. It does not predict several distinctive features of language, such as central embedding, large lexicons, or the lack of iconicity, which seem to serve communication purposes at the expense of learnability.