Implicit/Explicit Rules and Representations

In this position paper, we argue that careless reliance on AI to answer our questions and to judge our output is a violation of Grice’s Maxim of Quality as well as a violation of Lemoine’s legal Maxim of Innocence, performing an (unwarranted) authority fallacy, and while lacking assessment signals, committing Type II errors that result from fallacies of the inverse. What is missing in the focus on output and results of AI-generated and AI-evaluated content is, apart from paying proper tribute, (...) the demand to follow a person’s thought process (or a machine’s decision processes). In deliberately avoiding Neural Networks that cannot explain how they come to their conclusions, we introduce logic-symbolic inference to handle any possible epistemics any human or artificial information processor may have. Our system can deal with various belief systems and shows how decisions may differ for what is true, false, realistic, unrealistic, literal, or anomalous. As is, stota AI such as ChatGPT is a sorcerer’s apprentice. (shrink)

In some situations, we attribute intentional mental states to a person despite their inability to articulate the contents in question: these are implicit mental states. Attributions of implicit mental states raise certain philosophical challenges related to rationality, concept possession, and privileged access. In the philosophical literature, there are two distinct strategies for addressing these challenges, depending on whether the content attributions are personal-level or subpersonal-level. This paper explores the difference between personal-level and subpersonal-level approaches to implicit mental state attribution and (...) investigates the relationship between the two approaches. It concludes by highlighting the methodological and metaphilosophical commitments which can result in different perspectives on the relative priority of personal-level and subpersonal-level theories. (shrink)

Cancellability is an essential feature of implicatures. However, its reliability has been challenged by several cases and examples in which conversational implicatures seem to be hard or even impossible to cancel. Should it then be concluded that not all implicatures are cancellable, and therefore Grice's cancellability test should be weakened or abandoned? The present paper addresses this problem by drawing a distinction between theoretical and practical cancellability, where the latter concept captures the (un)reasonableness of explicit or contextual cancellation. By analyzing (...) three legal cases of misleading advertising, I show how the indefensibility of an interpretation can account for the practical uncancellability of an implicature. To explain and account for the reasonableness of an interpretation, the presumptive inferences involved must be reconstructed and evaluated considering different dimensions, such as the conversational context and the associated presumptions. (shrink)

I address the question of whether naturalism can provide adequate means for the scientific study of rules and rule-following behavior. As the term "naturalism" is used in many different ways in the contemporary debate, I will first spell out which version of naturalism I am targeting. Then I will recall a classical argument against naturalism in a version presented by Husserl. In the main part of the paper I will sketch a conception of rule-following behavior that is influenced by Sellars (...) and Haugeland. I will argue that rule-following is an essential part of human nature and insist in the social dimension of rules. Moreover, I will focus on the often overlooked fact that genuine rule-following behavior requires resilience and presupposes an inclination to calibrate one's own behavior to that of the other members of the community. Rule-following, I will argue, is possible only for social creatures who follow shared rules, which in turn presupposes a shared (first-person plural) perspective. This implies, however, that our scientific understanding of human nature has to remain incomplete as long as it does not take this perspective, which prima facie seems alien to it, into account. (shrink)

나는 이상한 책과 특별한 사람들에, 매우 익숙하지만, 호킨스는 어떤 종류의 진술의 "진실"의 열쇠로 근육 긴장을 테스트하기위한 간단한 기술의 그의 사용으로 인해 눈에 띄는 - 즉, 테스트되는 사람이 그것을 믿는지 여부, 하지만 정말 사실 여부! 잘 알려진 것은 사람들이 이미지, 소리, 터치, 냄새, 아이디어, 사람 등 노출되는 모든 것에 대해 자동적이고 무의식적인 생리학적, 심리적 반응을 보일 것이라는 것입니다. 그래서,, 그들의 진정한 감정을 알아 내기 위해 근육 독서는 전혀 급진적이지 않다, 막대기로 물을 찾는 중 스틱으로 사용하는 달리 (더 많은 근육 독서) "초자연적 (...) 인 과학"을 할 수 있습니다. 호킨스는 인지 부하의 증가에 대한 응답으로 팔의 근육에 있는 긴장 감소의 사용을 이렇게 누군가의 핑거의 일정한 압력에 응하여 팔이 떨어지는 원인이 되는 것을 기술합니다. 그는 '암시적 인식', '자동성' 등의 문구로 언급된 사회 심리학분야에서 오랫동안 확립되고 방대한 연구 노력이 있고, '운동학'을 사용하는 것이 하나의 작은 부분이라는 것을 알지 못하는 것 같다. 근육 톤 (자주 사용) 사회 심리학자 측정 EEG, 갈바닉 피부 응답 및 단어에 가장 자주 구두 응답, 문장, 이미지 또는 시간에 상황에 따라 시간에 변화 초에서 개월 자극 후 개월. Bargh와 Wegner와 같은 많은 사람들은 S1 (자동화 된 시스템 1)을 통해 인식없이 크게 배우고 행동하는 오토마톤이며 Kihlstrom 및 Shanks와 같은 많은 다른 사람들이 이러한 연구가 결함이 있으며 우리는 S2 (심의 시스템 2)의 생물이라고 말합니다. 호킨스는 더 높은 계급의 생각의 설명 심리학의 다른 영역에서와 같이, 아무 생각이 없는 것 같지만, "자동성"에 관한 상황은 Wittgenstein이 30 년대에 심리학의 불임과 불모의 이유를 설명 할 때와 마찬가지로 여전히 혼란스럽습니다. 그럼에도 불구하고, 이 책은 쉽게 읽을 수 있으며 일부 치료사와 영적 교사는 그것을 사용할 수 있습니다. 현대 의 두 시스템 보기에서인간의 행동에 대한 포괄적 인 최신 프레임 워크를 원하는 사람들은 내 책을 참조 할 수 있습니다'철학의 논리적 구조, 심리학, 민d와 루드비히 비트겐슈타인과 존 Searle의언어' 2nd ed (2019). 내 글의 더 많은 관심있는 사람들은 '이야기 원숭이를 볼 수 있습니다-철학, 심리학, 과학, 종교와 운명 행성에 정치 - 기사 및 리뷰 2006-2019 3 rd 에드 (2019) 및 21st 세기 4번째 에드 (2019) 및 기타에서 자살 유토피아 망상. (shrink)

Я очень привык к странным книгам и специальныхлюдей, но Хокинс выделяется из-за его использования простой техники для тестирования мышечного напряжения в качестве ключа к "истине" любого заявления бы то ни было, т.е., не только ли человек проходит тестирование верит, но действительно ли это так! Что хорошо известно, что люди будут показывать автоматические, бессознательные физиологические и психологические реакции на все, что они подвергаются-изображения, звуки, прикосновения, запахи, идеи, люди. Таким образом,, мышечное чтение, чтобы узнать их истинные чувства не является радикальным на всех, (...) в отличие от использования его в качестве обливания палкой (больше мышечного чтения), чтобы сделать "паранормальные науки". Хокинс описывает использование снижения напряжения в мышцах руки в ответ на увеличение когнитивной нагрузки, в результате чего рука падает в ответ на постоянное давление пальцев человека. Он, кажется, не знают, что есть давно установленных и обширных текущих исследований в социальной психологии называют такие фразы, как "неявное познание", "автоматизм" и т.д., и что его использование "кинезиологии" является одним крошечным разделом. В дополнение к мышечному тонусу (нечасто используемым) социальные психологи измеряют ЭЭГ, гальваническую реакцию кожи и наиболее часто вербальные ответы на слова, предложения, изображения или ситуации, иногда меняющиеся от секунд до нескольких месяцев после стимула. Многие, такие как Барг и Вегнер, принимают результаты означает, что мы автоматы, которые учатся и действуют в значительной степени без осведомленности через S1 (автоматизированная система 1) и многие другие, такие как Kihlstrom и Шанкс говорят, что эти исследования являются ошибочными, и мы существа S2 (совещательная система 2). Хотя Хокинс, кажется, понятия не имеет, как и в других областях описательной психологии высшего порядка мысли, ситуация в отношении "автоматики" по-прежнему, как хаотично, как это было, когда Витгенштейн описал причины бесплодия и бесплодия психологии в 30-х годов. Тем не менее, эта книга легко читать, и некоторые терапевты и духовные учителя могут найти его в использовании. Те, кто желает всеобъемлющего до современных рамок для человеческого поведения из современных двух systEms зрения могут проконсультироваться с моей книгой"Логическая структура философии, психологии, Минd иязык в Людвиг Витгенштейн и Джон Сирл" второй ред (2019). Те, кто заинтересован в более моих сочинений могут увидеть "Говоря обезьян - Философия, психология, наука, религия и политика на обреченной планете - Статьи и обзоры 2006-2019 3-й ed (2019) и suicidal утопических заблуждений в 21-мst веке 4-й ed (2019) th и другие. (shrink)

Vor einigen, Jahren habe ich den Punkt erreicht, an dem ich normalerweise aus dem Titel eines Buches oder zumindest aus den Kapiteltiteln erzähle, welche philosophischen Fehler gemacht werden und wie häufig. Bei nominell wissenschaftlichen Arbeiten können diese weitgehend auf bestimmte Kapitel beschränkt sein, die philosophisch werden oder versuchen, allgemeine Schlussfolgerungen über die Bedeutung oder langfristige-Bedeutung des Werkes zuziehen. Normalerweise sind die wissenschaftlichen Fakten jedoch großzügig mit philosophischem Kauderwelsch darüber, was diese Tatsachen bedeuten, verwogen. Die klaren Unterscheidungen, die Wittgenstein vor etwa (...) 80 Jahren zwischen wissenschaftlichen Fragen und deren Beschreibungen durch verschiedene Sprachspiele beschrieb, werden selten berücksichtigt, so dass man abwechselnd von der Wissenschaft begeistert und von ihrer inkohärenten Analyse bestürzt ist. So ist es mit diesem Band. Wenn man einen Geist mehr oder weniger wie unseren erschaffen soll, braucht man eine logische Struktur für Rationalität und ein Verständnis der beiden Denksysteme (Dual-Prozess-Theorie). Wenn man darüber philosophieren will, muss man die Unterscheidung zwischen wissenschaftlichen Faktenfragen und der philosophischen Frage verstehen, wie Sprache im streitigen Kontext funktioniert und wie man die Fallstricke von Reduktionismus und Scientismus vermeiden kann, aber Kurzweil ist, wie die meisten Verhaltensstudenten, weitgehend ahnungslos. Er ist verzaubert von Modellen, Theorien und Konzepten und dem Erklärungsdrang, während Wittgenstein uns gezeigt hat, dass wir nur beschreiben müssen, und dass Theorien, Konzepte usw. nur Wege der Verwendung von Sprache (Sprachspiele) sind, die nur einen Wert haben, da sie einen klaren Test haben (klare Wahrheitsmacher, oder wie John Searle (der berühmteste Kritiker der AI) gerne sagt, klare Bedingungen der Zufriedenheit (COS). Ich habe versucht, in meinen jüngsten Schriften damit einen Anfang zu machen. Wer aus der modernen zweisystems-Sichteinen umfassenden, aktuellen Rahmen für menschliches Verhalten wünscht, kann mein Buch "The Logical Structure of Philosophy, Psychology, Mindand Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019) konsultieren. Diejenigen,die sich für mehr meiner Schriften interessieren, können 'Talking Monkeys--Philosophie, Psychologie, Wissenschaft, Religion und Politik auf einem verdammten Planeten --Artikel und Rezensionen 2006-2019 3rd ed (2019) und Suicidal Utopian Delusions in the 21st Century 4th ed (2019) und andere sehen. (shrink)

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...) such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

ABSTRACTThis paper provides a naturalistic account of inference. We posit that the core of inference is constituted by bare inferential transitions, transitions between discursive mental representations guided by rules built into the architecture of cognitive systems. In further developing the concept of BITs, we provide an account of what Boghossian [2014] calls ‘taking’—that is, the appreciation of the rule that guides an inferential transition. We argue that BITs are sufficient for implicit taking, and then, to analyse explicit taking, we posit (...) rich inferential transitions, which are transitions that the subject is disposed to endorse. (shrink)

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual (...) structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation. (shrink)

This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it according to the needs emerging in different situational scenarios, is an important (...) aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer & Todd) due to their “bounded rationality” (Simon, 1957). In this perspective, one of the requirements that should be considered for the design, the realization and the evaluation of intelligent cognitively-inspired systems should be rep- resented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This requirement, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution. (shrink)

The overwhelming majority of those who theorize about implicit biases posit that these biases are caused by some sort of association. However, what exactly this claim amounts to is rarely specified. In this paper, I distinguish between different understandings of association, and I argue that the crucial senses of association for elucidating implicit bias are the cognitive structure and mental process senses. A hypothesis is subsequently derived: if associations really underpin implicit biases, then implicit biases should be modulated by counterconditioning (...) or extinction but should not be modulated by rational argumentation or logical interventions. This hypothesis is false; implicit biases are not predicated on any associative structures or associative processes but instead arise because of unconscious propositionally structured beliefs. I conclude by discussing how the case study of implicit bias illuminates problems with popular dual-process models of cognitive architecture. (shrink)

Perception is a first-person internal sensation induced within the nervous system at the time of arrival of sensory stimuli from objects in the environment. Lack of access to the first-person properties has limited viewing perception as an emergent property and it is currently being studied using third-person observed findings from various levels. One feasible approach to understand its mechanism is to build a hypothesis for the specific conditions and required circuit features of the nodal points where the mechanistic operation of (...) perception take place for one type of sensation in one species and to verify it for the presence of comparable circuit properties for perceiving a different sensation in a different species. The present work explains visual perception in mammalian nervous system from a first-person frame of reference and provides explanations for the homogeneity of perception of visual stimuli above flicker fusion frequency, the perception of objects at locations different from their actual position, the smooth pursuit and saccadic eye movements, the perception of object borders, and perception of pressure phosphenes. Using results from temporal resolution studies and the known details of visual cortical circuitry, explanations are provided for (a) the perception of rapidly changing visual stimuli, (b) how the perception of objects occurs in the correct orientation even though, according to the third-person view, activity from the visual stimulus reaches the cortices in an inverted manner and (c) the functional significance of well-conserved columnar organization of the visual cortex. A comparable circuitry detected in a different nervous system in a remote species-the olfactory circuitry of the fruit fly Drosophila melanogaster-provides an opportunity to explore circuit functions using genetic manipulations, which, along with high-resolution microscopic techniques and lipid membrane interaction studies, will be able to verify the structure-function details of the presented mechanism of perception. (shrink)

In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by providing (...) unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)

Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...) recent empirical work on analogical cognition as well as model applications to philosophical topics. Topics to be discussed include the epistemological distinction between implicit knowledge and explicit knowledge, the debate between empiricists and nativists, the frame problem, expertise, creativity and autism, cognitive architecture, and relational knowledge. Particular attention is given to Dedre Gentner and colleague’s structure-mapping theory – the most developed and widely accepted model of analogical cognition. (shrink)

Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to (...) meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities. (shrink)

There are two competing views of knowledge-how: Intellectualism and anti-intellectualism. According to the reductionist varieties of intellectualism defended by Jason Stanley and Timothy Williamson (2001) and Berit Brogaard (2007, 2008, 2009), knowledge-how simply reduces to knowledge-that. To a first approximation, s knows how to A iff there is a w such that s knows that w is a way to A. For example, John knows how to ride a bicycle if and only if there is a way w such that (...) John knows that w is a way to ride a bicycle. John Bengson and Marc Moffett (2007) defend an anti-reductionist version of intellectualism which takes knowledge-how to require, in addition, that s understand the concepts involved in her belief. According to the anti-intellectualist accounts originally defended by Gilbert Ryle (1946) and many others after him, knowledge-how requires the possession of a practical ability and so knowing that w (for some w) is a way to A does not suffice for knowing-how. For example, John knows how to ride a bicycle only if John has the ability to ride it; if John merely knows that w (for some w) is a way to ride a bicycle, John does not know how to ride a bicycle. Here I will argue for a conciliatory position that is compatible with the reductionist variety of intellectualism: knowledge-how is reducible to knowledge-that. But, I argue, there are knowledge states which are not justification-entailing and knowledge states which are not belief-entailing. Both kinds of knowledge state require the possession of practical abilities. I conclude by arguing that the view defended naturally leads to a disjunctive conception of abilities as either essentially involving mental states or as not essentially involving mental states. Only the former kind of ability is a kind of knowledge-state, viz. a knowledge-how state. (shrink)

Our focus in the article is to introduce a simple methodology of generating teaching-learning sequences using the semantic network techinque, followed by the emergent properties of such a network and their implications for the teaching-learning process (didactics) with marginal notes on epistemological implications. A collaborative portal for teachers, which publishes a network of prerequisites for teaching/learning any concept or an activity is introduced. The article ends with an appeal to the global community to contribute prerequisites of any subject to complete (...) the global roadmap for an altas being built on similar lines as Wikipedia. The portal is launched and waiting for community participation at http://www.gnowledge.org. (shrink)

The use of highly abstract mathematical frameworks is essential for building the sort of theoretical foundation for semantic integration needed to bring it to the level of a genuine engineering discipline. At the same time, much of the work that has been done by means of these frameworks assumes a certain amount of background knowledge in mathematics that a lot of people working in ontology, even at a fairly high theoretical level, lack. The major purpose of this short paper is (...) provide a (comparatively) simple model of semantic integration that remains within the friendlier confines of first-order languages and their usual classical semantics and logic. (shrink)

The degree to which information is encoded explicitly in a representation is related to the computational cost of recovering or using the information. Knowledge that is implicit in a system need not be represented at all, even implicitly, if the cost of recurring it is prohibitive.

Does self?knowledge help? A rationalist, presumably, thinks that it does: both that self?knowledge is possible and that, if gained through appropriate channels, it is desirable. Descartes notoriously claimed that, with appropriate methods of enquiry, each of his readers could become an expert on herself or himself. As well as the direct, first?person knowledge of self to which we are led in the Meditationes , we can also seek knowledge of our own bodies, and of the union of our minds and (...) our bodies: the latter forms of self?knowledge are inevitably imperfect, but are no less important in guiding our conduct in the search after truth. (shrink)

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...) entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators. (shrink)

A discussion of a number of ways that information can be present in a computer program without being explicitly represented.

This paper takes up the challenge which Carnap poses in his Aufbau: to make of it a basis for continued epistemological research. I try to close some gaps in Carnap’s original presentation and to make at least the first few steps of his constructional outline more accessible to the modern reader. Particularly emphasized is Carnap’s implicit recognition that, to be effective, “structural” models of epistemology (using logical symbols) must be complemented with “procedural” models (his “fictitious operations”). The paper shows how (...) a procedural model, a computer program,can “bypass” Nelson Goodman’s counter example to Carnap’s logical construction of “similarity circles”. (shrink)

Jerry Fodor has argued that concept acquisition cannot be a psychological or “rational-causal” process, but can only be a “brute-causal” process of acquisition. This position generates the “doorknob  DOORKNOB” problem: why are concepts typically acquired on the basis of experience with items in their extensions? I argue that Fodor’s taxonomy of causal processes needs supplementation, and characterize a third type: what I call “intelligible-causal processes.” Armed with this new category I present what I regard as a better response than (...) Fodor’s to the doorknob  DOORKNOB problem. (shrink)