Artificiell intelligens (AI) är ett ungt forskningsområde där många av de grundläggande problemen förefaller att vara av filosofisk art.1 Ämnet har sina filosofiska rötter dels i traditionen från Leibniz, Frege, Russell och Hilbert, som strävar efter att formalisera principerna för exakt tänkande, dels i den klassiska mekanismen: idén att människan är en maskin och att det mänskliga tänkandet är en mekanisk process. Som en första approximation kan vi säga att AI är det vetenskapliga studiet av hur man konstruerar och bygger (...) maskiner (datorer) som är intelligenta (eller uppvisar intelligent beteende). Det är lätt att se att denna forskning aktualiserar frågor av filosofiskt slag. Hit hör problem som rör tänkandets natur samt förhållandet mellan medvetandet och kroppen. Inom AI ses ofta datorns arbetssätt som en modell för människans kognitiva processer och man föreställer sig kanske att hjärnan är ett slags dator. Eftersom hjärnan liknas vid en dator och en dator tänks arbeta rent mekaniskt i enlighet med ett givet program (en given algoritm), så föreställer man sig att samma sak bör gälla för hjärnan. Analogin hjärna-dator leder till föreställningen att tänkande inte är något annat än en dators mekaniska manipulerande av symboler i enlighet med givna formella regler. Denna uppfattning om tänkandets natur förefaller samtidigt problematisk: mekanisk manipulation av symboler förutsätter ingen förståelse av symbolernas innebörd; genuint tänkande, å andra sidan, tycks förutsätta sådan förståelse. Ibland verkar också analogin hjärna-dator i den motsatta riktningen. Eftersom hjärnan liknas vid en dator och hjärnan har en mental dimension, så föreställer man sig kanske att samma sak måste gälla för vissa datorer: synen på hjärnan som en dator leder till tanken att det borde vara möjligt att konstruera datorer som bokstavligen tänker, känner och förnimmer. Man föreställer sig kanske att man skall kunna konstruera ett artificiellt medvetande. Vi är alla förtrogna med datorer som (i en viss mening) kan lösa förelagda uppgifter på ett intelligent sätt, men följer det därav att dessa datorer kan tänka? Inte tänker väl en schackspelande dator på schack? Hur intelligent dagens datorer än beter sig, så verkar det ändå inte rimligt att säga att de har tankar, känslor eller upplevelser. Men kan man utesluta att vi någon gång i framtiden kommer att kunna konstruera datorer som verkligen förstår, är medvetna, och tänker? Dessa frågor rymmer många begreppsliga oklarheter. Vad är intelligens, tänkande, förståelse, medvetande? Hur förhåller sig intelligens och tänkande till medvetandets fenomenella sida: upplevelser och känslor? Om vad slags ting är det meningsfullt att säga att de har intelligens, medvetande, mentala tillstånd, etc.? Är det meningsfullt att säga detta om en hjärna, en maskin, en dator? Vad menas med en maskin (dator)? Hur kan vi veta (avgöra) om ett ting har intelligens, tankar, känslor eller upplevelser? I denna uppsats avser vi att närmare belysa sambandet mellan filosofi och AI samt kritiskt granska några av AI-forskningens förutsättningar inom filosofi och logik. (shrink)

We discuss external and internal graphical and linguistic representational systems. We argue that a cognitive theory of peoples' reasoning performance must account for (a) the logical equivalence of inferences expressed in graphical and linguistic form, and (b) the implementational differences that affect facility of inference. Our theory proposes that graphical representation limit abstraction and thereby aid “processibility”. We discuss the ideas of specificity and abstraction, and their cognitive relevance. Empirical support both comes from tasks which involve the manipulation of external (...) graphics and tasks that do not. For the former, we take Euler's (1772) circles, provide a novel computational reconstruction, show how it captures abstractions, and contrast it with earlier construals and with Johnson‐Laird's (1983) mental models representations. We demonstrate equivalence of the graphical Euler system, and the nongraphical mental models system. For tasks not involving manipulation of external graphics, we discuss text comprehension, and the mental performance of syllogisms. By positing an internal system with the same specificity as Euler's circles, we cover the mental models data, and generate new empirical predictions. Finally, we consider how the architecture of working memory explains why such specific representations are relatively easy to store. (shrink)

When governments introduce controversial policies or face a risk of policy failure, officeholders try to avoid blame and justify their decisions by using various legitimation strategies. This paper focuses on the ways in which legitimations are expressed in government social media communication, using the Twitter posts of the British government’s Brexit department as an example. We show how governments may seek legitimacy by appealing to (1) the personal authority of individual policymakers, (2) the collective authority of (political) organisations, (3) the (...) impersonal authority of rules or documents, (4) the goals or effects of government policy, (5) ‘the will of the people’, and (6) time pressure. The results suggest that official legitimations in social media posts tend to rely more on references to authority and shared values rather than presentation of evidence and sound arguments. (shrink)

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in psychology (...) and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain. (shrink)

We advance a theoretical framework which combines recent insights of research in logic, psychology, and formal semantics, on the nature of diagrammatic representation and reasoning. In particular, we wish to explain the varied efficacy of reasoning and representing with diagrams. In general we consider diagrammatic representations to be restricted in expressive power, and we wish to explain efficacy of reasoning with diagrams via the semantical and computational properties of such restricted `languages'. Connecting these foundational insights (from semantics and complexity theory) (...) to the psychology of reasoning with diagrams requires us to develop the notion of the {\it availability} (to an agent) of {\it constraints} operating within representation systems, as a consequence of their direct semantic interpretation. Thus we offer a number of fundamental definitions as well as a research programme which aligns current efforts in the logical and psychological analysis of diagrammatic representation systems. (shrink)

Theoria, Volume 88, Issue 3, Page 494-528, June 2022.

Is there any argument for scepticism? The epistemic problem of the possibility of error. Arguments for scepticism rest on the assumption that knowledge claims are fallible. For this reason the concept of knowledge appears to be questionable. Since it is necessary to distinguish doubts from possible doubts, the arguments for scepticism appear to be unconvincing. If we take it into account that we know something that is immune to doubt, we should draw the conclusion that, contrary to scepticism, knowledge claims (...) have to be compatible with being fallible. Thus any knowledge claim is capable of being doubted. (shrink)

Learning with different kinds of ICT-based tools is an important issue in today's society. In this article we focus on how design of technology rich environments based on state of the art learning principles can give us new insights about how learning occur, and how we can develop new types of learning environments. Medical education constitutes the subject domain. There has been a considerable effort to develop 3D technologies in this field, and the article provides a careful review of how (...) these technologies are applied. There is, however, a substantial gap between these advances and the use of technologies in medical education. Related work proposes individualistic assumptions or metaphors that do not focus explicitly on learning and technology mediation. Based on theoretical analysis of previous literature in the field we argue that there is a need for a new unit of analysis that includes the relationship between individual and collective activity and the role of technology herein. The socio-cultural and especially activity theory is taken as the perspective which gives the possibility to develop the argumentation about the unit of analysis. The unit of analysis also has implications for design of 3D environments. The design principles are elaborated upon and examples are given in relation to an application called Matador (Medical Advanced Training in an Artificial Distributed Environment). Matador is aimed at developing a simulation environment for training in emergency medicine. (shrink)

Epistemic conditionals have often been thought to satisfy the Ramsey test : If A, then B is acceptable in a belief state G if and only if B should be accepted upon revising G with A. But as Peter Gärdenfors has shown, RT conflicts with the intuitively plausible condition of Preservation on belief revision. We investigate what happens if RT is retained while Preservation is weakened, or vice versa. We also generalize Gärdenfors' approach by treating belief revision as a relation (...) rather than as a function.In our semantic approach, the same relation is used to model belief revision and to give truth-conditions for conditionals. The approach validates a weak version of the Ramsey Test — essentially, a restriction of RT to maximally consistent belief states. (shrink)

The objective of our article is to show how “quality of evidence” and “imprecision,” as they are defined in Grading of Recommendations Assessment, Development, and Evaluation articles, may lead to confusion. We focus only on the context of systematic reviews.

Festschrift for Michiel van Lambalgen on the occasion of his retirement as professor of logic and cognitive science at the University of Amsterdam.

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed-world reasoning of particular interest for (...) human cognition. Byrne's “suppression” data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine-grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3-valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning. (shrink)

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed-world reasoning of particular interest for (...) human cognition. Byrne's “suppression” data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine-grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3-valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning. (shrink)

It is neither desirable nor possible to eliminate normative concerns from the psychology of reasoning. Norms define the most fundamental psychological questions: What are people trying to do, and how? Even if no one system of reasoning can be the norm, pure descriptivism is as undesirable and unobtainable in the psychology of reasoning as elsewhere in science.

In earlier papers (Lindström & Rabinowicz, 1989. 1990), we proposed a generalization of the AGM approach to belief revision. Our proposal was to view belief revision as a relation rather thanas a function on theories (or belief sets). The idea was to allow for there being several equally reasonable revisions of a theory with a given proposition. In the present paper, we show that the relational approach is the natural result of generalizing in a certain way an approach to belief (...) revision due to Adam Grove. In his (1988) paper, Grove presents two closely related modelings of functional belief revision, one in terms of a family of "spheres" around the agent's theory G and the other in terms of an epistemic entrenchment ordering of propositions. The "sphere"-terminology is natural when one looks upon theories and propositions as being represented by sets of possible worlds. Grove's spheres may be thought of as possible "fallback" theories relative to the agent's original theory: theories that he may reach by deleting propositions that are not "sufficiently" entrenched (according to standards of sufficient entrenchment of varying stringency). To put it differently, fallbacks are theories that are closed upwards under entrenchment The entrenchment ordering can be recovered from the family of fallbacks by the definition: A is at least as entrenched as B iff A belongs to every fallback to which B belongs. To revise a theory T with a proposition A, we go to the smallest sphere that contain A-worlds and intersect it with A. The relational notion of belief revision that we are interested in, results from weakening epistemic entrenchment by not assuming it to be connected. I.e., we want to allow that some propositions may be incomparable with respect to epistemic entrenchment. As a result, the family of fallbacks around a given theory will no longer have to be nested. This change opens up the possibility for several different ways of revising a theory with a given proposition. (shrink)

In medical research, it is not unusual that risks are ruled out without any specification the exact risk that was ruled out. This makes it difficult to balance expected health benefits and risk of harm when choosing between alternative treatment options. International guidelines for reporting medical research results are sufficiently specific when it comes to establishing health benefits. However, there is a lack of standards for reporting on ruling out risks. We argue that transparency is needed, as in the case (...) of non-inferiority trials. The Consolidated Standards of Reporting Trials and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements should be revised accordingly. (shrink)

The theories of belief change developed within the AGM-tradition are not logics in the proper sense, but rather informal axiomatic theories of belief change. Instead of characterizing the models of belief and belief change in a formalized object language, the AGM-approach uses a natural language — ordinary mathematical English — to characterize the mathematical structures that are under study. Recently, however, various authors such as Johan van Benthem and Maarten de Rijke have suggested representing doxastic change within a formal logical (...) language: a dynamic modal logic. Inspired by these suggestions Krister Segerberg has developed a very general logical framework for reasoning about doxastic change: dynamic doxastic logic (DDL). This framework may be seen as an extension of standard Hintikka-style doxastic logic with dynamic operators representing various kinds of transformations of the agent's doxastic state. Basic DDL describes an agent that has opinions about the external world and an ability to change these opinions in the light of new information. Such an agent is non-introspective in the sense that he lacks opinions about his own belief states. Here we are going to discuss various possibilities for developing a dynamic doxastic logic for introspective agents: full DDL or DDL unlimited. The project of constructing such a logic is faced with difficulties due to the fact that the agent’s own doxastic state now becomes a part of the reality that he is trying to explore: when an introspective agent learns more about the world, then the reality he holds beliefs about undergoes a change. But then his introspective (higher-order) beliefs have to be adjusted accordingly. In the paper we shall consider various ways of solving this problem. (shrink)