How do people understand questions about cause and prevent? Some theories propose that people affirm that A causes B if A's occurrence makes a difference to B's occurrence in one way or another. Other theories propose that A causes B if some quantity or symbol gets passed in some way from A to B. The aim of our studies is to compare these theories' ability to explain judgements of causation and prevention. We describe six experiments that compare judgements for causal (...) paths that involve a mechanism, i.e. a continuous process of transmission or exchange from cause to effect, against paths that involve no mechanism yet a change in the cause nevertheless brings about a change in the effect. Our results show that people prefer to attribute cause when a mechanism links cause to effect. In contrast, prevention is sensitive both to the presence of an interruption to a causal mechanism and to a change in the outcome in the absence of a mechanism. In this sense, ‘prevent’ means something different than ‘cause not'. We discuss the implications of our results for existing theories of causation. (shrink)

Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a (...) concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability. (shrink)

We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three experiments that (...) test the prediction that bottom-up inference should occur only when the actor's primary objective is known. The model fits intentionality judgments reasonably well with no free parameters. (shrink)

The likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another’s credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements and the conditional probabilities of (...) those conclusions given other related facts. For example, given that most lawyers make good sales people, what is the probability that most psychologists make good sales people? The results showed that when the fact and the conclusion had the same explanation the fact increased people’s willingness to believe the conclusion, but when they had different explanations the fact decreased the conclusion’s credibility. This decrease is attributed to explanation discounting; the explanation for the fact had the effect of reducing the plausibility of the explanation for the conclusion. (shrink)

Studies of categorical induction typically examine how belief in a premise (e.g., Falcons have an ulnar artery) projects on to a conclusion (e.g., Robins have an ulnar artery). We study induction in cases in which the premise is uncertain (e.g., There is an 80% chance that falcons have an ulnar artery). Jeffrey's rule is a normative model for updating beliefs in the face of uncertain evidence. In three studies we tested the descriptive validity of Jeffrey's rule and a related probability (...) theorem, the rule of total probability. Although these rules provided good approximations to mean judgments in some cases, the results from regression and correlation analyses suggest that participants focus on the parts of these rules that are associated with the highest overall probability. We relate our findings to rational models of judgment. (shrink)

Statements that share an explanation tend to lend inductive support to one another. For example, being told that Many furniture movers have a hard time financing a house increases the judged probability that Secretaries have a hard time financing a house. In contrast, statements with different explanations reduce one another s judged probability. Being told that Many furniture movers have bad backs decreases the judged probability that Secretaries have bad backs. I pose two questions concerning such discounting effects. First, does (...) the reduction depend on explanations being mutually incompatible or does it occur when explanations are deemed irrelevant to one another? I found that a small discounting effect occurred with statements that were blatantly unrelated. However, the discounting effect also depended on a factor external to the argument being judged; the composition of the argument set. Second, are explanation effects attributable to changes in the belief afforded statements or to response-specific changes resulting from misunderstanding of the probability rating task or response bias? The results implicate changes in belief. Prior belief influenced conditional probability more than argument strength judgements, as it would if participants understood the tasks in the same way as the experimenter. Also, conditional probability true and false judgements were complementary, suggesting no response bias. (shrink)

Response to "Predictive Policies" by R.S.McGowan Mr. McGowan has assumed that there is a clear distinction between inductive inferences and others, that we all know how to make the distinction, that we all agree that the inductive ones are somehow better or more reasonable than the alternatives, and I have criticised all of these assumptions. Further he hasformulated the philosophical problem of induction as the problem of showing why the inductive ones are better, and he has attempted to show that (...) inductive policiesas represented in his flow chart are better than non-inductive and counter-inductive ones. I have criticised some of the details of his argument and put forward the counter-claim that policies based on the weaker principles of desistence are better at avoiding contradictions and conform to past experience more closely than policies based on his strong principle of persistence. Accordingly, some modifications of his predictive rules have been suggested. Perhaps most importantly of all, I have argued that the assertion that one policy or inference is better or more rational than another is an incomplete assertion until a basis of comparison has been specified, since different policies may be better or more rational in relation to different bases, and I have indicated some possible approaches for further investigation of this point. A final line of investigation which should be mentioned is the problem of deciding which of two bases of comparison is better relative to some higher-order basis of comparison, a problem which may turn out to be very important in connexion with justifications of predictive policies. It seems that I have asked more questions than I have answered. Perhaps formulating them will help someone more familiar with the field than I am to find interesting answers. (shrink)

Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets (...) not having experienced earlier himself. Being somewhat of a convert, Sloman is a zealous campaigner for his point of view. Now a Reader in Cognitive Science at Sussex, he began his academic career in more orthodox philosophy and, by exposure to linguistics and AI, came to feel that all approaches to mind which ignore AI are missing the boat. I agree with him, and I am glad that he has written this provocative book. The tone of Sloman's book can be gotten across by this quotation (p. 5): "I am prepared to go so far as to say that within a few years, if there remain any philosophers who are not familiar with some of the main developments in artificial intelligence, it will be fair to accuse them of professional incom- petence, and that to teach courses in philosophy of mind, epistemology, aesthetics, philosophy of science, philosophy of language, ethics, metaphysics, and other main areas of philosophy, without discussing the relevant aspects of artificial intelligence will be as irresponsible as giving a degree course in physics which includes no quantum theory." -/- (The author now regrets the extreme polemical tone of the book.). (shrink)

"The Emperor's New Mind" by Roger Penrose has received a great deal of both praise and criticism. This review discusses philosophical aspects of the book that form an attack on the "strong" AI thesis. Eight different versions of this thesis are distinguished, and sources of ambiguity diagnosed, including different requirements for relationships between program and behaviour. Excessively strong versions attacked by Penrose (and Searle) are not worth defending or attacking, whereas weaker versions remain problematic. Penrose (like Searle) regards the notion (...) of an algorithm as central to AI, whereas it is argued here that for the purpose of explaining mental capabilities the architecture of an intelligent system is more important than the concept of an algorithm, using the premise that what makes something intelligent is not what it does but how it does it. What needs to be explained is also unclear: Penrose thinks we all know what consciousness is and claims that the ability to judge Go "del's formula to be true depends on it. He also suggests that quantum phenomena underly consciousness. This is rebutted by arguing that our existing concept of "consciousness" is too vague and muddled to be of use in science. This and related concepts will gradually be replaced by a more powerful theory-based taxonomy of types of mental states and processes. The central argument offered by Penrose against the strong AI thesis depends on a tempting but unjustified interpretation of Goedel's incompleteness theorem. Some critics are shown to have missed the point of his argument. A stronger criticism is mounted, and the relevance of mathematical Platonism analysed. Architectural requirements for intelligence are discussed and differences between serial and parallel implementations analysed. (shrink)

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two (...) or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures. (shrink)

This is a contribution to construction of a research roadmap for future cognitive systems, including intelligent robots, in the context of the euCognition network, and UKCRC Grand Challenge 5: Architecture of Brain and Mind. -/- A meeting on the euCognition roadmap project was held at Munich Airport on 11th Jan 2007. This document was in part a response to discussions at that meeting. An explanation of why specifying requirements is a hard problem, and why it needs to be done, along (...) with some suggestions for making progress, can be found in this presentation: http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0701 "What's a Research Roadmap For? Why do we need one? How can we produce one?" Working on that presentation made me realise that certain deceptively familiar words and phrases frequently used in this context (e.g. "robust". "flexible", "autonomous") appear not to need explanation because everyone understands them, whereas in fact they have obscure semantics that needs to be elucidated. Only then can we understand what the implications are for research targets. In particular, they need explanation and analysis if they are to be used to specify requirements and research goals, especially for publicly funded projects. -/- First draft analyses are presented here. In the long term I would like to expand and clarify those analyses, and to provide many different examples to illustrate the points made. This will probably have to be a collaborative research activity. (shrink)

For over half a century I have been interested in the role of intuitive spatial reasoning in mathematics. My Oxford DPhil Thesis (1962) was an attempt to defend Kant's philosophy of mathematics, especially his claim that mathematical proofs extend our knowledge (so the knowledge is "synthetic", not "analytic") and that the discoveries are not empirical, or contingent, but are in an important sense "a priori" (which does not imply "innate") and also necessarily true. -/- I had made my views clear (...) in courses on philosophy of science and mathematics when teaching at Sussex University (from 1964) which was why one of our former students, Mary Pardoe (then Mary Ensor) who had become a mathematics teacher informed me, while visiting the university, that she had found a new diagrammatic proof of the triangle sum theorem. I reported her proof in some papers and presentations on methods of representation and reasoning, e.g. here, but neither she nor I has encountered anyone else who knew about the proof. (shrink)

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an (...) agent‐based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self‐obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient. (shrink)

Maybe they have been made, but I missed them because I don’t read and listen enough, as most of my energies are focused elsewhere. Apologies if this is all old hat. Don’t feel you have to read on. In case others are interested, I shall put this on my web site at http://www.cs.bham.ac.uk/˜axs/gov/ My main point is that it is just silly to talk so much about universities and top-up fees without putting universities in the context of a complete policy (...) on post-school education. What I am offering is not a detailed policy, but a framework for thinking about the problem. Perhaps someone has already done this but I have never heard it mentioned in any of the public debates. I suspect that’s because this top-up fees issue is not part of a coherent policy but just a sort of religious commitment to a “new way”, like other things done by this government. I start with some comments on the fees issue, then go on to talk about how to think about post-school education, distinguishing several categories that need to be separated and funded differently as part of a joined-up system. (shrink)

This paper distinguishes two versions of Ryle's notion of 'logical geography'. Logical geography: The network of relationships between current uses of a collection of concepts. (Probably what Ryle meant by the term.) Logical topography Features of the portion of reality, or types of portions of reality, related to a given set of concepts, where the reality may be capable of being divided up in different ways using different networks of relationships between concepts. -/- Studying/analysing logical topography includes evaluating the alternative (...) divisions in terms of the theories or applications that are supported by the different sets of concepts, or perhaps some other uses of the concepts. -/- On re-reading Appendices III and IV of my Oxford DPhil thesis "Knowing and Understanding Relations between meaning and truth, meaning and necessary truth, meaning and synthetic necessary truth",, online here, I found to my surprise that several of the topics discussed below had been discussed in those two Appendices, and that the distinction between logical topography and logical geography was made in Appendix IV, discussing conceptual analysis, even though I did not use these words. (shrink)

What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual machinery which (...) we have only recently learnt how to design and build, and could not even have been thought about in Darwin’s time, can interact with the physical machinery in which they are implemented, without being identical with their physical implementation, nor mere aggregates of physical structures and processes. The existence of various kinds of virtual machinery (including both “platform” virtual machines that can host other virtual machines, e.g. operating systems, and “application” virtual machines, e.g. spelling checkers, and computer games) depends on complex webs of causal connections involving hardware and software structures, events and processes, where the specification of such causal webs requires concepts that cannot be defined in terms of concepts of the physical sciences. That indefinability, plus the possibility of various kinds of self-monitoring within virtual machinery, seems to explain some of the allegedly mysterious and irreducible features of consciousness that motivated Darwin’s critics and also more recent philosophers criticising AI. There are consequences for philosophy, psychology, neuroscience and robotics. (shrink)

John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong AI. This paper explores some (...) of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)

John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong AI. This paper explores some (...) of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)

How can a virtual machine X be implemented in a physical machine Y? We know the answer as far as compilers, editors, theorem-provers, operating systems are concerned, at least insofar as we know how to produce these implemented virtual machines, and no mysteries are involved. This paper is about extrapolating from that knowledge to the implementation of minds in brains. By linking the philosopher's concept of supervenience to the engineer's concept of implementation, we can illuminate both. In particular, by showing (...) how virtual machines can be implemented in causally complete physical machines, and still have causal powers, we remove some philosophical problems about how mental processes can be real and can have real effects in the world even if the underlying physical implementation has no causal gaps. This requires a theory of ontological levels. (shrink)

What most people seem not to have noticed is that there's another kind of obesity, a sort of ' mental obesity' which may be causing as much harm to the nation's health -- its mental and intellectual health.

The implication is that the majority of universities are inferior. A consequence of this is that whether such pronouncements are accurate or not they will influence decision-making in various quarters in such a way as to attract resources towards a small subset of the organisations, thereby amplifying differences that already exist, or, in some cases introducing real differences in quality where previously the alleged differences were spurious.

REVIEWS: This is a very helpful volume and a good addition to contemporary scholarship on the valuable and little-known Jaina contribution to philosophy. Prof.Nagin Shah has edited the book well, including clear introduction and a very complete index.

This book explores the relation of John Henry Newman's idea of conscience to what he called conscience "in the ordinary sense of the word," examining such neglected difficulties in this area as the relation between individual conscience and the authority of the church, and rights of conscience.

Islamic philosophy; Ibn al-Muṭahhar al-Ḥillī, al-Ḥasan ibn Yūsuf, 1250-1325; Muslim scholars; 13th century; history.