Scalar implicature and contrastive explanation

I argue for a subsumption of any version of Grice’s first quantity maxim posited to underlie scalar implicature, by developing the idea of implicature recovery as a kind of explanatory inference, as e.g. in science. I take the applicable model to be contrastive explanation, while following van Fraassen’s analysis of explanation as an answer to a why-question. A scalar implicature is embedded in such an answer, one that meets two probabilistic constraints: the probability of the answer, and ‘favoring’. I argue that besides having application at large, outside of linguistic interpretation, these constraints largely account not only for implicatures based on strength order, logical and otherwise, but also for unordered cases. I thus suggest that Grice’s maxim and its descendants are expressions of general explanatory constraints, as they happen to be manifested in this particular explanatory task. I conclude by briefly discussing how I accordingly view Grice’s system outside of scalar implicature.     

Explicating pluralism: Where the mind to molecule pathway gets off the track—Reply to Bickle

It is argued that John Bickle’s Ruthless Reductionism is flawed as an account of the practice of neuroscience. Examples from genetics and linguistics suggest, first, that not every mind-brain link or gene-phenotype link qualifies as a reduction or as a complete explanation, and, second, that the higher (psychological) level of analysis is not likely to disappear as neuroscience progresses. The most plausible picture of the evolving sciences of the mind-brain seems a patchwork of multiple connections and partial explanations, linking anatomy, mechanisms and functions across different domains, levels, and grain sizes. Bickle’s claim that only the molecular level provides genuine explanations, and higher level concepts are just heuristics that will soon be redundant, is thus rejected. In addition, it is argued that Bickle’s recasting of philosophy of science as metascience explicating empirical practices, ignores an essential role for philosophy in reflecting upon criteria for reduction and explanation. Many interesting and complex issues remain to be investigated for the philosophy of science, and in particular the nature of interlevel links found in empirical research requires sophisticated philosophical analysis.     

Computational explanation in neuroscience

According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.     

Philosophy between science and the humanities

Philosophy should avoid isolation, and should return to being curious and enthusiastic about explanation: about why- and how possibly-questions. The analytic and continental philosophical cultures should establish a dialogue, where each side brings out the distinctive qualities of its work while widening the scope of its concerns.     

Philosophy and Science Dialogue: Consciousness

This is a dialogue between a philosopher and a scientist about the scientific explanation of consciousness. What is consciousness? Does it admit of scientific explanation? If so, what must a scientific theory of consciousness be like in order to provide us with a satisfying explanation of its explanandum? And what types of entities might such a theory acknowledge as being conscious? Philosopher Owen Flanagan and scientist Giulio Tononi weigh in on these issues during an exchange about the nature and scientific explanation of consciousness.     

Verantwortung ohne Verständnis? Wie die Ethikdebatte zur Gentechnik von deren Wissenschaftstheorie abhängt

Responsibility Without Understanding? How the Debate on the Ethics of Genetic Engineering Depends on Its Philosophy of Science. The main thesis in this paper is that bioethics has no own criteria to judge the chances and risks of genetic engineering. But if we distinguish (1) between different types of genetic, (2) between genetic engineering as a set of methods for experimentation and genetic engineering as an industrial technique and (3) reconstruct the metaphors and the terminology in general, which are used by biologists describing their practice, it is possible to formulate such criteria. As the distinction between nature and culture is the result of human actions (not drawn by nature) and the communication about these actions and distinctions in a given cultural context, the criteria are the result of a discourse, in which not only biologists, but all members of a society argue about the reproduction and structuration of their society.     

Trustworthiness in explanation: The obligation to explain well

‘Scientific integrity’ certainly requires that data and references be beyond reproach. However, issues within the theory of scientific explanation suggest that there may be more to it than just this. While it is true that some contemporary, pragmatic analyses of explanation suffer from the ‘problem of relevance’ (an inability to ensure that explanations which are paradigmatic technically are relevant to the question being posed), it does not seem to be true that the addition of formal, metaphysical constraints is necessary to solve this problem. I argue that, when viewed as requests for help with an epistemic problem, explanation-seeking questions reveal the existence of a set of moral criteria centered in trust which, when satisfied, prevent trivial or irrelevant explanations from being offered, thereby broadening the concept of ‘scientific integrity’.     

The Formal Structure of Kind Representations

Kind representations, concepts like table, triangle, dog, and planet, underlie generic language. Here, we investigate the formal structure of kind representations—the structure that distinguishes kind representations from other types of representations. The present studies confirm that participants distinguish generic-supporting properties of individuals (e.g., this watch is made of steel) and accidental properties (e.g., this watch is on the nightstand). Furthermore, work dating back to Aristotle establishes that only some generic-supporting properties bear a principled connection to the kind, that is, are true of an individual by virtue of its being a member of a specific kind (e.g., telling time for a watch). The present studies tested the hypothesis that principled connections are part of the formal structure of kind representations. Specifically, they tested whether they structure a newly learned kind representation. Experiment 1 found that introducing a property of a newly encountered novel kind in any one of four linguistic frames that provide evidence that a property has a principled connection to a kind (e.g., “It has fur because it is a blick”) led participants to infer a different conceptual consequence of principled connections (i.e., “There is something wrong with this blick, which does not have fur”) for which they had no direct evidence. Two introduction frames that provided no evidence for principled connections (e.g., “Almost all blicks have fur”) did not generate the same consequence. Experiment 2 found that all of the targeted properties were generic licensing, irrespective of the introduction frame. That the distinction between properties that bear principled connections to their kinds, and merely generic-supporting properties structures novel kind representations, provides strong evidence that this distinction is part of the formal structure of kind representations.     

Rethinking the explanatory power of dynamical models in cognitive science

In this paper I offer an interventionist perspective on the explanatory structure and explanatory power of (some) dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical models need not refer to underlying mechanisms in order to be explanatory. I defend and illustrate this position in terms of dynamical models of the A-not-B error in developmental psychology as elaborated by Thelen and colleagues, and dynamical models of unintentional interpersonal coordination developed by Richardson and colleagues.