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Papers by Jonathan Waskan

Compte-rendu de l'ouvrage de P. Thagard intitule «L'esprit : introduction a la science co... more Compte-rendu de l'ouvrage de P. Thagard intitule «L'esprit : introduction a la science cognitive» (1996) qui presente les principaux themes de la recherche cognitive depuis sa creation dans les annees 1940-1950. Examinant la conception du raisonnement et de la rationalite defendue par Thagard comme le coeur meme de l'activite cognitive (planification, prise de decision, explication), l'A. propose une autre approche de la rationalite que celle des formats des representations mentales, et s'interesse aux autres phenomenes cognitifs que sont la perception, la memoire, les processus du langage, le controle moteur, exclus de la philosophie de l'esprit par la these de la modularite de Fodor

Models and Cognition, 2006

Cognitive Science, 2003

Theories concerning the structure, or format, of mental representation should (1) be formulated i... more Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax-sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction and manipulation of the cognitive equivalent of scale models. A reading of this hypothesis is provided that satisfies condition (1) and which, even though it may not fully satisfy condition (2), turns out (in light of the frame problem) to be the only known way to satisfy condition (3).

The Routledge Handbook Of Consciousness, 2018

Many philosophers of science believe that empirical psychology can contribute little to the philo... more Many philosophers of science believe that empirical psychology can contribute little to the philosophical investigation of explanations. They take this to be shown by the fact that certain explanations fail to elicit any relevant psychological events (e.g., familiarity, insight, intelligibility, etc.). We report results from a study suggesting that, at least among those with extensive science training, a capacity to render an event intelligible is considered a requirement for explanation. We also investigate for whom explanations must be capable of rendering events intelligible and whether or not accuracy is also viewed as a requirement.

Cognitive Science, 2014

Three Senses of ‘Explanation’ Jonathan Waskan (waskan@illinois.edu) Ian Harmon (iharmon2@illinois... more Three Senses of ‘Explanation’ Jonathan Waskan (waskan@illinois.edu) Ian Harmon (iharmon2@illinois.edu) Andrew Higgins (higgins9@illinois.edu) Joseph Spino (spino2@illinois.edu) Department of Philosophy, 810 S. Wright Street Urbana, IL 61801 USA Abstract ‘Explanation’ appears to be ambiguous between a representational-artifact, an objective, and a doxastic sense. That the distinctions between the three are still poorly understood we regard as an impediment to progress in the philosophy of science and as a source of the field’s resistance to greater integration with experimental psychology. We begin to elucidate the overlapping contours of the three sense of ‘explanation’ using a variation on Powell & Horne’s Semantic Integration paradigm, showing that both laypeople and scientists regard doxastic explanations as constitutive of representational-artifact, but not of objective, explanations and accuracy as closely connected to objective, but not representational-artifact, explanations....

Synthese, 2013

Many philosophers of science follow Hempel in embracing both substantive and methodological anti-... more Many philosophers of science follow Hempel in embracing both substantive and methodological anti-psychologism regarding the study of explanation. The former thesis denies that explanations are constituted by psychological events, and the latter denies that psychological research can contribute much to the philosophical investigation of the nature of explanation. Substantive anti-psychologism is commonly defended by citing cases, such as hyper-complex descriptions or vast computer simulations, which are reputedly generally agreed to constitute explanations but which defy human comprehension and, as a result, fail to engender any relevant psychological events. It is commonly held that the truth of the substantive thesis would lend support to the methodological thesis. However, the standard argument for the substantive thesis presumes that philosophers' own judgments about the aforementioned cases issues from mastery of the lay or scientific norms regarding the use of 'explanation.' Here we challenge this presumption with a series of experiments indicating that both lay and scientific populations require of explanations that they actually render their targets intelligible. This research not only undermines a standard line of argument for substan

... By the middle of the nineteenth century, it was discovered that the activity of the nervous s... more ... By the middle of the nineteenth century, it was discovered that the activity of the nervous system is also electrical in nature. Hermann von Helmholtz (18211894) managed to clock the speed at which nervous impulses travel. ...

Cognitive Science, Literature, and the Arts is the first student-friendly introduction to the use... more Cognitive Science, Literature, and the Arts is the first student-friendly introduction to the uses of cognitive science in the study of literature, written specifically for the non-scientist. Patrick Colm Hogan guides the reader through all of the major theories of cognitive science, focusing on those areas that are most important to fostering a new understanding of the production and reception of literature. This accessible volume provides a strong foundation of the basic principles of cognitive science, and allows us to begin to understand how the ...

Canadian Journal of Philosophy

Connection Science, Jul 1, 2010

We humans often respond effectively when faced with novel circumstances. This is because we are a... more We humans often respond effectively when faced with novel circumstances. This is because we are able to predict how particular alterations to the world will play out. Philosophers, psychologists, and computational modelers have long favored an account of this process that takes its inspiration from the truth-preserving powers of formal deduction techniques. There is, however, an alternative hypothesis that is better able to account for the human capacity to predict the consequences worldly alterations. This alternative takes its inspiration from the powers of truth preservation exhibited by scale models and leads to a determinate computational solution to the frame problem.

Philosophical Studies, 2011

The idea that certain aspects of human cognition involve the construction and utilization of non-... more The idea that certain aspects of human cognition involve the construction and utilization of non-sentential representations akin to pictures and scale models has been with us at least since Aristotle's De Anima (4 th-century B.C./1987). Among the most vocal latter-day critics of this idea are those, like Pylyshyn (1981, 1984, 2002, 2003), who favor an across-the-board computational theory of cognition, where 'computation' should hereafter be understood in what might be termed the strict sense that involves the application of syntax-sensitive rules to sentential data structures (e.g., as opposed to a looser sense of the term that allows for transformations to other kinds of information-bearing vehicles). As these critics know all-too well, computational theories of cognition automatically enjoy the status of being neurally realizable, at least for all practical purposes. One way of showing this is to note that a device that approximates a universal Turing machine can be constructed out of neuron-like processors that interact purely on the basis of biologically plausible operating principles. 1 In fact, McCulloch and Pitt's early work in this regard inspired von Neumann to show that this same sort of device can be created out of electronic components (e.g., vacuum tubes) (Boden 2006, 196). This latter achievement yielded a platform on which many high-level cognitive models and architectures would be constructed, which clearly accelerated the ascent of the computational theory cognition. Clearly it would do much to bolster the credibility of the theory that brains sometimes realize nonsentential images and models if it could be shown that we have equally good reason to believe that brains are capable, at least in principle, of realizing representations of this sort. For this reason, I have taken great pains to demonstrate that certain computer scientists and engineers have, by creating what are known as finite element models, inadvertently shown precisely this (Waskan 2003, 2006, 2008). What their work shows, in particular, is that because non-sentential images and models can be realized by (strict) computations, they too can-if not directly, then at least indirectly via computations-also be realized by neural machinations. I am realistic enough to know that many of those who favor an across-the-board computational theory of cognition, especially the philosophers among them, will reject these proposals, so what I will try to show here is that by accepting them they actually stand to gain a great deal. My efforts will be directed towards showing how the proposal that some computers realize non-sentential images and models can resolve one major facet of the 11 There are, of course, also important differences between InCoMs and scale models (Waskan 2005). 12 http://globetrotter.berkeley.edu/people/Searle/searle-con4.html (last accessed 9/25/08). 13 Searle bristles at this terminology, but I feel that it is perfectly apt in light of the foregoing exposition. 14 To overcome the limitations imposed by human memory and, worse still, by the fairly rigid constraints governing perception and thought, this project will surely require the construction of vast computational models of neural systems (see Waskan 2006, chapter 9). Admittedly, to the extent that these models do explain qualia, it will sometimes (viz., as concerns creatures with radically different perceptual organs) be akin to the way in which computational models of black holes or the big bang explain these occurrences. Apart from inspiring a wealth of useful metaphors, in the end they may not provide us with the exact kinds of insight and understanding that Mary and Nagel seek. Still, unlike their topic-neutral predecessors, these models and metaphors would, in their own way,

... The medium of thought: a model of representational and inferential productivity. Authors: Jon... more ... The medium of thought: a model of representational and inferential productivity. Authors: JonathanAndrew Waskan, Chairpersons: William Bechtel, Publication: ... An abstract is not available. top of page AUTHORS. Jonathan Andrew Waskan No contact information provided yet. ...

Much of the philosophical discussion of explanations has centered around two broad conceptions of... more Much of the philosophical discussion of explanations has centered around two broad conceptions of what sorts of 'things' explanations are, descriptive and objective. Proponents of each agree upon one thing: Psychology can contribute little to the study of explanations. They attempt to show this by pointing to cases of explanation where the commonly associated phenomenology of explanation (CAPE) (e.g., feelings of insight or understanding) is absent and cases where the CAPE is present without any explanations. All such arguments improperly exploit the ambiguity of 'explanation', but they do contain a kernel of truth. The CAPE is, in fact, not constitutive of explanation, not even in the oft-overlooked (third) psychological sense of the term. What appears to be essential is that one finds a happening intelligible. Here I propose a model of the psychological underpinnings of intelligibility and, ultimately, of what explanations are (in the psychological sense). I close by outlining how the psychological study of intelligibility may actually help to reveal the origins of all three concepts of explanation and, in turn, the origins of the judgments about explanation that have in large measure driven philosophical theorizing on the subject. 2 ECHOES OF HEMPEL The study of explanations became a going concern in Anglo-American philosophy in the mid-20 th century logical positivism was on the decline. To paint with broad strokes, positivistic philosophers of science sought to brand science as an institutional source of knowledge that is (unlike religion, astrology, and large swaths of philosophy) devoid of meaningless metaphysical speculations. The major sticking point was claims about theoretical goings-on (e.g., electrons), but positivists maintained that these claims could be straightforwardly 'reduced,' using the tools of formal logic, to claims about the observations that would confirm them. But there is, of course, much more to theoretical claims than meets the eye. They are typically linked in complex ways to, among other things, lots of other theoretical claims. The upshot is that even if a theoretical claim about a system were accurate, altering the system in a certain way would still not always yield the expected outcome. Theoretical claims make, it came to be acknowledged, do make assertions about happenings unseen, and in this way they also serve an important retrospective function: They provide explanations for how and why observed happenings unfold the way they do. 2.1 Hempel's Anti-psychologism Carl Hempel's seminal work on explanation appeared in the wake of the positivistic obsession with the prediction of observables. According to Hempel (1965), science is the product of two basic human motives. One is mankind's practical desire to improve his life through foresight and control over nature. Here predictive leverage is clearly quite important. The other is to be found in "his sheer intellectual curiosity, in his deep and persistent desire to know and to understand himself and his world" (333). This requires explanations. Hempel's own interest in explanation stems from a similar motive, from his deep and persistent desire to know and to understand, "What is the nature of the explanations empirical sciences can provide? What understanding of empirical phenomena do they convey?" (333). This is what might be called an ontological motive. According to Hempel, it could be served by examining the "form and function" of the various kinds of explanation provided by science (333). In addition, like most

Model-based reasoning in science is often carried out in an attempt to understand the kinds of me... more Model-based reasoning in science is often carried out in an attempt to understand the kinds of mechanical interactions that might give rise to particular occurrences. One hypothesis regarding in-the-head reasoning about mechanisms is that scientist rely upon mental models that are like scale models in crucial respects. Behavioral evidence points to the existence of these mental models, but questions remain about the neural plausibility of this hypothesis. This chapter will provide an overview of the psychological literature on mental models of mechanisms with a specific focus on the question of how representations that share the distinctive features of scale models might be realized by neural machinations. It is shown how lessons gleaned from the computational simulation of mechanisms and from neurological research on mental maps in rats can be applied to make sense of how neurophysiological processes might realize mental models. The goal of this chapter is to provide readers with a general introduction to the central challenge facing those would maintain that in-the-head model based reasoning about mechanisms in science is achieved through the use of scale-model-like mental representations. 1.1 Overview A central form of model-based reasoning in science, particularly in the special sciences, is model-based reasoning about mechanisms. This form of reasoning can be effected with the aid of external representational aids (e.g., formalisms, diagrams, and computer simulations) and through the in-the-head manipulation of representations. Philosophers of science have devoted most of their attention to the former, but the latter is arguably at the heart of most of what passes for explanatory understanding in science (Sec. 1.2). Psychologists have long theorized that the humans and other creatures (e.g., rats) reason about spatial, kinematic, and dynamic relationships through the use of mental representations, often termed mental models, that are structurally similar to scale models, though clearly the brain does not instantiate the very properties of a modeled system in the way that scale models do (1.3). A key challenge facing this view is thus to show that brains are capable of realizing representations that are like scale models in crucial respects. There have been several failed attempts to show precisely this, but a look at how computers are utilized to model mechanical interactions offers a useful way of understanding how brains might realize mental representations of the relevant sort (Sec. 1.4). This approach meshes well with current research on mental maps in rats. In addition, it has useful ramifications for research in A.I. and logic (Sec. 1.5), and it offers a promising account of the generative knowledge that scientists bring to bear when testing mechanistic theories while also shedding light on the role that external representations of mechanisms play in scientific reasoning (1.6). 1.2 Mechanistic Reasoning in Science A common reason that scientists engage in model-based reasoning is to derive information that will enable them to explain or predict the behavior of some target system. Model-based explanations provide scientists with a way of understanding how or why one or more explanandum occurrences came about. A good model-based explanation will typically provide the means for determining what else one ought to expect if that explanation is accurate 1that is, it will enable one to formulate predictions so that the explanation may (within widely known limits) be tested. 1 One must bear in mind, however, that models are often accurate only in certain respects and to certain degrees [2]. Model-based reasoning can, corresponding to the diversity of representational structures that count as modelsincluding external scale models, biological models, mathematical formalisms, and computer simulationstake many forms in science. As for what models represent, it is now widely accepted that mechanisms are one of the principal targets of modelbased reasoning. This is most obviously true in the non-basic sciences (e.g., biology, medicine, cognitive science, economics, and geology). In philosophy of science, much of the focus on mechanisms has thus far been on the role they play in scientific explanation. The idea that all genuine scientific explanation is mechanistic began to gain traction in contemporary philosophy of science with the work of Peter Railton, who claimed that "if the world is a machinea vast arrangement of nomic connectionsthen our theory ought to give us some insight into the structure and workings of the mechanism, above and beyond the capability of predicting and controlling its outcomes…" [1]. Inspired by Railton, Wesley Salmon abandoned his statistical-relevance model of explanation in favor of the view that "the underlying causal mechanisms hold the key to our understanding of the world" [3]. On this view, an "explanation of an event involves exhibiting that event as it is embedded in its causal network and/or displaying its internal causal structure"[4]. Salmon was working in the shadow of Carl Hempel's covering law model of explanation, according to which explanations involve inferences from statements describing laws and, in some cases, particular conditions. Salmon tended, in contrast, to favor an ontic account, according to which explanations are out in the world. He thought that progress in understanding those explanations requires 'exhibiting' the relevant mechanisms. However, even though he rejected representational and inferential accounts of explanation, he naturally recognized that reasoning about mechanisms, which requires representations (models), plays a big part in the process of exhibiting those mechanisms. A more recent formulation of the mechanistic account of explanation is supplied by Machamer, Darden, & Craver, who claim that "Mechanisms are entities and activities organized such that they are productive of regular changes from start or setup to finish or termination conditions" [5]. A central goal of science, on their view, is to formulate models, which take the form of descriptions of mechanisms that render target occurrences intelligible: Mechanism descriptions show how possibly, how plausibly, or how actually things work. Intelligibility arises...from an elucidative relation between the explanans (the setup conditions and intermediate entities and activities) and the explanandum (the termination condition or the phenomenon to be explained).... As with 'exhibiting' for Salmon, the process of 'elucidating' how setup conditions lead to termination conditions requires a significant contribution from model-based reasoning. Bechtel offers a related account of mechanisms. He claims: "A mechanisms is a structure performing a function in virtue of its component parts. The orchestrated functioning of the mechanism is responsible for one or more phenomena" [6]. As compared with other mechanists, Bechtel is much more explicit about the role that model-based reasoning plays in science and about the diverse forms of representation that may be involved (e.g., descriptions, diagrams, scale models, and animal models). He is, moreover, among the few to acknowledge the importance of 'in-the-head' model-based reasoning. He suggests that its central form may