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A reappraisal of the covering-law model in cognitive science Raoul Gervais Centre for Logic and Philosophy of Science Ghent University.

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Presentation on theme: "A reappraisal of the covering-law model in cognitive science Raoul Gervais Centre for Logic and Philosophy of Science Ghent University."— Presentation transcript:

1 A reappraisal of the covering-law model in cognitive science Raoul Gervais Centre for Logic and Philosophy of Science Ghent University

2 Main claims and two assumptions: Main thesis: CL-explanations are indispensable in cognitive science Complementary position: both CL and mechanistic explanation matter Assumption 1: capacities are an important type of explanandum Assumption 2: (ontological) every cognitive capacity has a mechanism responsible for it

3 Overview State of the debate: CL model of explanation has gone out of fashion The attention has shifted from CL towards mechanistic explanations Imbalance in debate: CL used to get too much attention, now it receives too little Yet CL model is important if we want to understand the explanatory practice of cognitive science

4 Overview Four reasons why CL is indispensable: – Sometimes, it’s the only thing we have – Heuristic value: they can suggest new explananda – Intrinsically valuable, as they provide understanding – They allow us to understand how mechanistic models generalize

5 State of the debate Consensus among mechanists that CL is poorly equipped for understanding explanation in the cognitive sciences (and life sciences in general) Not all explanations are arguments No laws

6 State of the debate In this sense, the covering law model is inaccurate when it states that all science consists of a search for real “general laws.” One can read an entire article in Science on research findings in biology and not encounter anything a scientist would call a general law. (D’Andrade 1986 p. 22). The received view of scientific explanation in philosophy (the deductive-nomological or D-N model) holds that to explain a phenomenon is to subsume it under a law. However, most actual explanations in the life sciences do not appeal to laws specified in the D-N model. (Bechtel & Abrahamsen 2005 p )

7 State of the debate For these three reasons [accidental generalizations, explanatorily irrelevant premises, and the failure of nomic expectability], the CL model of explanation has generally faded from philosophical currency. Also for these three reasons, the CL model is not an especially useful starting place for thinking about the norms of explanation in neuroscience (Craver 2007 p. 40). Given the ubiquity of references to mechanism in biology, and sparseness of reference to laws, it is a curious fact that mechanistic explanation was mostly neglected in the literature of 20 th century philosophy of science. This was due both to the emphasis placed on physics and to the way in which explanation in physics was construed. (Bechtel & Abrahamsen 2005 p. 423)

8 State of the debate Machamer, Darden & Craver (2000) Thinking about mechanisms Most cited paper in PoS over the past three years: 32 times ( ) Laudan (1981) A confutation of Convergent Realism: 2 nd with 20 citations Hempel and Oppenheim (1948) Studies in the logic of explanation: 18 th with 8 citations

9 State of the debate Idea that CL is so dominant philosophers of science do not pay enough attention to mechanisms is outdated Let us not make the reversed mistake: to focus exclusively on mechanisms, to the neglect of CL explanations

10 Example Spatial navigation Spatial cognition Animal cognition: capacity of the homing pigeon (Columba livia) to navigate Pigeon can home over large distances There must be a mechanism responsible

11 Example Pigeons can home both in sunny weather and on clouded days Two explananda: – E1: Pigeons can home on sunny days – E2: Pigeons can home on clouded days

12 Example Pigeon navigation depends on the sun as reference point Internal solar compass – L1: Pigeons have an internal solar compass – L2: All animals with an internal solar compass can find their way back home on sunny days – E1:Pigeons have the capacity to find their way back home on sunny days

13 Example E2 means the solar compass cannot be the whole story W. T. Keeton (1974): magnetic compass – L3Pigeons have a magnetic compass – L4All animals with a magnetic compass have the capacity to home on clouded days – E2 Pigeons have the capacity to home on clouded days

14 Example L5Pigeons have a sun compass and a back up magnetic compass that works only on clouded days L6All animals with a sun compass and a magnetic compass that works only on cloudy days, have the capacity to home on sunny days even if they carry a magnet around their neck E3Pigeons have the capacity to home on sunny days, even if they carry a magnet around their neck

15 Reflections CL explanations posit a mechanism without describing it in any detail “Pigeons have a solar compass” means: In the body of pigeons there are entities (of which we don’t know where they are and what they look like) that have certain unknown activities and are organized in an unknown way. These entities, activities and organization ensure that pigeons have the capacity (on sunny days) to determine the angle they have to maintain relative to sun “Pigeons have a magnetic compass” means: In the body of pigeons there are entities (of which we don’t know where they are and what the look like) that have certain unknown activities and are organized in an unknown way. These entities, activities and organization ensure that pigeons have the capacity (on clouded days) to determine the angle they have to maintain relative to the magnetic field of the earth

16 Reflections Unknown mechanism: if one agrees that this is the meaning of the laws L1 and L3, the explanations are non-mechanistic (because no information is given about the entities, activities or organization). However, from an ontological point of view they presuppose a mechanism: the ‘law’ cannot be true unless there is a mechanism But are they interesting?

17 The only thing we’ve got Still impossible to give a mechanistic explanation for homing capacity of pigeons Magnetic compass: – Recent proposal (Fleissner et al. 2003): iron particles (SPM) in the upper beak. But only candidates (idem. p. 360). – Controversy over the function of the iron particles (Treiber et al. 2012) – “…our work reveals that the sensory cells that are responsible for trigeminally mediated magnetic sensation in birds remain undiscovered. These enigmatic cells may reside in the olfactory epithelium, a sensory structure that has been implicated in magnetoreception in the rainbow trout” (Treiber et al p. 369) – Interesting heuristics: ‘let’s now look at the nasal cavity, because that’s where trouts have it’ – a piece of analogy reasoning that is quite different from the heuristics envisaged by mechanists (not even a mechanism sketch in terms of Machamer, Darden and Craver 2000), yet it offers a new hypothesis

18 Heuristically useful CL explanations that posit but do not describe a mechanism suggest new explananda E4: Why do pigeons have the capacity (on sunny days) to determine the angle they have to maintain relative to the sun? E5: Why do pigeons have the capacity (on clouded days) to determine the angle they have to maintain relative to the magnetic field of the earth? CL-explanations as a stepping stone towards mechanistic explanations

19 Understanding CL explanations provide us with means to understand contrasts E6 Why do pigeons have the capacity to find their way back home while other sedentary birds do not have this capacity? E7 Why do woodcocks migrate during the night, while pigeons cover distances during the day? CL explanations developed allow us to understand these contrasts

20 Model generalization “Why do pigeons home” versus “How do pigeons home?” More generally: Q1: Why does S perform C? Q2: How does S perform C? Q3: How do systems S 1…n perform C? A3: Systems S 1…n perform C through mechanism M, as shown by model A

21 Model generalization E8: Why does S perform C through M? – CSystem S is part of a set S 1…n – LAll systems S 1…n perform C through M (as shown by A) – E8System S performs C through M

22 Model generalizaiton Contrasts again E9: Why does S not perform C (or perform C*), while systems S 1…n do perform C? E10: How does system S perform C, given that manner M (the way systems S 1…n perform C) is not available? – Impaired functions and restored capacities – Systems homology and nomic expectability – Universally quantified statements versus exemplar models

23 Objection: how much of Hempel is retained? Logical conditions of adequacy for DN explanations: R1: The explanandum must be a logical consequence of the explanans. R2: The explanans must contain general laws, and these must be essential for the derivation of the explanandum. R3: The explanans must have empirical content, that is it must be capable, at least in principle, of test by experiment or observation. Empirical condition of adequacy for DN explanations: R4: The sentences in the explanans must be true.

24 Mitchell “Rather than bemoan the failure of biological generalizations to live up to the normative definition of exceptionless universality, the pragmatic approach suggests a different philosophical project. To understand the multiple relations among scientific generalizations one must first explore the parameters which make generalizations useful in grounding expectation in a variety of context” (1997, p. S478). Parameters: – Degree of accuracy – being attuned to specified goals of intervention – Levels of ontology – generalizations about populations should should describe ‘structural relations between trait-groups’

25 Summarizing Four reasons why CL explanations are indispensible in cognitive science: – Historically speaking, sometimes it’s the only thing we have (solar compass are shorthand terms for unknown mechanisms; other examples might be cognitive maps and circadian rhythms). – Heuristically useful – Provide understanding – Allow us to make sense of model generalization


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