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Plausibility versus richness in mechanistic models

Raoul Gervais (UGent) and Erik Weber (UGent)
(2013) PHILOSOPHICAL PSYCHOLOGY. 26(1). p.139-152
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Abstract
In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of how-possibly, to how-plausibly, to how-actually models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models.
Keywords
Explanation, Mechanism, Mechanistic Explanation, Models, Plausibility, Richness, PERCEPTION, INTERACTIVE ACTIVATION MODEL, UNDERSTANDING FACE RECOGNITION

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Citation

Please use this url to cite or link to this publication:

Chicago
Gervais, Raoul, and Erik Weber. 2013. “Plausibility Versus Richness in Mechanistic Models.” Philosophical Psychology 26 (1): 139–152.
APA
Gervais, R., & Weber, E. (2013). Plausibility versus richness in mechanistic models. PHILOSOPHICAL PSYCHOLOGY, 26(1), 139–152.
Vancouver
1.
Gervais R, Weber E. Plausibility versus richness in mechanistic models. PHILOSOPHICAL PSYCHOLOGY. 2013;26(1):139–52.
MLA
Gervais, Raoul, and Erik Weber. “Plausibility Versus Richness in Mechanistic Models.” PHILOSOPHICAL PSYCHOLOGY 26.1 (2013): 139–152. Print.
@article{3086510,
  abstract     = {In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of how-possibly, to how-plausibly, to how-actually models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models.},
  author       = {Gervais, Raoul and Weber, Erik},
  issn         = {0951-5089},
  journal      = {PHILOSOPHICAL PSYCHOLOGY},
  language     = {eng},
  number       = {1},
  pages        = {139--152},
  title        = {Plausibility versus richness in mechanistic models},
  url          = {http://dx.doi.org/10.1080/09515089.2011.633693},
  volume       = {26},
  year         = {2013},
}

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