Abstract
The Explanatory Model of Scientific Understanding (EMU) is a deflationary thesis recently advocated by Kareem Khalifa (Philosophy of Science, 79, 15–37, 2012). EMU is committed to two key ideas: all understanding-relevant knowledge is propositional in nature; and the abilities we use to generate understanding are merely our usual logical reasoning skills. In this paper I provide an argument against both ideas, suggesting that scientific understanding requires a significant amount of non-propositional knowledge not captured by logical relations. I use the Inferential Model of Scientific Understanding to reveal how we can better represent what constitutes understanding a scientific event. In particular, this model accounts for not only logical and probabilistic inferences, but also those conceptual associations and categorizations we must make to comprehend an explanation.
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Notes
In this paper my concern is with understanding scientific events using scientific explanations traditionally construed. I am not going to discuss understanding models, theories, or other potential objects of understanding.
It is important to remain neutral here with regard to our views on meaning and understanding as they relate to concepts. I will say more about this shortly, but we should not be tempted to say that for my daughter to know the meaning of a word requires she be able to make explanatory inferences on its basis—that would unjustifiably smuggle inferential abilities into the example.
See Wilson and Keil (2000) for a number of studies revealing that children often seem to understand without being able to explain back what they know.
Psychologists refer to this kind of knowledge as residing in ‘semantic memory’ (Lieberman 2004)
Many psychologists recognize a distinction between semantic knowledge, comprehension, and problem solving (Kintsch 1998).
For more on the way psychologists distinguish between problem solving and comprehension see the essays in Part I and Part IV of Holyoak and Morrison (2005).
Indeed this is very much what de Regt advocates.
For a nice introduction to these ideas see Margolis and Laurence (1999).
I omit case (iii) and problem-solving ability because I claim to have already established it to be too strong a demand for understanding.
Literal knowledge is a term here lifted from the reading comprehension literature in cognitive psychology. It refers merely to highly superficial knowledge of a text.
I have already argued that this goes well beyond mere propositional knowledge.
You don’t have to like this approach, but it does provide us with an accessible route to differentiating understanding from mere semantic knowledge. Similar routes should be possible under the other three approaches to modeling the mind, but showing that is a separate task.
The psychology literature on mental models and how they reflect aspects of human cognition is vast. A good starting point is Johnson-Laird (2005) and references therein. What I describe in the text is just one form of mental model and a very general form of algorithms.
Holland et al. (1986) make good use of this example for a different purpose.
Notice this is an epistemic account and doesn’t commit us to a unified ontology for the world—the thesis that the world is actually unified. It may well be that nature is not unified but just rewards us as if she were. We make diachronic predictive inferences, and if they are not disconfirmed they are reinforced. The world itself may not have the underlying nature we suppose, but empirically its structure is reflected in our successful reinforcement of cognitive associations. Unificatory understanding is thus explained not by the question-begging idea that nature must be unified because it is understandable, but by the common-sense notion that it only need appear to be. We can thus avoid committing to more metaphysics than we need, which is a virtue of the inferential account.
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Newman, M. EMU and inference: what the explanatory model of scientific understanding ignores. Euro Jnl Phil Sci 4, 55–74 (2014). https://doi.org/10.1007/s13194-013-0075-0
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DOI: https://doi.org/10.1007/s13194-013-0075-0