Abstract
The practice of conceptual analysis has undergone a revival in recent years. Although the extent of its role in philosophy is controversial, many now accept that conceptual analysis has at least some role to play. Granting this, I consider the relevance of empirical investigation to conceptual analysis. I do so by contrasting an extreme position (anti-empirical conceptual analysis) with a more moderate position (non-empirical conceptual analysis). I argue that anti-empirical conceptual analysis is not a viable position because it has no means for resolving conceptual disputes that arise between seemingly competent speakers of the language. This is illustrated by considering one such dispute that has been pressed by a prominent advocate of anti-empirical conceptual analysis: Bennett and Hacker (2003) assert that psychological predicates only logically apply to whole living animals, but many scientists and philosophers use the terms more broadly. I argue that to resolve such disputes we need to empirically investigate the common understanding of the terms at issue. I then show how this can be done by presenting the results of three studies concerning the application of “calculates” to computers.
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Notes
As distinguished from secondary or non-literal meanings, and bearing no relation to what David Chalmers has called “primary intentions.” This is discussed further in Section 1. I take the advocates of conceptual analysis that we will consider to hold that the terms they are interested in (philosophically interesting common terms) each have a primary meaning and that these are reflected in the standard employment of those terms in the general linguistic community (for our purposes, English speakers). For the sake of argument I will assume that this is correct and that the relevant terms do have such primary meanings.
This list is, of course, not exhaustive of the positions one might hold on the issue; rather, it marks a few important points on what might be thought of as a spectrum—from empirical investigation being essential to conceptual analysis having no role to play.
My primary concern will be with the claim, detailed in the following section, that we can examine the use of common terms without empirically investigating how those terms are employed in the linguistic community. While Bennett and Hacker might seem to suggest such an investigation in this passage, they actually hold that there is no need for competent speakers of the language to examine how other people in the linguistic community use those terms.
Just how widely shared a conceptual intuition must be is not clear in the literature; and, while this is an interesting and important question, it is not one that I can do justice to here.
It is plausible that the term “calculate” was once restricted to human beings and only applied to machines metaphorically. Accepting this, however, it nonetheless seems that the death of the metaphor might well signal the lifting of the animal restriction for the term (see Camp 2006, 161).
Participants were asked to rate each sentence on a scale from 1 (“sounds weird”) to 7 (“sounds natural”). Sentences included “Acme Corporation believes that its profit margin will soon increase” (M = 6.1) and “Acme Corporation intends to release a new product this January” (M = 6.3).
Planned analysis showed that the mean for this question was significantly below the neutral point of 4 (t(30) = −2.186; p = 0.037 (two-tailed)). This might be because recognizing that the statement is literally false, participants sought a metaphorical meaning. Unlike the other literal-false sentence—“some birds are mammals”—a figurative understanding readily presents itself: As one participant indicated in the margin of the survey, some bachelors are married to their jobs.
Jobs jails: t(30) = 8.897, p < 0.001 (two-tailed); rumors viruses: t(30) = 8.945, p < 0.001 (two-tailed); libraries goldmines: t(30) = 7.106, p < 0.001 (two-tailed); lies boomerang: t(30) = 6.950, p < 0.001 (two-tailed).
Jobs jails: t(30) = −7.025, p < 0.001 (two-tailed); rumors viruses: t(30) = −5.587, p < 0.001 (two-tailed); libraries goldmines: t(30) = −4.744, p < 0.001 (two-tailed); lies boomerang: t(30) = −4.465, p < 0.001 (two-tailed).
This approach was suggested by Mark Phelan. See Phelan (2010) for a discussion of paraphrasing figurative language.
For example, “added up” was aggregated with “added” and “added together”; while “summed” was aggregated with “summed up,” “took the sum,” and “produced the sum.”
This was tested using a logistic regression with the agent (cashier or cash register) treated as the response and the paraphrase given for “calculated” as a categorical predictor. The model was
$$ \log {\hbox{it}}\left( \pi \right) = {\beta_0} + {\beta_1}{{\hbox{I}}_1} + {\beta_2}{{\hbox{I}}_2} + {\beta_3}{{\hbox{I}}_3} + {\beta_4}{{\hbox{I}}_4} + \varepsilon $$where I1 is 1 if the description was “Summed” and 0 otherwise, I2 is 1 if the description was “Figured Out” and 0 otherwise, I3 is 1 if the description was “Rang Up” and 0 otherwise, and I4 is 1 if the description was “Determined” and 0 otherwise. The intercept gives the estimate for “Added Up.” A likelihood ratio test accepts the hypothesis that β 1 = β 2 = β 3 = β 4 = 0. The p-value of the test is 0.5298 (the chi-square test statistic was 3.17 with four degrees of freedom). Furthermore, the point estimate for β 0 is 0.0000 and the local p-value of the test that β 0 = 0 is 1.0000. What this means is that the odds ratio of “cashier” to “cash register” is 1:1 and none of the responses is a significant predictor. So, knowing the way a person paraphrased “calculated” does not help you to predict whether the sentence they were given was about a cashier or a cash register.
Additional terms not shown were “find out,” “totaled,” “came up with,” “formulated,” and “processed” with six occurrences for paraphrases of the second sentence (two for “find out” and one each for the other terms) and none for the first sentence.
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Acknowledgements
This research was assisted by a Dissertation Completion Fellowship, which is part of the Andrew W. Mellon / American Council of Learned Societies Early Career Fellowship Program. The author would like to thank Peter Machamer (many of the ideas in this article emerged in discussions with him over the course of writing our (2005)), Edouard Machery, Jonathan Livengood, Peter Gildenhuys, the audience at the 1st annual Interdisciplinary Approach to Philosophical Issues Conference, an anonymous referee for the Review of Philosophy and Psychology, and the editors of this special issue for their insightful comments and suggestions; he would also like to thank Jonathan Livengood for his assistance with the logistic regression in Section 5.2 and Mark Phelan for suggesting that experiment.
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Sytsma, J. The Proper Province of Philosophy. Rev.Phil.Psych. 1, 427–445 (2010). https://doi.org/10.1007/s13164-010-0032-1
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DOI: https://doi.org/10.1007/s13164-010-0032-1