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Causation, Responsibility, and Typicality

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Abstract

There is ample evidence that violations of injunctive norms impact ordinary causal attributions. This has struck some as deeply surprising, taking the ordinary concept of causation to be purely descriptive. Our explanation of the findings—the responsibility view—rejects this: we contend that the concept is in fact partly normative, being akin to concepts like responsibility and accountability. Based on this account, we predicted a very different pattern of results for causal attributions when an agent violates a statistical norm. And this pattern has been borne out by the data (Sytsma et al. 2012; Livengood et al. 2017; Sytsma n.d.-a). These predictions were based on the responsibility attributions that we would make. In this paper, I extend these previous findings, testing responsibility attributions. The results confirm the basis of our predictions, showing the same pattern of effects previously found for causal attributions for both injunctive norms and statistical norms. In fact, the results for responsibility attributions are not statistically significantly different from those previously found for causal attributions. I argue that this close correspondence lends further credence to the responsibility view over competing explanations of the impact of norms on causal attributions.

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Notes

  1. See, for example, Alicke (1992); Knobe and Fraser (2008); Hitchcock and Knobe (2009); Sytsma et al. (2012); Reuter et al. (2014); Kominsky et al. (2015); Livengood et al. (2017); Icard et al. (2017); Kominsky and Phillips (2019); Livengood and Sytsma (2020). Under “injunctive norms” I include both prescriptive norms (what should be done) and proscriptive norms (what should not be done), although “prescriptive norm” is often used to refer to both.

  2. See Sytsma et al. (2012); Livengood et al. (2017); Sytsma et al. (2019); Livengood and Sytsma (2020); Sytsma and Livengood (n.d.); Sytsma (n.d.-a, b, c, d). While discussions of the impact of injunctive norms on causal attributions often describe these in terms of moral judgments, this is arguably too strong. In keeping with Sytsma et al. (2012), I’ll instead speak of broadly moral evaluations, where this is intended to correspond with the injunctive norms at issue. Finally, our thesis is first a claim about the ordinary use of causal attributions in English. Most of the work at issue in this paper have been conducted on English-speakers (but see Samland and Waldmann 2016, Grinfeld et al. 2020). How far our thesis extends to other languages is an interesting open question.

  3. That we are highly attuned to norms and their violation is hardly controversial (e.g., Sripada and Stich 2007), even as there is a great deal of disagreement about why we are so attuned and how this came about.

  4. See Hitchcock and Knobe (2009); Halpern and Hitchcock (2015); Kominsky et al. (2015); Phillips et al. (2015); Icard et al. (2017); Kominsky and Phillips (2019).

  5. Sytsma (n.d.-a, b) and Sytsma and Livengood (n.d.) raise further issues for the counterfactual view.

  6. In their first experiment, Grinfeld et al. (2020) used strength measures for responsibility and causation finding corresponding effects for a sample drawn from Amazon Mechanical Turk. By contrast, in their fourth experiment they found a predicted dissociation between strength measures of responsibility and causation based on epistemic perspective. It should be noted, however, that the study was conducted in Hebrew using university students, and that there were potentially important differences between the measures used, the responsibility question asking about the agent’s responsibility for success or failure (“To what extent is the candidate responsible for his success/failure in the exam?”) while the causal question asked about the agent’s action with regard to simply the outcome (“To what extent did the study of the candidate cause the outcome in the exam?”). Further work is needed to confirm that the dissociation occurs in English and when using matching queries.

  7. In addition to these views, other prominent accounts in the literature include Alicke’s blame view (e.g., Alicke 1992; Alicke et al. 2011) and Samland and Waldmann’s pragmatic view (e.g., Samland and Waldmann 2016). I’ll return to these views briefly in Section 3.

  8. This effect is typically explained in terms of violations of a specific type of injunctive norm, what Hitchcock and Knobe term norms of proper functioning. We hold that such effects can be explained in terms of the application of the same normative concept at play in causal attributions concerning agents. In line with this, research indicates that people have a tendency to take an agentive perspective on nature as a whole, including what philosophers would think of as non-agents (e.g., Bloom 2007; Rose 2015, and Rose and Schaffer 2017).

  9. While more work is needed here, it is possible that statistical norms have different effects for causal attributions concerning non-agents than for agents. One possibility is that statistical norms might be taken to bear more directly on the injunctive norms typically taken to be applicable to non-agents. Here the focus has been on norms of proper functioning—what things should do in designed or otherwise regular systems—and violations of typicality might plausibly be understood as violations of such a norm (e.g., a component of a machine doing something other than its typical behavior would not just violate a statistical norm, but an injunctive norm insofar as the component does something it wasn’t designed to do).

  10. I tested just the conditions from Sytsma et al. (2012) where the administrative assistant did not violate a norm, making a slight modification to the conditions where Professor Smith did not violate an injunctive norm: in the original study it was stated that the agents were able to take pens; in the replication it was instead stated that they were allowed to take pens to more clearly mark permissibility.

  11. See Sytsma (n.d.-a) for vignettes. For the first four probes the attributions were “Professor Smith is responsible for the problem” and “The administrative assistant is responsible for the problem”; for the second four, the administrative assistant was named “John” and the attribution tested was “John is responsible for the problem.”

  12. Participants for each study in this paper were recruited through advertising for a free personality test on Google. In addition to answering the questions reported below, participants were asked basic demographic questions and after the philosophical questions were given a 10-item Big Five personality inventory. In line with Sytsma (n.d.-a), participants were restricted to native English-speakers who were 16 years of age or older. Participants for Study 1 were 61.8% women (three non-binary), average age 34.4 years, ranging from 16 to 90. Given the higher percentage of women, the four ANOVAs described below were run with gender as a third between-participants factor. No significant gender effects were found.

  13. Professor Smith, Population-level: F(1, 322) = 1.34, p = 0.25, η2 = 0.003. Administrative Assistant, Population-level: F(1, 322) = 0.18, p = 0.67, η2 = 0.001. Professor Smith, Agent-level: F(1, 327) = 0.002, p = 0.96, η2 = 0.000. Administrative Assistant, Agent-level: F(1, 327) = 2.82, p = 0.094, η2 = 0.008.

  14. Professor Smith: statistical norm, F(1, 322) = 0.23, p = 0.63, η2 = 0.001; injunctive norm, F(1, 322) = 68.3, p = 3.7e−15, η2 = 0.17. Administrative Assistant: statistical norm, F(1, 322) = 0.19, p = 0.66, η2 = 0.001; injunctive norm, F(1, 322) = 4.07, p = 0.045, η2 = 0.012.

  15. Professor Smith: statistical norm, F(1, 327) = 37.2, p = 3.0e−9, η2 = 0.096; injunctive norm, F(1, 327) = 19.6, p = 1.3e−5, η2 = 0.051. Administrative Assistant: statistical norm, F(1, 327) = 13.2, p = 3.3e−4, η2 = 0.038; injunctive norm, F(1, 327) = 1.64, p = 0.20, η2 = 0.005.

  16. There was a borderline significant three-way interaction, however, for the Administrative Assistant in the agent-level conditions: F(1, 327) = 3.37, p = 0.067, η2 = 0.010.

  17. Professor Smith, Population-level: injunctive norm, F(1, 164) = 34.1, p = 2.8e-8, η2 = 0.17. Administrative Assistant, Population-level: no significant effects. Professor Smith, Agent-level: statistical norm, F(1, 166) = 30.5, p = 1.2e-7, η2 = 0.14; injunctive norm, F(1, 166) = 13.8, p = 2.7e-4, η2 = 0.066. Administrative Assistant, Agent-level: statistical norm, F(1, 166) = 4.34, p = 0.039, η2 = 0.025; borderline significant interaction, F(1, 166) = 3.73, p = 0.055, η2 = 0.021.

  18. Population-level: t(83.986) = 4.94, p = 2.0e-6, d = 1.06, one-tailed; W = 1414.5, p = 7.3e-6. Agent-level: t(81.84) = 2.61, p = 0.0054, d = 0.57, one-tailed; W = 1166, p = 0.0047.

  19. Population-level: t(79.967) = 3.49, p = 4.0e−4, d = 0.77, one-tailed; W = 1185.5, p = 5.7e−4. Agent-level: t(83.846) = 3.16, p = 0.0011, d = 0.67, one-tailed; W = 1254.5, p = 0.0012.

  20. t(81.538) = 4.23, p = 3.0e−5, d = 0.92, one-tailed; W = 443.5, p = 2.8e−5.

  21. t(83.404) = 3.66, p = 2.2e−4, d = 0.79, one-tailed; W = 520.5, p = 2.2e−4.

  22. The results for this condition have recently been replicated (Sytsma n.d.-c), including that the same effects were seen using a between-participants design with participants either receiving the probe from the first page or a slightly modified stand-alone version of the probe from the second page.

  23. That participants don’t infer that Lauren knows about the problem with the mainframe is confirmed by Studies 3 and 4 in Sytsma (n.d.-c).

  24. See Livengood et al. (2017) for vignettes.

  25. 69.1% women (six non-binary), average age 39.5 years, ranging from 16 to 86. The ANOVAs described below were run with gender as a second between-participants factor. No significant gender effects were found.

  26. Page 1: Term, F(1, 518) = 1.25, p = 0.26, η2 = 0.002; Condition, F(4, 518) = 1.10, p = 0.36, η2 = 0.008. Interaction, F(4, 518) = 0.44, p = 0.78, η2 = 0.003. Page 2: Term, F(1, 518) = 0.34, p = 0.56, η2 = 0.001; Term*Condition, F(4, 518) = 1.79, p = 0.13, η2 = 0.013; Term*Condition, F(4, 518) = 1.43, p = 0.22, η2 = 0.011.

  27. Page 1: F(1, 254) = 0.79, p = 0.53, η2 = 0.012. Page 2: F(1, 254) = 2.82, p = 0.026, η2 = 0.043.

  28. Page 1, typicality not specified: t(50) = 5.29, p = 1.4e−6, d = 0.74, one-tailed; V = 131, p = 6.8e−6. Page 2, typicality not specified: t(50) = 7.78, p = 1.8e−10, d = 1.09, one-tailed; V = 1079, p = 9.4e−8. Page 1, agent-level typical: t(49) = 6.93, p = 4.2e−9, d = 0.98, one-tailed; V = 49, p = 4.0e−7. Page 2, agent-level typical: t(49) = 4.14, p = 6.9e−5, d = 0.58, one-tailed; V = 843, p = 3.8e−4. Page 1, agent-level atypical: t(53) = 7.09, p = 1.6e−9, d = 0.96, one-tailed; V = 71, p = 8.3e−8. Page 2, agent-level atypical: t(53) = 5.27, p = 1.3e−6, d = 0.72, one-tailed; V = 898.5, p = 2.5e−5. Page 1, population-level typical: t(54) = 5.10, p = 2.3e−6, d = 0.69, one-tailed; V = 141, p = 1.6e−5. Page 2, population-level typical: t(54) = 11.6, p < 2.2e−16, d = 1.57, one-tailed; V = 1457, p = 8.5e−10. Page 1, population-level atypical: t(48) = 3.30, p = 9.2e−4, d = 0.47, one-tailed; V = 225.5, p = 0.0022. Page 2, population-level atypical: t(48) = 6.93, p = 4.8e−9, d = 0.99, one-tailed; V = 861.5, p = 4.4e−7.

  29. Page 1, agent-level typical vs. agent-level atypical: t(101.95) = 0.20, p = 0.85, d = 0.038, two-tailed; W = 1391, p = 0.78. Page 2, agent-level typical vs. agent-level atypical: t(101.42) = 0.68, p = 0.50, d = 0.13, two-tailed; W = 1205.5, p = 0.33. Page 1, population-level typical vs. population-level atypical: t(92.697) = 0.49, p = 0.62, d = 0.098, two-tailed; W = 1342, p = 0.97. Page 2, population-level typical vs. population-level atypical: t(91.502) = 1.44, p = 0.15, d = 0.29, two-tailed; W = 1511.5, p = 0.22.

  30. Page 1, agent-level typical vs. agent-level atypical: t(105.98) = 1.53, p = 0.13, d = 0.29, two-tailed; W = 1209, p = 0.12. Page 2, agent-level typical vs. agent-level atypical: t(104.4) = 0.54, p = 0.59, d = 0.10, two-tailed; W = 1585.5, p = 0.35. Page 1, population-level typical vs. population-level atypical: t(93.331) = 0.17, p = 0.87, d = 0.034, two-tailed; W = 1205.6, p = 0.81. Page 2, population-level typical vs. population-level atypical: t(93.261) = 0.54, p = 0.59, d = 0.11, two-tailed; W = 1384.5, p = 0.25.

  31. Agent-level, Page 1: Term, F(1, 318) = 1.35, p = 0.25, η2 = 0.004; Norm, F(2, 318) = 0.75, p = 0.47, η2 = 0.005; Term*Norm, F(2, 318) = 0.91, p = 0.40, η2 = 0.006. Agent-level, Page 2: Term, F(1, 318) = 1.29, p = 0.26, η2 = 0.004; Norm, F(2, 318) = 0.18, p = 0.84, η2 = 0.001; Term*Norm, F(2, 318) = 2.13, p = 0.12, η2 = 0.013. Population-level, Page 1: Term, F(1, 310) = 0.21, p = 0.64, η2 = 0.001; Norm, F(2, 310) = 0.43, p = 0.65, η2 = 0.003; Term*Norm, F(2, 310) = 0.031, p = 0.97, η2 = 0.000. Population-level, Page 2: Term, F(1, 310) = 0.94, p = 0.33, η2 = 0.003; Norm, F(2, 310) = 1.68, p = 0.19, η2 = 0.011; Term*Norm, F(2, 310) = 0.28, p = 0.76, η2 = 0.002.

  32. Agent-level, Page 1: F(2, 152) = 0.46, p = 0.63, η2 = 0.006. Agent-level, Page 2: F(2, 152) = 1.77, p = 0.17, η2 = 0.023. Population-level, Page 1: F(2, 152) = 0.24, p = 0.79, η2 = 0.003. Population-level, Page 2: F(2, 152) = 1.17, p = 0.31, η2 = 0.015.

  33. While philosophers often distinguish between moral responsibility and causal responsibility (e.g., Talbert 2019), the impact of injunctive norms on responsibility attributions in the scenarios tested would seem to indicate that participants were calling on a concept more like the former.

  34. Hindriks explains the effect for intentionality judgments in terms of a misalignment between what should motivate the agent and the agent’s actual reasons for acting, and he argues that the same holds for responsibility attributions. He then takes his explanation to provide reason to reject the call for a common explanation of the effects for intentionality and other folk-psychological concepts, on the one hand, and causal attributions on the other. The reason Hindriks offers is that “causing is a more objective notion deciding and acting intentionally,” such that “it would be implausible if the attitudes of the acting agent bore systematically on what she caused” (2014, 67). Interestingly, Sauer (2014) arrives at the opposite conclusion for basically the same reason. Accepting the call for a common explanation, he holds that we should reject accounts that would not apply to causal attributions, assuming for instance that “whether or not an agent had a certain type of causal impact on the unfolding events does not depend on the described agents’ beliefs” (491). There is evidence, however, that whether or not the agent knows that the outcome will occur if she acts impacts causal attributions (e.g., Sytsma n.d.-c).

  35. Alternatively, some of the attributions might be thought to run in sequence, for instance with the effect of alternative possibilities on responsibility attributions running through causal attributions. Further, it is worth noting that one would expect other factors to play a role in some attributions. For instance, Cushman (2008) presents evidence that blame judgments are far more sensitive to consequences and how they came about than judgments about the wrongness of an agent’s action. We would expect these findings for blame attributions to extend to causal attributions and responsibility attributions, and plausibly the counterfactual view would make the same prediction.

  36. As noted above, the responsibility view does not make a specific suggestion concerning the role of the alternative possibilities we consider in arriving at broadly moral evaluations, but is open to the possibility that they play a role. Similarly, we consider the exact relationship between causal attributions and responsibility attributions to be an open question. For instance, it could be that these run in sequence. In fact, in Sytsma (n.d.-d) participants were also asked to rate a responsibility attribution. Including this question in the search produced the same model, but with the effect of the wrongness judgments on causal attributions running through responsibility attributions.

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Acknowledgements

I want to thank Joshua Knobe and Jonathan Kominsky for very helpful feedback on previous drafts of this paper.

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    Justin Sytsma

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Sytsma, J. Causation, Responsibility, and Typicality. Rev.Phil.Psych. 12, 699–719 (2021). https://doi.org/10.1007/s13164-020-00498-2

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