Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior.
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John Doris offers a similar, though not identical, analysis of character traits. According to his view, “global” traits have two primary features:
Consistency Character and personality traits are reliably manifested in trait-relevant behavior across a diversity of trait-relevant eliciting conditions that may vary widely in their conduciveness to the manifestations of the trait in question.
Stability Character and personality traits are reliably manifested in trait-relevant behavior over iterated trials of similar trait-relevant eliciting conditions. (Doris 2002, p. 22)
Doris also mentions a third feature of character traits, evaluative integration, which is not relevant for our current purposes.
There are also cross-cultural differences in the extent to which individuals fall prey to the correspondence bias. While the correspondence bias is present to some extent across cultures (Choi et al. 1999; Krull et al. 1999; Norenzayan et al. 2003), it appears that members of "individualist" societies are particularly susceptible to it; meanwhile, members of "collectivist" societies seem to pay more attention to situational factors and the presence of social constraints (Choi and Nisbett 1998; Miyamoto and Kitayama 2002). This is consistent with the broad finding that members of collectivist cultures display a habitual tendency to attend to situational factors and contexts (Kitayama et al. 2003). These habitual patterns of attention seem to make members of "collectivist" cultures better able to correct their initial dispositionalist attributions.
Beliefs about one’s own character traits could figure in practical reasoning. But this observation is of little help to the simulation-theorist: surely, this kind of self-reflection is uncommon in the first-person case, and it would be bizarre if we nevertheless believed that other people frequently engage in it. Moreover, beliefs about one’s character seem like they would have a very different effect on behavior than character itself. If I reflect on my own impulsivity, for instance it will probably lead me to be less impulsive.
System 1 strategies are “fast, relatively effortless routines that occur without our awareness,” while System 2 strategies are “slow routines which require the expenditure of mental effort and are subject to consciousness and deliberative control” (Fiebich and Coltheart 2015, p. 238).
Fiebich and Coltheart distinguish between non-linguistic trait attributions and linguistic trait attributions. Non-linguistic trait attributions occur when an agent does not possess a linguistic concept of a trait (i.e. the word ‘generosity’). These only consist in associations between particular behaviors, situations, and agents, and would only allow for predicting similar behaviors in similar situations. Linguistic trait attributions, in contrast, involve the possession of a linguistic concept of a trait, and would facilitate a whole network of predictions.
I am skeptical of this distinction for two reasons. First, non-linguistic trait attribution, on this account, does not seem to involve trait-based reasoning at all: traits are supposed to be enduring, internal properties of individuals, but these non-linguistic trait attributions seem to consist only in superficial behavioral associations. Second, this distinction implicitly assumes that the only way to possess a concept of a trait is through language. But there is ample reason to think that even pre-linguistic or non-linguistic entities can possess concepts (e.g. Call and Tomasello 2008; Carey 2009). While linguistic concepts undoubtedly enrich and expand our trait attribution abilities, there is no reason to think that non-linguistic trait attribution is as impoverished as Fiebich and Coltheart (2015) make it out to be.
There is considerable variation amongst the different versions of predictive coding. Some theorists have taken the extreme position that prediction error signals are the only information carried via bottom-up input systems (Clark 2015; Friston and Kiebel 2009; Hohwy 2013), while others allow that traditional bottom-up information-processing compliments top-down prediction (Bar 2007; Spratling 2016). On my account, trait information (e.g. via facial features) is sometimes initially processed in a bottom-up fashion; as such, I disavow the idea that bottom-up input systems only carry prediction errors.
For example, the explanatory status of the Bayesian aspect of these models is a vexed question. Some theorists are explicit that the Bayesian formalism is intended to capture only the computational level of description, abstracted away from implementational, mechanistic details (Chater et al. 2006), while others seem to be making claims about the actual algorithms that support predictive processes (Friston and Kiebel 2009). While some have charged that ultimately, Bayesian models amount to “just-so” stories with little explanatory value (Jones and Love 2011), there are reasonable answers to such challenges (Zednik and Jäkel 2016), and plausible ways to interpret the various aspects of Bayesian models that render them empirically tractable (Icard 2016).
This is one way in which character traits may serve as an inferential heuristic: without this over-hypothesis, mindreaders would begin their action-predictions with a flat probability distribution over all the mental state hypotheses consistent with their current behavioral observations, which would give rise to an inverse problem. Instead, trait attributions bias the prior probability distribution towards a subset of mental-state hypotheses, which the predictor can proceed to test. Even if this distribution is in fact erroneous, it still serves as a means of bootstrapping our initial mental-state predictions, which then allow us to update our priors accordingly.
Members of “collectivist” cultures, who habitually attend to contextual factors, no doubt benefit from such attentional effects in their comparative resistance to the correspondence bias.
Thanks to an anonymous reviewer for suggesting this.
This may be an instance of what Cimpian and Salomon call the ‘inherence heuristic’—a “fast, intuitive heuristic leads people to explain many observed patterns in terms of the inherent features of the things that instantiate these patterns” (Cimpian and Salomon 2014, p. 461)—and a precursor to psychological essentialism about certain social categories (Gelman 2004; Haslam et al. 2006; Rhodes et al. 2012).
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I would like to thank Peter Carruthers, Georges Rey, Andrew Knoll, Joseph Jebari, and Charles Starkey for comments on drafts of this paper, and Julius Schönherr, Moonyong Song, Shen Pan, Yichi Zhang, Aida Roige Mas, Kalewold Hailu Kalewald, and Casey Enos for helpful discussion. This research was supported by a Social Sciences and Humanities Research Council Doctoral Fellowship (#752-2014-0035).
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Westra, E. Character and theory of mind: an integrative approach. Philos Stud 175, 1217–1241 (2018). https://doi.org/10.1007/s11098-017-0908-3
- Trait attribution
- Theory of mind
- Bayesian predictive coding