How stereotypes shape our perceptions of other minds

So what do stereotyping and mindreading have to do with one another? There are some obvious differences, of course: stereotypes are mainly about groups, while mindreading is mainly about individuals. But intuitively, it seems like knowing about somebody’s social group membership could tell you a lot about what they think: if I tell you that I am a liberal, for instance, that should tell you a lot about my beliefs, values, and social preferences — valuable information, when it comes to predicting and interpreting my behaviour.

Some philosophers and psychologists, such as Kristin Andrews, Anika Fiebich and Mark Coltheart, have suggested that stereotypes and mindreading may actually be alternative strategies for predicting and interpreting behaviour (Andrews 2012; Fiebich & Coltheart 2015). That is, it may be that sometimes we use stereotypes instead of mindreading to figure out what a person is going to do. 

According to one such proposal (Fiebich & Coltheart 2015), stereotypes allow us to predict behaviour because they encode associations between social categories, situations, and behaviours. Thus, one might form a three-way association between the social category police, the situation donut shops, and the behaviour eating donuts, which would lead one to predict that, when one sees a police officer in a donut shop, he or she will likely be eating a donut. 

A more complex version of this associationist approach would be to associate social groups with particular traits labels (as per the SCM), and thus consist in four-way associations between social categories, trait labels, situations, and behaviours (Fiebich & Coltheart 2015; Andrews 2012). Thus, one might come to the trait of generosity with leaving large tips in restaurants, and associate the social category of uncles with generosity, and thereby come to expect uncles to leave large tips in restaurants. One might then explain this behaviour by referring to the uncle’s generosity. 

The key thing to notice about these accounts is that their predictions do not rely at all upon mental-state attributions. This is by design: these proposals are meant to show that we often don’t need mindreading to predict or interpret behaviour.

One problem for this sort of view comes from its invocation of “situations.” What information, one might wonder, is contained within the scope of a particular “situation”? Surely, a situation does not include everything about the state of the world at a given moment. 

Situations are probably meant to pick out local states of affairs. But not all the facts about a local state of affairs will be relevant to behaviour prediction. The presence of mice in the kitchen of a restaurant, for instance will not affect your predictions about the size of your uncle’s tip. It might, however, affect our predictions about the behaviour of the health inspector, should one suddenly arrive. 

Which local facts are predictively useful will ultimately depend upon their relevance to the agent whose behaviour we are predicting. But whether or not a fact is relevant to an agent will depend upon that agent’s beliefs about the local state of affairs, as well as her goals and desires. 

If this is how representations of predictively useful situations are computed, then the purportedly non-mentalistic proposal given above really includes a tacit appeal to mindreading. If this is not how situations are computed, then we are owed an explanation for how the non-mentalistic behaviour-predictor arrives at predictively useful representations of situations that do not depend upon considerations of relevance.

Instead of treating mindreading and stereotypes as separate forms of behaviour-prediction and interpretation, we might instead explore the ways in which stereotypes might inform mindreading. The key to this approach, I suggest, lies in the fact that stereotypes encode information about personality traits. 

In many ways, personality traits are like mental states: they are unobservable mental properties of individual, and they are causally related to behaviour. But they also differ in one key respect: their temporal stability. Beliefs and desires are inherently unstable: a belief that P can be changed by the observation of not‑P; a desire for Q can be extinguished by the attainment of Q. 

Personality traits, in contrast, cannot be extinguished or abandoned based on everyday events. Rather, they tend to last throughout a person’s lifetime, and manifest themselves in many different ways across many different situations. 

A unique feature of personality traits, in other words, is that they are highly stable mental entities (Doris 2002). So when stereotypes ascribe traits to groups, they are ascribing a property that one could reasonably expect to remain consistent across many different situations.

The temporal properties of mental states are extremely relevant for mindreading, especially in models that employ Bayesian Predictive Coding (Kilner & Frith 2007; Koster-Hale & Saxe 2013; Hohwy & Palmer 2014; Hohwy 2013; Clark 2015). To see why, let’s start with an example:

Suppose that we believe that Laura is thirsty, and have attributed to her the goal of getting a drink (G). As goals go, this one is relatively short-term (unlike, say, the goal of getting a PhD). But we know that in order to achieve (G), we predict that Laura must form a number of even shorter-term sub-goals: 

• (G1) get the juice from the fridge, and 

• (G2) pour herself a glass of juice. 

But each of these requires the formation of still shorter-term sub-sub-goals: 

• (G1a) walk over to kitchen, 

• (G1b) open fridge door, 

• (G1c) remove juice container, 

• (G2a) remove cup from cupboard, 

• (G2b) pour juice into cup. 

Predicting Laura’s behaviour in this context thus begins with the ascription of a longer-duration mental state (G), followed by the ascription of successively shorter-term mental-state attributions (G1, G2, G1a, G1b, G1c, G2a, G2b).

As mindreaders, we can use attributions of more abstract, temporally extended mental states to make top-down inferences about more transient mental states. At each level in this action-prediction hierarchy, we use higher-level goal-attributions to constrain the space of possible sub-goals that the agent might form. We then use our prior experience to select the most likely sub-goal from the hypothesis space, and the process repeats itself.

Ultimately, this yields fairly fine-grained expectations about motor-intentions, which manifest themselves as mirror-neuron activity (Kilner & Frith 2007; Csibra 2008). Action-prediction thus plays out as a descent from more stable mental-state attributions to more transient ones, which ultimately bottom out in highly concrete expectations about behaviour.

Personality traits, which are distinguished by their high degree of temporal stability, fit naturally into the upper levels of this action-prediction hierarchy. Warmth traits, for instance, can tell us about the general preferences of an agent: a generous person probably has a general preference for helping others, while a greedy person probably has a general desire to enrich herself. These broad preference-attributions can in turn inform more immediate goal-attributions, which can then be used to predict behaviour.

This role for representations of personality traits in mental-state inference fits well with what we know about how we reason about traits more generally. For instance, we often make extremely rapid judgments about the warmth and competence traits of individuals based on fairly superficial evidence, such as facial features (Todorov et al. 2008); we also tend to over attribute the causes of behaviour to personality traits, rather than situational factors — a phenomenon commonly known as the “fundamental attribution error” or the “correspondence bias (Gawronski 2004; Ross 1977; Gilbert et al. 1995). 

Prioritizing personality traits makes a lot of sense if they form the inferential basis for more complex forms of behaviour prediction. It also makes sense that this aspect of mindreading would need to rely on fast, rough-and-ready heuristics, since personality trait information would need to be inferred very quickly in order to be useful in action-planning.

From a computational perspective, thus, using personality traits to make inferences about behaviour makes a lot of sense, and might make mindreading more efficient. But in exchange for this efficiency, we make a very disturbing trade. 

Stereotypes, which can be activated rapidly based on easily available perceptual cues provide the mindreading system with a rapid means for storing trait information (Mason et al. 2006; Macrae et al. 1994). With this speed comes one of the most morally pernicious forms of human social cognition, one that helps to perpetuate discrimination and social inequality.

The picture I’ve painted in this post is, admittedly, rather pessimistic. But just because the roots of discrimination are cognitively deep, we should not conclude that it is inevitable. 

More recent work from McGlothlin and Killen (2010) should give us some hope: while children from racially homogeneous schools (who had little direct contact with members of other races) tended to show signs of biased intention-attribution, McGlothlin and Killen also found that children from racially heterogeneous schools (who had regular contact with members of other races) did not display such signs of bias. Evidently, intergroup contact is effective in curbing the development of stereotypes — and, by extension, biased mindreading.