Month: November 2021

Although having a history as old as the social and behavioral sciences (and for some, as old as philosophical reflections on the mind itself), dual-process models of cognition have been with us only for a bit over two decades, becoming established in cognitive and social psychology in the late 1990s (see Sloman, 1996 and Smith and DeCoster, 2000 for foundational reviews). The implicit measurement revolution provided the “data” side to the theoretical and computational modeling side, thus fomenting further theoretical and conceptual development (Strack & Deutsch, 2004; Gawronski & Bodenhausen, 2006). Although not without its critics, the dual-process approach has now blossomed into an interdisciplinary framework useful for studying learning, perception, thinking, and action (Lizardo et al., 2016). In sociology, dual-process ideas were introduced by way of the specific dual-process model of moral reasoning developed by Jonathan Haidt (2001) in Steve Vaisey’s (2009) now classic and still heavily cited paper. Sociological applications of the dual-process framework for specific research problems now abound, with developments on both the substantive and measurement sides (Miles, 2015; Miles et al. 2019; Melamed et al. 2019; Srivastava & Banaji, 2011).

The dual-process framework revolves around the ideal-typical distinction between two “modes” or “styles” of cognition (Brett & Miles, 2021). These are now very familiar. One is the effortful, usually conscious, deliberate processing of serially presented information, potentially available for verbal report (as when reasoning through a deductive chain or doing a hard math problem in your head). The other is the seemingly effortless, automatic, usually unconscious, associative processing information (as when a solution to a problem just “comes” to you, or when you just “know” something without seemingly having gone through steps to reach the solution). This last is usually referred to as intuitive, automatic, or associative “Type 1” cognition, and the former is usually referred to as effortful, deliberate, or non-automatic “Type 2” cognition.

As with many hard and fast distinctions, there is the virtue of simplification and analytic power, but there is the limitation, evident to all, that the differentiation between Type 1 versus Type 2 cognition occludes as much as it reveals. For instance, people wonder about the existence of “mixed” types of cognition or iterative cycles between the two modes or the capacity of one mode (usually Type 2) to override the outputs of the other (usually Type 1). It seems like the answer to all these wonders is a general “yes.” We can define a construct like “automaticity” to admit various “in-between” types (Moors & De Houwer, 2006), suggesting that a pure dichotomy is too simple (Melnikoff & Bargh, 2018). And yes, the two types of cognition interact and cycle (Cunningham & Zelazo, 2007). The interactive perspective is even built into some measurement strategies, which rely on overloading or temporarily overwhelming the deliberate system to force people to respond with intuitive Type 1 cognition (as in so-called “cognitive load” techniques; see Miles, 2015 for a sociological application).

Another sort of wonder revolves around whether these are the only types of cognition that exist. Are there any more types? Accordingly, some analysts speak of “tri” or “quad” process models and the like (Stanovich, 2009). It seems, therefore, that field is moving toward a taxonomic approach to the study of cognitive processes. However, the criteria or “dimensions” around which such taxonomies are to be constructed are in a state of flux. As I noted in a previous post, moving toward a taxonomic approach is generally a good thing. Moreover, the field of memory research is a good model for how to build taxonomic theory in cognitive social science (CSS), especially since the “kinds” typically studied in CSS are usually “motley” (natural kinds that decompose into fuzzy subkinds). When studying motley kinds and organizing into fruitful taxonomies, it is essential to focus on the analytic dimensions and let the chips fall where they may. This is different from thinking up “new types” of cognition from the armchair in unprincipled ways, where the dimensions that define the types are ill-defined (as with previous attempts to talk about tri-process models of cognition and the like). Moreover, the dimensional approach leaves things open to discover surprising “subkinds” that join properties that we would consider counter-intuitive.

Accordingly, an upshot of everyone now accepting (even begrudgingly) some version of the dual-process theory is that we also agree that the cognitive-scientific kind “cognition” is itself motley! That is, whatever it is, cognition is not a single kind of thing. Right now, we kind of agree that it is at least two things (as I said, an insight that is as old as the Freudian distinction between primary and secondary process), but it is likely that it could be more than two. In this post, I’d like to propose one attempt to define the possible dimensional space from which a more differentiated typology of cognitive processes can be constructed.

Taxonomizing Cognition

So if we needed to choose dimensions to taxonomize cognition, where would we begin? I think a suitable candidate is to pick two closely aligned dimensions of cognition that people thought were fused or highly correlated but now are seen as partially orthogonal. For example, in a previous post on the varieties of “implictness” (which is arguably the core dimension of cognition that defines the core distinction in dual-process models), I noted that social and cognitive psychologists differentiate between two criteria for deeming something “implicit.” First, a-implicitness uses an “automaticity” criterion. Here, cognition is implicit if it is automatic and explicit if it is deliberate or effortful. Second, there is u-implicitness, which uses a(n) (un)consciousness criterion. Here, cognition is implicit if it occurs outside of consciousness, and it is explicit if it is conscious.

I implied (but did not explicitly argue) in that post that maybe these two dimensions of explicitness could come apart. If they can, these seem like pretty good criteria to build a taxonomy of cognitive process kinds that goes beyond two! This is precisely what the philosophers Nicholas Shea and Chris Frith did in a paper published in 2016 in Neuroscience of Consciousness. Cross-classifying the type of processing (deliberate v. automatic) against the type of representations over which the processing occurs (conscious v. unconscious), yields a new “type” of cognition which they refer to as “Type 0 cognition.”

In Shea and Frith’s taxonomy, our old friend Type 1 cognition refers to the automatic processing of initially conscious representations, typically resulting in conscious outputs. In their words, “[t]ype 1 cognition is characterized by automatic, load-insensitive processing of consciously represented inputs; outputs are typically also conscious.” (p.4). This definition is consistent with Evans’s (2019) more recent specification of Type 1 cognition as working-memory independent cognition that still uses working memory to “deposit” the output of associative processing. In Evans’s words,

While Type 1 processes do not require the resources of working memory or controlled attention for their operation (or they would be Type 2) they do post their products into working memory in a way that many autonomous processes of the brain do not. Specifically, they bring to mind judgements or candidate responses of some kind accompanied by a feeling of confidence or rightness in that judgement (p. 384).

For Shea and Frith (2016), on the other hand, our other good friend, Type 2 cognition, refers to the deliberate, effortful processing of conscious representations. In their words,

Type 2 cognition is characterized by deliberate, non-automatic processing of conscious representations. It is sensitive to cognitive load: type 2 processes interfere with one another. Type 2 cognition operates on conscious representations, typically in series, over a longer timescale than type 1 cognition. It can overcome some of the computational limitations of type 1 cognition, piecemeal, while retaining the advantage of being able to integrate information from previously unconnected domains. It is computation-heavy and learning-light: with its extended processing time, type 2 cognition can compute the correct answer or generate optimal actions without the benefit of extensive prior experience in a domain (p. 5).

By way of contrast with these familiar faces, our new friend Type 0 cognition refers to the automatic processing of non-conscious representations. Shea and Frith see isolating Type 0 cognition as a separate cognitive-process subkind as their primary contribution. Previous work, in their view, has run Type 0 and Type 1 cognition together, to their analytic detriment. Notably, they argue for the greater (domain-specific) efficiency and accuracy of Type 0 cognition over Type 1. They note that various deficiencies of Type 1 cognition identified in such research programs as the “heuristics and biases” literature come from the fact that, in Type 1 cognition, there is a mismatch between process and representation because automatic/associative processes are recruited to deal with conscious representational inputs.

For instance, Type 1 cognition is at work when Haidt asks people whether they would wear Hitler’s t-shirt, and they say “ew, no way!” but are unable to come up with a morally reasonable reason why (or make up an implausible one on the spot). Type 1 moral cognition “misfires” here because the associative (“moral intuition”) system was recruited to process conscious inputs, relied on an associative/heuristic process to generate an answer (in this case, based on implicit contact, purity, and contagion considerations), and produced a conscious output, the origins of with subjects are completely unaware of (and is thus forced to retrospectively confabulate using Type 2 cognition). The same goes for judgment and decision-making producing answers to questions when engaging in the base-rate fallacy, using a representativeness heuristic, and the like (Kahneman, 2011). 

The types of cognition for which a match is made in heaven between process and representation (like Type 2 and their Type 0) result in adaptive cognitive processes that “get the right answer.” Type 2 cognition refers to domain-general problems requiring information integration and the careful weighing of alternatives. In Type 0 cognition, this refers to domain-specific problems requiring fast, adaptive cognitive processing and action control, where consciousness (if it were to rear its ugly head) would spoil the fun and impair the effectiveness of the cognitive system to do what is supposed to do, similar to athletes who “choke” when they become conscious of what they are doing (see Beilock, 2011).

So, what is Type 0 cognition good for? Shea and Frith point to things like the implicit learning of probabilistic action/reward contingencies after many exposures (e.g., reinforcement learning), where neither the probabilities nor the learning process is consciously represented, and the learning happens via associative steps. As they note, in “model-free reinforcement learning can generate optimal decisions when making choices for rewards, and feedback control can compute optimal action trajectories…non-conscious representation goes hand-in-hand with correct performance” (p. 3). In the same way, “Type 0 cognition is likely to play a large role in several other domains, for example in the rich inferences which occur automatically and without consciousness in the course of perception, language comprehension and language production” (ibid).

Organizing the Types

So, where does Shea and Frith’s taxonomy of cognitive process kinds leave us? Well, maybe something like the dimensional typology shown in Figure 1. It seems like at least three different cognitive process kinds are well-defined, especially if you are convinced that we should distinguish Type 0 from Type 1 cognition (and I think I am).

However, as I argued earlier, a key advantage of beginning with dimensions in any taxonomical exercise is that we may end up with a surprise. Here, it is the fact that a fourth potential type of cognition now appears in the lower-right quadrant, one that no one has given much thought to before. Type ??? cognition: deliberate processing of unconscious representations. Can this even be a thing? Shea and Frith do note this implication of their taxonomic exercise but think it is too weird. They even point out that it may be a positive contribution of their approach to have discovered this “empty” slot in cognitive-process-kind space. In their words, “[w]hat of the fourth box? This would be the home of deliberate processes acting on non-conscious representations. It seems to us that there may well be no type of cognition that fits in this box. If so, that is an important discovery about the nature of consciousness” (p. 7).

Nevertheless, are things so simple? Maybe not. The Brains Blog dedicated a symposium to the paper in 2017 in which three authors provided commentaries. Not surprisingly, some of the commenters did not buy the “empty slot” argument. In their comment, Jacob Berger points to some plausible candidates for Shea and Frith’s Type ??? cognition (referred to as “Type 0.5 cognition”). This includes the (somewhat controversial) work of Dijksterhuis, Aarts, and collaborators (e.g., Dijksterhuis & Nordgren, 2006; Dijksterhuis & Aarts, 2010) on “unconscious thought theory” (UTT) (see Bargh, 2011 for a friendly review). In the UTT paradigm, participants are asked to make seemingly deliberate choices between alternatives, with a “right” answer aimed at maximizing a set of quality dimensions. At the same time, conscious thinking is impaired via cognitive load. The key result is that participants who engage in this “unconscious thinking” end up making choices that are as optimal as people who think about it reflectively. So, this seems to be a case of a deliberate thinking process operating over unconscious representations.

Berger does anticipate an objection to UT as being a candidate for Type ??? cognition, which itself brings up an issue with critical taxonomic ramifications:

S&F might reply that such [UT] cases are not genuinely unconscious because, like examples of type-1 cognition, they involve conscious inputs and outputs. But if this processing is not type 0.5, then it is hard to see where S&F’s taxonomy accommodates it. The cognition does not seem automatic, akin to the processing of type 0 or type 1 of which one is unaware (it seems, for example, rather domain general); nor does it seem to be a case of type-2 cognition, since one is totally unaware of the processing that results in conscious outputs. Perhaps what is needed is an additional distinction between the inputs/outputs of a process’ being conscious and the consciousness of states in the intervening processing. In type-1 cognition, the inputs/outputs are conscious, but the states involved in the automatic processing are not; in type-2, both are conscious. We might therefore regard Dijksterhuis’ work as an instance of ‘type-1.5’ cognition: conscious inputs/outputs, but deliberative unconscious processing.

Thus, Berger proposes to dissociate not only conscious/unconscious representations from deliberate/automatic processing but also adds the dimension of whether the inputs and outputs of the cognitive process and its intervening steps are themselves conscious or unconscious. Berger’s implied taxonomy can thus be represented as in Figure 2.

Figure 2 clarifies that the actual mystery type does not connect conscious inputs and outputs with deliberate unconscious processing (UT), but a type linking unconscious inputs and outputs with deliberate unconscious processing (the new Type ???). Also, the figure makes clear that the proper empty slot is a type of cognition conjoining unconscious inputs and outputs with deliberate conscious processing; this bizarre and implausible combination can indeed be ruled out on a priori grounds. Note, in contrast, that if there is such a thing as deliberate unconscious processing (and the jury is still out on that), there is no reason to rule out the new Type ??? cognition shown in Figure 2 on a priori grounds (as Shea and Frith tried to do with Berger’s Type 1.5). For instance, Bargh (2011) argues that unconscious goal pursuit (a type of unconscious thought) can be triggered outside of awareness (unconscious input) and also has behavioral consequences (e.g., trying hard on a task) that subjects may also be unaware of (unconscious output). In this sense, Bargh’s unconscious goal pursuit would qualify as a candidate for Type ??? cognition. So, following Berger’s recommendation, we end up with five (I know an ugly prime) candidate cognition types. 

So, What?

Is all we are getting after all of this a more elaborate typology? Well, yes. And that is good! However, I think the more differentiated approach to carving the cognitive-process world also leads to some substantive insight. I refer in particular to Shea and Frith’s introduction of the Type 0/Type 1 distinction. For instance, in a recent review (and critique) of dual-process models of social cognition, Amodio proposes an “interactive memory systems” account of attitudes and impression formation (“Social Cognition 2.0”) that attempts to go beyond the limitations of the traditional dual-process model (“Social Cognition 1.0”).

Amodio’s argument is wide-ranging, but his primary point is that there are multiple memory systems and that a conception of Type 1 cognition as a single network of implicit concept/concept associations over which unconscious cognition operates is incomplete. In addition to concept/concept associations, Amodio points to other types of associative learning, including Pavlovian (affective) and instrumental (reinforcement learning). Amodio’s primary point is that something like an “implicit attitude,” insofar as it recruits multiple but distinct (and dissociable) forms of memory and learning subserved by different neural substrates, is not a single kind of thing (a taxonomical exercise for the future!). This dovetails nicely with the current effort to taxonomize cognitive processes. Thus, a standard conceptual association between categories of people and valenced traits operates via Type 1 cognition. However, it is likely that behavioral approach/avoid tendencies toward the same type of people, being the product of instrumental/reinforcement learning mechanisms, operate via Shea and Frith’s Type 0 cognition.

References

Bargh, J. A. (2011). Unconscious Thought Theory and Its Discontents: A Critique of the Critiques. Social Cognition, 29(6), 629–647.

Beilock, S. L. (2011). Choke. The secret of performing under pressure. London: Constable.

Brett, G., & Miles, A. (2021). Who Thinks How? Social Patterns in Reliance on Automatic and Deliberate Cognition. Sociological Science, 8, 96–118.

Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: a social cognitive neuroscience perspective. Trends in Cognitive Sciences, 11(3), 97–104.

Dijksterhuis, A., & Aarts, H. (2010). Goals, attention, and (un)consciousness. Annual Review of Psychology, 61, 467–490.

Evans, J. S. B. T. (2019). Reflections on reflection: the nature and function of type 2 processes in dual-process theories of reasoning. Thinking & Reasoning, 25(4), 383–415.

Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: an integrative review of implicit and explicit attitude change. Psychological Bulletin, 132(5), 692–731.

Haidt, J. (2001). The emotional dog and its rational tail: a social intuitionist approach to moral judgment. Psychological Review, 108(4), 814–834.

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

Lizardo, O., Mowry, R., Sepulvado, B., Stoltz, D. S., Taylor, M. A., Van Ness, J., & Wood, M. (2016). What are dual process models? Implications for cultural analysis in sociology. Sociological Theory, 34(4), 287–310.

Melamed, D., Munn, C. W., Barry, L., Montgomery, B., & Okuwobi, O. F. (2019). Status Characteristics, Implicit Bias, and the Production of Racial Inequality. American Sociological Review, 84(6), 1013–1036.

Melnikoff, D. E., & Bargh, J. A. (2018). The mythical number two. Trends in cognitive sciences, 22(4), 280-293.

Miles, A. (2015). The (Re)genesis of Values: Examining the Importance of Values for Action. American Sociological Review, 80(4), 680–704.

Miles, A., Charron-Chénier, R., & Schleifer, C. (2019). Measuring Automatic Cognition: Advancing Dual-Process Research in Sociology. American Sociological Review, 84(2), 308–333.

Moors, A., & De Houwer, J. (2006). Automaticity: a theoretical and conceptual analysis. Psychological Bulletin, 132(2), 297–326.

Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 3.-22

Smith, E. R., & DeCoster, J. (2000). Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 4(2), 108–131.

Srivastava, S. B., & Banaji, M. R. (2011). Culture, Cognition, and Collaborative Networks in Organizations. American Sociological Review, 76(2), 207–233.

Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri-process theory? In J. S. B. T. Evans (Ed.), In two minds: Dual processes and beyond , (pp (Vol. 369, pp. 55–88). Oxford University Press.

Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 8(3), 220–247.

Vaisey, S. (2009). Motivation and Justification: A Dual-Process Model of Culture in Action. American Journal of Sociology, 114(6), 1675–1715.