Knowing Ourselves Together: The Cultural Origins of Metacognition
Authors: Cecilia Heyes, Dan Bang, Nicholas Shea, Christopher D. Frith, Stephen M. Fleming
Journal: Trends in Cognitive Sciences (2020)

Human metacognition involves discrimination, interpretation, and broadcasting of subtle cues indicating the rightness of ongoing thought and behaviour.We propose that human metacognition is made fit for purpose by cultural evolution rather than genetic evolution.In particular, we present evidence that the effective discrimination, interpretation, and broadcasting of metacognitive cues depends on cultural learning.The cultural origins hypothesis advances a programme of research on the development of metacognition, cultural variation, individual differences, and cross-species comparisons. Metacognition – the ability to represent, monitor and control ongoing cognitive processes – helps us perform many tasks, both when acting alone and when working with others. While metacognition is adaptive, and found in other animals, we should not assume that all human forms of metacognition are gene-based adaptations. Instead, some forms may have a social origin, including the discrimination, interpretation, and broadcasting of metacognitive representations. There is evidence that each of these abilities depends on cultural learning and therefore that cultural selection might shape human metacognition. The cultural origins hypothesis is a plausible and testable alternative that directs us towards a substantial new programme of research. Metacognition – the ability to represent, monitor and control ongoing cognitive processes – helps us perform many tasks, both when acting alone and when working with others. While metacognition is adaptive, and found in other animals, we should not assume that all human forms of metacognition are gene-based adaptations. Instead, some forms may have a social origin, including the discrimination, interpretation, and broadcasting of metacognitive representations. There is evidence that each of these abilities depends on cultural learning and therefore that cultural selection might shape human metacognition. The cultural origins hypothesis is a plausible and testable alternative that directs us towards a substantial new programme of research. How do cognitive mechanisms become fit for purpose? They are all complex products of nature and nurture, but who or what designs the features that enable cognitive processes to do their jobs? How come visual systems can see, learning mechanisms can learn, and reasoning processes can reason? In many cases, gene-based selection (see Glossary) leads the design team. The visual system can see primarily because it has been honed by natural selection over biological generations. Variant systems were genetically inherited, and, through differential reproduction, those that were better at processing visual information proliferated while the others died out. In some cases, intentional design is also involved [1.Dennett D.C. From Bacteria to Bach and Back: The Evolution of Minds. W. W. Norton, 2017Google Scholar]. The cognitive mechanisms enabling you to read these words were designed in part by educationalists. The people who teach us to read, and designers of literacy programmes, make new cognitive mechanisms from old parts. With foresight and deliberation, they turn mechanisms that were designed by genetic evolution for small object recognition into a cognitive system for reading [2.Dehaene S. Dehaene-Lambertz G. Is the brain prewired for letters?.Nat. Neurosci. 2016; 19: 1192-1193Crossref PubMed Scopus (16) Google Scholar]. For some cognitive mechanisms, cultural selection is a third member of the design team, alongside genetic evolution and intentional design. Recent evidence suggests that a range of cognitive mechanisms, including imitation and mindreading (or theory of mind), have been shaped by a cultural selection process analogous to gene-based selection [3.Atzil S. et al.Growing a social brain.Nat. Hum. Behav. 2018; 2: 624-636Crossref PubMed Google Scholar, 4.Germar M. Mojzisch A. Learning of social norms can lead to a persistent perceptual bias: a diffusion model approach.J. Exp. Soc. Psychol. 2019; 84: 103801Crossref Scopus (1) Google Scholar, 5.Heyes C.M. Frith C.D. The cultural evolution of mind reading.Science. 2014; 344: 1243091Crossref PubMed Scopus (157) Google Scholar, 6.Ho M.K. et al.Social is special: a normative framework for teaching with and learning from evaluative feedback.Cognition. 2017; 167: 91-106Crossref PubMed Scopus (9) Google Scholar, 7.Heyes C. Empathy is not in our genes.Neurosci. Biobehav. Rev. 2018; 95: 499-507Crossref PubMed Scopus (11) Google Scholar]. In this cultural evolutionary process, variants arise in individual development, rather than by genetic mutation, and are inherited via social interaction rather than DNA. Good variants are culturally learned (e.g., copied) by more agents, but, unlike intentional design, this need not be because the teachers or the learners understand what makes them good. In this opinion article, we suggest that an important kind of metacognition has been made fit for purpose primarily by the latter two members of the team – intentional design and cultural evolution – rather than genetic selection. Here, we focus on the role of cultural evolution. We survey evidence that explicit metacognition (Box 1) is social in origin, and we outline an empirical programme that would allow the cultural origins hypothesis to be further developed and tested. First, however, we outline the many functions of metacognition in individual and group decision-making.Box 1Explicit MetacognitionIn this article we are concerned with what we call explicit metacognition. A representation is explicit, in our sense, when it is conscious and represented in working memory so that it can be used by processes of cognitive control. Thus, a hallmark of explicit metacognition is that it is sensitive to concurrent processing load. Humans typically communicate explicit metacognitive representations verbally. For example, we can tell others when we are uncertain about what we have seen. However, we can also communicate nonverbally about our explicit metacognitive states; and it is an open question whether language is necessary for an individual to have the capacity for explicit metacognition.Metacognition also operates in implicit processes that are automatic and relatively insensitive to cognitive load. The contrast between explicit and implicit metacognition can be seen in research on error monitoring in skilled typists [130.Logan G.D. Crump M.J.C. Cognitive illusions of authorship reveal hierarchical error detection in skilled typists.Science. 2010; 330: 683-686Crossref PubMed Scopus (86) Google Scholar]. Automatic monitoring processes make skilled typists fractionally slower on the next keystroke after they have made an error. Explicit metacognition, by contrast, allows the typist to report that they have made an error. The factors that affect implicit and explicit metacognition in these scenarios are experimentally dissociable.Metacognition is sometimes assumed to require consciousness, but here we adopt the more liberal definition that does not presuppose that metacognitive processes are conscious. So, a nonconscious representation or evaluation of a cognitive state or process can count as metacognitive. Explicit metacognition does require consciousness but note that our usage does not make explicit synonymous with conscious (which is another common usage). Our use is more restrictive. It excludes automatic metacognitive processes that do not depend on working memory and are insensitive to cognitive load, even if they involve conscious states like feelings of fluency.We can further distinguish two ways in which a metacognitive assessment of a decision can be computed [48.Fleming S.M. Daw N.D. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation.Psychol. Rev. 2017; 124: 91-114Crossref PubMed Scopus (103) Google Scholar]. First-order confidence is based wholly on the state or states used to take the decision itself. Second-order confidence is computed by a separate system and considers further factors (see Figure 2 in main text). Explicit metacognition is typically the result of a second-order computation. In this article we are concerned with what we call explicit metacognition. A representation is explicit, in our sense, when it is conscious and represented in working memory so that it can be used by processes of cognitive control. Thus, a hallmark of explicit metacognition is that it is sensitive to concurrent processing load. Humans typically communicate explicit metacognitive representations verbally. For example, we can tell others when we are uncertain about what we have seen. However, we can also communicate nonverbally about our explicit metacognitive states; and it is an open question whether language is necessary for an individual to have the capacity for explicit metacognition. Metacognition also operates in implicit processes that are automatic and relatively insensitive to cognitive load. The contrast between explicit and implicit metacognition can be seen in research on error monitoring in skilled typists [130.Logan G.D. Crump M.J.C. Cognitive illusions of authorship reveal hierarchical error detection in skilled typists.Science. 2010; 330: 683-686Crossref PubMed Scopus (86) Google Scholar]. Automatic monitoring processes make skilled typists fractionally slower on the next keystroke after they have made an error. Explicit metacognition, by contrast, allows the typist to report that they have made an error. The factors that affect implicit and explicit metacognition in these scenarios are experimentally dissociable. Metacognition is sometimes assumed to require consciousness, but here we adopt the more liberal definition that does not presuppose that metacognitive processes are conscious. So, a nonconscious representation or evaluation of a cognitive state or process can count as metacognitive. Explicit metacognition does require consciousness but note that our usage does not make explicit synonymous with conscious (which is another common usage). Our use is more restrictive. It excludes automatic metacognitive processes that do not depend on working memory and are insensitive to cognitive load, even if they involve conscious states like feelings of fluency. We can further distinguish two ways in which a metacognitive assessment of a decision can be computed [48.Fleming S.M. Daw N.D. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation.Psychol. Rev. 2017; 124: 91-114Crossref PubMed Scopus (103) Google Scholar]. First-order confidence is based wholly on the state or states used to take the decision itself. Second-order confidence is computed by a separate system and considers further factors (see Figure 2 in main text). Explicit metacognition is typically the result of a second-order computation. Explicit metacognition uses conscious representations in working memory to monitor or evaluate – and often to control – cognitive states and processes. Explicit metacognition (here metacognition, when not qualified) is sensitive to cognitive load, and is typically slow, deliberate, and verbally reportable [8.Shea N. Frith C.D. Dual-process theories and consciousness: the case for 'Type Zero' cognition.Neurosci. Conscious. 2016; 2016niw005Crossref PubMed Google Scholar,9.Shea N. et al.Supra-personal cognitive control and metacognition.Trends Cogn. Sci. 2014; 18: 186-193Abstract Full Text Full Text PDF PubMed Scopus (118) Google Scholar]. It yields feelings of knowing and confidence judgements, allowing us to think and report 'I'm sure' and 'I'm not so sure' about our perceptions, memories, and decisions. The adoption of frameworks inherited from psychophysics and signal detection theory has made possible the objective measurement of metacognitive ability in laboratory tasks, by assessing the bias and sensitivity of judgments of confidence in relation to task performance [10.Fleming S.M. Lau H.C. How to measure metacognition.Front. 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In cultural learning, by contrast with other kinds of social learning, what the receiver learns through social interaction with the sender is similar to, and causally dependent on, what the sender knows [44.Heyes C. Is morality a gadget? Nature, nurture and culture in moral development.Synthese. 2019; (Published online October 11, 2019. https://doi.org/10.1007/s11229-019-02348-w)Crossref Scopus (0) Google Scholar]. Cultural learning often, but not always, involves teaching. The sender may intend to communicate information to the receiver, or instead involuntarily leak information that is picked up by the receiver. If metacognition is acquired through cultural learning, it may be fit for purpose not because of gene-based selection and intentional design, but also due to cultural selection – a selection process operating on variants transmitted culturally over generations of learners. Here, we survey evidence that metacognition is acquired through cultural learning. A stronger claim would be that metacognition is made fit for purpose by cultural selection; acquired through cultural learning and rendered adaptive by a process of natural selection acting on the culturally learned variants. There is currently less evidence in support of the stronger claim, partly because it has not yet been seriously investigated. However, there is evidence of adaptively relevant variation in metacognitive ability (i.e., in the relevant phenotype) across cultural groups and societies. Given the timescales involved, this is unlikely to be the result of gene-based selection. It is therefore plausible that cultural selection has been at work, selecting the adaptive variants in the metacognitive abilities observed in different cultural groups. Current models of metacognition suggest that a range of first-order monitoring signals need to be re-represented by the metacognitive system in order to become available for the kind of intra- and suprapersonal control functions highlighted above [9.Shea N. et al.Supra-personal cognitive control and metacognition.Trends Cogn. Sci. 2014; 18: 186-193Abstract Full Text Full Text PDF PubMed Scopus (118) Google Scholar,45.Clark A. Karmiloff-Smith A. The cognizer's innards: a psychological and philosophical perspective on the development of thought.Mind Lang. 1993; 8: 487-519Crossref Scopus (125) Google Scholar,46.Cleeremans A. Connecting conscious and unconscious processing.Cogn. Sci. 2014; 38: 1286-1315Crossref PubMed Scopus (12) Google Scholar] (Figure 1). Many of these first-order signals are encapsulated within the perception-action loop. 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Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation.Psychol. Rev. 2017; 124: 91-114Crossref PubMed Scopus (103) Google Scholar]. This is consistent with the popular inferential view of how metamemory judgments are formed [49.Koriat A. Monitoring one's own knowledge during study: a cue-utilization approach to judgments of learning.J. Exp. Psychol. Gen. 1997; 126: 349-370Crossref Google Scholar], and implies that first-order monitoring signals need to be discriminated and interpreted by the metacognitive system. In the following, we identify three components that comprise the capacity for metacognition (Figure 1): (i) discrimination – distinguishing metacognitive feelings from one another, and from feelings that do not arise from metacognitive computations; (ii) interpretation – working out the significance of metacognitive representations, for example, whether ease of processing indicates that an object is familiar; and (iii) broadcasting – learning efficient communicative conventions for sharing metacognitive representations with other agents. As we introduce each of these components, and in the section that follows, we survey evidence that their development depends on cultural learning, and we identify opportunities to test this hypothesis further in future research. Relevant inputs for metacognition must be distinguished from one another (e.g., stimulus visibility versus decision confidence) and from interoceptive signals, including emotional states (e.g., low confidence versus fear). It would be maladaptive to share within the cognitive system, or broadcast to other agents, feelings that reflect states of the body or the world as if they represent properties of cognitive representations and processes. For instance, fear of a bear should not be mistaken for uncertainty about whether one has seen a bear. However, exactly this kind of crosstalk can be observed in laboratory experiments on metacognition. For example, when people were briefly flashed a face with a disgusted expression, their confidence in an incidental perceptual task was subtly modulated [50.Allen M. et al.Unexpected arousal modulates the influence of sensory noise on confidence.eLife. 2016; 5: 1-17Crossref Scopus (52) Google Scholar]. 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