Abstract
The goal of this paper is to explore forms of metacognition that have rarely been discussed in the extensive psychological and philosophical literatures on the topic. These would comprise explicit (as opposed to merely implicit or procedural) instances of meta-representation of some set of mental states or processes in oneself, but without those representations being embedded in anything remotely resembling a theory of mind, and independent of deployment of any sort of concept-like representation of the mental. Following a critique of some extant suggestions made by Nicholas Shea, the paper argues that appraisals of the value of cognitive effort involve the most plausible instances of this kind of metacognition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The simplified model presented here ignores top-down influences on evaluative learning, of the sort that figure in nocebo and placebo effects. It also ignores the influence of background mood (Eldar et al. 2016), and glosses over the fact that a value acquired from previous evaluative learning will be a weighted average of the values experienced in the past, with more recent rewards being counted more heavily in proportion to the learning rule.
Notice that Shea does not claim that the error signal is meta-representational on the grounds that it represents something about an action (that is, a movement caused by an intention, hence a partly mental entity). This is for good reason: the action-representations involved in evaluative learning are coded entirely in sensorimotor format.
Thus far Shea (2014) doesn’t disagree. For on p. 322 he writes that the correctness-condition for a stored value (in my notation, m) is that, “[m] is accurate iff the average reward payoff that would be achieved by repeatedly choosing [the thing or action] in the current environment is [m].” Note the implication here, by the way, that the stored value for Xs is at least implicitly tensed. The system is designed to track values in potentially changing circumstances. In fact, it can be helpful to think of VAL(Xs = m) as a sort of generic representation, the evaluative equivalent of a generic belief like, “Birds fly.” Although learned from previous experience, it gives rise to expectations of future Xs, somewhat as the generic belief that birds fly would lead you to expect that the next bird you encounter will fly.
Note that in his 2018 book Shea still explicitly endorses a meta-representational account of reward-prediction error signals (albeit in passing), so it isn’t anachronistic to employ his later theory of representational content to evaluate the earlier account. Note, too, that Shea’s varitel semantics includes two basic kinds of representing relation. One is informational, with an internal symbol causally co-varying (in the right circumstances and in the right way) with the represented property or thing. The other is a form of structural mapping, with the relations among a set of internal symbols mirroring the relations among a set of external entities. It is the first of these sorts or representing relation that is relevant here. This is because error signals are singular in occurrence, rather than doing their work via the relations they stand in to a set of similar signals.
Note that in the quoted passage Shea describes the error-signal d as telling the down-stream system to revise its expected value for the thing in question. He intends this quite seriously. He thinks that the error signal has imperative, or directive, content as well as indicative content. (That is, he thinks it is what Millikan 1995, calls a “pushmi-pullyu” representation.) But this is ill-motivated. The error signal no more has an imperative content than does visual perception of something unexpected. The perceptual content serves to update one’s beliefs about the likelihood of events in the environment. But it doesn’t direct one to update one’s beliefs.
It is worth noting that reward-value representations aren’t just representations of adaptive value and disvalue; they are actually what Millikan (1995) calls “pushmi-pullyu” representations. For expectations of value directly motivate actions designed to achieve or avoid the valued or disvalued things in question. Moreover, note the difference between this case and the error signals themselves, which Shea (2014) claims have imperative content. For it is not true that anything that causes a change in an organism (e.g. in a stored value) is an imperative. Imperatives serve to cause / motivate action.
Notice that in cases where one consciously and reflectively does something decision-like—such as articulating in inner speech, “I will stop and think about this one”—a decision has already been taken to engage controlled processing. (In pausing to articulate those words one is already stopping to think.) The inner-speech performance serves as an expression of that decision. My target in the discussion that follows are the (putative) unconscious decisions that initiate controlled processing.
To be clear, I will not be claiming that LCA models actually succeed in providing the best explanation of the phenomenon we call “deciding to think / stop thinking.” My claim, rather, is negative. It is that, given the viability of such models and their popularity in psychology, it would be hard to establish—and certainly controversial to claim—that there is an explicit metacognitive representation-type picked out by the phrase “decision to engage controlled processing.” Note, too, that although LCA models are a specific type of diffusion decision model (Forstmann et al. 2016), nothing of significance turns on this distinction for our purposes.
Note, however, that this isn’t to claim that all affective states in general are tied to a representation of something. Moods, in particular, are affective states that are free-floating—or, perhaps better, that color everything—rather than being tied to some thing or type of thing in particular.
Note that if different forms of executive engagement are to be evaluated separately, as I hint at here (e.g. focused attention versus response inhibition), then the model I am proposing would require there to be distinct signals sent to evaluative systems from each component kind of executive control. The default settings for each of these signals would be negative, but evaluative learning might alter their values in particular types of context independently of one another. Note, too, that there is unlikely to be anything resembling perceptual constancies in this domain. (Mental effort doesn’t have to be identified across a wide range of differing signals; a single signal, or a single signal for each type of effort, will do.) So one cannot appeal to Burge’s (2010) framework to argue that despite the absence of mental-state concepts mental effort is represented as such.
References
Beck, J. (2015). Analogue magnitude representations: A philosophical introduction. British Journal for the Philosophy of Science, 66, 829–855.
Beck, J. (2019). Perception is analog: The argument from Weber’s law. Journal of Philosophy, 116, 314–349.
Bermúdez, J. (2003). Thinking without words. Oxford: Oxford University Press.
Bermúdez, J. (2015). Nonconceptual mental content. In E. Zalta (Ed.), Stanford encyclopedia of philosophy. https://plato.stanford.edu/archives/fall2015/entries/content-nonconceptual
Bilgrami, A. (2006). Self-knowledge and resentment. Cambridge, MA: Harvard University Press.
Boly, M., Balteau, E., Schnakers, C., Degueldre, C., Moonen, G., Luxen, A., et al. (2007). Baseline brain activity fluctuations predict somatosensory perception in humans. Proceedings of the National Academy of Sciences, 104, 12187–12192.
Burge, T. (2010). Origins of objectivity. Oxford: Oxford University Press.
Byrne, A. (2018). Transparency and self-knowledge. Oxford: Oxford University Press.
Cacioppo, J., & Petty, R. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116–131.
Camp, E. (2004). The generality constraint, nonsense, and categorical restrictions. Philosophical Quarterly, 54, 209–231.
Carruthers, P. (2011). The opacity of mind. Oxford: Oxford University Press.
Carruthers, P. (2015). The centered mind. Oxford: Oxford University Press.
Carruthers, P. (2017). Are epistemic emotions metacognitive? Philosophical Psychology, 30, 58–78.
Carruthers, P. (2018). Valence and value. Philosophy and Phenomenological Research, 97, 658–680.
Cassam, Q. (2014). Self-knowledge for humans. Oxford: Oxford University Press.
Christoff, K., Irving, Z., Fox, K., Spring, N., & Andrews-Hanna, J. (2016). Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews Neuroscience, 17, 718–731.
Corbetta, M., & Shulman, G. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215.
Corbetta, M., Patel, G., & Shulman, G. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58, 3063–3124.
Cutter, B., & Tye, M. (2011). Tracking representationalism and the painfulness of pain. Philosophical Issues, 21, 90–109.
Dayan, P., & Berridge, K. (2014). Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation. Cognitive, Affective, and Behavioral Neuroscience, 14, 473–492.
Dehaene, S. (1997). The number sense. London: Penguin Press.
Delton, A., & Sell, A. (2014). The co-evolution of concepts and motivation. Current Directions in Psychological Science, 23, 115–120.
Dickinson, A., & Balleine, B. (2002). The role of learning in the operation of motivational systems. In C. R. Gallistel (Ed.), Stevens handbook of experimental psychology. New York: Wiley.
Dokic, J. (2012). Seeds of self-knowledge: Noetic feelings and metacognition. In M. Beran, J. Brandl, J. Perner, & J. Proust (Eds.), Foundations of metacognition. Oxford: Oxford University Press.
Dufau, S., Grainger, J., & Ziegler, J. (2012). How to say “no” to a nonword: A leaky competing accumulator model of lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 1117–1128.
Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Thousand Oaks: Sage Publications.
Eisenberger, R. (1992). Learned industriousness. Psychological Review, 99, 248–267.
Eldar, E., Rutledge, R., Dolan, R., & Niv, Y. (2016). Mood as representation of momentum. Trends in Cognitive Sciences, 20, 15–24.
Evans, G. (1982). The varieties of reference. Oxford: Oxford University Press.
Evans, J., & Over, D. (1996). Rationality and reasoning. Hove, East Sussex: Psychology Press.
Evans, J. (2010). Thinking twice: Two minds in one brain. Oxford: Oxford University Press.
Evans, J., & Stanovich, K. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8, 223–241.
Fernández, J. (2013). Transparent minds: A study of self-knowledge. Oxford: Oxford University Press.
Flavell, J. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906–911.
Fodor, J. (2015). Burge on perception. In S. Laurence & E. Margolis (Eds.), The conceptual mind. Cambridge: MIT Press.
Forstmann, B., Ratcliff, R., & Wagenmakers, E.-J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641–666.
Gruber, R., Schiestl, M., Boeckle, M., Frohnwieser, A., Miller, R., Gray, R. D., et al. (2019). New Caledonian crows use mental representations to solve metatool problems. Current Biology, 29, 686–692.
Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27, 377–442.
Hagger, M., Chatzisarantis, N., Alberts, H., Anggono, C., Batailler, C., Birt, A., et al. (2016). A multilab preregistered replication of the ego-depletion effect. Perspectives on Psychological Science, 11, 546–573.
Hesselmann, G., Kell, C., & Kleinschmidt, A. (2008). Ongoing activity fluctuations in hMT+ bias the perception of coherent visual motion. Journal of Neuroscience, 28, 14481–14485.
Hosking, J., Crocker, P., & Winstanley, C. (2016). Prefrontal cortical inactivations decrease willingness to expend cognitive effort on a rodent cost/benefit decision-making task. Cerebral Cortex, 26, 1529–1538.
Inzlicht, M., Shenhav, A., & Olivola, C. (2018). The effort paradox: Effort is both costly and valued. Trends in Cognitive Sciences, 22, 337–349.
Izard, V., Sann, C., Spelke, E., & Streri, A. (2009). Newborn infants perceive abstract numbers. Proceedings of the National Academy of Sciences, 106, 10382–10385.
Jeannerod, M. (2006). Motor cognition. Oxford: Oxford University Press.
Jordan, K., MacLean, E., & Brannon, E. (2008). Monkeys match and tally quantities across senses. Cognition, 108, 617–625.
Kahneman, D. (2015). Thinking, fast and slow. New York: Farrah, Strauss, & Giroux.
Karten, H. (2015). Vertebrate brains and evolutionary connectomics: On the origins of the mammalian “neocortex”. Philosophical Transactions of the Royal Society B, 370, 20150060.
Kirk, C., McMillan, N., & Roberts, W. (2014). Rats respond for information: Metacognition in a rodent? Journal of Experimental Psychology: Animal Learning and Cognition, 40, 249–259.
Kurzban, R. (2010). Does the brain consume additional glucose during self-control tasks? Evolutionary Psychology, 8, 244–259.
Kurzban, R. (2016). The sense of effort. Current Opinion in Psychology, 7, 67–70.
Kurzban, R., Duckworth, A., Kable, J., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36, 661–726.
Le Pelley, M. (2012). Metacognitive monkeys or associative animals? Simple reinforcement learning explains uncertainty in nonhuman animals. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 686–708.
Masicampo, E., & Baumeister, R. (2008). Toward a physiology of dual-process reasoning and judgment. Psychological Science, 19, 255–260.
Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: The dynamics of willpower. Psychological Review, 106, 3–19.
Millikan, R. (1995). Pushmi-pullyu representations. Philosophical Perspectives: AI, Connectionism and Philosophical Psychology, 9, 185–200.
Mischiati, M., Lin, H.-T., Herold, P., Imler, E., Olberg, R., & Leonardo, A. (2015). Internal models direct dragonfly interception steering. Nature, 517, 333–338.
Mysore, S., & Knudsen, E. (2013). A shared inhibitory circuit for both exogenous and endogenous control of stimulus selection. Nature Neuroscience, 16, 473–478.
Nelson, T., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. In G. Bower (Ed.), The psychology of learning and information. Cambridge: Academic Press.
Nicholson, T., Williams, D., Grainger, C., Lind, S., & Carruthers, P. (2019). Relationships between implicit and explicit uncertainty monitoring and mindreading: Evidence from autism spectrum disorder. Consciousness and Cognition, 70, 11–24.
Odic, D. (2018). Children’s intuitive sense of number develops independently of their perception of area, density, length, and time. Developmental Science, 21, e12533.
Peacocke, C. (1992). A study of concepts. Cambridge, MA: MIT Press.
Proust, J. (2014). The philosophy of metacognition. Oxford: Oxford University Press.
Sauce, B., Wass, C., Smith, A., Kwan, S., & Matzel, L. (2014). The external-internal loop of interference: Two types of attention and their influence on the learning abilities of mice. Neurobiology of Learning and Memory, 116, 181–192.
Seyfarth, R., Cheney, D., & Marler, P. (1980). Vervet monkey alarm calls: Semantic communication in a free-ranging primate. Animal Behavior, 28, 1070–1094.
Shea, N. (2014). Reward prediction error signals are meta-representational. Noûs, 48, 314–341.
Shea, N. (2018). Representation in cognitive science. Oxford: Oxford University Press.
Shenhav, A., Cohen, J. D., & Botvinick, M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19, 1286–1291.
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T., Cohen, J. D., et al. (2017). Toward and rational and mechanistic account of mental effort. Annual Reviews in Neuroscience, 40, 99–124.
Shipstead, Z., Lindsey, D., Marshall, R., & Engle, R. (2014). The mechanisms of working memory capacity: Primary memory, secondary memory, and attention control. Journal of Memory and Language, 72, 116–141.
Sloman, S. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22.
Smith, J. D., Couchman, J., & Beran, M. (2014). Animal metacognition: A tale of two comparative psychologies. Journal of Comparative Psychology, 128, 115–131.
Smith, J. D., Shields, W., & Washburn, D. (2003). The comparative psychology of uncertainty monitoring and meta-cognition. Behavioral and Brain Sciences, 26, 317–373.
Stanovich, K. (1999). Who is rational? Studies of individual differences in reasoning. Mahwah: Erlbaum.
Taylor, A., Elliffe, D., Hunt, G., & Gray, R. (2010). Complex cognition and behavioral innovation in New Caledonian crows. Proceedings of the Royal Society B: Biological Sciences, 277, 2637–2643.
Templer, V., Lee, K., & Preston, A. (2017). Rats know when they remember: Transfer of metacognitive responding across odor-based delayed match-to-sample tests. Animal Cognition, 20, 891–906.
Tsukahara, J., Harrison, T. L., Draheim, C., Martin, J. D., & Engle, R. (2020). Attention control: The missing link between sensory discrimination and intelligence. Attention, Perception, and Psychophysics,
Tye, M. (2000). Consciousness, color, and content. Cambridge: MIT Press.
Usher, M., & McClelland, J. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108, 550–592.
Vadillo, M., Gold, N., & Osman, M. (2016). The bitter truth about sugar and willpower: The limited evidential value of the glucose model of ego depletion. Psychological Science, 27, 1207–1214.
von Bayern, A., Danel, S., Auersperg, A., Mioduszewska, B., & Kacelnik, A. (2018). Compound tool construction by New Caledonian crows. Nature Scientific Reports, 8, 15676.
Winstanley, C., & Floresco, S. (2016). Deciphering decision making: Variation in animal models of effort- and uncertainty-based choice reveals distinct neural circuitries underlying core cognitive processes. Journal of Neuroscience, 36, 12069–12079.
Wolpert, D., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience, 3, 1212–1217.
Wolpert, D., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317–1329.
Acknowledgments
I am grateful to the anonymous referees for their insightful comments on previous versions of this article.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Carruthers, P. Explicit nonconceptual metacognition. Philos Stud 178, 2337–2356 (2021). https://doi.org/10.1007/s11098-020-01557-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11098-020-01557-1