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Do Process-1 simulations generate the epistemic feelings that drive Process-2 decision making?

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Abstract

We apply previously developed Chu space and Channel Theory methods, focusing on the construction of Cone-Cocone Diagrams (CCCDs), to study the role of epistemic feelings, particularly feelings of confidence, in dual process models of problem solving. We specifically consider “Bayesian brain” models of probabilistic inference within a global neuronal workspace architecture. We develop a formal representation of Process-1 problem solving in which a solution is reached if and only if a CCCD is completed. We show that in this representation, Process-2 problem solving can be represented as multiply iterated Process-1 problem solving and has the same formal solution conditions. We then model the generation of explicit, reportable subjective probabilities from implicit, experienced confidence as a simulation-based, reverse engineering process and show that this process can also be modeled as a CCCD construction.

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Notes

  1. The role of an infomorphism contrasts with the superficially similar treatment of Ehresmann and Vanbremeersch (2007), Ehresmann and Gomez-Ramirez (2015), which basically implements maps between neurons or co-activated functional assemblies of neurons (christened “cat-neurons”).

  2. A “pattern” in this latter approach models an activation pattern [a “cat-neuron” in the terminology of Ehresmann and Vanbremeersch (2007)] over a subset of conditionally jointly activated neurons or, more technically, a class of functionally equivalent (in the context of the overall categorization system) jointly activated subsets of neurons. Such a “cat-neuron” is the colimit, in the construction of Ehresmann and Vanbremeersch (2007), of all such (classes of) synchronous assemblies of neurons that are activated by the same input, the colimit representing a kind of “binding agent” in a hierarchical structure.

  3. Dennett (2017) characterizes Process-1 as relatively inflexible “competence without comprehension” that is available, to varying degrees, to all organisms. Process-2 is, from an evolutionary point of view, more recent and possibly unique to human cognition [see also Evans (2010); Frankish (2010)]. The paradigmatic descriptions in, e.g., Kahneman (2011) can reasonably be tied to cultural theories of ‘duality’, as in the anthropological setting of Nisbett (2003) who compares the traditional modes of Western analytic/rational-based thinking to those of the Asian intuitive/holistic thinking. The warnings of Henrich et al. (2010) are clearly of relevance here.

  4. Barrett (2017) also emphasizes that such allostasis maintenance functions must be universal across animals. The DMN is best known as the primary locus of self-referential rumination (e.g., Buckner et al. 2008; Qin and Northoff 2011) and hence much of Process-2 thought functions that are presumably human specific. Evidence that individual differences in DMN connectivity correlate with genetic differences observable in pedigree studies (Glahn et al. 2010) suggests significant recent evolutionary change. Whether ancient and recent functions of the DMN can be teased apart at the architectural level remains to be seen.

  5. In this respect, Arango-Muñoz (2014) suggests that some epistemic feelings are non-conceptual experiences. An individual need not have to realize specific concepts of certainty or uncertainty in order to have epistemic feelings, such as feeling certain or uncertain about something (e.g., on a dull day an individual may grab at an umbrella before leaving home while thinking about details of her conference paper). In a similar spirit, Proust (2015) proposes epistemic feelings to be both intentional and directional, and thus seen as types of mental affordances that induce a cognitive response, and then determine how to implement it.

References

  • Abramsky S (2012) Big toy models: representing physical systems as Chu spaces. Synthese 186:697–718

    Google Scholar 

  • Adámek J, Herrlich H, Strecker GE (2004) Abstract and concrete categories: the joy of cats. Wiley, New York. Available at http://katmat.math.uni-bremen.de/acc. Accessed 26 May 2019

  • Adams EW (1998) A primer of probabilistic logic. University of Chicago Press, Chicago

    Google Scholar 

  • Alain C, Arnott SR, Hevenor S, Graham S, Grady CL (2001) “What” and “where” in the human auditory system. Proc Natl Acad Sci USA 98:12301–12306

    CAS  PubMed  PubMed Central  Google Scholar 

  • Allwein G (2004) A qualititative framework for Shannon Information theories. In: ‘NSPW ’04 Proceedings of 2004 workshop on new security paradigms’, ACM, Nova Scotia, Canada, New York, pp 23–31

  • Allwein G, Moskowitz IS, Chang L-W (2004) A new framework for Shannon information theory. Technical Report A801024 Naval Research Laboratory, Washington, p 17

  • Allwein G, Yang Y, Harrison WL (2011) Qualitative decision theory via Channel Theory. Log Log Philos 20:1–30

    Google Scholar 

  • Amabile TM, Barsade SG, Mueller JS, Staw BM (2005) Affect and creativity at work. Adm Sci Q 50:367–403

    Google Scholar 

  • Arango-Muñoz S (2014) The nature of epistemic feelings. Philos Psychol 27(2):193–211

    Google Scholar 

  • Arnsten AFT (2009) Stress signalling pathways that impair prefrontal cortex structure and function. Nat Rev Neurosci 10:410–422

    CAS  PubMed  PubMed Central  Google Scholar 

  • Awodey S (2010) Category theory, oxford logic guides, vol 62. Oxford University Press, Oxford

    Google Scholar 

  • Baars BJ, Franklin S (2003) How conscious experience and working memory interact. Trends Cognit Sci 7:166–172

    Google Scholar 

  • Baars BJ, Franklin S, Ramsoy TZ (2013) Global workspace dynamics: Cortical “binding and propagation” enables conscious contents. Front Psychol 4:200

    PubMed  PubMed Central  Google Scholar 

  • Bach DR, Dolan RJ (2012) Knowing how much you don’t know: a neural organization of uncertainty estimates. Nat Rev Neurosci 13:572–586

    CAS  PubMed  Google Scholar 

  • Badcock PB, Friston KJ, Ramstead MJD, Ploeger A, Hohwy J (2019) The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior. Cognit Affect Neurosci (In press). https://doi.org/10.3758/s13415-019-00721-3

  • Bar M (2009) The proactive brain: memory for predictions. Philos Trans R Soc B Biol Sci 364(1521):1235–1243

    Google Scholar 

  • Bargh JA, Ferguson MJ (2000) Beyond behaviorism: on the automaticity of higher mental processes. Psychol Bull 126:925–945

    CAS  PubMed  Google Scholar 

  • Barr M (1979) Autonomous categories, with an appendix by Po Hsiang Chu, vol 752. Lecture Notes in Mathematics. Springer, Berlin

    Google Scholar 

  • Barr M (1991) Autonomous categories and linear logic. Math Struct Comput Sci 1:159–178

    Google Scholar 

  • Barrett LF, Tugade MM, Engle RW (2004) Individual differences in working memory capacity and dual-process theories of the mind. Psychol Bull 130(4):553–573

    PubMed  PubMed Central  Google Scholar 

  • Barrett LF (2017) The theory of constructed emotion: an active inference account of interoception and categorization. Soc Cognit Affect Neurosci 12(1):1–23. https://doi.org/10.1093/scan/nsw154

    Article  Google Scholar 

  • Barrett LF, Simmons WK (2015) Interoceptive predictions in the brain. Nat Rev Neurosci 16(7):419–429

    CAS  PubMed  PubMed Central  Google Scholar 

  • Barwise J, Seligman J (1997) Information flow: the logic of distributed systems, vol 44. Cambridge tracts in theoretical computer science. Cambridge University Press, Cambridge

    Google Scholar 

  • Barwise J (1997) Information and impossibilities. Notre Dame J Form Log 38(4):488–515

    Google Scholar 

  • Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ (2012) Canonical microcircuits for predictive coding. Neuron 76:695–711

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I (2016) Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. eLife 5:e18566

    PubMed  PubMed Central  Google Scholar 

  • Blake R, Shiffrar M (2007) Perception of human motion. Ann Rev Psychol 58:47–73

    Google Scholar 

  • Block N (1995) On a confusion about a function of consciousness. Behav Brain Sci 18:227–247

    Google Scholar 

  • Brown R, Porter T (2006) Category theory: an abstract setting for analogy and comparison. What is category theory? Advanced Studies in Mathematics and Logic. Polimetrica Publisher, Italy, pp 257–274

    Google Scholar 

  • Brown H, Adams RA, Parees I, Edwards M, Friston K (2013) Active inference, sensory attenuation and illusions. Cognit Process 14(4):411–427

    Google Scholar 

  • Buckner R, Andrews-Hanna J, Schacter D (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38

    PubMed  Google Scholar 

  • Carter O, Hohwy J, van Boxtel J, Lamme V, Block N, Koch C, Tsuchiya N (2018) Conscious machines: defining questions. Science 2018:400

    Google Scholar 

  • Chadhuri R, Knoblauch K, Gariel M-A, Kennedy H, Wang X-J (2015) A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88:419–431

    Google Scholar 

  • Chater N (2018) The Mind is Flat. The remarkable shallowness of the improvising brain. Yale University Press, New Haven and London

    Google Scholar 

  • Clark A (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci 36:181–204

    PubMed  Google Scholar 

  • Cordier J-M, Porter T (1989) Shape theory: categorical methods of approximation. Ellis Horwood, Hemel Hempstead

    Google Scholar 

  • Cover TM, Thomas JA (2006) Elements of information theory. Wiley, New York

    Google Scholar 

  • Craig AD (2009) How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci 10:59–70

    CAS  PubMed  Google Scholar 

  • Craig AD (2010) The sentient self. Brain Struct Funct 214:563–577

    PubMed  Google Scholar 

  • Critchley HD, Eccles J, Garfinkel SN (2013) Interaction between cognition, emotion, and the autonomic nervous system. In: Buijs RM, Swaab DF (eds) Handbook of clinical neurology, vol 117, 3rd edn. Autonomic nervous system. Elsevier, Amsterdam, pp 59–77

    Google Scholar 

  • Csikzentmihalyi M (2014) Flow and the Foundations of Positive Psychology. Springer, Dordrecht

    Google Scholar 

  • Cushman F (2020) Rationalization is rational. Behav Brain Sci 43:e28. https://doi.org/10.1017/S0140525X19001730

    Article  Google Scholar 

  • Dajani DR, Uddin LQ (2015) Demystifying cognitive flexibility: implications for clinical and developmental neuroscience. Trends Neurosci 38:571–578

    CAS  PubMed  PubMed Central  Google Scholar 

  • Damásio A (1994) Descartes’ error: emotion, reason and the human brain. Putnam, New York

    Google Scholar 

  • de Finetti B (1974) Theory of probability, vol I. Wiley, New York

    Google Scholar 

  • Debruyne H, Portzky M, Van den Eynde F, Audenaert K (2000) Cotard’s syndrome: a review. Curr Psychiatry Rep 11:197–202

    Google Scholar 

  • Dehaene S, Naccache L (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79:1–37

    CAS  PubMed  Google Scholar 

  • Dehaene S, Sergent C, Changeux J-P (2003) A neuronal network modeling subjective reports and objective physiological data during conscious perception. Proc Natl Acad Sci USA 100(14):8520–8525

    CAS  PubMed  PubMed Central  Google Scholar 

  • Dehaene S, Charles L, King J-R, Marti S (2014) Toward a computational theory of conscious processing. Curr Opin Neurobiol 25:76–84

    CAS  PubMed  Google Scholar 

  • Dehaene S, Lau H, Kouider S (2017) What is consciousness, and could machines have it? Science 358:486–492

    CAS  PubMed  Google Scholar 

  • Dennett DC (2017) From bacteria to bach and back: the evolution of minds. Penguin, London

    Google Scholar 

  • Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE (2008) A dual-networks architecture of top–down control. Trends Cognit Sci 12:99–105

    Google Scholar 

  • Dretske F (1981) Knowledge and the flow of information. MIT Press, Cambridge

    Google Scholar 

  • Dzharfarov EN, Kon M (2018) On universality of classical probability with contextually labeled random varaibles. J Math Psychol 85:17–24

    Google Scholar 

  • Dzhafarov EN, Kujala JV (2017a) Lecture notes in computer science. In: Barros JA, Coecke B, Pothos E (eds) Contextuality-by-Default 2.0: systems with binary random variables, vol 10106. Springer, Berlin, pp 16–32

    Google Scholar 

  • Edelman GM, Gally JA, Baars BJ (2011) Biology of consciousness. Front Psychol 73(1):43–52. https://doi.org/10.3389/fpsyg.2011.00004

    Article  Google Scholar 

  • Ehresmann AC, Vanbremeersch J-P (2007) Memory evolutive systems; hierarchy, emergence, cognition. Studies in multidisciplinarity. Elsevier, New York

    Google Scholar 

  • Ehresmann AC, Gomez-Ramirez J (2015) Conciliating neuroscience and phenomenology via category theory. Prog Biophys Mol Biol 119:347–359

    PubMed  Google Scholar 

  • Eichenbaum H, Yonelinas AR, Ranganath C (2007) The medial temporal lobe and recognition memory. Ann Rev Neurosci 30:123–152

    CAS  PubMed  Google Scholar 

  • Eriksson L, Hájek A (2007) What are degrees of belief? Stud Log 86:183–213

    Google Scholar 

  • Evans JSt BT (2006) The heuristic-analytic theory of reasoning: extension and evaluation. Psychon Bull Rev 13(3):378–395

    PubMed  Google Scholar 

  • Evans JStBT (2008) Dual processing accounts of reasoning, judgement and social cognition. Annu Rev Psychol 59:255–278

    PubMed  Google Scholar 

  • Evans J St BT (2010) Intuition and reasoning: A dual process perspective. Psychol Inq 21(4):313–326 (Special Issue on Intuition (October-December 2010))

    Google Scholar 

  • Evans J St B T, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 8:223–241

    PubMed  Google Scholar 

  • Feinberg TE, Roane DM (2005) Delusional misidentification. Psychiatr Clin N Am 28:665–683

    Google Scholar 

  • Feldman H, Friston K (2010) Attention, uncertainty, and free energy. Front Hum Neurosci 4:215. https://doi.org/10.3389/fnhum.2010.00215

    Article  PubMed  PubMed Central  Google Scholar 

  • Fields C (2002) Why do we talk to ourselves? J Expt Theor Artif Intell 14:255–272

    Google Scholar 

  • Fields C (2011) Trajectory recognition as the basis for object individuation: a functional model of object file instantiation and object token encoding. Front Psychol Percept Sci 2:49

    Google Scholar 

  • Fields C (2012) The very same thing: extending the object token concept to incorporate causal constraints on individual identity. Adv Cognit Psychol 8:234–247

    Google Scholar 

  • Fields C (2013) Metaphorical motion in mathematical reasoning: further evidence for pre-motor implementation of structure mapping in abstract domains. Cognit Process 14:217–229

    Google Scholar 

  • Fields C (2016) Visual re-identification of individual objects: a core problem for organisms and AI. Cognit Process 17:1–13

    Google Scholar 

  • Fields C (2017) Object Permanence. In: Shackelford TK, Weekes-Shackelford VA (eds) Encyclopedia of evolutionary psychological science. Springer, New York (Chap. 2373)

    Google Scholar 

  • Fields C, Glazebrook JF (2019) A mosaic of Chu spaces and channel theory I: category-theoretic concepts and tools. J Expt Theor Artif Intell 31(2):177–213. https://doi.org/10.1080/0952813X.2018.1544282

    Article  Google Scholar 

  • Fields C, Glazebrook JF (2019) A mosaic of Chu spaces and channel theory II: applications to object identification and mereological complexity. J Exper Theor Artif Intell 31(2):237–265. https://doi.org/10.1080/0952813X.2018.1544285

    Article  Google Scholar 

  • Fields C, Glazebrook JF (2020) Representing measurement as a thermodynamic symmetry breaking. Symmetry 12:810. https://doi.org/10.3390/sym12050810

    Article  Google Scholar 

  • Firestone C, Scholl BJ (2016) Cognition does not affect perception: evaluating the evidence for “top-down” effects. Behav Brain Sci 39:e229

    PubMed  Google Scholar 

  • Fleming SM, Daw ND (2017) Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation. Psychol Rev 124:91–114

    PubMed  PubMed Central  Google Scholar 

  • Flombaum JI, Scholl BJ, Santos LR (2008) Spatiotemporal priority as a fundamental principle of object persistence. In: Hood B, Santos L (eds) The origins of object knowledge. Oxford University Press, New York, pp 135–164

    Google Scholar 

  • Fodor JA (1983) The modularity of mind. MIT/Bradford, Cambridge

    Google Scholar 

  • Folke T, Jacobsen C, Fleming SM, De Martino B (2016) Explicit representation of confidence informs future value-based decisions. Nat Hum Behav 1:0002

    Google Scholar 

  • Frankish K (2010) Dual-process and dual system theories of reasoning. Philos Compass 5(10):914–926. https://doi.org/10.1111/j.1747-9991.2010.00330.x

    Article  Google Scholar 

  • Friston KJ (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11:127–138

    CAS  PubMed  Google Scholar 

  • Friston KJ, Kiebel S (2009) Predictive coding under the free-energy principle. Philos Trans R Soc Lond B 364:1211–1221

    Google Scholar 

  • Friston KJ, Kilner J, Harrison L (2006) A free energy principle for the brain. J Physiol 100:70–87

    Google Scholar 

  • Friston KJ, Rigoli F, Ognibene D, Mathys C, FitzGerald T, Pezzulo G (2015) Active inference and epistemic value. Cognit Neurosci 6:187–214

    Google Scholar 

  • Gentner D (1983) Structure-mapping: a theoretical framework for analogy. Cognit Sci 7(2):155–170

    Google Scholar 

  • Glahn DC, Winkler AM, Kochunov P, Almasy L, Duggirala R, Carless MA, Curran JC, Olvera RL, Laird AR, Smith SM, Beckmann CF, Fox PT, Blangero J (2010) Genetic control over the resting brain. Proc Natl Acad Sci USA 10(7):1223–1228

    Google Scholar 

  • Gratus J, Porter T (2006) A spatial view of information. Theor Comput Sci 365:206–215

    Google Scholar 

  • Grossberg S (2013) Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw 37:1–47

    PubMed  Google Scholar 

  • Gschwind M, Picard F (2016) Ecstatic epileptic seizures: a glimpse into the multiple roles of the insula. Front Behav Neurosci 10:21

    PubMed  PubMed Central  Google Scholar 

  • Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of the human cerebral cortex. PLoS Biol 6(7):e159

    PubMed  PubMed Central  Google Scholar 

  • Heitz RP (2014) The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front Neurosci 8:150

    PubMed  PubMed Central  Google Scholar 

  • Henrich J, Heine SJ, Norenzayan A (2010) The weirdest people in the world. Behav Brain Sci 33:61–83

    PubMed  Google Scholar 

  • Hochstein S, Ahissar M (2002) View from the top: hierarchies and reverse hierarchies in the visual system. Neuron 36:791–804

    CAS  PubMed  Google Scholar 

  • Hohwy J (2013) The predictive mind. Oxford University Press, Oxford

    Google Scholar 

  • Joffily M, Coricelli G (2013) Emotional valence and the free-energy principle. PLoS Comput Biol 9(6):e1003094

    CAS  PubMed  PubMed Central  Google Scholar 

  • Johnson-Laird PN (1977) Procedural semantics. Cognition 5:189–214

    Google Scholar 

  • Josipovic J (2019) Nondual awareness: consciousness-as-such as non-representational reflexivity. Prog Brain Res 244:273–298

    PubMed  Google Scholar 

  • Kahneman D, Frederick S (2002) Representativeness revisited: attribute substitution in intuitive judgement. In: Gilovich T, Griffin D, Kahneman D (eds) Heuristics and biases: the psychology of inituitive judgement. Cambridge University Press, Cambridge, pp 49–81

    Google Scholar 

  • Kahneman D (2011) Thinking. Fast and Slow. Penguin, London

    Google Scholar 

  • Kahneman D, Triesman A, Gibbs BJ (1992) The reviewing of object files: object-specific integration of information. Cognit Psychol 24:175–219

    CAS  PubMed  Google Scholar 

  • Kalfoglou Y, Schorlemmer M (2003) IF-Map: an ontology-mapping method based on information-flow theory. In: Spaccapietra S, March S, Aberer K (eds) Journal on Data Semantics I. Lecture Notes in Computer Science, vol 2800. Springer, Berlin, pp 107–127

    Google Scholar 

  • Kanai R, Komura Y, Shipp S, Friston K (2015) Cerebral hierarchies: predictive processing, precision and the pulvinar. Philos Trans R Soc B 370:20140169

    Google Scholar 

  • Keifer M, Pulvermüller F (2012) Conceptual representations in mind and brain: theoretical developments, current evidence and future directions. Cortex 7:805–825

    Google Scholar 

  • Kikuchi M, Nagasaka I, Toyoda S, Kitamura S (2003) A mathematical model of interactions in artifact environment. In: Proceedings of SICE annual conference 2003, Fukui University, Fukui, pp 2085–2090

  • Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:67–680

    Google Scholar 

  • Knill DC, Pouget A (2004) The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci 27:712–719

    CAS  PubMed  Google Scholar 

  • Koch C, Massimini M, Boly M, Tononi G (2016) Neural correlates of consciousness: progress and problems. Nat Rev Neurosci 17:307–320

    CAS  PubMed  Google Scholar 

  • Koriat A (2012) The self-consistency model of subjective confidence. Psychol Rev 119(1):80–113

    PubMed  Google Scholar 

  • Koriat A, Levy-Radot R (1999) Processes underlying metacognitive judgements: information-based and experience-based monitoring of one’s own knowledge. In: Chaiken S, Trope Y (eds) Dual-process theories in social psychology. Guilford Press, London-New York, pp 483–502

    Google Scholar 

  • Kosslyn SM, Ganis G, Thompson WL (2001) Neural foundations of imagery. Nat Rev Neurosci 2:635–642

    CAS  PubMed  Google Scholar 

  • Kounios J, Beeman M (2015) The Eureka factor: creative insights and the brain. Windmill, London

    Google Scholar 

  • Lawson RP, Rees G, Friston KJ (2014) An aberrant precision account of autism. Front Hum Neurosci 8:302

    PubMed  PubMed Central  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    CAS  PubMed  Google Scholar 

  • Mahon BZ, Caramazza A (2009) Concepts and categories: a cognitive neuropsychological perspective. Annu Rev Psychol 60:27–51

    PubMed  PubMed Central  Google Scholar 

  • Maloney LT, Zhang H (2010) Decision-theoretic models of visual perception and action. Vision Res 50:2362–2374

    PubMed  Google Scholar 

  • Mashour GA, Roelfsema P, Changeux J-P, Dehaene S (2020) Conscious processing and the global neuronal workspace hypothesis. Neuron 105:776–798

    CAS  PubMed  PubMed Central  Google Scholar 

  • McCall R, Franklin S (2013) Cortical learning algorithms with predictive coding for a systems-level cognitive architecture. In: Second annual conference on advances in cognitive systems poster collection, pp 149–166

  • McClelland JL (1998) Connectionist models and Bayesian inference. In: Oakford M, Chater N (eds) Rational models of cognition. Oxford University Press, Oxford, pp 21–53

    Google Scholar 

  • Melnikoff DE, Bargh JA (2018) The mythical number two. Trends Cognit Sci 22:280–293

    Google Scholar 

  • Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 214(5–6):655–667

    PubMed  PubMed Central  Google Scholar 

  • Meyniel F, Sigman M, Mainen ZF (2015) Confidence as Bayesian probability: from neural origins to behavior. Neuron 88:78–92

    CAS  PubMed  Google Scholar 

  • Moors A, De Houwer J (2006) Automaticity: a theoretical and conceptual analysis. Psychol Bull 132:297–326

    PubMed  Google Scholar 

  • Navajas J, Hindocha C, Foda H, Keramati M, Latham PE, Bahrami B (2017) The idiosyncratic nature of confidence. Nat Hum Behav 1:810–818

    PubMed  PubMed Central  Google Scholar 

  • Nhuy N, Van Quang N (2001) Chu spaces and conditional probabilities. Acta Math Vietnam 26(2):241–248

    Google Scholar 

  • Nisbett R (2003) The geography of thought: how asians and westerners think differently... and why. Free Press, New York

    Google Scholar 

  • Old LJ, Priss U (2001) Metaphor and information flow. In: Proccedings of the 12th midwest artificial intelligence and cognitive science conference, pp 99–104

  • Papadopoulos BK, Syropoulos A (2000) Fuzzy sets and fuzzy relational structures as Chu spaces. Int J Uncertain Fuzziness Knowl Based Syst 8(4):471–479

    Google Scholar 

  • Parmigiani G, Inoue LYT (2009) Decision theory: principles and approaches. Wiley, Chichester

    Google Scholar 

  • Parr T, Rees G, Fiston KJ (2018) Computational neuropsychology and Bayesian inference. Front Hum Neurosci 12(61):14

    Google Scholar 

  • Paz L, Insabato A, Zylberberg A, Deco G, Sigman M (2016) Confidence through consensus: a neural mechanism for uncertainty monitoring. Sci Rep 6:21830

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pliushch I (2015) The extension of the indicator-function of feelings (A commentary on Joëlle Proust). In: Metzinger T, Windt JM (eds.) Open MIND 31(C). Frankfurt am Main: MIND Group, p 12. https://doi.org/10.15502/978395857039

  • Pratt V (1995) Chu spaces and their interpretation as concurrent objects. Lect Notes Comput Sci 1000:392–405

    Google Scholar 

  • Pratt V (1997) Types as Processes, via Chu spaces. Invited paper. In: Proceedings ‘Express’97: fourth workshop on expressiveness in concurrency’. Santa Margherita, Italy, Electronic Notes in Theoretical Computer Science, vol 7, p 21. http://www.elsevier.nl/locate/entcs/volume7.html

  • Pratt V (1999) Chu spaces. School on Category Theory and Applications. (Coimbra 1999), Vol. 21 of Textos Mat. Sér. B, University of Coimbra, Coimbra, pp 39–100

  • Pratt V (1999) Chu spaces from the representational viewpoint. Ann Pure Appl Log 96:319–333

    Google Scholar 

  • Proust J (2015) The representational structure of feelings. In: Metzinger T, Windt JM (eds.) Open MIND 31(T) Frankfurt am Main: MIND Group, p 25. https://doi.org/10.15502/9783958570047

  • Pylyshyn Z (1984) Computation and cognition. MIT Press, Cambridge

    Google Scholar 

  • Qin P, Northoff G (2011) How is our self related to midline regions and the default-mode network? NeuroImage 57:1221–1233

    PubMed  Google Scholar 

  • Roederer J (2010) Information and its role in nature. Springer, Heidelberg

    Google Scholar 

  • Rosenblatt F (1961) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, Washington

    Google Scholar 

  • Sanborn AN, Chater N (2016) Bayesian brains without probabilities. Trends Cognit Sci 20(12):883–893

    Google Scholar 

  • Sathian K, Lacey S, Stilla R, Gibson GO, Deshpanda G, Hu X, LaConte S, Glielmi C (2011) Dual pathways for haptic and visual perception of spatial and texture information. Neuroimage 57:462–475

    CAS  PubMed  Google Scholar 

  • Schendan HE, Maher SM (2009) Object knowledge during entry-level categorization is activated and modified by implicit memory after 200 ms. NeuroImage 44:1423–1438

    PubMed  Google Scholar 

  • Schuman CD, Potok TE, Patton RM, Birdwell D, Dean ME, Rose GS, Plank JS (2017) A survey of neuromorphic computing and neural networks in hardware. Preprint arXiv:1705.06963v1 [cs.NE]

  • Schwarz N (2012) Feelings-as-information theory. In: Van Lange PAM, Kruglanski AW, Higgins ET (eds) Handbook of theories of social psychology. Sage Publications Ltd, Thousand Oaks, pp 289–308

    Google Scholar 

  • Seligman J (2009) Channels: From logic to probability. In: Sommaruga G (ed) Formal theories of information, vol 5363. Lecture Notes in Computer Science. Springer, Berlin, pp 193–233

    Google Scholar 

  • Sequeira H, Hot P, Silvert L, Delaplanque S (2009) Electrical autonomic correlates of emotion. Int J Psychophysiol 71:50–56

    PubMed  Google Scholar 

  • Sergent C, Baillet S, Dehaene S (2005) Timing of the brain events underlying access to consciousness during the attentional blink. Nat Neurosci 8:1391–1400

    CAS  PubMed  Google Scholar 

  • Seth AK, Suzuki K, Critchley HD (2012) An interoceptive predictive coding model of conscious presence. Front Psychol 2(395):16. https://doi.org/10.3389/fpsyg.2011.00395

    Article  Google Scholar 

  • Seth AK (2013) Interoceptive inference, emotion, and the embodied self. Trends Cognit Sci 17(11):565–573

    Google Scholar 

  • Shanahan M (2012) The brain’s connective core and its role in animal cognition. Philos Trans R Soc B 367:2704–2714

    Google Scholar 

  • Shaw MP (1999) On the role of affect in scientific discovery. In: Russ SW (ed) Affect, creative experience and psychological adjustment. Taylor & Francis, London, pp 147–162

    Google Scholar 

  • Shea N, Frith CD (2019) The Global Workspace needs metacognition. Trends Cognit Sci 23(7):560–571

    Google Scholar 

  • Shechner T, Bar-Haim Y (2016) Threat monitoring and attention-bias modification in anxiety and stress-related disorders. Curr Dir Psychol Sci 25:431–437

    Google Scholar 

  • Shipp S, Adams RA, Friston KJ (2013) Reflections on agranular architecture: predictive coding in the motor cortex. Trends Neurosci 36:706–716

    CAS  PubMed  PubMed Central  Google Scholar 

  • Simon HA (1967) Motivational and emotional controls of cognition. Psychol Rev 74:29–39

    CAS  PubMed  Google Scholar 

  • Simon HA (1972) Theories of bounded rationality. In: McGuire CB, Radner R (eds) Decision and organization. North Holland, Amsterdam

    Google Scholar 

  • Simons JS, Garrison JR, Johnson MK (2017) Brain mechanisms of reality monitoring. Trends Cognit Sci 21:462–473

    Google Scholar 

  • Sloman R (1996) The empirical case for two systems of reasoning. Psychol Bull 119(1):3–22

    Google Scholar 

  • Sporns O (2013) Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol 23:162–171

    CAS  PubMed  Google Scholar 

  • Sporns O, Honey CJ (2006) Small worlds inside big brains. Proc Natl Acad Sci USA 111:15220–15225

    Google Scholar 

  • Spratling MW (2016) Predictive coding as a model of cognition. Cognit Proc 17:279–305

    CAS  Google Scholar 

  • Spratling MW (2017) A hierarchical predictive coding model of object recognition in natural images. Cognit Comput 9:151–167

    CAS  PubMed  Google Scholar 

  • Treisman A (2006) Object tokens, binding and visual memory. In: Zimmer HD, Mecklinger A, Lindenberger U (eds) Handbook of binding and memory: perspectives from cognitive neuroscience. Oxford University Press, Oxford, pp 315–338

    Google Scholar 

  • Tversky A, Kahneman D (1973) Availability: a heuristic for judging frequency and probability. Cognit Psychol 4:207–232

    Google Scholar 

  • Tversky A, Kahneman D (1974) Judgement under uncertainty: heuristics and biases. Science 185(4157):1124–1131

    CAS  PubMed  Google Scholar 

  • Uddin LQ (2015) Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 16:55–61

    CAS  PubMed  Google Scholar 

  • van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cognit Sci 17(12):683–695

    Google Scholar 

  • Van de Cruys S (2017) Affective value in the predictive mind. In: Metzinger T, Wiese W (eds.) Philosophy and Predictive Processing. Frankfurt am Main: MIND Group, p 21. https://doi.org/10.15502/9783958573253

  • Van de Cruys S, Evers K, Van der Hallen R, Van Eylen L, Boets B, de-Wit L, Wagemans J (2014) Precise minds in uncertain worlds: predictive coding in autism. Psychol Rev 121:649–675

    PubMed  Google Scholar 

  • Wallace R (2005) Consciousness: a mathematical treatment of the global neuronal workspace. Springer, New York

    Google Scholar 

  • Wason PC, Evans J St BT (1975) Dual processes in reasoning. Cognition 3(2):141–154

    Google Scholar 

  • Whyte CJ (2019) Integrating the global neuronal workspace into the framework of predictive processing: towards a working hypothesis. Consiousness Cognit 73:102763

    Google Scholar 

  • Wickelgren WA (1977) Speed-accuracy tradeoff and information-processing dynamics. Acta Psychol 41:67–85

    Google Scholar 

  • Winkielman P, Schwarz N, Fazendeiro T, Reber R (2003) The hedonic marking of processing fluency: implications for evaluation judgement. In: Musch J, Klauer KC (eds) The psychology of evaluation: affective processes in cognition and emotion. Erlbaum, Mahwah, pp 189–217

    Google Scholar 

  • Zimmer HD, Ecker UKD (2010) Remembering perceptual features unequally bound in object and episodic tokens: neural mechanisms and their electrophysiological correlates. Neurosci Biobehav Rev 34:1066–1079

    PubMed  Google Scholar 

  • Zmigrod S, Hommel B (2010) Temporal dynamics of unimodal and multimodal feature binding. Atten Percept Psychophys 72:142–152

    PubMed  Google Scholar 

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Acknowledgements

We wish to thank two anonymous referees for their constructive criticism and suggestions which helped to improve the overall presentation of this paper. We also thank the Handling Editor for further assessment and useful comments.

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  1. Caunes Minervois, France

    Chris Fields

  2. Department of Mathematics and Computer Science, Eastern Illinois University, 600 Lincoln Ave., Charleston, IL, 61920–3099, USA

    James F. Glazebrook

  3. Department of Mathematics, University of Illinois at Urbana, Champaign, Urbana, IL, 61801, USA

    James F. Glazebrook

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  1. Chris Fields
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Fields, C., Glazebrook, J.F. Do Process-1 simulations generate the epistemic feelings that drive Process-2 decision making?. Cogn Process 21, 533–553 (2020). https://doi.org/10.1007/s10339-020-00981-9

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