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Nonhuman rationality: a predictive coding perspective

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Abstract

How can we rethink ‘rationality’ in the wake of animal and artificial intelligence studies? Can nonhuman systems be rational in any nontrivial sense? In this paper, we propose that all organisms, under certain circumstances, exhibit rationality to a diverse degree and aspect in the sense of the standard picture (SP): Their inferential processes conform to logic and probability rules. We first show that according to Calvo and Friston (J R Soc Interface 14(131):20170096, 2017) and Orlandi (2018), all biological systems must embody a top-down process (active inference) to minimize free energy. Next, based on Maddy’s (Second philosophy, Oxford University Press, Oxford, 2007; The logical must: Wittgenstein on logic, Oxford University Press, Oxford, 2014) analysis, we argue that this inferential process conforms to logic and probability rules; thus, it satisfies the SP, which explains the rudimentary logic and arithmetic (e.g., categorizing and numbering) found among pigeons and mice. We also hold that the mammalian brain is only one among many ways of implementing rationality. Finally, we discuss data from microorganisms to support this view.

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Notes

  1. Stein (1996, p. 4) first coined the term and defined that “to be rational is to reason in accordance with principles of reasoning that are based on rules of logic, probability, and so forth.” However, he proposed the pragmatic picture of rationality to replace the SP. Stein’s (1996) proposal, albeit insightful, is not the focus of this paper for two reasons. First, while the SP has its limits, it remains the received view (Nichols and Samuels 2016). Stein himself (1996, p.4) also pointed out that both rationality thesis and anti-rationality thesis “are typically based on what I call the standard picture.” Second, Stein holds that logic and probability “are normative principles of reasoning” (p.4), but the SP can have a descriptive reading too (Please see footnote 3 and the example of Kepler’s laws in Sect. 3). Since we only deal with the descriptive rationality in this paper, Stein’s valuable criticism of the normative SP is not discussed here.

  2. In his De Anima, Aristotle states that everything is made out of form and matter, and the form of organisms, called the soul, can consist of a rational soul (e.g., human), an animal soul (e.g., animal), and a nutritive soul (e.g., plant). Humans have different souls from animals and plants, and rationality is partly what characterizes the essence of humans—or more precisely, humans that are not women, children, slaves, or barbarians. Aristotle believed that rationality could not be reduced to elements that are more basic and is not a capacity but a distinctive manner of having power (Boyle 2012, 2016).

  3. This Enlightenment notion of rationality has at least two readings: normative and descriptive. The former holds that the SP is what rationality should be and often has a rigid explanation of SP. In contrast, the latter holds that the SP is what rationality actually is and often has a weaker explanation of SP, e.g., Nichols and Samuels’ (2016) interpretation of the term ‘accord’ in their expression of SP.

  4. As regards the boundaries of the SP rationality, a nonhuman organism can easily fail to exhibit rationality due to insufficient resources (energy, time, etc.) or confusing environmental stimuli (see footnote 11). For example, Temnothorax ants can behave irrationality (violate optimal probability) when a third relatively unattractive option is presented (Edwards and Pratt 2009). Besides, one may wonder what is the point to argue that all organisms can exhibit rationality. A quick reply is that just as the claim that all life on earth is carbon-based, it helps us to better characterize the commonality of all life and lifts the limits of the anthropomorphist perspective on the nature of rationality and intelligence.

  5. Boghossian (2014) clearly states that his account is not about Kahneman’s (2011) System 1 reasoning (i.e., automatic, fast, subpersonal, and not-control).

  6. A simplified version is P(H|E) = P(E|H)P(H)/P(E), where P(E) > 0. P(H|E) is the post probability of hypothesis H, given evidence E. P(H|E) is the likelihood that H is true and E occurs.

  7. Two points should be clarified about plant movement. First, movement presupposes time, and time is relative. For humans, cypresses with thousands of years of longevity seem to be still, but cypresses are not. This relativity makes it easier to overlook the movement of trees. Second, if movement includes releasing chemical signals for cross-species and within-species communication, then plants are quite successful as well (Baldwin and Schultz 1983; González-Teuber et al. 2014; Mancuso and Viola 2015).

  8. Please see the final section for this further issue.

  9. For example, to some reducible physicalists, because brain = mind > intelligence (incl. perception and action) > cognition (excl. perception and action) > rationality (higher criterion), rationality requires the brain. To some advocates of the embodied-embedded-extended mind, because mind (incl. part of the world) > intelligence (incl. body, perception, and action) > cognition (interaction between perception and action) > rationality (finest intelligence) > brain (inside the skull), rationality requires mind. Since the brain is indispensable to the mind, rationality cannot exist without the brain.

  10. For someone who insists that the brain is necessary to intelligence, the claim of plant neurobiology (e.g., Brenner et al. 2006; Calvo 2016) is the only way of supporting plant intelligence.

  11. An E. coli makes mistakes as well. If it keeps swimming toward the toxin instead, then its generalization is incorrect rather than invalid. In such a case, the bacterium does not conform to the rules and fails to satisfy the SP rationality; thus, it cannot be regarded as rational.

  12. Examples include motor control, sensory integration, decision-making, and social behavior such as cell-to-cell communication and cooperation (Allman 2000; Bayliss et al. 2012; Hellingwerf 2005; Koraimann and Wagner 2014; Perkins and Peter 2009; Shapiro 2007; Ben-Jacob 2004; Refardt et al. 2013).

  13. They can live freely as single cells, but they can also aggregate to form multicellular reproductive structures. They also exhibit intelligent characteristics similar to those seen in eusocial insects.

References

  • Addessi E, Paglieri F, Focaroli V (2011) The ecological rationality of delay tolerance: insights from capuchin monkeys. Cognition 119(1):142–147

    PubMed  Google Scholar 

  • Allman J (2000) Evolving brains. Scientific American Library, New York

    Google Scholar 

  • Agrillo C, Petrazzini MEM, Bisazza A (2017) Numerical abilities in fish: a methodological review. Behav Process 141:161–171

    Google Scholar 

  • Ayala FJ (1987) Understanding extinction. BioScience 37(6):426–428

    Google Scholar 

  • Baldwin I, Schultz JC (1983) Talking trees. Science 221(4607):277–279

    CAS  PubMed  Google Scholar 

  • Bayliss DL, Walsh JL, Iza F, Shama G, Holah J, Kong MG (2012) Complex responses of microorganisms as a community to a flowing atmospheric plasma. Plasma Process Polym 9(6):597–611

    CAS  Google Scholar 

  • Ben-Jacob E, Becker I, Shapira Y, Levine H (2004) Bacterial linguistic communication and social intelligence. Trends Microbiol 12(8):366–372

    PubMed  Google Scholar 

  • Boghossian P (2014) What is inference? Philos Stud 169(1):1–18

    Google Scholar 

  • Boghossian P (2016) Rationality, reasoning and rules: reflections on Broome’s rationality through reasoning. Philos Stud 173(12):3385–3397

    Google Scholar 

  • Boyle M (2012) Essentially rational animals. Rethink Epistemol 2:395–428

    Google Scholar 

  • Boyle M (2016) Additive theories of rationality: a critique. Eur J Philos 24:527–555

    Google Scholar 

  • Boysen ST (2006) Effects of symbols on chimpanzee cognition. In: Hurley SL, Nudds M (eds) Rational animals? Oxford University Press, Oxford

    Google Scholar 

  • Brenner ED, Stahlberg R, Mancuso S, Vivanco J, Baluška F, Van Volkenburgh E (2006) Plant neurobiology: an integrated view of plant signaling. Trends Plant Sci 11(8):413–419

    CAS  PubMed  Google Scholar 

  • Broome J (2014) Comments on Boghossian. Philos Stud. https://doi.org/10.1007/s11098-012-9894-7

    Article  Google Scholar 

  • Carruthers P (2013) Animal minds are real,(distinctively) human minds are not. Am Philos Q 50(3):233–248

    Google Scholar 

  • Calvo P (2016) The philosophy of plant neurobiology: a manifesto. Synthese 193(5):1323–1343

    Google Scholar 

  • Calvo P, Friston K (2017) Predicting green: really radical (plant) predictive processing. J R Soc Interface 14(131):20170096

    PubMed  PubMed Central  Google Scholar 

  • Calvo P, Baluška F, Sims A (2016) “Feature detection” vs. “predictive coding” models of plant behavior. Front Psychol 7:1505

    PubMed  PubMed Central  Google Scholar 

  • Calvo Garzón P, Keijzer F (2011) Plants: adaptive behavior, root-brains, and minimal cognition. Adapt Behav 19(3):155–171

    Google Scholar 

  • Call J, Tomasello M (eds) (2020) The gestural communication of apes and monkeys. Psychology Press, Hove

    Google Scholar 

  • Chase VM, Hertwig R, Gigerenzer G (1998) Visions of rationality. Trends Cogn Sci 2(6):206–214

    CAS  PubMed  Google Scholar 

  • Christiansen MH, Chater N (2015) The language faculty that wasn’t: a usage-based account of natural language recursion. Front Psychol 6:1182

    PubMed  PubMed Central  Google Scholar 

  • Clark A (2016) Surfing uncertainty: prediction, action and the embodied mind. Oxford University Press, Oxford

    Google Scholar 

  • Crane T (2004) The mechanical mind. Rutledge, London

    Google Scholar 

  • Darwin C (1880) The power of movement in plants. John Murray, London

    Google Scholar 

  • Delalez NJ (2014) Bacterial flagella: flagellar motor. eLS. Wiley, Chichester

    Google Scholar 

  • Descartes R, Maclean I (2006) Discourse on the method of rightly conducting the reason and seeking truth in the sciences. Oxford University Press, Oxford

    Google Scholar 

  • Edwards SC, Pratt SC (2009) Rationality in collective decision-making by ant colonies. Proc R Soc B 276:3655–3661

    PubMed  Google Scholar 

  • Friston K (2008) Hierarchical models in the brain. PLoS Comput Biol 4(11):e1000211

    PubMed  PubMed Central  Google Scholar 

  • Friston K (2014) Predictive processing and active inference. In: Cognitive processing, vol 15, no 1, pp S19–S19. Tiergartenstrasse 17, D-69121 Springer, Heidelberg

  • Fodor JA (1983) The modularity of mind: an essay on faculty psychology. MIT Press, Cambridge

    Google Scholar 

  • Ford BJ (2010) The secret power of the single cell. New Sci 206(2757):26–27

    Google Scholar 

  • Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175(1):63–72

    PubMed  Google Scholar 

  • Gagliano M, Vyazovskiy VV, Borbély AA, Grimonprez M, Depczynski M (2016) Learning by association in plants. Sci Rep 6:38427

    CAS  PubMed  PubMed Central  Google Scholar 

  • Garrod S, Gambi C, Pickering MJ (2014) Prediction at all levels: forward model predictions can enhance comprehension. Lang Cognit Neurosci 29(1):46–48

    Google Scholar 

  • Van Gelder T (1995) What might cognition be, if not computation? J Philos 92(7):345–381

    Google Scholar 

  • Godfrey-Smith P (2016) Other minds: the octopus, the sea, and the deep origins of consciousness. Farrar, Straus and Giroux, New York

    Google Scholar 

  • González-Teuber M, Kaltenpoth M, Boland W (2014) Mutualistic ants as an indirect defence against leaf pathogens. New Phytol 202(2):640–650

    PubMed  Google Scholar 

  • Graham AC (1992) Unreason within reason: Essays on the outskirts of rationality. Open Court, La Salle

    Google Scholar 

  • Hall D, Ames R (1995) Anticipating China: thinking through the narratives of Chinese and Western culture. State University of New York Press, Albany, NY, p 141

    Google Scholar 

  • Hellingwerf KJ (2005) Bacterial observations: a rudimentary form of intelligence? Trends Microbiol 13:152–158

    CAS  PubMed  Google Scholar 

  • Herman LM, Uyeyama RK, Pack AA (2008) Bottlenose dolphins understand relationships between concepts. Behav Brain Sci 31(02):139–140

    Google Scholar 

  • Hirsch EA (1995) On construction of a symbolic realization of hyperbolic automorphisms of the torus. Zapiski Nauchnykh Seminarov POMI 223:137–139

    Google Scholar 

  • Hlobil U (2014) Against Boghossian, Wright and Broome on inference. Philos Stud 167(2):419–429

    Google Scholar 

  • Hopp SL, Owren MJ, Evans CS (eds) (2012) Animal acoustic communication: Sound analysis and research methods. Springer, Berlin

    Google Scholar 

  • Hung TW (2015) How sensorimotor interactions enable sentence imitation. Mind Mach 25(4):1–18

    Google Scholar 

  • Hung TW (2016) Rationality and Escherichia coli. In: Hung TW, Lane TJ (eds) Rationality: constraints and contexts. Elsevier, Amsterdam, pp 227–240

    Google Scholar 

  • Hung T-w (2019) How Did Language Evolve? Some Reflections on the Language Parasite Debate. Biological Theory 14 (4):214–223

    Google Scholar 

  • Hurley S (2001) Perception and action: alternative views. Synthese 129(1):3–40

    Google Scholar 

  • Hurley S (2008) The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behav Brain Sci 31(01):1–22

    PubMed  Google Scholar 

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

    Google Scholar 

  • Hohwy J (2018) Prediction error minimization in the brain. In: Sprevak M, Colombo M (eds) Preprint of chapter for Routledge handbook to the computational mind. Routledge, Oxford

    Google Scholar 

  • Jensen ME, Moss CF, Surlykke A (2005) Echolocating bats can use acoustic landmarks for spatial orientation. J Exp Biol 208(23):4399–4410

    PubMed  Google Scholar 

  • Kahneman D (2011) Thinking, fast and slow. Macmillan, New York

    Google Scholar 

  • Kirman A (1993) Ants, rationality, and recruitment. Q J Econ 108(1):137–156

    Google Scholar 

  • Koraimann G, Wagner MA (2014) Social behavior and decision making in bacterial conjugation. Front Cell Infect Microbiol 4(54):4

    Google Scholar 

  • Kunita I et al (2013) Adaptive path-finding and transport network formation by the amoeba-like organism physarum. In: Suzuki Y, Nakagaki T (eds) Natural computing and beyond. Proceedings in information and communications technology, vol 6. Springer, Tokyo

    Google Scholar 

  • Lee NYL (2016) Cross-cultural differences in thinking: some thoughts on psychological paradigms. In: Hung TW, Lane TJ (eds) Rationality: constraints and contexts. Elsevier, Amsterdam, pp 61–73

    Google Scholar 

  • Lin H (2016) Bridging the logic-based and probability-based approaches to artificial intelligence. In: Hung TW, Lane TJ (eds) Rationality: constraints and contexts. Elsevier, Amsterdam, pp 215–225

    Google Scholar 

  • Liu Y, Liao J, Zhu B, Wang E, Ding J (2006) Crystal structures of the editing domain of Escherichia coli leucyl-tRNA synthetase and its complexes with Met and Ile reveal a lock-and-key mechanism for amino acid discrimination. Biochem J 394:399–407

    CAS  PubMed  PubMed Central  Google Scholar 

  • Livio M (2003) The golden ratio: the story of PHI, the world’s most astonishing number. Broadway, New York

    Google Scholar 

  • Lewis AG, Bastiaansen M (2015) A predictive coding framework for rapid neural dynamics during sentence-level language comprehension. Cortex 68:155–168

    PubMed  Google Scholar 

  • Lupyan G, Clark A (2015) Words and the world: predictive coding and the language-perception-cognition interface. Curr Dir Psychol Sci 24(4):279–284

    Google Scholar 

  • Maddy P (2007) Second philosophy. Oxford University Press, Oxford

    Google Scholar 

  • Maddy P (2014) The logical must: Wittgenstein on logic. Oxford University Press, Oxford

    Google Scholar 

  • Magnasco MO (1997) Chemical kinetics is Turing universal. Phys Rev Lett 78(6):1190

    CAS  Google Scholar 

  • Marcus GF (2008) Kluge: the haphazard construction of the human mind. FF, London

    Google Scholar 

  • Mancuso S, Viola A (2015) Brilliant green: the surprising history and science of plant intelligence. Island Press, Washington

    Google Scholar 

  • Millikan R (2006) Styles of rationality. In: Hurley S, Nudds M (eds) Rational animals. Oxford University Press, Oxford, pp 117–126

    Google Scholar 

  • Miletto Petrazzini ME, Bertolucci C, Foà A (2018) Quantity discrimination in trained lizards (Podarcis sicula). Front Psychol 9:274

    PubMed  PubMed Central  Google Scholar 

  • Montoya-Lerma J, Giraldo-Echeverri C, Armbrecht I, Farji-Brener A, Calle Z (2012) Leaf-cutting ants revisited: towards rational management and control. Int J Pest Manag 58(3):225–247

    Google Scholar 

  • Nakagaki T, Yamada H, Tóth Á (2000) Intelligence: maze-solving by an amoeboid organism. Nature 407(6803):470–470

    CAS  PubMed  Google Scholar 

  • Nichols S, Samuels R (2016) Bayesian psychology and human rationality. In: Hung TW, Lane TJ (eds) Rationality: constraints and contexts. Elsevier, Amsterdam, pp 17–36

    Google Scholar 

  • Nisbett RE, Peng K, Choi I, Norenzayan A (2001) Culture and systems of thought: holistic versus analytic cognition. Psychol Rev 108(2):291

    CAS  PubMed  Google Scholar 

  • Novoplansky A (2016) Future perception in plants. In: Nadine M (ed) Anticipation across disciplines. Springer, Cham, pp 57–70

    Google Scholar 

  • Okada K, Matchin W, Hickok G (2018) Neural evidence for predictive coding in auditory cortex during speech production. Psychon Bull Rev 25(1):423–430

    PubMed  Google Scholar 

  • Orlandi N (2018) Predictive perceptual systems. Synthese 195(6):2367–2386

    Google Scholar 

  • Ozasa K, Lee J, Song S, Maeda M, Hara M (2012) Optical analog feedback in euglena-based neural network computing. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. UCNC 2012. Lecture notes in computer science, vol 7445. Springer, Berlin

    Google Scholar 

  • Papineau D, Heyes C (2006) Rational or associative? Imitation in Japanese quail. In: Hurley SL, Nudds M (eds) Rational animals? Oxford University Press, Oxford

    Google Scholar 

  • Peng K, Nisbett RE (1999) Culture, dialectics, and reasoning about contradiction. Am Psychol 54:741–754

    Google Scholar 

  • Pepperberg I (2006) Intelligence and rationality in parrots. In: Hurley SL, Nudds M (eds) Rational animals? Oxford University Press, Oxford

    Google Scholar 

  • Perkins TJ, Peter SS (2009) Strategies for cellular decision making. Mol Syst Biol 5(1):326

    PubMed  PubMed Central  Google Scholar 

  • Piffer L, Petrazzini MEM, Agrillo C (2013) Large number discrimination in newborn fish. PLoS ONE 8(4):e62466

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pickering MJ, Garrod S (2013) An integrated theory of language production and comprehension. Behav Brain Sci 36(4):329–347

    PubMed  Google Scholar 

  • Pinker S (1994) The language instinct: how the mind creates language. Harper Collins, New York

    Google Scholar 

  • Posamentier AS, Lehmann I (2007) The fabulous fibonacci numbers. Prometheus Books, Amherst

    Google Scholar 

  • Refardt D, Bergmiller T, Kümmerli R (2013) Altruism can evolve when relatedness is low: evidence from bacteria committing suicide upon phage infection. Proc R Soc B Biol Sci 280(1759):20123035

    Google Scholar 

  • Richardson K (2010) The evolution of intelligent systems: how molecules became minds. Palgrave Macmillan, London

  • Richardson K (2012) Heritability lost; intelligent found: intelligence is integral to the adaptation and survival of all organisms faced with changing environments. EMBO Rep 13:591–595

    CAS  PubMed  PubMed Central  Google Scholar 

  • Samuels R, Stich S, Bishop M (2002) Ending the rationality wars: how to make disputes about human rationality disappear. In: Elio R (ed) Common sense, reasoning, and rationality. Oxford University Press, Oxford, pp 236–268

    Google Scholar 

  • Samuels R, Stich S, Faucher L (2004) Reason and rationality. In: Niiniluoto I, Sintonen M, Woleński J (eds) Handbook of epistemology. Springer, Dordrecht, pp 131–179

    Google Scholar 

  • Shapiro JA (2007) Bacteria are small but not stupid: Cognition, natural genetic engineering and socio-bacteriology. Stud Hist Philos Biol Biomed Sci 38:807–819

    CAS  PubMed  Google Scholar 

  • Shapiro E (2012) A mechanical turing machine: blueprint for a biomolecular computer. Interface Focus 2(4):497–503

    PubMed  PubMed Central  Google Scholar 

  • Stanley HE, Buldyrev SV, Goldberger AL, Havlin S, Mantegna RN, Ossadnik SM, Peng CK, Sciortino Stillwell J (2006) Yearning for the impossible: the surprising truths of mathematics. A K Peters, Natick

    Google Scholar 

  • Stein E (1996) Without good reason. Clarendon Press, Oxford

    Google Scholar 

  • Stevens JR, King AJ (2013) The lives of others: social rationality in animals. Oxford University Press, Oxford

    Google Scholar 

  • Smet AF, Byrne RW (2014) Interpretation of human pointing by African elephants: generalisation and rationality. Anim Cogn 17(6):1365–1374

    PubMed  Google Scholar 

  • Tamir DI, Thornton MA (2018) Modeling the predictive social mind. Trends Cognit Sci 22(3):201–212

    Google Scholar 

  • Trewavas A (2012) (2012) Plants are intelligent too. EMBO Rep 13:772–773

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tschudin AJP (2006) Belief attribution tasks with dolphins: what social minds can reveal about animal rationality. In: Hurley SL, Nudds M (eds) Rational animals? Oxford University Press, Oxford

    Google Scholar 

  • Turing A (1950) Computing machinery and intelligence. Mind LIX(236):433–460

    Google Scholar 

  • Vertosick FT (2002) The genius within: discovering the intelligence of every living thing. Harcourt, New York

    Google Scholar 

  • Waliszewski P, Konarski J (2002) Fractal structure of space and time is necessary for the emergence of self-organization, connectivity, and collectivity in cellular system. In: Losa GA, Merlini D, Nonnenmacher TF, Weibel ER (eds) Fractals in biology and medicine, vol 3. Birkhauser, Basel, pp 15–24

    Google Scholar 

  • Whiten A (2011) The scope of culture in chimpanzees, humans and ancestral apes. Philos Trans R Soc B Biol Sci 366(1567):997–1007

    Google Scholar 

  • Williamson T (2012) Boghossian and Casalegno on understanding and inference. Dialectica 66(2):237–247

    Google Scholar 

  • Willemet R (2013) Reconsidering the evolution of brain, cognition, and behavior in birds and mammals. Front Psychol 4:396

    PubMed  PubMed Central  Google Scholar 

  • Wright C (2014) Comment on Paul Boghossian, “The nature of inference.” Philos Stud. https://doi.org/10.1007/s11098-012-9892-9

    Article  Google Scholar 

  • Wolpert DM, Doya K, Kawato M (2003) A unifying computational framework for motor control and social interaction. Philos Trans R Soc B Biol Sci 358(1431):593–602

    Google Scholar 

  • Xu F, Tenenbaum JB (2007) Word learning as Bayesian inference. Psychol Rev 114(2):245

    PubMed  Google Scholar 

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This research is sponsored by the Ministry of Science and Technology, Taiwan, under grant no. 107-2410-H-001-101-MY3.

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    Tzu-Wei Hung

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Hung, TW. Nonhuman rationality: a predictive coding perspective. Cogn Process 22, 353–362 (2021). https://doi.org/10.1007/s10339-020-01009-y

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