Why animals are not robots

Abstract

In disciplines traditionally studying expertise such as sociology, philosophy, and pedagogy, discussions of demarcation criteria typically centre on how and why human expertise differs from the expertise of artificial expert systems. Therefore, the demarcation criteria has been drawn between robots as formalized logical architectures and humans as creative, social subjects, creating a bipartite division that leaves out animals. However, by downsizing the discussion of animal cognition and implicitly intuiting assimilation of living organisms (LOs) to robots, key features to explain why human expertise is crucially different from robot expertise are neglected. In the absence of clarification of fundamental cognitive principles of LOs, cognitively robots may appear persuasively closer to humans when they are in fact not. In this paper, I will discuss essential features of organic cognition to emphasise why animals are not like robots at all. The purpose is to add a third category when comparing humans and robots to make a tripartite division that consists of (a) humans (b) LOs, and (c) machines. I will argue that LOs, adapted to ever-changing circumstances, are qualitatively different from robots. Humans, in the sense of belonging to the biological class of LOs, also share this central feature. In addition, however, humans alone possess and use language in a way that turns cognition from a predominantly online to off-line activity (e.g. Wilson Psychonomic Bulletin & Review 9(4), 625–635 2002) that introduces truly abstract thinking. In the end I introduce the concept of ‘Linguification’ to dissect the particular mechanisms sustaining abstract thinking in the explanation of what makes humans distinct from robots and LOs.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    To biologists, this categorization is of course immensely crude, given that cognitive abilities are always species-specifically defined.

  2. 2.

    The Mirror Self Recognition test introduced by Gallup (1970), though somewhat controversial (see Schilhab 2004), has been widely used as a method to assess self-consciousness in humans and non-humans.

  3. 3.

    When engaging in mimeomorphic actions, humans may expose these mechanical features too and thus momentarily belong to the category of LOs and robots: “The human per complicated animal (that is, the human engaged in mimeomorphic actions) – is continuous with the animal and physical world. We are just like complicated cats, dogs, trees and sieves. When doing things we are just complicated sets of mechanisms” (Collins 2010, p. 104).

  4. 4.

    In the words of Godfrey-Smith (2002, p.135): “We can think of cognition as a biological toolkit used to control behaviour; a collection of capacities which, in combination, allow organisms to achieve various kinds of adaptive coordination between their actions and the world. This toolkit typically includes the capacities for perception, internal representation of the world, memory decision-making and the production of behavior”. However, the abilities that enable a particular organism to cope are not fixed across species but rely on the history and life conditions of specific species.

  5. 5.

    This understanding forms a platform for a semiotic, or sign-theoretic, understanding of organismic life. In the view of Hoffmeyer (2010, p. 189): “Biosemiotics suggests that living systems should be studied as semiotic systems in their own right. This idea is based on the belief that the poverty of the information discourse in biological sciences results from the reductive neglect of the interpretive aspect of biological information”.

  6. 6.

    Though interpretative abilities are shared across the biological world, and not just applicable to “dolphins and chimps and a few other species” as suggested by Collins (2010, p. 25), subdivisions of the ability are still thinkable, especially if we apply cognitive functions such as attention, awareness and consciousness. Godfrey-Smith (2002) poses: “As the term “toolkit” suggests we need not to find some single set of tools across all the organisms with cognitive capacities; different organisms have different collections of behaviour control devices, according to their circumstances and history. Furthermore, the list I gave of some core elements of the toolkit (perception, internal representation of the world, memory, learning) should not be seen as describing a set of recognizable and distinct “modules” found in the same form in all cognitive systems that have them. Rather, this is a set of capacities realized in different ways in different organisms, capacities that shade into each other and off into other, noncognitive parts of the biological machinery” (p. 135).

  7. 7.

    For similar discussions on imminent attention as constitutive of feeding activities in birds, see Dukas (2009).

  8. 8.

    The lego-thinking is not an invention from robotics but thrives also within the cognitive sciences. For instance, the substituting of particular brain structures for cognitive abilities corroborates rejections of pain-experiencing fish (for instance the lack of neocortical regions see Rose 2007, see also Chandroo et al. 2004). The explanation is based on reducing cognition to simpler building blocks (lego-bricks) that organisms may or may not possess. Unfortunately, the exclusive emphasis on stagnant properties of particular brain structures is stripped of considerations of developmental and integrative properties of cognition and thus misses out on the fundamental nature of cognition.

  9. 9.

    See also the concept of ‘repair’ in Collins (2010) and Collins and Kusch (1998).

  10. 10.

    Cycling has always been focal to discussions of skills, for instance see Collins (2010) for discussions of bicycling robots.

  11. 11.

    The distinction between cognition sustained by direct experiences and cognition that takes place without has elsewhere been referred to as on-line and off-line cognition (Wilson 2002). No doubt, humans are experts on the ability to activate images without external support in so-called off-line cognition (see also Wilson 2008; Stjernfelt 2012).

  12. 12.

    Within science and technology, the knowledge categories of ‘contributory expertise’ and ‘interactional expertise’ were introduced (Collins 2004; Collins and Evans 2002; Collins et al. 2006). Collins and Evans (2002) employ these terms to illuminate differences between kinds of expertise. Interactional expertise is remarkable for its exclusive dependence on linguistic (in the sense of linguistically carried) knowledge without the simultaneous contribution of relevant direct experiences.

  13. 13.

    Here, ‘abstract’ knowledge is to be understood as knowledge of phenomena that are not directly experienced. This breaks somewhat with the common understanding that ‘abstract’ means lacking in physical attributes. Borghi and Cimatti object to the colloquial understanding and posit (2012, p. 23): “there is not a concrete-abstract dichotomy. In fact, we consider the dimension of concreteness and abstractness as a continuum. […] at one extreme of this continuum there are pure natural kind concepts (DOG), followed by complex artifacts (TRAIN), by simple artifacts (HAMMER), and then by nominal concepts of the social role category (TEACHER). At the other extreme there are the pure nominal concepts, the content of which is established by definition, like ODD NUMBER”.

    The emphasis on the lack of relevant experiential content as demarcation criterion for the abstract used here is somewhat connected to the idea of abstractions that are ripped from context as discussed by Barsalou (2009), insofar relevant perceptual context is in fact missing.

  14. 14.

    Barsalou (2003, 2009) introduced the concept of situated conceptualization by which the concept of ‘linguification’ is inspired. Barsalou defines situated conceptualization as (2005, s. 620): “a multimodal simulation that supports one specific course of situated action with a particular category instance”. Linguification picks out those cases of situated conceptualization in which linguistic concepts become part of the neural correlate of the ‘particular category instance’. Well-established linguification processes may then offer themselves as handles that, with proper use by the interlocutor, may re-enact previous experiences.

  15. 15.

    The notion that embodied experiences also inform language at an advanced linguistic level is not new. Different approaches attempt to explain the meaning of abstract words and sentences by focusing on either metaphorical analogies, where image schemas obtained in a particular situation are applied in a new context (Lakoff and Johnson 1980), an action-based view in which motor responses are recruited showing the so-called action compatibility effect (ACE) (Glenberg et al. 2008), or on the relation between abstract words and internal references (as proposed by Barsalou and Wiemer Hastings 2005). Additionally, the WAT hypothesis (Borghi and Cimatti 2009; see Borghi et al. for reference, 2011) emphasises that words are tools irrespective of their reference and takes the perspective of Wittgenstein (Borghi and Cimatti 2012 p. 22): “We conceive words of a language as a set of tools that allow the user to perform a given activity”. To Borghi and Cimatti, the tool perspective that includes the social component will help solve the problem of meaning of abstract words and thus distinguish between meaning of concrete and abstract words (MCW and MAW respectively) (p.22): “In the case of acquisition of concrete word meanings, categories are grounded primarily in perception and action systems, and linguistic labels contribute in constraining the boundaries of grounded categories. In the use of abstract words, the opposite mechanisms might be adopted. Abstract words are more difficult to learn because they activate a much more complex set of situations, objects, human activities and so on”. Hence, in this view, MCW and MAW differ due to the differences in situations in which words are used. Though WAT accentuates the significance of language and linguistic dynamics, the WAT hypothesis is not explicit about which particular linguistic-cognitive mechanisms apply to either MCW or MAW situations. Williams et al. (2009, p. 1257) introduce the concept of scaffolding as “a process through which humans readily integrate incoming information with extant knowledge structures”. Scaffolding is then “the passive, natural process through which new concepts are formed, especially in early childhood. Features of abstract or less understood concepts are mapped onto existing and well-understood concepts, such that the structure of the developmentally earlier, primary concept is retained in the newly constructed concept. This structure imbues the newer concept with meaning” (ibid.).

    However, the actual mechanism during linguistic activities by which such scaffolding is accomplished has not been clarified. In relation to the idea of interactional expertise, for instance Selinger (2003) proposes processes of extrapolation.

References

  1. Arrabales, R., Ledezma, A., & Sanchis, A. (2010). ConsScale: a pragmatic scale for measuring the level of consciousness in artificial agents. Journal of Consciousness Studies, 17(3–4), 131–164.

    Google Scholar 

  2. Barsalou, L. W. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London B, 358, 1177–1187.

    Article  Google Scholar 

  3. Barsalou, L.W. (2005). Situated conceptualization. In Cohen, H. & Lefebvre, C. (Eds.) Handbook of categorization in cognitive science, (pp. 619–650) Elsevier.

  4. Barsalou, L. W., & Wiemer-Hastings, K. (2005). Situating abstract concepts. In D. Pecher & R. Zwaan (Eds.), Grounding cognition: The role of perception and action in memory, language, and thought (pp. 129–163). New York: Cambridge University Press.

    Google Scholar 

  5. Barsalou, L. W. (2009). Simulation, situated conceptualization, and prediction. Philosophical Transactions of the Royal Society B, 364, 1281–1289.

    Article  Google Scholar 

  6. Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowlegde in modality‐specific systems. Trends in Cognitive Sciences, 7(2), 84–91.

    Google Scholar 

  7. Bjork, E. L., & Bjork, R. (2011). Learning: Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. Gernsbacher, R. Pew, L. Hough, & J. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64). New York: Worth Publishers.

    Google Scholar 

  8. Borghi, A. M., & Cimatti, F. (2009). Words as tools and the problem of abstract words meanings. In N. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2304–2309). Amsterdam: Cognitive Science Society.

    Google Scholar 

  9. Borghi, A. M., & Cimatti, F. (2012). Words are not just words: the social acquisition of abstract words. RIFL, 5, 22–37.

  10. Borghi, A. M., Flumini, A., Cimatti, F., Marocco, D., & Scorolli, C. (2011). Manipulating objects and telling words: a study on concrete and abstract words acquisition. Frontiers in Psychology, 2, 1–14.

    Google Scholar 

  11. Byrne, R. (1995). The thinking ape: Evolutionary origins of intelligence. Oxford: Oxford University Press.

    Book  Google Scholar 

  12. Calvo, P. & Gomila, A. (2008) Eds. Handbook of cognitive science. An embodied approach. Elsevier.

  13. Cashman, T. (2008). What connects the map to the territory. In J. Hoffmeyer (Ed.), A legacy for living systems. Gregory Bateson as precursor for biosemiotics (pp. 45–58). Copenhagen: Springer.

    Google Scholar 

  14. Chandroo, K. P., Duncan, I. J. H., & Moccia, R. D. (2004). Can fish suffer?: perspectives on sentience, pain, fear and stress. Applied Animal Behaviour Science, 86, 225–250.

    Article  Google Scholar 

  15. Clark, A. (2008). Supersizing the mind: Embodiment, action and cognitive extension. Oxford: Oxford University Press.

    Book  Google Scholar 

  16. Collier, J. (2012). Interpretants. In D. Favareau, P. Cobley, & K. Kull (Eds.), A more developed sign: Interpreting the work of Jesper Hoffmeyer (pp. 175–177). Tartu: Tartu University Press.

    Google Scholar 

  17. Collins, H. (2004). Interactional expertise as a third kind of knowledge. Phenomenology and the Cognitive Sciences, 3, 125–143.

    Article  Google Scholar 

  18. Collins, H. (2010). Tacit and explicit knowledge. Chicago: The University of Chicago Press.

    Book  Google Scholar 

  19. Collins, H. M. (2011). Language and practice. Social Studies of Science, 41(2), 271–300.

    Article  Google Scholar 

  20. Collins, H. M. (2012). Language as a repository of tacit knowledge. In T. Schilhab, F. Stjernfelt, & T. Deacon (Eds.), The symbolic species evolved (pp. 225–239). Dordrecht: Springer.

    Google Scholar 

  21. Collins, H. M., & Evans, R. (2002). The third wave of science studies: studies of expertise and experience. Social Studies of Science, 32, 235–296.

    Article  Google Scholar 

  22. Collins, H. M., & Evans, R. (2007). Rethinking expertise. Chicago: University of Chicago Press.

    Book  Google Scholar 

  23. Collins, H., Evans, R., Ribeiro, R., & Hall, M. (2006). Experiments with interactional expertise. Studies in History and Philosophy of Science 37(a), 656–674.

    Google Scholar 

  24. Collins, H., & Kusch, M. (1998). The shape of actions. What humans and machines can do. Massachusetts: Massachusetts Institute of Technology.

    Google Scholar 

  25. Deacon, T. W. (1997). The symbolic species. New York: W. W. Norton & Company.

    Google Scholar 

  26. Deacon, T. W. (2012). Incomplete nature. How mind emerged from matter. New York: W. W. Norton & Company.

    Google Scholar 

  27. Dukas, R. (2009). Evolutionary biology of limited attention. In L. Tommasi, M. A. Peterson, & L. Nadel (Eds.), Cognitive biology: Evolutionary and developmental perspectives on mind, brain and behaviour (pp. 147–161). Massachusetts: Massachusetts Institute of Technology.

    Google Scholar 

  28. Gallup, G. G., Jr. (1970). Chimpanzees: self recognition. Science, 167(3914), 86–87.

    Article  Google Scholar 

  29. Glenberg, A. M., Sato, M., Cattaneo, L., Riggio, L., Palumbo, D., & Buccino, G. (2008). Processing abstract language modulates motor system activity. Quarterly Journal of Experimental Psychology, 61(6), 905–919.

    Article  Google Scholar 

  30. Godfrey-Smith, P. (2002). Environmental complexity, signal detection, and the evolution of cognition. In M. Bekoff, C. Allen, & G. M. Burghardt (Eds.), The cognitive animal (pp. 135–149). Massachusetts: Massachusetts Institute of Technology.

    Google Scholar 

  31. González, J., Barros-Loscertales, A., Pulvermüller, F., Meseguer, V., Sanjuán, A., Belloch, V., et al. (2006). Reading cinnamon activates olfactory brain regions. NeuroImage, 32, 906–912.

    Article  Google Scholar 

  32. Gärdenfors, P., Brinck, I., & Osvath, M. (2012). The tripod effect: Co-evolution of co-operation, cognition and communication. In T. Schilhab, F. Stjernfelt, & T. Deacon (Eds.), The symbolic species evolved (pp. 192–223). Dordrecht: Springer.

    Google Scholar 

  33. Hesslow, G. (2012). Current status of the simulation theory of cognition. Brain Research, 1428, 71–79.

    Article  Google Scholar 

  34. Hodges, B. (2009). Ecological pragmatics. Values, dialogical arrays, complexity, and caring. Pragmatics & Cognition, 17(3), 628–652.

    Article  Google Scholar 

  35. Hoffmeyer, J. (2010). Semiotic freedom: An emerging force. In P. Davies & N. H. Gregersen (Eds.), Information and the nature of reality: From Physics to Metaphysics (pp. 185–204). Cambridge: Cambridge University Press.

    Google Scholar 

  36. Hoffmeyer, J. (2012). The natural history of intentionality. A biosemiotic approach. In T. Schilhab, F. Stjernfelt, & T. Deacon (Eds.), The symbolic species evolved (pp. 97–116). Dordrecht: Springer.

    Google Scholar 

  37. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: Chicago University Press.

    Google Scholar 

  38. Lovibond, P. F., & Shanks, D. R. (2002). The role of awareness in Pavlovian conditioning: empirical evidence and theoretical implications. Journal of Experimental Psychology: Animal Behavior Processes, 28(1), 3–26.

    Google Scholar 

  39. Maturana, H. R., & Varela, F. J. (1998). The tree of knowledge: The biological roots of human understanding. Massachusetts: Shambhala Publishing.

    Google Scholar 

  40. Noble, W., & Davidson, I. (1996). Human evolution, language and mind. Hong Kong: Cambridge University Press.

    Google Scholar 

  41. Pecher, D., Boot, I., & Van Dantzig, S. (2011). Abstract concepts: Sensory-motor grounding, metaphors, and beyond. In B. Ross (Ed.), The psychology of learning and motivation, 54 (pp. 217–248). Burlington: Academic Press.

    Google Scholar 

  42. Phelps, E.A. (2005). The interaction of emotion and cognition: The relation between the human amygdala and cognitive awareness. The new unconscious. Hassin, R. R., Uleman, J. S. and Bargh, J. A. Oxford: Oxford University Press: 61–76.

  43. Pulvermüller, F. (2011). Meaning and the brain: the neurosemantics of referential, interactive and combinatorial knowledge. Journal of Neurolinguistics. doi:10.1016/j.jneuroling.2011.03004.

    Google Scholar 

  44. Raposo, A., Moss, H. E., Stamatakis, E. A., & Tyler, L. K. (2009). Modulation of motor and premotor cortices by actions, action words and action sentences. Neuropsychologia, 47, 388–396.

    Article  Google Scholar 

  45. Ribeiro, R., & Collins, H. (2007). The bread-making machine: tacit knowledge and two types of actions. Organization Studies, 28, 1417–1433.

    Article  Google Scholar 

  46. Rose, J. D. (2007). Anthropomorphism and ‘mental welfare’ of fishes. Diseases of Aquatic Organisms, 75, 139–154.

    Article  Google Scholar 

  47. Sheckley, B. G., & Bell, S. (2006). Experience, consciousness, and learning: implications for instruction. New Directions for Adult and Continuing Education, 110, 43–53.

    Article  Google Scholar 

  48. Schilhab, T. S. S. (2002). Anthropomorphism and mental state attribution. Animal Behaviour, 63, 1021–1026.

    Article  Google Scholar 

  49. Schilhab, T. S. S. (2004). What mirror self-recognition in nonhumans can tell us about aspects of self. Biology and Philosophy, 19(1), 111–126.

    Article  Google Scholar 

  50. Schilhab, T. (2011a). Neural perspectives on ‘Interactional expertise’: the plasticity of language. Journal of Consciousness Studies, 18(7–8), 99–116.

    Google Scholar 

  51. Schilhab, T. (2011b). Derived embodiment and imaginative capacities in interactional expertise, Phenomenology and the Cognitive Sciences 2011, doi:10.1007/s11097-011-9232-0.

  52. Schilhab, T. (2013). On derived embodiment: A response to Collins. Phenomenology and the Cognitive Sciences 2011, doi:10.1007/s11097-012-9265-z.

  53. Selinger, E. (2003). The necessity of embodiment: the Dreyfus- Collins debate. Philosophy Today, 47, 266–279.

    Article  Google Scholar 

  54. Sheets-Johnstone, M. (2007). Consciousness: a natural history. Synthesis Philosophica, 44(2), 283–299.

    Google Scholar 

  55. Stjernfelt, F. (2012). The evolution of semiotic self-control. In T. Schilhab, F. Stjernfelt, & T. Deacon (Eds.), The symbolic species evolved (pp. 39–63). Dordrecht: Springer.

    Google Scholar 

  56. Sutton, J., McIlwain, D., Christensen, W., & Geeves, A. (2011). Applying intelligence to the reflexes: embodied skills and habits between Dreyfus and Descartes. Journal of the British Society for Phenomenology, 42(1), 78–103.

    Article  Google Scholar 

  57. Takeno, J. (2008). A robot succeeds in 100% mirror image cognition. International Journal on Smart Sensing and Intelligent systems, 1(4), 891–911.

    Google Scholar 

  58. Tylén, K., Weed, E., Wallentin, M., Roepstorff, A., & Frith, C. D. (2010). Language as a tool for interacting minds. Mind & Language, 25(1), 3–29.

    Article  Google Scholar 

  59. Vázquez-Ibar, J. L., Guan, I., Weinglass, A. B., Verner, G., Gordillo, R., & Kaback, H. R. (2004). Sugar recognition by the Lactose Permease of Escherichia coli. Journal of Biological Chemistry, 279, 49214–49221.

    Article  Google Scholar 

  60. Wetzel, N., Widmann, A., Berti, S., & Schröger, E. (2006). The development of involuntary and voluntary attention from childhood to adulthood: a combined behavioral and event-related potential study. Child Neurophysiology, 117, 2191–2203.

    Article  Google Scholar 

  61. Williams, L. E., Huang, J. Y., & Bargh, J. A. (2009). The scaffolded mind: higher mental processes are grounded in early experience of the physical world. European Journal of Social Psychology, 39, 1257–1267.

    Article  Google Scholar 

  62. Wilson, M. (2002). Six views on embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–635.

    Article  Google Scholar 

  63. Wilson, M. (2008). How did we get from there to here? An evolutionary perspective on embodied cognition. In Calvo, P. & Gomila, T. (Eds.), Directions for an Embodied Cognitive Science: Towards an Integrated Approach. (pp. 375–393). Elsevier.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Theresa S. S. Schilhab.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Schilhab, T.S.S. Why animals are not robots. Phenom Cogn Sci 14, 599–611 (2015). https://doi.org/10.1007/s11097-013-9342-y

Download citation

Keywords

  • Robots
  • Animal cognition
  • Linguification
  • Contextuality
  • Derived embodiment
  • Language