A widespread assumption in experimental comparative (animal) cognition is that, barring compelling evidence to the contrary, the default hypothesis should postulate the simplest cognitive ontology (mechanism, process, or structure) consistent with the animal’s behavior. I call this assumption the principle of cognitive simplicity (PoCS). In this essay, I show that PoCS is pervasive but unjustified: a blanket preference for the simplest cognitive ontology is not justified by any of the available arguments. Moreover, without a clear sense of how cognitive ontologies are to be carved up at the joints—and which tools are appropriate for the job—PoCS rests on shaky conceptual ground.
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In fairness to the researchers, one may argue that many research projects do not require an explicit commitment to a single definition of cognition. For example, in narrowly scoped experiments, such as asking whether a Western Scrubjay appears to possess episodic-like memory (autobiographical event memory in non-human animals), it is not necessary to specify whether episodic-like memory is meaningfully cognitive (Clayton et al. 2001). What is required is not a general definition of cognition, but a clearly articulated strategy for identifying the behavioral markers of episodic-like memory. To put this point more generally, whether the hypothesized trait counts as cognitive is presumed to be a separate question from how to set up and evaluate experimental tests of that trait. However, these questions are not entirely separate, since the former plays a role in establishing what comes to count as the default hypothesis. Therefore, while the science “works” just fine without broad agreement regarding concepts central to its inquiry—just as biology works without a philosophically unassailable definition of life—the experimental programs in comparative cognition are shaped by PoCS.
For another example of the PoCS assumption, see Foote and Crystal’s (2012) rejection of the metacognitive explanation in response to the development of a non-metacognitive model by Smith et al. (2008). They write, “Clearly, putative evidence for metacognition in rats is critically undermined when a non-metacognition model can produce the observed pattern of behavior” (Foote and Crystal 2012, 188). However, it is important to note that comparative cognition researchers, such as Foote and Crystal, may have other rational reasons than simplicity for favoring associative explanations. For example, models of associative learning are better developed than, e.g., metacognitive models. Devising tests that further refine such models is, therefore, possible, while refining metacognitive models is not possible at present. The availability of these models, moreover, makes associative hypotheses more predictive than the alternatives. While this reason has its own shortcomings, it is perfectly rational for scientists to prefer hypotheses that have generated well-specified models over hypotheses that have yet to be fully fleshed out and which continue to make only rough predictions. I develop this idea in my dissertation ([present author] 2013). For a discussion of pre-empirical preferences in comparative cognition see also Buckner (2013).
Shettleworth is not, I should note, suggesting that her argument is grounds for a general simplicity preference.
The Taxonomic Ubiquity Argument is based on more than behavioral data, as an anonymous reviewer helpfully notes: In fact, as the review notes, the ubiquity of associative-learning has been established by non-behavioral observations, such as evidence of physical mechanisms that can support associative-learning in such invertebrates as the Aplysia sea slug. Behavioral data is, of course, supplemented with neuroanatomical data, which may be used to further the Taxonomic Ubiquity Argument. In fact, the Aplysia example supports my conclusion rather than imperil it: the mere presence of a physical realizer of associative learning does not establish that only associative learning is responsible for the observed behavior. Such an inference successfully shows that associative learning is possible across a broad range of taxa, but not that other cognitive abilities are therefore likely to be absent, as the Taxonomic Ubiquity Argument holds.
To see why recent origins suggest rarity, consider the following simple example. Imagine a very simple tree with one ancestral node, A, representing the ancestral species, and ten branches representing ten new species, S1–S10. If a trait, T, is ancient, then it will be found in A and is, therefore, more likely to be found in S1–S10 as well (depending on how conserved T happens to be). However, if T is a recent development, i.e., if it arose in S7, then it would have to be recreated in the other nine branches as well, which is an unlikely scenario. Therefore, the recent origins of T strongly suggest its relative rarity.
The reader may note that Shettleworth’s argument in the quoted passage draws on the TUA as well as on the SA. In keeping with the theme of drawing out the arguments in favor of PoCS, I have elected to distinguish between these two arguments for the purposes of this essay.
Also of note is Karin-D’Arcy’s implication that the canon is an anti-anthropomorphism principle (Karin-D’Arcy 2005).
Sober is not responding to Karin-D’Arcy. His aim is to show that no re-interpretation of the Canon in evolutionary terminology will issue in the parsimony reading of the Canon that has become so popular. Along the way, Sober offers what he considers to be the best interpretation of Morgan’s actual intent (Sober 2005).
A homology is “a similarity inherited from a common ancestor” (Sober 2005, 94).
A homoplasy is “a similarity that is the result of two or more independent derivations of the trait” (ibid).
It is now clear that we cannot reliably infer common developmental mechanisms from the fact that a trait is taxically homologous (Wagner 2007). Furthermore, as Powell and Shea (forthcoming) argue, behavioral homology relations can be preserved notwithstanding a complete turnover in the underlying developmental mechanisms of a trait in one or both lineages, and despite even a shift in the inheritance system (genetic, cultural, etc.) through which the trait is transmitted.
Buckner C (2011) Two approaches to the distinction between cognition and ‘mere association’. Int J Comp Psychol 24:314–348
Buckner C (2013) Morgan’s Canon, meet Hume’s Dictum: avoiding anthropofabulation in cross-species comparisons. Biol Philos 28:853–871
Carruthers P (2008) Meta-cognition in animals: a skeptical look. Mind Lang 23:58–89
Clayton NS, Dickinson A (1998) Episodic-like memory during cache recovery by scrub jays. Nature 395:272–278
Clayton NS, Griffiths DP, Emery NJ, Dickinson A (2001) Elements of episodic-like memory in animals. Philos Trans R Soc Biol 356:1483–1491. doi:10.1098/rstb.2001.0947
Conway MS (2003) Life’s solution: inevitable humans in a lonely universe. Cambridge University Press, Cambridge
Cruse H, Wehner R (2011) No need for a cognitive map: decentralized memory for insect navigation. Public Libr Sci Comput Biol 7:e1002009. doi:10.1371/journal.pcbi.1002009
Crystal JD, Foote AL (2009) Metacognition in animals. Comp Cogn Behav 4:1–16
Dally JM, Emery NJ, Clayton NS (2006) Food-Caching western scrub-jays keep track of who was watching when. Science 16:1662–1665. doi:10.1126/science.1126539
de Waal FBM, Ferrari PF (2010) Towards a bottom-up perspective on animal and human cognition. Trends Cogn Sci 14:201–207
Dickinson A (2012) Associative learning and animal cognition. Philos Trans R Soc Biol Sci 367:2733–2743. doi:10.1098/rstb.2012.0220
Dugas-Ford J, Rowell JJ, Ragsdale CW (2012) Cell-type homologies and the origins of the neocortex. PNAS 109:16974–16979
Emery NJ, Clayton NS (2004) The mentality of crows: convergent evolution of intelligence in corvids and apes. Science 306:1903–1907
Fitzpatrick S (2008) Doing away with Morgan’s Canon. Mind Lang 23:224–246
Foote AL, Crystal JD (2007) Metacognition in the rat. Curr Biol 17:551–555
Foote AL, Crystal JD (2012) Play it again: a new method for testing metacognition in animals. Anim Cogn 15:187–199
Gallistel CR (2008) Learning and representation. In: Menzel R, Byrne J (eds) Learning and memory: a comprehensive reference. Elsevier, New York, pp 227–242
Güntürkün O (2012) The convergent evolution of neural substrates for cognition. Psychol Resour 76:212–219. doi:10.1007/s00426-011-0377-9
Heyes C (2012) Simple minds: a qualified defense of associative learning. Philos Trans R Soc Biol Sci 367:2695–2703
Karin-D’Arcy MR (2005) The modern role of Morgan’s canon in comparative psychology. Int J Comp Psychol 18:179–201
Le Pelley ME (2012) Metacognitive monkeys or associative animals simple reinforcement learning explains uncertainty in nonhuman animals. J Exp Psychol Learn Mem Cogn 38:686–708. doi:10.1037.a0026478
Mameli M, Bortolotti L (2006) Animal rights, animal minds, and human mindreading. J Med Ethics 32:84–89
Morgan LC (1894) An introduction to comparative psychology. Scribner’s, New York
Pitt D (2012) Mental representation. In: Edward N. Zalta (ed) The Stanford Encyclopedia of philosophy (Winter 2012 Edition). http://plato.stanford.edu/archives/win2012/entries/mental-representation/
Povinelli DJ, Barth J (2005) Reinterpreting behavior: a human specialization? Behav Brain Sci 28:712–713
Powell R, Shea N (forthcoming) Homology across inheritance systems. Biol Philos
Shettleworth SJ (2009) Cognition, evolution, and behavior: edition II. Oxford University Press, Oxford
Shettleworth SJ (2012) Modularity, comparative cognition and human uniqueness. Philos Trans R Soc Biol Sci 367:2794–2802. doi:10.1098/rstb.2012.0211
Shettleworth SJ (2013) Fundamentals of comparative cognition. Oxford University Press, New York
Smith JD, Beran MJ, Couchman JJ, Coutinho MVC (2008) The comparative study of metacognition: sharper paradigms, safer inferences. Psychon Bull Rev 15:679–691
Sober E (2005) Comparative psychology meets evolutionary biology: Morgan’s canon and cladistic parsimony. In: Daston L, Mitman G (eds) Thinking with animals: new perspectives on anthropomorphism. Columbia University Press, New York, pp 85–99
Taylor AH, Knaebe B, Gray RD (2012) An end to insight? New Caledonian crows can spontaneously solve problems without planning their actions. Proc Royal Soc B Biol Sci 279(1749):4977–4981
Thomas RK (2001) Lloyd Morgan’s cannon: a history of misrepresentation. http://htpprints.yorku.ca/archive/00000017
van der Vaart E, Verbrugge R, Hemelrijk CK (2012) Corvid re-caching without ‘theory of mind’: a model. Public Libr Sci Online 7(3):e32904. doi:10.1371/journal.pone.0032904
Wagner GP (2007) The developmental genetics of homology. Nat Rev Genet 8:473–479
Wimpenny JH, Weir AAS, Clayton L, Rutz C, Kacelnik A (2009) Cognitive processes associated with sequential tool use in new Caledonian crows. Public Libr Sci Online 4:6471
Zentall TR (2001) The case for a cognitive approach to animal learning and behavior. Behav Process 54:65–78
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Meketa, I. A critique of the principle of cognitive simplicity in comparative cognition. Biol Philos 29, 731–745 (2014). https://doi.org/10.1007/s10539-014-9429-z
- Comparative cognition
- Animal cognition
- Cognitive evolution