Joined: Oct. 2005
No offense to their finding lines of evidence I did not yet mention: there is nothing at all in regards to cognitive science, not even a computer model of the underlying learning mechanism.
Actually, that is complete and total bullshit, not to put too fine a point on it, a baldfaced Trump-level lie. But you are you, so what else should we have expected?
With regards to not involving a computer model: the current paper is partly based on R.A. Watson, et al., 2010, Optimisation in ‘self-modelling’ complex adaptive systems. Complexity, 16: 17–26. The current paper includes output from the computer model in R.A. Watson et al., 2014, The evolution of phenotypic correlations and ‘developmental memory’. Evolution, 68:1124–1138, which is exactly and precisely a model of the underlying learning mechanism. It is not exactly the sort of computer model that you intended to refer to, but nonetheless it's much better than yours as it actually does something interesting. See also R.A. Watson et al., 2010, Associative memory in gene regulation networks, in H. Fellermann et al. (eds.), Proceedings of the Artificial Life Conference XII, MIT Press, p. 194–202.
In the latest paper he also presents a lot of algorithms that could easily be turned into models, except that it's pretty clear where they are going, and he bases his work on many other people's published computer models, so yes, computer models are involved.
Contrary to your entirely uninformed opinion, Watson's paper includes rather a lot of stuff about cognitive science.
He also provides a lot of very carefully defined terms, which is a lovely touch that you would be advised to follow. He also writes very clearly, so ditto.
His work shows that he has been steadily laying a foundation for the exceptional claims that he wishes to make. That’s a very good way to develop one's ideas into a theory in science.
Last but not least, he provides a lengthy and surprisingly fascinating bibliography of 89 papers, many of which sound appropriate to your nonsense, but very few of which you appear to have read. Items of potential interest include:
L. Bettencourt. The rules of information aggregation and emergence of collective intelligent behavior. Top. Cogn. Sci., 1 (2009), pp. 598–620.
R.A. Watson, et al. Transformations in the scale of behavior and the global optimization of constraints in adaptive networks. Adapt. Behav., 19 (2011), pp. 227–249.
P. Adams. Hebb and Darwin. J. Theoret. Biol., 195 (1998), pp. 419–438.
C. Fernando, et al. Selectionist and evolutionary approaches to brain function: a critical appraisal. Front. Comput. Neurosci., 6 (2012), p. 24.
D. Hofstadter. Analogy as the core of cognition. D. Gentner (Ed.), et al., The Analogical Mind: Perspectives from Cognitive Science, MIT Press (2001), pp. 499–538.
J.J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A., 79 (1982), pp. 2554–2558.
M. Parter, et al. Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput. Biol., 4 (2008), p. e1000206.
N. Kashtan, U. Alon. Spontaneous evolution of modularity and network motifs. Proc. Natl. Acad. Sci. U.S.A., 102 (2005), pp. 13773–13778.
C. Fernando. Design for a Darwinian brain: part 1. Philosophy and neuroscience. N.F. Lepora (Ed.), et al., Biomimetic and Biohybrid Systems, Springer (2013), pp. 71–82.
T. Mitchell. Machine Learning. McGraw Hill (1997)
C.R. Shalizi. Dynamics of Bayesian updating with dependent data and misspecified models. Electron. J. Stat., 3 (2009), pp. 1039–1074.
G.E. Hinton. Learning multiple layers of representation. Trends Cogn. Sci., 11 (2007), pp. 428–434.
J.J. Hopfield, D.W. Tank. Computing with neural circuits: a model. Science, 233 (1986), pp. 625–633.
R.A. Watson, et al. Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions. Evol. Biol. (2015) (in press).
R.C. O’Reilly, Y. Munakata. Computational Explorations in Cognitive Neuro-science: Understanding the Mind by Simulating the Brain. MIT Press (2000)
T. Börgers, R. Sarin. Learning through reinforcement and replicator dynamics. J. Econ. Theory, 77 (1997), pp. 1–14.
J. Clune, et al. The evolutionary origins of modularity. Proc. R. Soc. B Biol. Sci., 280, p. 20122863.