I provide some historical perspective on the development of the “minimally cognitive behavior” research program and its relation to “minimal cognition.”
AgmonE.BeerR. D. (2014). The dynamics of action switching in an evolved agent. Adaptive Behavior, 22, 3–20.
2.
AgreP.ChapmanD. (1987). Pengi: An implementation of a theory of activity. In Proceedings AAAI-87: Proceedings of the Sixth National Conference on Artificial Intelligence (pp. 268–272). American Institute of Aeronautics and Astronautics.
3.
AizawaK. (2017). Cognition and behavior. Synthese, 194, 4269–4288.
4.
BarandiaranX.MorenoA. (2006). On what makes certain dynamical systems cognitive: A minimally cognitive organization program. Adaptive Behavior, 14, 171–185.
5.
BeerR. D. (1990). Intelligence as adaptive behavior: An experiment in computational neuroethology. Academic Press.
6.
BeerR. D. (1992). A dynamical systems perspective on autonomous agents (Technical report CES-92-11). Department of Computer Engineering and Science, Case Western Reserve University.
7.
BeerR. D. (1995a). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173–215.
8.
BeerR. D. (1995b). Computational and dynamical languages for autonomous agents. In PortR.van GelderT. (Eds.), Mind as motion: Explorations in the dynamics of cognition (pp. 121–147). The MIT Press.
9.
BeerR. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In MaesP.MataricM.MeyerJ.PollackJ.WilsonS. (Eds.), From animals to animats 4: Proceedings of the fourth international conference on simulation of adaptive behavior (pp. 421–429). The MIT Press.
10.
BeerR. D. (2003a). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11, 209–243.
11.
BeerR. D. (2003b). Arches and stones in cognitive architecture. Adaptive Behavior, 11, 299–305.
12.
BeerR. D.GallagherJ. G. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior, 1, 91–122.
13.
BeerR. D.WilliamsP. L. (2015). Information processing and dynamics in minimally cognitive agents. Cognitive Science, 39, 1–38.
14.
BrancazioN.Segundo-OrtinM.McGivernP. (2020). Approaching minimal cognition: Introduction to the special issue. Adaptive Behavior, 28, 401–405.
15.
BrooksR. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.
16.
ClarkA.ToribioJ. (1994). Doing without representing?Synthese, 101, 401–431.
17.
CliffD. (1991). Computational neuroethology: A provisional manifesto. In MeyerJ.-A.WilsonS. W. (Eds.), From animals to animats 3: Proceedings of the first international conference on the simulation of adaptive behavior (pp. 29–39). The MIT Press.
18.
CliffD.HarveyI.HusbandsP. (1993). Explorations in evolutionary robotics. Adaptive Behavior, 2, 73–110.
19.
DennettD. C. (1978). Why not the whole iguana?Behavioral and Brain Sciences, 1, 103–104.
20.
DennettD. C. (1983). Intentional systems in cognitive ethology: The “Panglossian Paradigm” defended. Behavioral and Brain Sciences, 6, 343–390.
21.
Di PaoloE. A.HarveyI. (2003). Decisions and noise: The scope of evolutionary synthesis and dynamical analysis. Adaptive Behavior, 11, 284–288.
22.
FavelaL. H.MartinJ. (2017). “Cognition” and dynamical cognitive science. Minds and Machines, 27, 331–355.
23.
HarveyI.Di PaoloE.WoodR.QuinnM.TuciE. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life, 11, 79–98.
24.
IzquierdoE.HarveyI.BeerR. D. (2008). Associative learning on a continuum in evolved dynamical neural networks. Adaptive Behavior, 16, 361–384.
25.
KeijzerF. A. (2003). Making decisions does not suffice for minimal cognition. Adaptive Behavior, 11, 266–269.
26.
LyonP. (2020). Of what is “minimal cognition” the half-baked version?Adaptive Behavior, 28, 407–424.
27.
MaturanaH. R.VarelaF. J. (1980). Autopoiesis and cognition: The realization of the living. D. Reidel.
28.
MorenoA.UmerezJ.IbañezJ. (1997). Cognition and life: The autonomy of cognition. Brain and Cognition, 34, 107–129.
29.
PhattanasriP.ChielH. J.BeerR. D. (2007). The dynamics of associative learning in evolved model circuits. Adaptive Behavior, 15, 377–396.
30.
SlocumA. C.DowneyD. C.BeerR. D. (2000). Further experiments in the evolution of minimally cognitive behavior: From perceiving affordances to selective attention. In MeyerJ.BerthozA.FloreanoD.RoitblatH.WilsonS. (Eds.), From animals to animats 6: Proceedings of the sixth international conference on simulation of adaptive behavior (pp. 430–439). The MIT Press.
31.
SuchmanL. A. (1987). Plans and situated actions. Cambridge University Press.
32.
van GelderT. (1995). What might cognition be if not computation?Journal of Philosophy, 92, 345–381.
33.
WilliamsP. L.BeerR. D. (2013). Environmental feedback drives multiple behaviors from the same neural circuit. In LioP.MiglinoO.NicosiaG.NolfiS.PavoneM. (Eds.), Advances in artificial life: ECAL 2013 (pp. 268–275). The MIT Press.
34.
WilliamsP. L.BeerR. D.GasserM. (2008a). An embodied dynamical approach to relational categorization. In LoveB. C.McRaeK.SloutskyV.M. (Eds.), Proceedings of the 30th annual conference of the cognitive science society (pp. 223–228). Cognitive Science Society.
35.
WilliamsP. L.BeerR. D.GasserM. (2008b). Evolving referential communication in embodied dynamical agents. In BullockS.NobleJ.WatsonR.BedauM.A. (Eds.), Artificial life XI: Proceedings of the eleventh international conference on the simulation and synthesis of living systems (pp. 702–709). The MIT Press.
36.
WinogradT.FloresF. (1986). Understanding computers and cognition: A new foundation for design. Ablex.
37.
YamauchiB.BeerR. D. (1994). Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior, 2, 219–246.