Memory may be broadly regarded as information gained from past experience that is available in the service of ongoing and future adaptive behavior. The biological implementation of memory shares little with memory in synthetic cognitive systems where it is typically regarded as a passive storage structure. Neurophysiological evidence indicates that memory is neither passive nor centralized. A review of the relevant literature in the biological and computer sciences is conducted and a novel methodology is applied that incorporates neuroethological approaches with general biological inspiration in the design of synthetic cognitive systems: a case study regarding episodic memory provides an illustration of the utility of this methodology. As a consequence of applying this approach to the reinterpretation of the implementation of memory in synthetic systems, four fundamental functional principles are derived that are in accordance with neuroscientific theory, and which may be applied to the design of more adaptive and robust synthetic cognitive systems: priming, cross-modal associations, cross-modal coordination without semantic information transfer, and global system behavior resulting from activation dynamics within the memory system.
AggletonJ.BrownM. (1999). Episodic memory, amnesia, and the hippocampal–anterior thalamic axis. Behavioural and Brain Sciences, 22, 425–489.
2.
AggletonJ.BrownM. (2006). Interleaving brain systems for episodic and recognition memory. Trends in Cognitive Sciences, 10, 454–463.
3.
AggletonJ.PearceJ. (2001). Neural systems underlying episodic memory: insights from animal research. Behavioural and Brain Sciences, 356, 1467–1482.
4.
AllanK.WildingE.RuggM. (1998). Electrophysiological evidence for dissociable processes contributing to recollection. Acta Psychol., 98, 231–252.
5.
AndersonJ. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22.
6.
AndersonJ. (1993). Rules of the mind. Lawrence Erlbaum Associates, Inc.
7.
AndersonJ. R.BothellD.ByrneM. D.DouglassS.LebiereC.QinY. (2004). An integrated theory of the mind. Psychological Review, 111, 1036–1060.
8.
ArbibM. A.FellousJ.-M. (2004). Emotions: from brain to robot. Trends in Cognitive Sciences, 8, 554–561.
9.
AtkinsonR. C.ShiffrinR. M. (1968). Human memory: a proposed system and its control processes. In SpenceK. W.SpenceJ. T. (Eds.), The psychology of learning and motivation (pp. 89–195). New York: Academic Press.
10.
BaarsB. (1988). A cognitive theory of consciousness. Cambridge University Press.
11.
BaarsB.FranklinS. (2007). An architectural model of conscious and unconscious brain functions: Global workspace theory and IDA. Neural Networks, 20, 955–961.
12.
BaarsB.FranklinS. (2009). Consciousness is computational: the LIDA model of global workspace theory. International Journal of Machine Consciousness, 1, 23–32.
13.
BaddeleyA. (2000). The episodic buffer: A new component of working memory?Trends in Cognitive Sciences, 4, 417–423.
14.
BaddeleyA.HitchG. (1974). Working memory. In BowerG. H. (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.
15.
BaddeleyA. D. (2001). The concept of episodic memory. In BaddeleyA.AggletonJ.ConwayM. (Eds.), Episodic memory: new directions in research (pp. 1–10). Oxford: Oxford University Press.
16.
BarM. (2007). The proactive brain: using analogies and associations to generate predictions. Trends in Cognitive Sciences, 11, 280–289.
17.
BaxterP. (2010). Foundations of a constructivist memory-based approach to cognitive robotics. Unpublished doctoral dissertation, University of Reading.
18.
BaxterP.BrowneW. (2009). The role of internal value systems for a memory-based robotic architecture. In CañameroL.OudeyerP.-Y.BalkeniusC. (Eds.), Proceedings of the Ninth International Conference on Epigenetic Robotics (Vol. 146, pp. 195–196). Lund: LUCS.
19.
BaxterP.BrowneW. (2010). Memory as the substrate of cognition: a developmental cognitive robotics perspective. In JohanssonB.SahinE.BalkeniusC. (Eds.), Proceedings of the Tenth International Conference on Epigenetic Robotics (Vol. 147, pp. 19–26). Lund: LUCS.
20.
BaxterP.BrowneW. (2011). Memory-based cognitive framework: a low-level association approach to cognitive architectures. In KampisG.KarsaiI.SzathmáryE. (Eds.), European Conference on Artificial Life (ECAL 09), LNCS (Vol. 5777, pp. 402–409). Berlin: Springer-Verlag.
21.
BaxterP.WoodR.BelpaemeT.NalinM. (2011). On memory systems for companion robots: implementation methodologies and legal implications. In HoW.LimM.BromC. (Eds.), Proceedings of the 2nd AISB Symposium on Human Memory for Artificial Agents (pp. 30–34).
22.
BeerR. (1990). Intelligence as adaptive behaviour. New York: Academic Press.
23.
BickhardM. H. (2009). The biological foundations of cognitive science. New Ideas in Psychology, 27, 75–84.
24.
BoucherL.DienesZ. (2003). Two ways of learning associations. Cognitive Science, 27, 807–842.
25.
BromC.LukavskyJ. (2008). Episodic memory for human-like agents and human-like agents for episodic memory. In AAAI Fall Symposium 2008: Biologically Inspired Cognitive Architectures (pp. 42–47) Menlo Park, CA: AAAI Press.
26.
BromC.LukavskyJ. (2009). Towards virtual characters with a full episodic memory II: The episodic memory strikes back. In DeckerK.SichmanJ. S.SierraC.CastelfranchiC., (Eds.), Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009) (pp. 1–9), Richland, SC: AAMAS.
27.
BromC.LukavskyJ. (2010). Episodic memory for human-like agents and human-like agents for episodic memory. International Journal of Machine Consciousness, 2, 227–244.
28.
BullL. (2004). Applications of learning classifier systems. In BullL. (Ed), Applications of Learning Classifier Systems (pp. 260–275), Berlin: Springer-Verlag.
29.
BullL.KovacsT. (2005). Foundations of learning classifier systems: An introduction. In BullL.KovacsT. (Eds.), Foundations of learning classifier systems (pp. 1–20). Berlin: Springer-Verlag.
30.
BurgessN.DonnettJ. G.JefferyK. J.O’KeefeJ. (1997). Robotic and neuronal simulation of the hippocampus and rat navigation. Philosophical Transactions of the Royal Society of London B, 352, 1535–1543.
31.
CanameroL. (2005). Emotion understanding from the perspective of autonomous robots research. Neural Networks, 18, 445–455.
32.
CassimatisN. L.TraftonJ. G.BugajskaM. D.SchultzA. C. (2004). Integrating cognition, perception and action through mental simulation in robots. Robotics and Autonomous Systems, 49, 13–23.
33.
ClaytonN.DallyJ.EmeryN. (2007). Social cognition by food-caching corvids: the western scrub jay as a natural psychologist. Philosophical Transactions of the Royal Society of London B, 362, 507–522.
34.
ClaytonN.DickinsonA. (1998). Episodic-like memory during cache recovery by scrub jays. Nature, 395, 272–274.
35.
ClaytonN.GriffithsD.EmeryN.DickinsonA. (2001). Elements of episodic-like memory in animals. In BaddeleyA.ConwayM.AggletonJ. (Eds.), Episodic memory: new directions in research (pp. 232–248). Oxford: Oxford University Press.
36.
ClaytonN.RussellJ. (2009). Looking for episodic memory in animals and young children: Prospects for new a minimalism. Neuropsychologia, 47, 2330–2340.
37.
ClaytonN.SalwiczekL. (2007). Episodic memory. Current Biology, 17, R189–R191.
38.
CliffD. (1991). Computational neuroethology: A provisional manifesto. In MeyerJ.-A.WilsonS. (Eds.), From Animals to Animats: Proceedings of the First International Conference on the Simulation of Adaptive Behavior (pp. 29–39), Cambridge, MA: MIT Press.
39.
CohenN.SquireL. (1980). Preserved learning and retention of pattern analyzing skill in amnesia: Dissociation of knowing how and knowing that. Science, 210, 207–209.
40.
CollinsA. M.LoftusE. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407–428.
41.
CowanN. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. Psychological Bulletin, 104, 163–191.
42.
CowanN. (1999). An embedded-processes model of working memory. In MiyakeA.ShahP. (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 62–101). Cambridge University Press.
43.
CraikF. I. M.LockhartR. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684.
44.
CrestaniF. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11, 453–482.
45.
CurranT. (2000). Brain potentials of recollection and familiarity. Memory and Cognition, 28, 923–938.
46.
CurranT.DeBuseC.WorochB.HirchmanE. (2006). Combined pharmacological and electrophysiological dissociation of familiarity and recollection. Journal of Neuroscience, 26, 1979–1985.
47.
DamasioA. (1994). Descartes error: emotion, reason and the human brain. New York: Grosset/Putnam.
48.
DerbinskyN.LairdJ. E. (2010). Extending Soar with dissociated symbolic memories. In LimM.HoW. C. (Eds.), Remembering Who We Are: Human Memory for Artificial Agents Symposium, AISB 2010 (pp. 31–37) SSAISB.
49.
DeutschT.GruberA.LangR. (2008). Episodic memory for autonomous agents. In 2008 Conference on Human System Interactions (pp. 621–626). Los Alamos, CA: IEEE.
50.
Di PaoloE.IizukaH. (2008). How (not) to model autonomous behaviour. BioSystems, 91, 409–423.
51.
DoddW.GutierrezR. (2005). The role of episodic memory and emotion in a cognitive robot. In IEEE International Workshop on Robot and Human Interactive Communication, RoMan 2005 (pp. 692–697). IEEE.
52.
DorffnerG.PremE. (1993). Connectionism, symbol grounding, and autonomous agents. In Proceedings of the 15th annual meeting of the Cognitive Science Society (pp. 144–148), Hillside, NJ: Lawrence Erlbaum Associates.
53.
DorffnerG.PremE.TrostH. (1993). Words, symbols, and symbol grounding. In CasatiR.SmithB.WhiteG. (Eds.), Philosophy and the cognitive sciences: Proceedings of the 16th international Wittgenstein symposium. Vienna: Hölder-Pichler-Tempsky.
54.
DudaR.ShortliffeE. (1983). Expert systems research. Science, 220, 261–268.
55.
FritzkeB. (1994). Growing cell structures–A self-organizing network for unsupervised and supervised learning. Neural Networks, 7, 1441–1460.
56.
FritzkeB. (1995). A growing neural gas network learns topologies. In TesauroG.TouretzkyD.LeenT. (Eds.), Advances in neural information processing systems 7. Cambridge, MA: MIT Press.
57.
FusterJ. (1997). Network memory. Trends in Neurosciences, 20, 451–459.
58.
FusterJ. (2000). Cortical dynamics of memory. International Journal of Psychophysiology, 35, 155–164.
59.
GardenforsP. (2004). Conceptual Spaces: the geometry of thought. Cambridge, MA: MIT Press.
60.
GlenbergA. M. (1997). What memory is for. The Behavioral and Brain Sciences, 20, 1–19 [discussion 19–55].
61.
GoertzelB.LianR.ArelI.GarisH. D.ChenS. (2010). World survey of artificial brains, Part II: Biologically inspired cognitive architectures. Neurocomputing, 74, 30–49.
62.
GorskiN. A.LairdJ. E. (2010). Learning to use episodic memory. Cognitive Systems Research, 12(2), 1–10.
63.
GrafP.SchacterD. (1984). Implicit and explicit memory for new associations in normal and amnesic subjects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 501–518.
64.
GriffithsD.DickinsonA.ClaytonN. (1999). Episodic memory: what can animals remember about their past?Trends in Cognitive Sciences, 3, 74–80.
65.
HarnadS. (1990). The symbol grounding problem. Physica D, 42, 335–346.
66.
HawesN.SlomanA.WyattJ.ZillichM.JacobssonH.KruijffG.-J. M.. (2007). Towards an integrated robot with multiple cognitive functions. In HolteR. C.HoweA. (Eds.), Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI 2008) (pp. 1548–1553). Menlo Park, CA: AAAI Press.
67.
HawesN.WyattJ. (2010). Engineering intelligent information-processing systems with cast. Advanced Engineering Informatics, 24, 27–39.
68.
Hayes-RothB. (1985). A blackboard architecture for control. Artificial Intelligence, 26, 251–321.
69.
HaykinS. (1999). Neural networks (2nd ed.). New Jersey: Prentice-Hall.
70.
HebbD. (1949). The organization of behavior. New York: John Wiley and Sons, Inc.
71.
HoW. C.DautenhahnK.LimM. Y.VargasP. A.AylettR.EnzS. (2009). An initial memory model for virtual and robot companions supporting migration and long-term interaction. In RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 277–284). IEEE.
72.
HoW. C.DautenhahnK.NehanivC. (2008). Computational memory architectures for autobiographic agents interacting in a complex virtual environment: a working model. Connection Science, 20, 21–65.
73.
HoW. C.WatsonS. (2006). Autobiographic knowledge for believable virtual characters. In Intelligent Virtual Agents (IVA 2006), LNAI (pp. 383–394). Berlin: Springer.
74.
HusbandsP. (2009). Never mind the iguana, what about the tortoise? Models in adaptive behavior. Adaptive Behavior, 17, 320–324.
75.
IshikawaM. (1996). Structural learning with forgetting. Neural Networks, 9, 509–521.
76.
JaegerH. (2002). Short term memory in echo state networks (Tech. Rep. GMD 152). Fraunhofer Institute for Autonomous Intelligent Systems.
77.
JohnssonM.BalkeniusC. (2009). Associative self-organizing maps. In DouradoA.RosaA.C.MadaniK. (Eds.), Proceedings of the International Joint Conference on Computational Intelligence (pp. 363-370), Setubal: INISTICC Press.
78.
KadlecR.BromC. (2010). I can (almost) remember what you are doing: from actions to tasks. In LimM.HoW. C. (Eds.), Remembering Who We Are - Human Memory for Artificial Agents Symposium, AISB 2010 (pp. 27–30). AISB.
79.
KawamuraK.GordonS. (2006). From intelligent control to cognitive control. In Eleventh International Symposium on Robotics and Applications (ISORA 2006).
80.
KawamuraK.PetersR. A.BodenheimerR. E.SarkarN.ParkJ.CliftonC. A.. (2004). A parallel distributed cognitive control system for a humanoid robot. International Journal of Humanoid Robotics, 1, 65–93.
81.
KelleyT. D. (2003). Symbolic and sub-symbolic representations in computational models of human cognition: What can be learned from biology?Theory and Psychology, 13, 847–860.
82.
KierasD. E.MeyerD. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human–Computer Interaction, 12, 391–438.
83.
KierasD. E.MeyerD. E.MuellerS.SeymourT. (1999). Insights into working memory from the perspective of the EPIC architecture for modelling skilled perceptual-motor and cognitive human performance. In MiyakeA.ShahP., Models of working memory: Mechanisms of active maintenance and executive control (pp. 183–223). Cambridge: Cambridge University Press.
KrichmarJ. L. (2008). The neuromodulatory system: A framework for survival and adaptive behavior in a challenging world. Adaptive Behavior, 16, 385–399.
86.
KrichmarJ. L.EdelmanG. M. (2002). Machine psychology: Autonomous behavior, perceptual categorisation and conditioning in a brain-based device. Cerebral Cortex, 12, 818–830.
87.
KrichmarJ. L.NitzD. A.GallyJ. A.EdelmanG. M. (2005). Characterising functional hippocampal pathways in a brain-based device as it solves a spatial memory task. Proceedings of the National Academy of Sciences of the USA, 102, 2111–2116.
88.
KuipersB. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119, 191–233.
89.
KuipersB.ByunY.-T. (1991). A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Robotics and Autonomous Systems, 8, 47–63.
90.
LangR. (2007). Dynamic neural networks. Unpublished doctoral dissertation, University of Reading.
91.
LangleyP.LairdJ. E.RogersS. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10, 141–160.
92.
LeeM. H.MengQ.ChaoF. (2007). Developmental learning for autonomous robots. Robotics and Autonomous Systems, 55, 750–759.
93.
LovettM. C.RederL. M.LebiereC. (1999). Modeling working memory in a unified architecture. In MiyakeA.ShahP. (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 135–182). Cambridge: Cambridge University Press.
94.
LuciwM.WengJ. (2010). Top-down connections in self-organizing Hebbian networks: Topographic class grouping. IEEE Transactions on Autonomous Mental Development, 2, 248–261.
95.
LungarellaM.MettaG.PfeiferR.SandiniG. (2003). Developmental robotics: a survey. Connection Science, 15, 151–190.
96.
MaesP. (1989). How to do the right thing. Connection Science, 1, 291–232.
97.
MallotH. A.BastenK. (2008). Embodied spatial cognition: Biological and artificial systems. Image and Vision Computing, 27, 1658–1670.
98.
MayesA.RobertsN. (2001). Theories of episodic memory. Philosophical Transactions of the Royal Society of London B, 356, 1395–1408.
99.
McKinstryJ. L.SethA. K.EdelmanG. M.KrichmarJ. L. (2008). Embodied models of delayed neural responses: Spatiotemporal categorization and predictive motor control in brain based devices. Neural Networks, 21, 553–561.
100.
MillerG. (2003). The cognitive revolution: a historical perspective. Trends in Cognitive Sciences, 7, 141–144.
101.
MirzaN.NehanivC.DautenhahnK.te BoekhorstR. (2007). Grounded sensorimotor interaction histories in an information theoretic metric space for robot ontogeny. Adaptive Behavior, 15, 167–187.
102.
MorrisR. (2001). Episodic-like memory in animals: psychological criteria, neural mechanisms and the value of episodic-like tasks to investigate animal models of neurodegenerative disease. Philosophical Transactions of the Royal Society of London B, 356, 1453–1465.
MunakataY.McClellandJ. L. (2003). Connectionist models of development. Developmental Science, 6, 413–429.
106.
NealM.TimmisJ. (2003). Timidity: A useful emotional mechanism for robot control?Informatica, 4, 197–204.
107.
NewellA. (1990). Unified theories of cognition (2nd ed.). Cambridge, MA: Harvard University Press.
108.
NewellA.SimonH. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19, 113–126.
109.
NuxollA. (2007). Enhancing intelligent agents with episodic memory. Unpublished doctoral dissertation, University of Michigan.
110.
NuxollA.LairdJ. (2007). Extending cognitive architecture with episodic memory. In HolteR. C.HoweA. (Eds.), AAAI Conference on Artificial Intelligence (pp. 1560–1565). AAAI Press.
111.
NuxollA.TecuciD.HoW. C.WangN. (2010). Comparing forgetting algorithms for artificial episodic memory systems. In LimM.HoW. C. (Eds.), Remembering Who We Are - Human Memory for Artificial Agents Symposium, AISB 2010 (pp. 14–20), SSAISB.
112.
NyhusE.CurranT. (2010). Functional role of gamma and theta oscillations in episodic memory. Neuroscience and Biobehavioural Reviews, 34, 1023–1035.
113.
OberauerK. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 411–421.
114.
O’ReillyR. C.BraverT. S.CohenJ. D. (1999). A biologically based computational model of working memory. In MiyakeA.ShahP. (Eds.) Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 375–411), Cambridge: Cambridge University Press.
115.
O’ReillyR. C.NormanK. A. (2002). Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework. Trends in Cognitive Sciences, 6, 505–510.
116.
PackardM.KnowltonB. (2002). Learning and memory functions of the basal ganglia. Annual Review of Neuroscience, 25, 563–593.
117.
PessoaL. (2008). On the relationship between emotion and cognition. Nature Reviews: Neuroscience, 9, 148–158.
118.
PetersR.HambuchenK. E.KawamuraK.WilkesD. M. (2001). The sensory ego-sphere as a short-term memory for humanoids. In Second IEEE-RAS International Conference on Humanoid Robots (pp. 451–459).
119.
PoucetB.Lenck-SantiniP.-P.Paz-VillagranV.SaveE. (2003). Place cells, neocortex and spatial navigation: a short review. Journal of Physiology, 97, 537–546.
120.
PrescottT. J.Montes GonzalezF. M.GurneyK.HumphriesM. D.RedgraveP. (2006). A robot model of the basal ganglia: Behavior and intrinsic processing. Neural Networks, 19, 31–61.
121.
QuintonJ.-C.BuissonJ.-C. (2008). Interactivist sensorimotor learning: Computational implementation and parallel optimization. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008).
122.
RollsE.TrevesA. (1998). Neural networks and brain function. Oxford: Oxford University Press.
123.
RumelhartD. E.NormanD. A. (1973). Active semantic networks as a model of human memory. In Third International Joint Conference on Artificial Intelligence (pp. 450–457) San Francisco: Morgan Kaufmann.
124.
SchönerG.DoseM.EngelsC. (1995). Dynamics of behavior: Theory and applications for autonomous robot architectures. Robotics and Autonomous Systems, 16, 213–245.
125.
SchutteA. R.SpencerJ. P.SchönerG. (2003). Testing the dynamic field theory: Working memory for locations becomes more spatially precise over development. Child Development, 74, 1393–1417.
126.
SethA. K. (2009). Don’t throw the baby iguana out with the bathwater. Adaptive Behavior, 17, 338–342.
127.
ShanahanM. (2005). Consciousness, emotion, and imagination: A brain inspired architecture for cognitive robotics. In AISB ’05 Workshop: Next Generation Approaches to Machine Consciousness (pp. 26–35). SSAISB.
128.
ShanahanM. (2008). A spiking neuron model of cortical broadcast and competition. Consciousness and Cognition, 17, 288–303.
129.
SiegelmannH. (2008). Analog-symbolic memory that tracks via reconsolidation. Physica D: Nonlinear Phenomena, 237, 1207–1214.
SlocumA.DowneyD.BeerR. (2000). Further experiments in the evolution of minimally cognitive behaviour: From perceiving affordances to selective attention. In MeyerJ.-A.BerthozA.FloreanoD.RoitblatH.WilsonS. (Eds.), From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior (pp. 430–439). Cambridge, MA: MIT Press.
132.
SlomanA.ChappellJ. (2005). The altricial-precocial spectrum for robots. In 19th International Joint Conference on Artificial Intelligence IJCAI’05 (Vol. 1, pp. 1187). San Francisco: Morgan Kaufmann Publishers.
133.
SowaJ. F. (Ed.). (1991). Principles of semantic networks: explorations in the representation of knowledge. San Mateo, CA: Morgan Kaufmann.
134.
SowaJ. F. (1992). Semantic networks. In Encyclopedia of artificial intelligence, Volume 5. John Wiley and Sons, Inc.
135.
SquireL. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171–177.
136.
SunR. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17, 341–373.
137.
SunR. (2007). The importance of cognitive architectures: an analysis based on clarion. Journal of Experimental and Theoretical Artificial Intelligence, 19, 159–193.
138.
SunR.MerrillE.PetersonT. (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25, 203–244.
139.
SuttonR.BartoA. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
140.
TaylorJ.HartleyM.TaylorN.PanchevC.KasderidisS. (2009). A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning. Image and Vision Computing, 27, 1641–1657.
141.
TempleC. M.RichardsonP. (2004). Developmental amnesia: A new pattern of dissociation with intact episodic memory. Neuropsychologia, 42, 764–781.
142.
TulvingE. (2001). Episodic memory and common sense: how far apart?Philosophical Transactions of the Royal Society of London B, 356, 1505–1515.
143.
TulvingE. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25.
144.
Van der VeldeF.de KampsM. (2006). Neural blackboard architectures of combinatorial structures in cognition. The Behavioral and Brain Sciences, 29, 37–70.
145.
VargasP. A.FreitasA. A.LimM.EnzS.HoW. C.AylettR. (2010). Forgetting and generalisation in memory modelling for robot companions: a data mining approach. In LimM.HoW. C. (Eds.), Remembering Who We Are - Human Memory for Artificial Agents Symposium, AISB 2010 (pp. 42–45) SSAISB.
146.
VargasP. A.HoW. C.LimM.EnzS.AylettR. (2009). To forget or not to forget: Towards a roboethical memory control. In MichaelsonG.AylettR. (Eds.) Proceedings of the Symposium Killer Robots vs Friendly Fridges: The Social Understanding of Artificial Intelligence (pp. 18-23), SSAISB.
147.
Vargha-KhademF.GadianD.MishkinM. (2001). Dissociations in cognitive memory: The syndrome of developmental amnesia. In BaddeleyA.ConwayM.AggletonJ. (Eds.), Episodic memory: New directions in research. Oxford: Oxford University Press.
148.
Vargha-KhademF.GadianD.WatkinsK. E.ConnellyW.Van PaesschenW.MishkinM. (1997). Differential effects of early hippocampal pathology on episodic and semantic memory. Science, 227, 376–380.
149.
WebbB. (2009). Animals versus animats: Or why not model the real iguana?Adaptive Behavior, 17, 269–286.
150.
WengJ. (2007). On developmental mental architectures. Neurocomputing, 70, 2303–2323.
151.
WengJ.McClellandJ.PentlandA.SpornsO.StockmanI.SurM.. (2001). Autonomous mental development by robots and animals. Science, 291, 599–600.
152.
WixtedJ. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235–269.
153.
WolffM.LoukavenkoE.WillB.Dalrymple-AlfordJ. (2008). The extended hippocampal-diencephalic memory system: Enriched housing promotes recovery of the flexible use of spatial representations after anterior thalamic lesions. Hippocampus, 18, 996–1007.
154.
WolpertD.GhahramaniZ.FlanaganJ. (2001). Perspectives and problems in motor learning. Trends in Cognitive Sciences, 5, 487–494.
155.
WoodR.BaxterP.BelpaemeT. (2010). A developmental perspective on memory centered cognition. In JohanssonB.SahinE.BalkeniusC. (Eds.), Proceedings of the Tenth International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (Vol. 149, pp. 183–184). Lund: LUCS.
156.
WoodR.Di PaoloE. (2007). New models for old questions: Evolutionary robotics and the ‘A not B’ error. In Almeida e CostaF.RochaL. M.CostaE.HarveyI.CoutinhoA. (Eds.), Advances in Artificial Life: Proceedings of the 9th European Conference on Artificial life., LNCS (Vol. 4648, pp. 1141–1150). Berlin: Springer-Verlag.
157.
WyattJ. L.HawesN. (2008). Multiple workspaces as an architecture for cognition. In SamsonovitchA.V. (Ed) 2008 Fall Symposium on Biologically Inspired Cognitive Architectures, Technical Report (AAAI) FS-08-04 (pp. 201–206), Menlo Park, CA: AAAI Press.
158.
WyssR.KonigP.VerschureP. (2006). A model of the ventral visual system based on temporal stability and local memory. PLoS Biology, 4(5), e120, 1–8.
159.
YoungR. M.LewisR. L. (1999). The Soar cognitive architecture and human working memory, (pp. 224–256). Cambridge: Cambridge University Press.
160.
ZiemkeT. (2008). On the role of emotion in biological and robotic autonomy. BioSystems, 91, 401–408.
161.
ZiemkeT.ThiemeM. (2002). Neuromodulation of reactive sensorimotor mappings as a short-term memory mechanism in delayed response tasks. Adaptive Behavior, 10, 185–199.
