Modular and hierarchical brain organization to understand assimilation,accommodation and their relation to autism in reaching tasks: a developmental robotics hypothesis
Restricted accessResearch articleFirst published online October, 2014
Modular and hierarchical brain organization to understand assimilation,accommodation and their relation to autism in reaching tasks: a developmental robotics hypothesis
By assimilation children embody sensorimotor experiences into already built mental structures. Conversely, by accommodation these structures are changed according to the child’s new experience. Despite the intuitive power of these concepts to trace the course of sensorimotor development, they have gradually lost ground in psychology. This likely due to the lack of brain-related views capturing the dynamic mechanisms underlying them. Here we propose that brain modular and hierarchical organization is crucial to understanding assimilation/accommodation. We devise an experiment where a bio-inspired modular and hierarchical mixture-of-experts model guides a simulated robot to learn different reaching tasks by trial-and-error. The model gives a novel interpretation of assimilation/accommodation based on the functional organization of the experts allocated through learning. Assimilation occurs when the model adapts a copy of the expert trained for solving a task, to face another task requiring similar sensorimotor mappings. Experts storing similar sensorimotor mappings belong to the same functional module. Accommodation occurs when the model uses non-trained experts to face tasks requiring different sensorimotor mappings (generating a new functional group of experts). The model also provides a new theoretical framework to investigate assimilation/accommodation impairment, and proposes that such impairment might be related to autism spectrum disorder.
AflaloT. N.GrazianoM. S. A. (2011). Organization of the macaque extrastriate visual cortex re-examined using the principle of spatial continuity of function. Journal of Neurophysiology, 105, 305–320.
2.
AlexanderG. E.DeLongM. R.StrickP. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381.
3.
AmesC.Fletcher-WatsonS. (2010). A review of methods in the study of attention in autism. Developmental Review, 30, 52–73.
4.
BaldassarreG. (2002). A modular neural-network model of the basal ganglia’s role in learning and selecting motor behaviours. Journal of Cognitive Systems Research, 3, 5–13.
5.
BaldassareG. (2011). What are intrinsic motivations? A biological perspective. In CangelosiA.TrieschJ.FaselI.RohlfingK.NoriF.OudeyerP.-Y.Nagai& Y. (Eds.), Proceedings of the IEEE international conference on development and learning (ICDL 2011) (vol. 2, pp. 1–8). Piscataway, NJ: IEEE Press.
6.
BaldassarreG.CaligioreD.MannellaF. (2013). The hierarchical organisation of cortical and basal-ganglia systems: a computationally-informed review and integrated hypothesis. In BaldassarreG.MirolliM. (Eds.), Computational and robotic models of the hierarchical organisation of behaviour (pp. 237–270). Berlin, Germany: Springer-Verlag.
7.
BaldassarreG.MannellaF.FioreV. G.RedgraveP.GurneyK.MirolliM. (2012). Intrinsically motivated action-outcome learning and goal-based action recall: A system-level bio-constrained computational model. Neural Networks, 41, 168–187.
8.
BaldassareG.MirolliM. (Eds.). (2013). Intrinsically motivated learning in natural and artificial systems. Berlin, Germany: Springer.
9.
BartoA. G.SinghS.ChentanezN. (2004). Intrinsically motivated learning of hierarchical collections of skills. In TrieschJ.JebaraT. (Eds.), Proceedings of the international conference on developmental learning (ICDL 2004) (pp. 112–119). Piscataway, NJ: IEEE Press.
10.
BerthierN. E. (1996). Learning to reach: A mathematical model. Developmental Psychology, 32, 811–823.
11.
BerthierN. E.CarricoR. L. (2010). Visual information and object size in infant reaching. Infant Behavior and Development, 33, 555–566.
12.
BerthierN. E.RosensteinM. T.BartoA. G. (2005). Approximate optimal control as a model for motor learning. Psychological Review, 112, 329–346.
13.
BlaesiS.WilsonM. (2010). The mirror reflects both ways: Action influences perception of others. Brain and Cognition, 72, 306–309.
14.
BodenM. A. (1994). Piaget. London, UK: Fontana Press.
15.
BoersmaM.KemnerC.Reus deM. A.CollinG.SnijdersT. M.HofmanD.Heuvel van denM. P. (2013). Disrupted functional brain networks in autistic toddlers. Brain Connectivity, 3, 41–49.
16.
BotvinickM. M.NivY.BartoA. (2008). Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective. Cognition, 113, 262–280.
17.
BurackJ. A.CharmanT.YirmiyaN.ZelazoP. R. (2001). Development and autism: Messages from developmental psychopathology. In BurackJ. A.CharmanT.YirmiyaN.ZelazoP. R. (Eds.), The Development of Autism: Perspectives From Theory and Research (pp. 3–16). Mahwah, NJ: Lawrence Erlbaum Associates.
18.
CaeyenberghsK.LeemansA.HeitgerM. H.LeunissenI.DhollanderT.SunaertS.SwinnenS. P. (2012). Graph analysis of functional brain networks for cognitive control of action in traumatic brain injury. Brain: A Journal of Neurology, 135, 1293–307.
19.
CalabrettaR.Di FerdinandoA.WagnerG. P.ParisiD. (2003). What does it take to evolve behaviorally complex organisms?Biosystems, 69, 245–262.
20.
CaligioreD.BorghiA. M.ParisiD.BaldassarreG. (2010). TRoPICALS: A computational embodied neuroscience model of compatibility effects. Psychological Review, 117, 1188–1228.
21.
CaligioreD.FischerM. H. (2013). Vision, action and language unified through embodiment. Psychological Research, 77, 1–6.
22.
CaligioreD.GuglielmelliE.ParisiD.BaldassarreG. (2010). A reinforcement learning model of reaching integrating kinematic and dynamic control in a simulated arm robot. In Proceedings of the IEEE international conference on development and learning (ICDL 2010) (pp. 211–218). Piscataway, NJ: IEEE Press.
23.
CaligioreD.MirolliM.ParisiD.BaldassarreG. (2010). A bioinspired hierarchical reinforcement learning architecture for modeling learning of multiple skills with continuous state and actions. In JohanssonB.ŞahinE.BalkeniusC. (Eds.), Proceedings of the 10th international conference on epigenetic robotics (EpiRob-2010) (pp. 27–34). Lund, Sweden: Lund University Press.
24.
CaligioreD.ParisiD.BaldassarreG. (2014). Integrating reinforcement learning, equilibrium points, and minimum variance to understand the development of reaching: A computational model. Psychological Review, 121, 389–421.
25.
CaligioreD.PezzuloG.MiallR. C.BaldassarreG. (2013). The contribution of brain sub-cortical loops in the expression and acquisition of action understanding abilities. Neuroscience and Biobehavioral Reviews, 37, 2504–2515.
CarricoR. L.BerthierN. E. (2008). Vision and precision reaching in 15-month-old infants. Infant Behavior and Development, 31, 62–70.
28.
Castro da SilvaB.BaldassarreG.KonidarisG.BartoA. (2014). Learning parameterized motor skills on a humanoid robot. In XiN.HamelW. R.TanJ.KroviV. N.BussM.ElhajjI.FuL.-C. (Eds.), Proceedings of the international conference on robotics and automation (ICRA 2014) (pp. 1–6). Piscataway, NJ: IEEE (Hong Kong, China, May 31–June 7 2014).
29.
Castro da SilvaB.KonidarisG.BartoA. (2012). Learning parameterized skills. In LangfordJ.PineauJ. (Eds.) Proceedings of the 29th international conference on machine learning (ICML 2012) (pp. 1679–1686). New York, NY: Omnipress.
30.
CatarinoA.AndradeA.ChurchesO.WagnerA. P.Baron-CohenS.RingH. (2013). Task-related functional connectivity in autism spectrum conditions: an EEG study using wavelet transform coherence. Molecular Autism, 4, 1–14.
31.
CattaneoL.Fabbri-DestroM.S.B.PieracciniC.MontiA.CossuG.RizzolattiG. (2007). Impairment of actions chains in autism and its possible role in intention understanding. Proceedings of the National Academy of Sciences of the United States of America, 104, 17825–17830.
32.
ChenZ. J.HeY.Rosa-NetoP.GermannJ.EvansA. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18, 2374–2381.
33.
ChersiF.MirolliM.PezzuloG.BaldassareG. (2013). A spiking neuron model of the cortico-basal ganglia circuits for goal-directed and habitual action learning. Neural Networks, 41, 212–224.
34.
CiancioA. L.ZolloL.BaldassareG.CaligioreD.GuglielmelliE. (2013). The role of learning and kinematic features in dexterous manipulation: a comparative study with two robotic hands. International Journal of Advanced Robotic Systems, 10(340), 1–21.
35.
CiancioA. L.ZolloL.GuglielmelliE.CaligioreD.BaldassarreG. (2011). Hierarchical reinforcement learning and central pattern generators for modeling the development of rhythmic manipulation skills. In Proceedings of the 1st joint international conference on development and learning and epigenetic robotics (ICDL-EpiRob-2011). (pp. 1–8). Piscataway, NJ: IEEE Press.
36.
ClearfieldM. W. (2011). Learning to walk changes infants’ social interactions. Infant Behavior and Development, 34, 15–25.
37.
CliftonR. K.MuirD. W.AshmeadD. H.ClarksonM. G. (1993). Is visually guided reaching in early infancy a myth?Child Development, 64, 1099–1110.
38.
DoyaK. (2000). Reinforcement learning in continuous time and space. Neural Computation, 12, 219–245.
39.
DrescherG. L. (1991). Made-up minds: A constructivist approach to artificial intelligence. Cambridge, MA: MIT Press.
40.
EdelmanG. M. (1987). Neural Darwinism: the theory of neuronal group selection. New York, NY: Basic Books.
41.
ElmanJ. L.BatesE.JohnsonM. H.Karmiloff-SmithA.ParisiD.PlunkettK. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press.
42.
EskenaziT.GrosjeanM.HumphreysG. W.KnoblichG. (2009). The role of motor simulation in action perception: A neuropsychological case study. Psychological Research, 73, 477–485.
43.
FahlmanS. E. (1988). An empirical study of learning speed in back-propagation networks. Pittsburgh, PA: Carnegie Mellon University.
44.
FahlmanS. E.LebiereC. (1990). The cascade-correlation learning architecture. In Advances in neural information processing systems 2 (pp. 524–532). San Francisco, CA: Morgan Kaufmann Publishers Inc.
45.
FernándezF.VelosoM. (2006). Probabilistic policy reuse in a reinforcement learning agent. In Proceedings of the 5th international joint conference on autonomous agents and multiagent systems (AAMAS06) (pp. 720–727). New York, NY: ACM Press.
46.
ForssellD. (2004). Descriptive versus generative scientific theories. In ForssellD. (Ed.), Perceptual control theory: A book of readings (pp. 39–44). Hayward, CA: Living Control Systems Publishing.
47.
FusterJ. M. (2001). The prefrontal cortex–an update: time is of the essence. Neuron, 30, 319–333.
48.
GernsbacherM. A.SauerE. A.GeyeH. M.SchweigertE. K.HillG. H. (2008). Infant and toddler oral- and manual-motor skills predict later speech fluency in autism. Journal of Child Psychology and Psychiatry, 49, 43–50.
49.
GittinsJ. C.JonesD. M. (1979). A dynamic allocation index for the discounted multiarmed bandit problem. Biometrika, 66, 561–565.
50.
GowenE.HamiltonA. (2012). Motor abilities in autism: A review using a computational context. Journal of Autism and Developmental Disorders, 43, 323–344.
51.
GraybielA. M. (1998). The basal ganglia and chunking of action repertoires. Neurobiology of Learning and Memory, 70, 119–136.
52.
GrazianoM. S.TaylorC. S.MooreT. (2002). Complex movements evoked by microstimulation of precentral cortex. Neuron, 34, 841–851.
53.
GrossbergS.SeidmanD. (2006). Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates. Psychological Review, 113, 483–525.
54.
GurneyK.PrescottT. J.RedgraveP. (2001a). A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biological Cybernetics, 84, 401–410.
55.
GurneyK.PrescottT. J.RedgraveP. (2001b). A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour. Biological Cybernetics, 84, 411–423.
56.
HamiltonA. F.GraftonS. (2007). The motor hierarchy: from kinematics to goals and intentions. In RosettiY.KawatoM.HaggardP. (Eds.), Attention performance (pp. 381–408). Oxford, UK: Oxford University Press.
57.
HarunoM.WolpertD. M.KawatoM. (2001). MOSAIC model for sensorimotor learning and control. Neural Computation, 13, 2201–2220.
58.
HeY.EvansA. (2010). Graph theoretical modeling of brain connectivity. Current Opinion in Neurology, 23, 341–350.
59.
HerbortO.OgnibeneD.ButzM. V.BaldassarreG. (2007). Learning to select targets within targets in reaching tasks. In DemirisY.ScassellatiB.MareschalD. (Eds.), Proceedings of the IEEE international conference on development and learning (ICDL 2007) (pp. 7–12). Piscataway, NJ: IEEE Press.
60.
HoukJ. C.AdamsJ. L.BartoA. G. (1995). A model of how the basal ganglia generate and use neural signals that predict reinforcement. In HoukJ. C.DavidsJ. L.BeiserD. G. (Eds.), Models of information processing in the basal ganglia (pp. 249–270). Cambridge, MA: MIT Press.
61.
HoukJ. C.BastianenC.FanslerD.FishbachA.FraserD.ReberP. J.SimoL. S. (2007). Action selection and refinement in subcortical loops through basal ganglia and cerebellum. Philosophical Transactions of the Royal Society, B: Biological Sciences, 362, 1573–1583.
62.
HoukJ. C.DavisJ.BeiserD. (Eds.). (1995). Models of information processing in the basal ganglia. Cambridge, MA: MIT Press.
63.
JacobsR. A.JordanM. I.NowlanS. J.HintonG. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.
64.
JordanM.JacobsR. (1994). Hierarchical mixtures of experts and the em algorithm. Neural Computation, 6, 181–214.
65.
KlinA.JonesW.SchultzR.VolkmarF.CohenD. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59, 809–816.
66.
KoberJ.PetersJ. (2009). Policy search for motor primitives in robotics. Advances in neural information processing systems, 21, 849–856.
MannellaF.MirolliM.BaldassarreG. (2010). The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat. In ToshC.RuxtonG. (Eds.), Modelling perception with artificial neural networks (pp. 93–113). Cambridge, UK: Cambridge University Press.
69.
MareschalD. (2003). Computational models of cognitive development. In NadelL. (Ed.), Encyclopaedia of Cognitive Science (pp. 533–539). London: Macmillan.
70.
McClellandJ. L. (1995). A connectionist approach to knowledge and development. In SimonT. J.HalfordG. S. (Eds.), Developing Cognitive Competence: New Approaches to Process Modeling (pp. 278–304). Hillsdale, NJ: Erlbaum.
71.
MeierJ. D.AflaloT. N.KastnerS.GrazianoM. S. (2008). Complex organization of human primary motor cortex: a high-resolution fMRI study. Journal of Neurophysiology, 100, 1800–1812.
72.
MeunierD.LambiotteR.BullmoreE. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4(200), 1–11.
73.
MiddletonF. A.StrickP. L. (1996). The temporal lobe is a target of output from the basal ganglia. Proceedings of the National Academy of Sciences of the United States of America, 93, 8683–8687.
74.
MiddletonF. A.StrickP. L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research Reviews, 31, 236–250.
75.
MorganS. (1986). Autism and Piaget’s theory: are the two compatible?Journal of Autism and Developmental Disorders, 16, 441–457.
76.
NishimotoR.TaniJ. (2009). Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study. Psychological Research, 73, 545–558.
77.
OudeyerP.-Y.KaplanF.HafnerV. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11, 265–286.
78.
ParisiD.SchlesingerM. (2002). Artificial life and Piaget. Cognitive Development, 17, 1301–1321.
79.
PenfieldW.BoldreyE. (1937). Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain: A Journal of Neurology, 60, 389–443.
80.
PiagetJ. (1951). Play, dreams and imitation in childhood. London, UK: Routledge.
81.
PiagetJ. (1953). The origins of intelligence in children. London, UK: Routledge.
82.
PougetA.LathamP. E. (2003). Population codes. In ArbibM. A. (Ed.), The handbook of brain theory and neural networks. Cambridge, MA: MIT Press.
83.
RasheedW. A. J.AliH. A. (2009). Generalization aspect of neural networks on upgrading assimilation structure into accommodating scheme. Journal of Computer Science, 5, 177–183.
84.
RingM.SchaulT.SchmidhuberJ. (2011). The two-dimensional organization of behavior. In CangelosiA.TrieschJ.FaselI.RohlfingK.NoriF.OudeyerP.-Y.Nagai… Y. (Eds.), Proceedings of the IEEE international conference on development and learning (ICDL 2011) (vol. 2, pp. 1–8). Piscataway, NJ: IEEE Press.
85.
RizzolattiG.CamardaR.FogassiL.GentilucciM.LuppinoG.MatelliM. (1988). Functional organization of inferior area 6 in the macaque monkey. II. Area F5 and the control of distal movements. Experimental Brain Research, 71, 491–507.
86.
RosenbaumD. A.LoukopoulosL. D.MeulenbroekR. G. M.VaughanJ.EngelbrechtS. E. (1995). Planning reaches by evaluating stored postures. Psychological Review, 102, 28–67.
87.
RosenthalJ.MassieH. N.WulffK. (1980). A comparison of cognitive development in normal and psychotic children in the first two years of life from home movies. Journal of Autism and Developmental Disorders, 10, 433–443.
88.
RumelhartD. E.HintonG. E.WilliamsR. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
89.
SandiniG.MettaG.VernonD. (2007). The iCub cognitive humanoid robot: an open-system research platform for enactive cognition. In LungarellaM.IidaF.BongardJ.PfeiferR. (Eds.), 50 Years of artificial intelligence. Essays dedicated to the 50th anniversary of artificial intelligence. Berlin, Germany: Springer-Verlag.
90.
SarlegnaF.BlouinJ.BrescianiJ. P.BourdinC.VercherJ. L.GauthierG. M. (2003). Target and hand position information in the online control of goal-directed arm movements. Experimental Brain Research, 151, 524–535.
91.
SchaalS.PetersJ.NakanishiJ.IjspeertA. (2005). Learning movement primitives. Robotics Research, 15, 561–572.
92.
SchembriM.MirolliM.BaldassarreG. (2007a). Evolution and learning in an intrinsically motivated reinforcement learning robot. In Almeida e CostaF.RochaL. M.CostaE.HarveyI.CoutinhoA. (Eds.), Proceedings of the 9th european conference on artificial life (ECAL 2007) (pp. 294–333). Berlin, Germany: Springer-Verlag.
93.
SchembriM.MirolliM.BaldassarreG. (2007b). Evolving childhood’s length and learning parameters in an intrinsically motivated reinforcement learning robot. In BerthouzeL.PrinceC. G.LittmanM.KozimaH.BalkeniusC. (Eds.), Proceedings of the 7th international conference on epigenetic robotics (EpiRob-2007) (pp. 141–148). Lund, Sweden: Lund University Press.
94.
SchembriM.MirolliM.BaldassarreG. (2007c). Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot. In DemirisY.ScassellatiB.MareschalD. (Eds.), Proceedings of the IEEE international conference on development and learning (ICDL 2007) (pp. 282–287). Piscataway, NJ: IEEE Press.
95.
SchlesingerM.CangelosiA. (2013). An introduction to developmental robotics. Cambridge, MA: MIT Press.
96.
SchultzR. T.GauthierI.KlinA.FulbrightR. K.AndersonA. W.VolkmarF.GoreJ. C. (2000). Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Archives of General Psychiatry, 57, 331–340.
97.
SchultzW.DayanP.MontagueP. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.
98.
SchwarzA. J.GozziA.BifoneA. (2009). Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI. NeuroImage, 47, 302–311.
99.
ShultzT. R. (2003). Computational developmental psychology. Cambridge, MA: MIT Press.
100.
ShultzT. R.SchmidtW. C.BuckinghamD.MareschalD. (1995). Developing cognitive competence: New approaches to process modeling. In SimonT.HalfordG. (Eds.), (pp. 347–362). Hillsdale, NJ: LEA Publishing.
101.
SinghS.LewisR.BartoA.SorgJ. (2010). Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development, 2, 70–82.
102.
StrickP. L.DumR. P.FiezJ. A. (2009). Cerebellum and nonmotor function. Annual Review of Neuroscience, 32, 413–434.
103.
SugimotoN.HarunoK.DoyaMKawatoM. (2012). MOSAIC for multiple-reward environments. Neural Computation, 24(3), 577–606.
104.
SuttonR. S.BartoA. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
105.
TaffoniF.FormicaD.SchiavoneG.ScorciaM.TomassettiA.Polizzi di SorrentinoE.GuglielmelliE. (2013). The “mechatronic board”: A tool to study intrinsic motivations in humans, monkeys, and humanoid robots. In BaldassarreG.MirolliM. (Eds.), Intrinsically motivated learning in natural and artificial systems (pp. 411–432). Berlin, Germany: Springer-Verlag.
106.
TaffoniF.VespignaniM.FormicaD.CavalloG.Polizzi di SorrentinoE.SabbatiniG.GuglielmelliE. (2012). A mechatronic platform for behavioral analysis of non human primates. Journal of Integrative Neuroscience, 11, 87–101.
107.
TaniJ.NolfiS. (1999). Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor. Neural Networks, 12, 1131–1141.
108.
TaylorM.StoneP. (2009). Transfer learning for reinforcement learning domains: A survey. The Journal of Machine Learning Research, 10, 1633–1685.
109.
ThelenE.CorbettaD.KammK.SpencerJ. P.SchneiderK.ZernickeR. F. (1993). The transition to reaching: mapping intention and intrinsic dynamics. Child Development, 64, 1058–1098.
110.
ThelenE.SmithL. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.
111.
ThillS.CaligioreD.BorghiA. M.ZiemkeT.BaldassarreG. (2013). Theories and computational models of affordance and mirror systems: An integrative review. Neuroscience and Biobehavioral Reviews, 37, 491–521.
112.
ThomasM. S. C.KnowlandV. C. P.Karmiloff-SmithA. (2011). Mechanisms of developmental regression in autism and the broader phenotype: a neural network modeling approach. Psychological Review, 118, 637–654.
TikhanoffV.CangelosiA.FitzpatrickP.MettaG.NataleL.NoriF. (2008). The iCub humanoid robot simulator. In MadhavanR.MessinaE. R. (Eds.), Proceedings of the IEEE workshop on performance metrics for intelligent systems. (pp. 57–61). Piscataway, NJ: IEEE Press.
115.
TommasinoP.CaligioreD.MirolliM.BaldassarreG. (2012). Reinforcement learning algorithms that assimilate and accommodate skills with multiple tasks. In Proceedings of the IEEE international conference on development and learning and epigenetic robotics (ICDL-EpiRob-2012) (pp. 1–8). Piscataway, NJ: IEEE Press.
116.
TommasinoP.CaligioreD.MirolliM.BaldassarreG. (submitted). Transfer expert reinforcement learning (TERL): A reinforcement learning architecture that transfers knowledge between skills when solving multiple tasks.
117.
van GeertP. (1998). A dynamic systems model of basic developmental mechanisms: Piaget, Vygotsky, and beyond. Psychological Review, 105, 634–677.
118.
von HofstenC. (2004). An action perspective on motor development. Trends in Cognitive Science, 8, 266–272.
119.
von HofstenC. (2007). Action in development. Developmental Science, 10, 54–60.
120.
WilliamsJ.ThomasP. R.MaruffP.WilsonP. H. (2008). The link between motor impairment level and motor imagery ability in children with developmental coordination disorder. Human Movement Science, 27, 270–285.
121.
YinH. H.KnowltonB. J. (2006). The role of the basal ganglia in habit formation. Nature Reviews Neuroscience, 7, 464–476.
