Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

3 Issues per year

Open Access
See all formats and pricing
In This Section

Conceptual Commitments of the LIDA Model of Cognition

Stan Franklin
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Steve Strain
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Ryan McCall
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Bernard Baars
  • Corresponding author
  • 6615 Fisher Ave. Falls Church, VA 22046, USA
  • Email:
Published Online: 2014-04-25 | DOI: https://doi.org/10.2478/jagi-2013-0002


Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses “conceptual commitments” and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.

Keywords : asynchrony; cognitive cycle; cognitive model; commitments; consciousness; embodied; Global Workspace Theory; learning; LIDA; memory; motivation; non-linear dynamics; theta-gamma coupling


  • Anderson, J. R. (2007). Using brain imaging to guide the development of a cognitive architecture. In W. D. Gray (Ed.), Integrated models of cognitive systems (pp. 49-62). New York: Oxford University Press.

  • Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An Integrated Theory of the Mind Psychological Review, 111( 4), 1036-1060.

  • Armstrong, I. T., & Mewhort, D. (1995). Repetition deficit in rapid-serial-visual-presentation displays: Encoding failure or retrieval failure? Journal of Experimental Psychology:Human Perception and Performance, 21(5), 1044.

  • Augustenborg, C. C. (2010). The Endogenous Feedback Network: A new approach to the comprehensive study of consciousness. Consciousness and Cognition. doi: 10.1016/j.concog.2010.03.007 [Crossref] [PubMed]

  • Baars, B., Franklin, S., & Ramsøy, T. (2013). Global workspace dynamics: Cortical “binding and propagation” enables conscious contents. Frontiers in Consciousness Research, 4, 200. doi: 10.3389/fpsyg.2013.00200 [Crossref]

  • Baars, Bernard J. (1988). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.

  • Baars, Bernard J. (2002). The conscious access hypothesis: origins and recent evidence. Trends inCognitive Science, 6, 47-52.

  • Baars, Bernard J., & Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Science, 7, 166-172.

  • Bach, J. (2008). Seven principles of synthetic intelligence. In P. Wang, G. Goertzel & S. Franklin (Eds.), Artif icial General Intelligence 2008: Proceedings of the First AGI Conference (pp. 63-74). Amsterdam: IOS Press.

  • Bach, J., Goertzel, B., & Iklé, M. (Eds.). (2012). Artificial General Intelligence: 5th InternationalConference. Oxford, UK: Springer.

  • Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower (Ed.), The Psychologyof Learning and Motivation. New York: Academic Press.

  • Barham, J. (1996). A dynamical model of the meaning of information. BioSystems, 38, 235-241. [Crossref] [PubMed]

  • Barsalou, L. W. (1999a). Perceptual symbol systems. Behavioral and Brain Sciences, 22(04), 577-609.

  • Barsalou, L. W. (1999b). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577-609.

  • Barsalou, L. W. (2008). Grounded cognition. Annu. Rev. Psychol., 59, 617-645. [Crossref]

  • Bjork, E. L., & Bjork, R. A. (1988). On the adaptive aspects of retrieval failure in autobiographical memory.

  • Block, N. (2007). Consciousness, Accessibility and the Mesh between Psychology and Neuroscience. Behavioral and Brain Sciences, 30, 481-548.

  • Boltea, A., & Goschke, T. (2008). Intuition in the context of object perception: Intuitive gestalt judgments rest on the unconscious activation of semantic representations. Cognition,108(3), 608-616. doi: 10.1016/j.cognition.2008.05.001 [Crossref]

  • Born, J., & Wagner, U. (2006). Memory Consolidat ion during Sleep: Role of Cortisol Feedback. Annals of the New York Academy of Sciences, 1032, 198 - 201. doi: 10.1196/annals.1314.020 [Crossref]

  • Brainerd, C. J., & Dempster, F. N. (1995). Interference and inhibition in cognition: Academic Press.

  • Bullock, T. H. (1993). Goals and Strategies in Brain Research: The Place of Comparative Neurology. In T. H. Bullock (Ed.), How Do Brains Work?: Papers of a ComparativeNeurophysiologist How Do Brains Work?: Papers of a Comparative Neurophysiologist. Boston: Birkhauser.

  • Campanella, J., & Rovee‐ Collier, C. (2005). Latent learning and deferred imitation at 3 months. Infancy, 7(3), 243-262. [Crossref]

  • Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Knight, R. T. (2006). High gamma power is phase-locked to theta oscillat ions in human neocortex. Science, 313(5793), 1626-1628.

  • Canolty, R. T., & Knight, R. T. (2010). The functional role of cross -frequency coupling. Trendsin Cognitive Sciences, 14(11), 506-515. doi: 10.1016/j.tics.2010.09.001 [Crossref]

  • Cansino, S. (2009). Episodic memory decay along the adult lifespan: A review of behavioral and neurophysiological evidence. International Journal of Psychophysiology, 71(1), 64-69. [Crossref]

  • Chamizo, V. D., & Mackintosh, N. (1989). Latent learning and latent inhibit ion in maze discriminations. The Quarterly Journal of Experimental Psychology, 41(1), 21-31.

  • Chandler, C. C. (1991). How memory for an event is influenced by related events: Interference in modified recognition tests. Journal of Experimental Psychology: Learning, Memory, andCognition, 17, 115-125.

  • Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: news from the front. Trends in Cognitive Sciences, 2(10), 406-416.

  • Connor, D., & Shanahan, M. (2010). A computational model of a global neuronal workspace with stochastic connections. Neural Netw, 23(10), 1139-1154. doi: S0893-6080(10)00143-7 [pii]10.1016/j.neunet.2010.07.005 [PubMed]

  • Conway, Martin A. (2001). Sensory-perceptual episodic memory and its context: autobiographical memory. Philos. Trans. R. Soc. Lond B., 356, 1375-1384.

  • Conway, M. A. (2002). Sensory-perceptual episodic memory and its context: Autobiographical memory. In A. Baddeley, M. Conway & J. Aggleton (Eds.), Episodic Memory. Oxford: Oxford University Press.

  • Craik, F. I., Routh, D. a., Broadbent, D., & Craik, F. (1983). On the Transfer of Information from Temporary to Permanent Memory [and Discussion]. Philosophical Transactions of theRoyal Society of London. B, Biological Sciences, 302(1110), 341-359.

  • Cutsuridis, V., Hussain, A., & Taylor, J. G. (2011). Perception-Action Cycle: Models,Architectures, and Hardware (Vol. 1): Springer.

  • Daum, M. M., Sommerville, J. A., & Prinz, W. (2009). Disentangling embodied and symbolic modes of social understanding. European Journal of Social Psychology, 39(7), 1214-1216. [Crossref]

  • de Garis, H., & Goertzel, B. (2009a). Report on the 2nd International Conference on Artificial General Intelligence (AGI-09). AI Magazine, 115~116.

  • de Garis, H., & Goertzel, B. (2009b). Report on the First Conference on Artificial General Intelligence (AGI-08). AI Magazine, 30, 121-123.

  • de Vega, M., Glenberg, A., & Graesser, A. (Eds.). (2008). Symbols and Embodiment: Debates onmeaning and cognition. Oxford: Oxford University Press.

  • Dennett, D. (2005). Sweet Dreams : Philosophical Obstacles to a Science of Consciousness. Cambridge, MA: MIT Press.

  • Dennis, J. L., & Schutter, G. (2004). Extending the global workspace theory to emotion: Phenomenality without access. Consciousness and Cognition, 13(3), 539-549.

  • Designing Intelligent Robots: Reintegrating AI II. (2012). AAAI Spring Symposium 2013 Retrieved December 21, 2012, from http://people.csail.mit.edu/gdk/dir2/index.html

  • Dijkstra, T. M. H., Schöner, G., & Gielen, C. C. A. M. (1994). Temporal stability of the actionperception cycle for postural control in a moving visual environment. Experimental BrainResearch, 97(3), 477-486.

  • Doesburg, S., Green, J., McDonald, J., & Ward, L. (2009). Rhythms of Consciousness: Binocular Rivalry Reveals Large-Scale Oscillatory Network Dynamics Mediating Visual Perception. PLoS ONE, 4(7), e6142. doi: 10.1371/journal.pone.0006142 [Crossref]

  • Drescher, Gary L. (1991). Made-Up Minds: A Constructivist Approach to Artificial Intelligence. Cambridge, MA: MIT Press.

  • Edelman, Gerald M., & Tononi, G. (2000). A Universe of Consciousness. New York: Basic Books.

  • Eimer, M., & Schlagecken, F. (2003). Response facilitation and inhibit ion in subliminal priming. Biological Psychology, 64, 7-26. [Crossref]

  • Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review,102(2), 211-245. [PubMed] [Crossref]

  • Faghihi, U., & Franklin, S. (2012). The LIDA Model as a Foundational Architecture for AGI. In P. Wang & B. Goertzel (Eds.), Theoretical Foundations of Artificial General Intelligence (pp. 105-123). Paris: Atlantis Press.

  • Faghihi, U., McCall, R., & Franklin, S. (2012). A Computational Model of Attentional Learning in a Cognitive Agent. Biologically Inspired Cognitive Architectures, 2, 25-36.

  • Franklin, S. (1997). Autonomous Agents as Embodied AI. Cybernetics and Systems, 28, 499-520.

  • Franklin, S., & Baars, B. J. (2010). Spontaneous remembering is the norm: What integrative models tell us about human consciousness and memory. In John H. Mace (Ed.), The Act ofRemembering: Toward an understanding of how we recall the past. Oxford: Blackwell.

  • Franklin, S., Baars, B. J., Ramamurthy, U., & Ventura, M. (2005). The Role of Consciousness in Memory. Brains, Minds and Media, 1, 1-38.

  • Franklin, S., & Ramamurthy, U. (2006). Motivations, Values and Emotions: Three sides of the same coin Proceedings of the Sixth International Workshop on Epigenetic Robotics (Vol. 128, pp. 41-48). Paris, France: Lund University Cognitive Studies.

  • Franklin, S., Strain, S., Snaider, J., McCall, R., & Faghihi, U. (2012). Global Workspace Theory, its LIDA model and the underlying neuroscience. Biologically Inspired CognitiveArchitectures, 1, 32-43. doi: 10.1016/j.bica.2012.04.001 [Crossref]

  • Franks, N. R., Hooper, J. W., Dornhaus, A., Aukett, P. J., Hayward, A. L., & Berghoff, S. M. (2007). Reconnaissance and latent learning in ants. Proceedings of the Royal Society B:Biological Sciences, 274(1617), 1505-1509.

  • Freeman, W. J. (2002). The limbic action-perception cycle controlling goal-directed animal behavior. Neural Networks, 3, 2249-2254.

  • Freeman, W. J. (2003). A neurobiological theory of meaning in perception. Part 1. Information and meaning in nonconvergent and nonlocal brain dynamics. International Journal ofBifurcation and Chaos, 13, 2493-2511.

  • Fuster, J. (2006). The cognit: a network model of cortical representation. International Journal ofPsychophysiology, 60, 125-132.

  • Fuster, J. M. (2002). Physiology of executive functions: The perception-action cycle.

  • Fuster, J. M. (2004). Upper processing stages of the perception-action cycle. Trends in CognitiveSciences, 8(4), 143-145. [PubMed]

  • Fuster, J. M., & Bressler, S. L. (2012). Cognit activation: a mechanism enabling temporal integration in working memory. Trends in Cognitive Sciences, 16(4), 207-218.

  • Gaillard, R., Dehaene, S., Adam, C., Clémenceau, S., Hasboun, D., et al. (2009). Converging intracranial markers of conscious access. PLoS Biology, 7(3), e1000061. doi: 10.1371/journal.pbio.1000061 [Crossref]

  • Glenberg, A. M. (1997). What memory is for. Behavioral and Brain Sciences, 20(01), 1-19.

  • Glenberg, A. M., & Robertson, D. A. (2000). Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning. Journal of Memory and Language,43(3), 379-401.

  • Goertzel, B., & Pennachin, C. (2007). Artificial General Intelligence. Berlin: Springer.

  • Goertzel, B., & Wang, P. (Eds.). (2007). Advances in artificial general intelligence : concepts, architectures and algorithms. Amsterdam; Washington, DC: IOS Press.

  • Graves, Laurel A., Heller, Elizabeth A., Pack, Allan I., & Abel, T. (2003). Sleep Deprivation Selectively Impairs Memory Consolidat ion for Contextual Fear Conditioning. Learning &Memory, 10, 168-176.

  • Gunzelmann, G., Gluck, K. A., Van Dongen, H. P. A., O’Connor, R. M., & Dinges, D. F. (2005). A Neurobehaviorally Inspired ACT-R Model of Sleep Deprivation: Decreased Performance in Psychomotor Vigilance. In B. G. Bara, L. Barsalou & M. Bucciarelli (Eds.), Proceedings of the Twenty-Seventh Annual Meeting of the Cognitive Science Society (pp. 857-862). Mahwah, NJ: Lawrence Erlbaum Associates.

  • Hofstadter, D. R., & Mitchell, M. (1995). The Copycat Project: A model of mental fluidity and analogy-making. In K. J. Holyoak & J. Barnden (Eds.), Advances in connectionist andneural computation theory, Vol. 2: logical connections (pp. 205-267). Norwood N.J.: Ablex.

  • Izhikevich, E. M., & Edelman, G. M. ( 2008). Large-Scale Model of Mammalian Thalamocortical Systems. PNAS, 105, 3593-3598. [Crossref]

  • Jimenez, L. (2003). Attention and implicit learning: John Benjamins Publishing Company.

  • Johnston, V. S. (1999). Why We Feel: The Science of Human Emotions. Reading, MA: Perseus Books.

  • Kaelbling, L. P. (1994). Associative Reinforcement Learning: A Generate and Test Algorithm. Machine Learning, 15(3), 299-319. [Crossref]

  • Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive Architectures: Research Issues and Challenges. Cognitive Systems Research, 10(2), 141-160. doi: 10.1016/j.cogsys.2006.07.004 [Crossref]

  • Lewin, K. (1951). Field theory in sociall science: selected theoretical papers. New York: Harper & Row.

  • Longo, M. R. (2009). What's embodied and how can we tell? European Journal of SocialPsychology, 39(7), 1207-1209.

  • Madl, T., Baars, B. J., & Franklin, S. (2011). The Timing of the Cognitive Cycle. PLoS ONE,6(4), e14803. doi: 10.1371/journal.pone.0014803 [Crossref]

  • Madl, T., & Franklin, S. (2012, April 13-15). A LIDA-based Model of the Attentional Blink. Paper presented at the 11th International Conference on Cognitive Modeling, Berlin.

  • Maes, P. (1989). How to do the right thing. Connection Science, 1, 291-323.

  • McGaugh, J. L. (2000). Memory--a Century of Consolidat ion. Science, 287(5451), 248-251. doi: 10.1126/science.287.5451.248 [Crossref]

  • Miller, R. R., & Matzel, L. D. (2006). Retrieval failure versus memory loss in experimental amnesia: definitions and processes. Learning & Memory, 13(5), 491-497. [PubMed]

  • Nadel, L., Hupbach, A., Gomez, R., & Newman-Smith, K. (2012). Memory formation, consolidation and transformation. Neuroscience & Biobehavioral Reviews. [PubMed]

  • Neisser, U. (1976). Cognition and Reality: Principles and Implications of Cognitive Psychology San Francisco: W. H. Freeman.

  • Newell, A. (1973). You can’t play 20 questions with nature and win: Pro- jective comments on the papers of this symposium. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press.

  • Nyhus, E., & Curran, T. (2010). Functional role of gamma and theta oscillations in episodic memory. Neuroscience & Biobehavioral Reviews, 34(7), 1023-1035. [PubMed]

  • Osipova, D., Takashima, A., Oostenveld, R., Fernández, G., Maris, E., & Jensen, O. (2006). Theta and gamma oscillat ions predict encoding and retrieval of declarative memory. TheJournal of neuroscience, 26(28), 7523-7531.

  • Panksepp, J. (2005). Affective consciousness: Core emotional feelings in animals and humans. Consciousness and Cognition, 14, 30-80.

  • Pfeifer, R., & Bongard, J. C. (2006). How the body shapes the way we think: a new view of intelligence: MIT press.

  • Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning andVerbal Behavior, 6, 855-863.

  • Remondes, M., & Schuman, Erin M. (2004). Role for a cortical input to hippocampal area CA1 in the consolidation of a long-term memory. Nature, 431, 699-703.

  • Roseman, I. J., & Smith, C. A. (2001). Appraisal theory: Overview, assumptions, varieties, controversies Appraisal processes in emotion: Theory, methods, research (pp. 3-19). New York: Oxford University Press.Samsonovich, A. V. (2008, November 7-9). BiologicallyInspired Cognitive Architectures: Papers from the AAAI Fall Symposium. Paper presented at the AAAI Fall Symposia, Arlington, Virginia.

  • Samsonovich, A. V. (2010). Toward a Unified Catalog of Implemented Cognitive Architectures. In A. V. Samsonovich, K. R. Jóhannsdóttir, A. Chella & B. Goertzel (Eds.), Proceeding ofthe 2010 Conference on Biologically Inspired Cognitive Architectures (pp. 195-244). Amsterdam: IOS Press.

  • Samsonovich, A. V., & Johannsdottir, K. R. (Eds.). (2011). Biologically Inspired Cognitive Architectures 2011 - Proceedings of the Second Annual Meeting of the BICA Society (Vol. 233). Amsterdam: IOS Press.

  • Samsonovich, A. V., Jóhannsdóttir, K. R. J., Chella, A., & Goertzel, B. (Eds.). (2010). Biologically Inspired Cognitive Architectures 2010 - Proceedings of the First AnnualMeeting of the BICA Society (Vol. 221). Amsterdam: IOS Press.

  • Sauseng, P., Griesmayr, B., Freunberger, R., & Klimesch, W. (2010). Control mechanisms in working memory: a possible function of EEG theta oscillat ions. Neuroscience &Biobehavioral Reviews, 34(7), 1015-1022.

  • Sauseng, P., Klimesch, W., Gruber, W. R., & Birbaumer, N. (2008). Cross-frequency phase synchronization: a brain mechanism of memory matching and attention. NeuroImage,40(1), 308-317. [Crossref] [PubMed]

  • Schmidhuber, J., Thorisson, K. R., & Looks, M. (2011, August 3-6). Artif icial GeneralIntelligence , Proceedings. Paper presented at the 4th International Conference, AGI 2011, Mountain View, CA, USA.

  • Sederberg, P. B., Kahana, M. J., Howard, M. W., Donner, E. J., & Madsen, J. R. (2003). Theta and gamma oscillations during encoding predict subsequent recall. The Journal ofNeuroscience, 23(34), 10809.

  • Sergent, C., & Dehaene, S. (2004). Neural processes underlying conscious perception: Experimental findings and a global neuronal workspace framework. Journal of Physiology-Paris, 98(4-6), 374-384. doi: 10.1016/j.jphysparis.2005.09.006 [Crossref]

  • Shanahan, M. P. (2006). A Cognitive Architecture that Combines Internal Simulation with a Global Workspace. Consciousness and Cognition, 15, 433-449.

  • Shiffrin, R. M. (1970). Forgetting: Trace erosion or retrieval failure? Science; Science.

  • Silverta, L., Delplanquea, S., Bouwalerha, H., Verpoorta, C., & Sequeira, H. (2004). Autonomic responding to aversive words without conscious valence discrimination. Int. J. Psychophysiol., 53, 135-145. [Crossref]

  • Sims, C. R., & Gray, W. D. (2004). Episodic versus semantic memory: An exploration of modelsof memory decay in the serial attention paradigm. Paper presented at the 6th international conference on cognitive modeling (ICCM-2004), Pittsburgh, PA.

  • Sloman, A. (Ed.). (1999). What Sort of Architecture is Required for a Human-like Agent? Dordrecht, Netherlands: Kluwer Academic Publishers.

  • Smith, C. A., & Kirby, L. D. (2001). Toward delivering on the promise of appraisal theory. In K. R. Scherer, A. Schorr & T. Johnstone (Eds.), Appraisal processes in emotion: Theory,Methods, Research (pp. 121-138). New York: Oxford University Press. Snaider, J., McCall, R., & Franklin, S. (2009). Time Production and Representation in aConceptual and Computational Cognitive Model. Paper presented at the AAAI Fall Symposium on Biologically Inspired Cognitive Architecture, Washington, DC.

  • Snaider, J., McCall, R., & Franklin, S. (2011). The LIDA Framework as a General Tool for AGI. Paper presented at the The Fourth Conference on Artificial General Intelligence (Springer Lecture Notes in Artificial Intelligence), Mountain View, California, USA.

  • Stickgold, R., & Walker, Matthew P. (2005). Memory consolidation and reconsolidat ion: what is the role of sleep? Trends Neurosci., 28, 408-415. [Crossref]

  • Strain, S. F., Franklin, S., Heck, D. H., & Baars, B. J. (in preparation). Brain rhythms, cognitive cycles and mental moments.

  • Sukthankar, G. (2000). Face recognition: a critical look at biologically-inspired approaches: Carnegie Mellon University, the Robotics Institute.

  • Sun, R., & Franklin, S. (2007). Computational Models of Consciousness: A Taxonomy and some Examples. In P. D. Zelazo & M. Moscovitch (Eds.), Cambridge Handbook ofConsciousness (pp. 151-174). New York: Cambridge University Press.

  • Sun, R., & Naveh, I. (2004). Simulating organizat ional decision-making using a cognitively realistic agent model. Journal of Artif icial Societies and Social Simulation, 7(3).

  • Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112(1), 159-192. [Crossref] [PubMed]

  • Taylor, J. G. (2011). A Review of Models of Consciousness. In V. Cutsuridis, A. Hussain & J. G. [PubMed]

  • Taylor (Eds.), Perception-Action Cycle: Models, Architectures, and Hardware (pp. 335-357).

  • Tononi, G. (2008). Consciousness as integrated information: a provisional manifesto. BiologicalBulletin, 215, 216-242,.

  • Tort, A. B. L., Komorowski, R. W., Manns, J. R., Kopell, N. J., & Eichenbaum, H. (2009). Theta-gamma coupling increases during the learning of item-context associations. PNAS,106(49), 20942-20947 [Crossref]

  • Tulving, E., & Schacter, D. L. (1990). Priming and human memory systems. Science, 247, 301-306.

  • Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science andHuman Experience. Cambridge, MA: MIT press.

  • Wallace, R. (2005). Consciousness: A Mathematical Treatment of the Global Neuronal Workspace Model. New York: Springer.

  • Wamsley, E. J., Tucker, M., Payne, J. D., Benavides, J. A., & Stickgold, R. (2010). Dreaming of a learning task is associated with enhanced sleep dependent memory consolidation. Current Biology. [PubMed]

  • Wang, P., Goertzel, B., & Franklin, S. (2008). Artificial General Intelligence 2008. Amsterdam: IOS Press.

  • Wiedemann, C. (2007). Memory consolidation...while you are sleeping. [10.1038/nrn2084]. NatRev Neurosci, 8(2), 86-87.

  • Yang, J., Xu, X., Du, X., Shi, C., & Fang, F. (2011). Effects of unconscious processing on implicit memory for fearful faces. PLoS ONE, 6(2), e14641.

  • Yerkes, R. M., & Dodson, J. D. (1908). The Relationship of Strength of Stimulus to Rapidity of Habit Formation. Journal of Comparative Neurology and Psychology, 18, 459-482.

  • Zhang, Q. (2009). A computational account of dreaming: Learning and memory consolidation. Cognitive Systems Research, 10(2), 91-101

About the article

Received: 2013-04-09

Accepted: 2013-07-08

Published Online: 2014-04-25

Published in Print: 2013-06-01

Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2013-0002. Export Citation

© Stan Franklin et al.. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC-ND 3.0)

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

José-Antonio Cervantes, Luis-Felipe Rodríguez, Sonia López, Félix Ramos, and Francisco Robles
Cognitive Computation, 2015

Comments (0)

Please log in or register to comment.
Log in