References
- Alvarado, N., Adams, S. S., & Burbeck, S. (2002). The role of emotion in an architecture of mind. IBM Research.
- Baars, B. (1988). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.
- Baars, B., & Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Science, 7, 166–172.
- Bach, J. (2003). The micropsi agent architecture. Paper presented at the Proceedings of ICCM-5, international conference on cognitive modeling, Bamberg, Germany.
- Bach, J. (2009). Principles of Synthetic Intelligence: Psi: An Architecture of Motivated Cognition. Oxford: Oxford University Press.
- Bach, J. (2012). Modeling Motivation and the Emergence of Affect in a Cognitive Agent Theoretical Foundations of Artificial General Intelligence (pp. 241-262): Springer.
- Barto, A. G. (2007). Temporal difference learning. Scholarpedia, 2(11), 1604.
- Belavkin, R. V. (2001a). Modelling the inverted-U effect with ACT-R. In Erik M. Altmann, Wayne D. Gray, A. Cleeremans & Christian D. Schunn (Eds.), Proceedings of the 2001 Fourth International Conference on Cognitive Modeling (pp. 296). Hillsdale, NJ Lawrence Erlbaum Associates.
- Belavkin, R. V. (2001b). The role of emotion in problem solving. Paper presented at the Proceedings of the AISB’01 Symposium on emotion, cognition and affective computing, Heslington, York, England.
- Berridge, K. C., & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology, 199(3), 457-480. doi: 10.1007/s00213-008-1099-6
- Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Research Reviews, 28(3), 309-369.
- Bindra, D. (1978). How adaptive behavior is produced: a perceptual-motivational alternative to response reinforcements. Behavioral and Brain Sciences, 1(01), 41-52.
- Bogacz, R., Usher, M., Zhang, J., & McClelland, J. L. (2007). Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. Philos Trans R Soc Lond B Biol Sci.
- Breazeal, C. (1998). A Motivational System for Regulating Human-Robot Interaction. Paper presented at the AAAI98, Madison, WI.
- Camras, L. A. (2011). Differentiation, dynamical integration and functional emotional development. Emotion Review, 3(2), 138-146.
- Cañamero, D. (1997). Modeling motivations and emotions as a basis for intelligent behavior. Paper presented at the Proceedings of the first international conference on Autonomous agents.
- Canamero, Lola D. (2003). Designing Emotions for Activity Selection in Autonomous Agents. In R. Trappl, P. Petta & S. Payr (Eds.), Emotions in Humans and Artifacts (pp. 115-148). Cambridge, MA: MIT Press.
- Cannon, W. B. (1927). The James-Lange theory of emotions: A critical examination and an alternative theory. The American Journal of Psychology, 39(1/4), 106-124.
- Cannon, W. B. (1929). Organization For Physiological Homeostasis. Physiol Rev., 9, 399-431.
- Conway, M. (2001). Sensory–perceptual episodic memory and its context: autobiographical memory. Philos. Trans. R. Soc. Lond B., 356, 1375–1384.
- D’Mello, S., Ramamurthy, U., Negatu, A., & Franklin, S. (2006). A Procedural Learning Mechanism for Novel Skill Acquisition. In T. Kovacs & James A. R. Marshall (Eds.), Proceeding of Adaptation in Artificial and Biological Systems, AISB’06 (Vol. 1, pp. 184–185). Bristol, England: Society for the Study of Artificial Intelligence and the Simulation of Behaviour.
- Damasio, A. (2003). Looking for Spinoza: Joy, Sorrow and the Feeling Brain. New York: Harcourt.
- Damasio, A. (1999). The Feeling of What Happens. New York: Harcourt Brace.
- Daw, N., Niv, Y., & Dayan, P. (2005). Actions, policies, values, and the basal ganglia. In E. Bezard (Ed.), Recent Breakthroughs in Basal Ganglia Research.
- Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. [Research Support, Non-U.S. Gov’t]. Nature Neuroscience, 8(12), 1704-1711. doi: 10.1038/nn1560
- Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10, 204–211.
- Diener, E. (1999). Introduction to the special section on the structure of emotion. Journal of personality and Social Psychology, 76(5), 803.
- Dijkstra, T. M. H., Schöner, G., & Gielen, C. C. A. M. (1994). Temporal stability of the action-perception cycle for postural control in a moving visual environment. Experimental Brain Research, 97(3), 477-486.
- Dong, D., & Franklin, S. (2014). Sensory Motor System: Modeling the process of action execution. Paper presented at the Proceedings of the 36th Annual Conference of the Cognitive Science Society.
- Dong, D., & Franklin, S. (2015). A New Action Execution Module for the Learning Intelligent Distribution Agent (LIDA): The Sensory Motor System. Cognitive Computation. doi: 10.1007/s12559-015-9322-3.
- Dorner, D., & Hille, K. (1995). Artificial souls: motivated emotional robots. Paper presented at the IEEE International Conference on Systems, Man and Cybernetics, Vancouver, BC, Canada.
- Drescher, Gary L. (1991). Made-Up Minds: A Constructivist Approach to Artificial Intelligence. Cambridge, MA: MIT Press.
- Ekman, P., Sorenson, E. R., & Friesen, W. V. (1969). Pan-cultural elements in facial displays of emotion. Science, 164(3875), 86-88.
- Faghihi, U., McCall, R., & Franklin, S. (2012). A Computational Model of Attentional Learning in a Cognitive Agent. Biologically Inspired Cognitive Architectures, 2, 25-36.
- Faghihi, U., Estey, C., McCall, R., & Franklin, S. (2015). A Cognitive Model Fleshes Out Kahneman’s Fast and Slow Systems. Biologically Inspired Cognitive Architectures, 11, 38-52.
- Faghihi, U., Nkambou, R., Poirier, P., & Fournier-Viger, P. (2009). Emotional Learning and a Combined Centralist-Peripheralist Based Architecture for a More Efficient Cognitive Agent. Paper presented at the 7th IEEE International Conference on Industrial Technology (ICIT 2009).
- Fellous, J.-M. (2004). From human emotions to robot emotions. Architectures for Modeling Emotion: Cross-Disciplinary Foundations, American Association for Artificial Intelligence, 39-46.
- Fishbach, A., Roy, S. A., Bastianen, C., Miller, L. E., & Houk, J. C. (2005). Kinematic properties of on-line error corrections in the monkey. Experimental Brain Research, 164(4), 442–457.
- Franklin, S. (1995). Artificial Minds. Cambridge, Ma: MIT Press.
- Franklin, S. (2000). Deliberation and Voluntary Action in ‘Conscious’ Software Agents. Neural Network World, 10, 505–521
- Franklin, S. (2003). IDA: A Conscious Artifact? Journal of Consciousness Studies, 10, 47–66.
- Franklin, S., & Baars, B. (2010). Two Varieties of Unconscious Processes. In E. Perry, D. Collerton, H. Ashton & F. LeBeau (Eds.), New Horizons in the Neuuroscience of Consciousness (pp. 91–102). Amsterdam: John Benjamin.
- 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., & Graesser, A. C. (1997). Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents Intelligent Agents III (pp. 21–35). Berlin: Springer Verlag.
- Franklin, S., Kelemen, A., & McCauley, L. (1998). IDA: A Cognitive Agent Architecture IEEE Conf on Systems, Man and Cybernetics (pp. 2646–2651). Menlo Park, CA: IEEE Press.
- Franklin, S., Madl, T., D’Mello, S., & Snaider, J. (2014). LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning. IEEE Transactions on Autonomous Mental Development., PP(99), 1 doi: 10.1109/TAMD.2013.2277589
- Franklin, S., Madl, T., Strain, S., Faghihi, U., Dong, D., Kugele, S., . . . Chen, S. (2016). A LIDA cognitive model tutorial. Biologically Inspired Cognitive Architectures, 105-130. doi: 10.1016/j.bica.2016.04.003
- 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 Cognitive Architectures, 1, 32-43. doi: 10.1016/j.bica.2012.04.001
- Franklin, S., Strain, S., McCall, R., & Baars, B. (2013). Conceptual Commitments of the LIDA Model of Cognition. Journal of Artificial General Intelligence, 4(2), 1-22. doi:10.2478/jagi-2013-0002
- Freeman, W. J. (2002). The limbic action-perception cycle controlling goal-directed animal behavior. Neural Networks, 3, 2249-2254.
- Fum, D., & Stocco, A. (2004). Memory, Emotion, and Rationality: An ACT-R interpretation for Gambling Task results. Paper presented at the ICCM.
- Fuster, J. M. (2004). Upper processing stages of the perception–action cycle. Trends in Cognitive Sciences, 8(4), 143-145.
- Gallagher, M., McMahan, R. W., & Schoenbaum, G. (1999). Orbitofrontal cortex and representation of incentive value in associative learning. The Journal of neuroscience, 19(15), 6610-6614.
- Gmytrasiewicz, P. J., & Lisetti, C. L. (2002). Emotions and personality in agent design and modeling Game theory and decision theory in agent-based systems (pp. 81-95): Springer.
- Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.
- Hollerman, J., & Schultz, W. (1998). Dopamine Neruons Report an Error in the Temproal Prediction of Reward during Learning. Nature Neuroscience, 1, 304-309.
- Huys, Q. J., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Comput Biol, 8(3), e1002410.
- James, W. (1884). II.—What is an emotion? Mind(34), 188-205.
- James, W. (1890). The Principles of Psychology. Cambridge, MA: Harvard University Press.
- Johnston, Victor S. (1999). Why We Feel:The Science of Human Emotions. Reading MA: Perseus Books.
- Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American economic review, 93(5), 1449-1475.
- Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
- Kalis, A., Kaiser, S., & Mojzisch, A. (2013). Why we should talk about option generation in decision-making research. Front. Psychol, 4(555), 10.3389.
- Keller, L. R., & Ho, J. L. (1988). Decision problem structuring: Generating options. Systems, Man and Cybernetics, IEEE Transactions on, 18(5), 715-728.
- Klein, G., Wolf, S., Militello, L., & Zsambok, C. (1995). Characteristics of skilled option generation in chess. Organizational Behavior and Human Decision Processes, 62(1), 63-69.
- Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in cognitive sciences, 13(11), 479-487.
- Laird, John E., J, E., Newell, A., & Rosenbloom, Paul S. P. S. (1987). SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33, 1–64.
- Lang, P. J., & Davis, M. (2006). Emotion, motivation, and the brain: Reflex foundations in animal and human research. In G. E. M. J. J. K. S. Anders & D. Wildgruber (Eds.), Progress in Brain Research (Vol. Volume 156, pp. 3-29): Elsevier.
- Lazarus, R. (1991). Emotion and adaptation. New York: Oxford University Press.
- LeDoux, J. E. (2006). Emotional Memory: In Search of Systems and Synapsesa. Annals of the New York Academy of Sciences, 702(1), 149-157.
- Lee-Johnson, C. P., & Carnegie, D. A. (2009). Robotic Emotions: Navigation with Feeling. In J. Vallverdú & D. Casacuberta (Eds.), Handbook of Research on Synthetic Emotions and Sociable Robotics (pp. 88-117): IGI Global.
- Liddell, B. J., Brown, K. J., Kemp, A. H., Barton, M. J., Das, P., Peduto, A., . . . Williams, L. M. (2005). A direct brainstem–ìamygdala–cortical ‘alarm’ system for subliminal signals of fear. NeuroImage, 24(1), 235-243.
- Lucantonio, F., Stalnaker, T. A., Shaham, Y., Niv, Y., & Schoenbaum, G. (2012). The impact of orbitofrontal dysfunction on cocaine addiction. Nature Neuroscience, 15(3), 358-366.
- MacDonald, K. (2008). Effortful Control, Explicit Processing and the Regulation of Human Evolved Predispositions. Psychological Review, 115(4), 012–1031.
- Madl, T., Baars, B. J., & Franklin, S. (2011). The Timing of the Cognitive Cycle. PLoS ONE, 6(4), e14803.
- Madl, T., & Franklin, S. (2012). A LIDA-based Model of the Attentional Blink. Proceedings of the 11th International Conference on Cognitive Modelling, 283-288.
- Madl, T., Franklin, S., Chen, K., & Trappl, R. (2013). Spatial Working Memory in the LIDA Cognitive Architecture. In R. West & T. Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modelling (pp. 384-390). Ottawa, Canada: Carleton University.
- Maes, P. (1989). How to do the right thing. Connection Science, 1, 291–323.
- Marieb, E. N., & Hoehn, K. (2007). Human Anatomy & Physiology (Seventh ed.). San Francisco, CA: Pearson Benjamin Cummings.
- Marinier, R., & Laird, J. E. (2008). Emotion-driven reinforcement learning. Cognitive science, 115-120.
- Marinier, R. P., Laird, J. E., & Lewis, R. L. (2009). A computational unification of cognitive behavior and emotion. Cognitive Systems Research, 10(1), 48-69.
- McCall, R., Franklin, S., & Friedlander, D. (2010). Grounded Event-Based and Modal Representations for Objects, Relations, Beliefs, Etc. Paper presented at the FLAIRS-23, Daytona Beach, FL.
- McCall, R. J. (2014). Fundamental motivation and perception for a systems-level cognitive architecture. The University of Memphis.
- Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529.
- Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. The Journal of neuroscience, 16(5), 1936-1947.
- Negatu, A. (2006). Cognitively Inspired Decision Making for Software Agents: Integrated Mechanisms for Action Selection, Expectation, Automatization and Non-Routine Problem Solving: Ph.D. Dissertation, The University of Memphis, Memphis TN USA.
- Neisser, U. (1976). Cognition and Reality: Principles and Implications of Cognitive Psychology San Francisco: W. H. Freeman.
- O’Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003). Temporal difference models and reward-related learning in the human brain. Neuron, 38(2), 329-337.
- Pasquereau, B., Nadjar, A., Arkadir, D., Bezard, E., Goillandeau, M., Bioulac, B., . . . Boraud, T. (2007). Shaping of motor responses by incentive values through the basal ganglia. Journal of Neuroscience, 27, 1176-1183.
- Phelps, E. A. (2006). Emotion and Cognition: Insights from Studies of the Human Amygdala. Annual Review of Psychology, 57(1), 27-53. doi: doi:10.1146/annurev.psych.56.091103.070234
- Picard, R. (1997). Affective Computing. Cambridge MA: The MIT Press.
- Picard, R. W. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1–2), 55-64. doi: 10.1016/s1071-5819(03)00052-1
- Purves, D., Brannon, E. M., Cabeza, R., Huettel, S. A., LaBar, K. S., Platt, M. L., & Woldorff, M. G. (2008). Principles of cognitive neuroscience (Vol. 83): Sinauer Associates Sunderland, MA.
- Raab, M., de Oliveira, R. F., & Heinen, T. (2009). How do people perceive and generate options? Progress in brain research, 174, 49-59.
- Richard, J. M., & Berridge, K. C. (2011). Nucleus accumbens dopamine/glutamate interaction switches modes to generate desire versus dread: D1 alone for appetitive eating but D1 and D2 together for fear. The Journal of neuroscience, 31(36), 12866-12879.
- 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.
- Rowe, J., Hughes, L., Eckstein, D., & Owen, A. M. (2008). Rule-Selection and Action-Selection have a Shared Neuroanatomical Basis in the Human Prefrontal and Parietal Cortex. Cerebral Cortex, 18, 2275-2285. doi: 10.1093/cercor/bhm249
- Schoenbaum, G., Takahashi, Y., Liu, T. L., & McDannald, M. A. (2011). Does the orbitofrontal cortex signal value? Annals of the New York Academy of Sciences, 1239(1), 87-99.
- Shin, Y. K., Proctor, R. W., & Capaldi, E. J. (2010). A review of contemporary ideomotor theory. Psychological Bulletin, 136(6), 943-974. doi: 10.1037/a0020541
- Sloman, A. (1998). Damasio, Descartes, Alarms and Meta-management Proceedings Symposiumon Cognitive Agents: Modeling Human Cognition. San Diego: IEEE.
- Sloman, A. (1999). What Sort of Architecture is Required for a Human-like Agent? In M. Wooldridge & A. S. Rao (Eds.), Foundations of Rational Agency (pp. 35–52). Dordrecht, Netherlands: Kluwer Academic Publishers.
- Sloman, A., & Croucher, M. (1981). Why robots will have emotions.
- Smith, K. S., Berridge, K. C., & Aldridge, J. W. (2011). Disentangling pleasure from incentive salience and learning signals in brain reward circuitry. Proceedings of the National Academy of Sciences, 108(27), E255-E264.
- Snaider, J., McCall, R., & Franklin, S. (2011). The LIDA Framework as a General Tool for AGI. Paper presented at the Artificial General Intelligence (AGI-11), Mountain View, CA.
- Squire, L. R., & Kandel, E. R. (2000). Memory: From mind to molecules: Macmillan.
- Sun, R. (2009). Motivational representations within a computational cognitive architecture. Cognitive Computation, 1(1), 91-103.
- Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
- Thompson, R. F., & Madigan, S. A. (2007). Memory. Princeton, NJ: Princeton University Press.
- Tindell, A. J., Smith, K. S., Berridge, K. C., & Aldridge, J. W. (2009). Dynamic computation of incentive salience: ‘wanting’ what was never ‘liked’. The Journal of Neuroscience, 29(39), 12220-12228.
- Toates, F. M. (1986). Motivational systems: CUP Archive.
- Ward, P., Suss, J., Eccles, D. W., Williams, A. M., & Harris, K. R. (2011). Skill-based differences in option generation in a complex task: A verbal protocol analysis. Cognitive processing, 12(3), 289-300.
- Watkins, C. J. C. H. (1989). Learning from Delayed Rewards. Ph.D. thesis, Cambridge University.
- Westen, D. (1999). The Scientific Status of Unconscious Processes: Is Freud Really Dead? Journal of the American Psychoanalytic Association, 47(4), 1061-1106. doi: 10.1177/000306519904700404
- Wimmer, G. E., & Shohamy, D. (2012). Preference by Association: How Memory Mechanisms in the Hippocampus Bias Decisions. Science, 338(6104), 270-273. doi: 10.1126/science.1223252
- 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.
- Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007). Event Perception: A Mind–Brain Perspective. Psychological Bulletin, 133(2), 273–293.
- Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological bulletin, 127(1), 3.