Mechanisms for Robust Cognition

Cognitive Science

Volumn 39, Issue 6, 2015, Pages 1131-1171

  Walsh, Matthew M ^a   Gluck, Kevin A ^a  

^a AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

Adaptability; Cognitive systems; Computer simulation; Redundancy; Robustness; System control

Indexed keywords

ATTENTION; BRAIN; COGNITION; COMPUTER SIMULATION; EVOLUTION; HUMAN; PHYSIOLOGY; PSYCHOLOGICAL MODEL;

ATTENTION; BIOLOGICAL EVOLUTION; BRAIN; COGNITION; COMPUTER SIMULATION; HUMANS; MODELS, PSYCHOLOGICAL;

EID: 84937682915     PISSN: 03640213     EISSN: 15516709     Source Type: Journal    
DOI: 10.1111/cogs.12192     Document Type: Article

References (137)

1. Agrawal, A. A., Laforsch, C., & Tollrian, R. (1999). Transgenerational induction of defenses in animals and plants. Nature, 401, 60-63.
2. Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471-484.
3. Anderson, J. R. (2007). How can the human mind occur in the physical universe. New York: Oxford University Press.
4. Anderson, J. R., & Lebiere, C. (2003). The Newell Test for a theory of cognition. Behavioral and Brain Sciences, 26, 587-637.
5. Bach, J. (2009). Principles of synthetic intelligence: PSI: An architecture of motivated cognition. New York: Oxford University Press.
6. Balleine, B. W., & O'Doherty, J. P. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35, 48-69.
7. Barkai, N., & Leibler, S. (1997). Robustness in simple biochemical networks. Nature, 387, 913-917.
8. Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15, 200-209.
9. Berg, J., Tymoczko, J., & Stryer, L. (2002). Biochemistry (5th ed.). New York: W. H. Freeman.
10. Bitmore, K. (2009). Rational decisions. Princeton, NJ: Princeton University Press.
11. Bontempi, F., Giuliani, L., & Gkoumas, K. (2007). Handling the exceptions: Dependability of systems and structural robustness, 3rd International Conference on Structural Engineering, Mechanics and Computation (pp. 104-110). Cape Town, South Africa..
12. Borbély, A. A., & Achermann, P. (1999). Sleep homeostasis and models of sleep regulation. Journal of Biological Rhythms, 6, 559-570.
13. Borst, J. P., & Anderson, J. R. (2013). Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network. Proceedings of the National Academy of Sciences, USA, 110, 1628-1633.
14. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624-652.
15. Bowers, J. S. (2009). On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience. Psychological Review, 116, 220-251.
16. Brown, L. E., & Rosenbaum, D. A. (2002). Motor control: Models. In L. Nadel (Ed.), Encyclopedia of cognitive science, 3 (pp. 127-133). London: Macmillan.
17. Cain, N. H., Barreiro, A. K., Shalden, M., & Shea-Brown, E. (2013). Neural integrators for decision making: A favorable tradeoff between robustness and sensitivity. Journal of Neurophysiology, 109, 2542-2559.
18. Canisius, T. D. G. (Ed.) (2011). Structural robustness design for practising engineers. COST Action TU0601. Available at http://www.cost.eu/media/publications/11-38-Robustness-of-Structures-Final-Report-of-COST-Action-TU0601. Accessed March 14, 2014.
19. Chater, N., & Brown, G. D. A. (2008). From universal laws of cognition to specific cognitive models. Cognitive Science, 32, 36-67.
20. Chee, M. W., & Choo, W. C. (2004). Functional imaging of working memory after 24 hr of total sleep deprivation. Journal of Neuroscience, 24, 4560-4567.
21. Chee, M. W. I., Tan, J. C., Zheng, H., Parimal, S., Weissman, D. H., Zagorodnov, V., & Dinges, D. F. (2008). Lapsing during sleep deprivation is associated with distributed changes in brain activation. Journal of Neuroscience, 28, 5519-5528.
22. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, 240-247.
23. Csete, M., & Doyle, J. (2002). Reverse engineering of biological complexity. Science, 295, 1664-1669.
24. Davies, D. R., & Parasuraman, R. (1982). The psychology of vigilance. London: Academic Press.
25. Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8, 1704-1711.
26. Desmurget, M., & Grafton, S. (2000). Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences, 4, 423-431.
27. Dinges, D. F., & Powell, J. W. (1985). Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behavior Research Methods, Instruments, & Computers, 17, 652-655.
28. Doran, S. M., van Dongen, H. P., & Dinges, D. F. (2001). Sustained attention performance during sleep deprivation: Evidence of state instability. Archives of Italian Biology: Neuroscience, 139, 253-267.
29. Drummond, S. P., Brown, G. G., Salamat, J. S., & Gillin, J. C. (2004). Increasing task difficulty facilitates the cerebral compensatory response to total sleep deprivation. Sleep, 27, 445-451.
30. Durmer, J. S., & Dinges, D. F. (2005). Neurocognitive consequences of sleep deprivation. Seminars in Neurology, 25, 117-129.
31. Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, USA, 98, 13763-13768.
32. Erev, I., & Barron, G. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112, 912-931.
33. Flack, J. C., Hammerstein, P., & Krakauer, D. C. (2012). Robustness in biological and social systems. In J. R. Stevens & P. Hammerstein (Eds.), Evolution and the mechanisms of decision making. Strüngmann Forum Report, vol. 11, J. Lupp, series ed. (pp. 129-149). Cambridge, MA: MIT Press.
34. Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press.
35. Frederick, S., Loewenstein, G., & O'Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40, 351-401.
36. Friston, K. J., & Price, C. J. (2003). Degeneracy and redundancy in cognitive anatomy. Trends in Cognitive Sciences, 7, 151-152.
37. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58.
38. Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451-482.
39. Gluck, K. A., McNamara, J. M., Brighton, H., Dayan, P., Kareev, Y., Krause, J., Kurzban, R., Selten, R., Stevens, J. R., Voelkl, B., & Wimsatt, W. C. (2012). Robustness in a variable environment. In J. R. Stevens & P. Hammerstein (Eds.), Evolution and the mechanisms of decision making. Strüngmann Forum Report, vol. 11, J. Lupp, series ed. (pp. 195-214). Cambridge, MA: MIT Press.
40. Gluck, M. A., & Myers, C. E. (1997). Psychobiological models of hippocampal function in learning and memory. Annual Review of Psychology, 48, 481-514.
41. Gluck, K. A., & Pew, R. W. (Eds.) (2005). Modeling human behavior with integrated cognitive architectures: Comparison, evaluation, and validation. Mahwah, NJ: Erlbaum.
42. Goodwin, G. C., Graebe, S. F., & Salgado, M. E. (2001). Control system design. Englewood Cliffs, NJ: Prentice Hall.
43. Gratch, J., & Marsella, S. (2007). The architectural role of emotion in cognitive systems. In W. D. Gray (Ed.), Integrated models of cognitive systems (pp. 230-242). New York: Oxford University Press.
44. Gray, R. (2011). Looming auditory collision warnings for driving. Human Factors, 53, 63-74.
45. Gunzelmann, G., Gross, J. B., Gluck, K. A., & Dinges, D. F. (2009). Sleep deprivation and sustained attention performance. Integrating mathematical and cognitive modeling. Cognitive Science, 33, 880-910.
46. Habeck, C., Rakitin, B. C., Moeller, J., Scarmeas, N., Zarahn, E., Brown, T., & Stern, Y. (2004). An event-related fMRI study of the neurobehavioral impact of sleep deprivation on performance of a delayed-match- to-sample task. Cognitive Brain Research, 18, 306-321.
47. Hafner, M., Koeppl, H., Hasler, M., & Wagner, A. (2009). "Glocal" robustness analysis and model discrimination for circadian oscillators. PLOS Computational Biology, 5, 1-10.
48. Hagen, E. H., & Hammerstein, P. (2005). Evolutionary biology and the strategic view of ontogeny: Genetic strategies in the life course. Research in Human Development, 2(87), 101.
49. Hammerstein, P., Hagen, E. H., Herz, A. V. M., & Herzel, H. (2006). Robustness: A key to evolutionary design. Biological Theory, 1, 90-93.
50. Hammerstein, P., & Stevens, J. R. (2012). Six reasons for invoking evolution in decision theory. In P. Hammerstein & J. R. Stevens (Eds.), Evolution and the mechanisms of decision making. Strüngmann Forum Report, vol. 11, J. Lupp, series ed. (pp. 1-17). Cambridge, MA: MIT Press.
51. Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999). From molecular to modular cell biology. Nature, 402, C47-C52.
52. Hatakeyama, T. S., & Kaneko, K. (2012). Generic temperature compensation of biological clocks by autonomous regulation of catalyst concentration. Proceedings of the National Academy of Sciences, USA, 109, 8109-8114.
53. Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations (pp. 77-109). Cambridge, MA: MIT Press.
54. Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations (pp. 282-317). Cambridge, MA: MIT Press.
55. Huaco, D. R., Bowders, J. J., & Loehr, J. E. (2012). Method to develop target levels of reliability for design using LRFD. In Transportation Research Board 91st Annual Meeting (No. 12-4327). Washington, DC.
56. Ioannou, P. A., & Sun, J. (1996). Robust adaptive control. Upper Saddle River, NJ: Prentice-Hall.
57. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79-87.
58. Jordan, M. I. (1996). Computational aspects of motor control and motor learning. In J. Moody, S. Hanson, & R. Lippmann (Eds.), Advances in neural information processing (pp. 985-993). San Mateo, CA: Morgan Kaufmann.
59. Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.
60. Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718-727.
61. Kitano, H. (2004). Biological robustness. Nature Neuroscience, 5, 826-837.
62. Kitano, H. (2007). Toward a theory of biological robustness. Molecular Systems Biology, 3, 1-7.
63. Kohonen, T. (1984). Self-organization and associative memory. New York: Springer-Verlag.
64. Konorsky, J. (1967). Integrative activity of the brain: An interdisciplinary approach. Chicago: University of Chicago Press.
65. Kurzban, R. (2010). Why everyone (else) is a hypocrite: Evolution and the modular mind. Princeton, NJ: Princeton University Press.
66. Larhlimi, A., Blachon, S., Selbig, J., & Nikoloski, Z. (2011). Robustness of metabolic networks: A review of existing definitions. Biosystems, 106, 1-8.
67. Lashley, K. S. (1950). In search of the engram. In J. F. Danielli, & R. Brown (Eds.), Physiological mechanisms in animal behavior (pp. 454-482). New York: Academic Press.
68. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46, 50-80.
69. Lim, J., & Dinges, D. F. (2008). Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129, 305-322.
70. Luwel, K., Lemaire, P., & Verschaffel, L. (2005). Children's strategies in numerosity judgment. Cognitive Development, 20, 448-471.
71. Luwel, K., Verschaffel, L., Onghena, P., & de Corte, E. (2003). Analysing the adaptiveness of strategy choices using the choice/no-choice method: The case of numerosity judgment. European Journal of Cognitive Psychology, 15, 511-537.
72. Mackworth, N. H. (1948). The breakdown of vigilance during prolonged visual search. Quarterly Journal of Experimental Psychology, 1, 6-21.
73. Marewski, J. N., & Schooler, L. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118, 393-437.
74. Marr, D. (1971). Simple memory: A theory for archicortex. Proceedings of the Royal Society of London, Series B, 841, 23-81.
75. Marr, D. (1982). Vision. New York: W. H. Freeman.
76. McCauley, P., Kalachev, L. V., Mollicone, D. J., Banks, S., Dinges, D. F., & van Dongen, H. P. A. (2013). Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance. Sleep, 36, 1987-1997.
77. Mummery, C. J., Patterson, K., Wise, R. J. S., Vandenbergh, R., Price, C. J., & Hodges, J. R. (1999). Disrupted temporal lobe connections in semantic dementia. Brain, 122, 61-73.
78. Myung, I. J., Navarro, D. J., & Pitt, M. A. (2006). Model selection by normalized maximum likelihood. Journal of Mathematical Psychology, 50, 167-179.
79. Nason, S., & Laird, J. E. (2005). Soar-RL: Integrating reinforcement learning with soar. Cognitive Systems Research, 6, 51-59.
80. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Cambridge University Press.
81. NISTIR 7396 (2007) Best practices for reducing the potential for progressive collapse in buildings. Gaithersburg, MD: National Institute of Standards and Technology (NIST).
82. Noppeney, U., Friston, K. J., & Price, C. J. (2004). Degenerate neuronal systems sustaining cognitive functions. Journal of Anatomy, 205, 433-442.
83. O'Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776.
84. Ohlsson, S. (2011). Deep learning: How the mind overrides experience. New York: Cambirdge University Press.
85. O'Reilly, R. C. (1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 2, 455-462.
86. O'Reilly, R. C., & Frank, M. J. (2006). Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18, 283-328.
87. Page, M. P. A. (2000). Connectionist modeling in psychology: A localist manifesto. Behavioral and Brain Sciences, 23, 443-512.
88. Pakkenberg, B., & Gundersen, H. J. G. (1997). Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology, 384, 312-320.
89. Park, H. J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342, 1238411.
90. Paus, T., Zatorre, R. J., Hofle, N., Caramanos, Z., Gotman, J., Petrides, M., & Evans, A. C. (1997). Time-related changes in neural systems underlying attention and arousal during the performance of an auditory vigilance task. Journal of Cognitive Neuroscience, 9, 392-408.
91. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 534-552.
92. Portas, C. M., Rees, G., Howseman, A. M., Josephs, O., Turner, R., & Frith, C. D. (1998). A specific role for the thalamus in mediating the interaction of attention and arousal in humans. Journal of Neuroscience, 18, 8979-8989.
93. Pratt, R. (2000). Flight control systems: Practical issues in design and implementation. United Kingdom: Institute of Engineering and Technology.
94. Price, C. J., & Friston, K. J. (2002). Degeneracy and cognitive anatomy. Trends in Cognitive Sciences, 6, 416-421.
95. Pylyshyn, Z. W. (1999). Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. Behavioral and Brain Sciences, 22, 341-423.
96. Redish, A. D., Jensen, S., & Johnson, A. (2008). A unified framework for addiction: Vulnerabilities in the decision process. Behavioral and Brain Sciences, 31, 415-487.
97. Reinman, G., Ayer, T., Davan, T., Devore, M., Finley, S., Glanovski, J., Gray, L., Hall, B., Jones, C., Learned, A., Mesaros, E., Morris, R., Pinero, S., Russo, R., Stearns, E., Teicholz, M., Teslik-Welz, W., & Yudichak, D. (2012). Design for variation. Quality Engineering, 24, 317-345.
98. Reppert, S. M., & Weaver, D. R. (2002). Coordination of circadian timing in mammals. Nature, 418, 935-941.
99. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207-236.
100. Rittle-Johnson, B., & Siegler, R. S. (1999). Learning to spell: Variability, choice, and change in children's strategy use. Child Development, 70, 332-348.
101. Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A parallel distributed processing approach. Cambridge, MA: MIT Press.
102. Rumelhart, D. E., & Todd, P. M. (1993). Learning and connectionist representations. In D. E. Meyer & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience (pp. 3-30). Cambridge, MA: MIT Press.
103. Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362-380.
104. Saper, C. B., Scammell, T. E., & Lu, J. (2005). Hypothalamic regulation of sleep and circadian rhythms. Nature, 437, 1257-1263.
105. Sastry, S., & Bodson, M. (1989). Adaptive control: Stability, convergence, and robustness. Englewood Cliffs, NJ: Prentice-Hall.
106. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1-27.
107. Scott, J. J., & Gray, R. (2008). A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Human Factors, 50, 264-275.
108. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed developmental model of word recognition and naming. Psychological Review, 96, 523-568.
109. Siegler, R. S. (1987). The perils of averaging data over strategies: An example from children's addition. Journal of Experimental Psychology: General, 116, 250-264.
110. Siegler, R. S., Adolph, K. E., & Lemaire, P. (1996). Strategy choices across the life span. In L. R. Reder (Ed.), Implicit memory and metacognition (pp. 79-121). Mahwah, NJ: Erlbaum.
111. Siegler, R. S., & Lemaire, P. (1997). Older and younger adults' strategy choices in multiplication: Testing predictions of ASCM using the choice/no-choice method. Journal of Experimental Psychology: General, 126, 71-92.
112. Simon, H. A. (1982). Comments. In M. S. Clark & S. T. Fiske (Eds.), Affect and cognition: The 17th Annual Carnegie Symposium on cognition (pp. 333-342). Mahwah, NJ: Erlbaum.
113. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.
114. Squire, L. (1987). Memory and brain. New York: Oxford University Press.
115. Starossek, U., & Haberland, M. (2012). Robustness of structures. International Journal of Lifecycle Performance Engineering, 1, 3-21.
116. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
117. Taatgen, N. A., Huss, D., Dickison, D., & Anderson, J. R. (2008). The acquisition of robust and flexible cognitive skills. Journal of Experimental Psychology: General, 137, 548-565.
118. Thorndike, E. L. (1911). Animal intelligence: Experimental studies. New York: Macmillan.
119. Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience, 7, 907-915.
120. Veksler, V. D., Myers, C. W., & Gluck, K. A. (2014). SAwSu: An integrated model of associative and reinforcement learning (online early access). Cognitive Science, 38, 580-598.
121. Wagner, A. (2005). Robustness and evolvability in living systems. Princeton, NJ: Princeton University Press.
122. Walsh, M. M., & Anderson, J. R. (2009). The strategic nature of changing your mind. Cognitive Psychology, 58, 416-440.
123. Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience and Biobehavioral Reviews, 36, 1870-1884.
124. Walsh, M. M., & Anderson, J. R. (2013). Electrophysiological responses to feedback during the application of abstract rules. Journal of Cognitive Neuroscience, 25, 1986-2002.
125. Walsh, M. M., & Anderson, J. R. (2014). Navigating complex decision spaces: Problems and paradigms in sequential choice. Psychological Bulletin, 140, 466-486.
126. Walsh, M. M., Einstein, E. H., & Gluck, K. A. (2013). A quantification of robustness. Journal of Applied Research in Memory and Cognition, 2, 137-148.
127. Walsh, M. M., Gunzelmann, G., & Van Dongen, H. P. A. (2014). Comparing accounts of psychomotor vigilance impairment due to sleep loss. 36th Annual Conference of the Cognitive Science Society. Quebec City, Canada.
128. Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50, 433-441.
129. Warrington, E. K. (1975). The selective impairment of semantic memory. Quarterly Journal of Experimental Psychology, 27, 635-657.
130. Weinstein, M. C., & Fineberg, H. V. (1980). Clinical decision analysis. Philadelphia, PA: W. B. Saunders.
131. Wickelgren, W. A. (1977). Speed-accuracy tradeoff and information processing dynamics. Acta Psychologica, 41, 67-85.
132. Wolpert, D. M. (1997). Computational approaches to motor control. Trends in Cognitive Sciences, 1, 209-216.
133. Wolpert, D. M., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317-1329.
134. Wyatte, D., Curran, T., & O'Reilly, R. (2012). The limits of feedforward vision: Recurrent processing promotes robust object recognition when objects are degraded. Journal of Cognitive Neuroscience, 24, 2248-2261.
135. Yi, T. M., Huang, Y., Simon, M. I., & Doyle, J. (2000). Robust perfect adaptation in bacterial chemotaxis through integral feedback control. Proceedings of the National Academy of Sciences, USA, 97, 4649-4653.
136. Yin, H. H., Knowlton, B. J., & Balleine, B. W. (2004). Lesions of dorsolateral striatum preserve outcome expectancy but disrupt habit formation in instrumental learning. European Journal of Neuroscience, 19, 181-189.
137. Yin, H. H., Ostlund, S. B., Knowlton, B. J., & Balleine, B. W. (2005). The role of the dorsomedial striatum in instrumental conditioning. European Journal of Neuroscience, 22, 513-523.