Skip to main content Accessibility help
×
Home
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 6
  • Print publication year: 2008
  • Online publication date: June 2012

15 - Computational Models of Attention and Cognitive Control

from Part III - Computational Modeling of Various Cognitive Functionalities and Domains

Summary

Computational models are like the new kids in town for the field of decision making. This field is dominated by axiomatic utility theories or simple heuristic rule models. Decision theory has a long history, starting as early as the seventeenth century with probabilistic theories of gambling by Blaise Pascal and Pierre Fermat. In an attempt to retain the basic utility framework, constraints on utility theories are being relaxed, and the formulas are becoming more deformed. Recently, many researchers have responded to the growing corpus of phenomena that challenge traditional utility models by applying wholly different approaches. This chapter provides concrete illustration of how the computational approach can account for all of the behavioral paradoxes that have contested utility theories. The extent to which the other computational models have been successful in accounting for the results is also discussed.

References

Allport, A. (1989). Visual attention. In M. I. Posner (Ed.), Foundations of cognitive science (pp. 631–682). Cambridge, MA: The MIT Press.
Allport, A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set: Exploring the dynamic control of tasks. In C. Umilta & M. Moscovitch (Eds.), Attention and performance XV (pp. 421–452). Cambridge, MA: MIT Press.
Anderson, J. R. (1992). Automaticity and the ACT* theory. American Journal of Psychology, 105(2), 165–180.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 4(111), 1036–1060.
Ansorge, U. (2004). Top-down contingencies of nonconscious priming revealed by dual-task interference. The Quarterly Journal of Experimental Psychology: A, Human Experimental Psychology, 57(6), 1123–1148.
Aron, A. R., & Poldrack, R. A. (2006). Cortical and subcortical contributions to Stop signal response inhibition: Role of the subthalamic nucleus. The Journal of Neuroscience, 26(9), 2424–2433.
Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleusnorepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 403–450.
Barch, D. M., Carter, C. S., Braver, T. S., Sabb, F. W., Noll, D. C., & Cohen, J. C. (1999). Overt verbal responding during fMRI scanning: Empirical investigations of problems and potential solutions. Neuroimage, 10(6), 642–657.
Barch, D. M., Carter, C. S., & Cohen, J. D. (2004). Factors influencing Stroop performance in schizophrenia. Neuropsychology, 18(3), 477–484.
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652.
Braun, J., Koch, C., & Davis, L. J. (2001). Visual attention and cortical circuits. Cambridge, MA: MIT Press.
Braver, T. S., Barch, D. M., & Cohen, J. D. (1999). Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function. Biological Psychiatry, 46, 312–328.
Braver, T. S., & Bongiolatti, S. R. (2002). The role of frontopolar cortex in subgoal processing during working memory. Neuroimage, 15(3), 523–536.
Braver T. S., & Cohen J. D. (2000). On the control of control: The role of dopamine in regulating prefrontal function and working memory. In S. Monsell & J. Driver (Eds.), Attention and performance XVIII; control of cognitive processes (pp.713–737). Cambridge, MA: MIT Press.
Braver, T. S., & Cohen, J. D. (2001). Working memory, cognitive control, and the prefrontal cortex: Computational and empirical studies. Cognitive Processing, 2, 25–55.
Braver, T. S., Cohen, J. D., & Barch, D. M. (2002). The role of the prefrontal cortex in normal and disordered cognitive control: A cognitive neuroscience perspective. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 428–448). Oxford, UK: Oxford University Press.
Braver, T. S., Cohen, J. D., & Servan-Schreiber, D. (1995). Neural network simulations of schizophrenic performance in a variant of the CPT-AX: A predicted double dissociation. Schizophrenia Research, 15(1–2), 110.
Broadbent, E. D. (1958). Perception and communication. London: Pergamon.
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307(5712), 1118–1121.
Brown, J. W., Reynolds, J. R., & Braver, T. S. (2007). A computational model of fractionated conflict-control mechanisms in task-switching. Cognitive Psychology, 55, 37–85.
Brunel, N., & Wang, X.-J. (2001). Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. Journal of Computational Neuroscience, 11, 63–85.
Bundesen, C. (1990). A theory of visual attention. Psychological Review, 97(4), 523–547.
Bundesen, C. (1998). A computational theory of visual attention. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 353(1373), 1271–1281.
Bundesen, C., Habekost, T., & Kyllingsbaek, S. (2005). A neural theory of visual attention: Bridging cognition and neurophysiology. Psychological Review, 112(2), 291–328.
Burgess, P. W., Scott, S. K., & Frith, C. D. (2003). The role of the rostral frontal cortex (area 10) in prospective memory: A lateral versus medial dissociation. Neuropsychologia 41(8), 906–918.
Byrne, M. D., & Anderson, J. R. (2001). Serial modules in parallel: The psychological refractory period and perfect time-sharing. Psychological Review, 108(4), 847–869.
Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D., & Cohen, J. D. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280(5364), 747–749.
Cave, K. R. (1999). The FeatureGate model of visual selection. Psychological Research, 62(2–3), 182–194.
Chang, L., Speck, O., Miller, E. N., Braun, J., Jovicich, J., Koch, C. et al. (2001). Neural correlates of attention and working memory deficits in HIV patients. Neurology, 57(6), 1001–1007.
Cohen, J. D., Braver, T. S., & Brown, J. W. (2002). Computational perspectives on dopamine function in prefrontal cortex. Current Opinion in Neurobiology 12, 223–229.
Cohen, J. D., Braver, T. S., & O’Reilly, R. C. (1996). A computational approach to prefrontal cortex, cognitive control and schizophrenia: Recent developments and current challenges. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 351(1346), 1515–1527.
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332–361.
Cohen, J. D., & Huston, T. A. (1994). Progress in the use of interactive models for understanding attention and performance. In C. Umiltá & M. Moscovitch (Eds.), Attention and performance XV (pp. 1–19). Cambridge, MA: MIT Press.
Cohen, J. D., Romero, R. D., Farah, M. J., & Servan-Schreiber, D. (1994). Mechanisms of spatial attention: The relation of macrostructure to microstructure in parietal neglect. Journal of Cognitive Neuroscience, 6(4), 377–387.
Cohen, J. D., & Servan-Schreiber, D. (1992). Context, cortex, and dopamine: A connectionist approach to behaviour and biology in schizophrenia. Psychological Review, 99, 45–77.
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.
De Pisapia, N., & Braver, T. S. (2006). A model of dual control mechanisms through anterior cingulate and prefrontal cortex interactions. Neurocomputing, 69(10–12), 1322–1326.
De Pisapia, N., & Braver, T. S. (2007). Functional specializations in lateral prefrontal cortex associated with the integration and segregation of information in working memory. Cerebral Cortex, 17, 993–1006.
Deco, G. (2001). Biased competition mechanisms for visual attention in a multimodular neurodynamical system. In S. Wermter, J. Austin, D. Willshaw, and M. Elshaw (eds.), Emergent neural computational architectures based on neuroscience: Towards neuroscience-inspired computing (pp. 114–126). Berlin: Springer.
Deco, G., & Lee, T. S. (2004). The role of early visual cortex in visual integration: A neural model of recurrent interaction. The European Journal of Neuroscience, 20(4), 1089–1100.
Deco, G., Pollatos, O., & Zihl, J. (2002). The time course of selective visual attention: Theory and experiments. Vision Research, 42(27), 2925–2945.
Deco, G., & Rolls, E. T. (2002). Object-based visual neglect: A computational hypothesis. The European Journal of Neuroscience, 16(10), 1994–2000.
Deco, G., & Rolls, E. T. (2003). Attention and working memory: A dynamical model of neuronal activity in the prefrontal cortex. The European Journal of Neuroscience, 18(8), 2374–2390.
Deco, G., & Rolls, E. T. (2004). A neurody-namical cortical model of visual attention and invariant object recognition. Vision Research, 44(6), 621–642.
Deco, G., & Rolls, E. T. (2005a). Attention, short-term memory, and action selection: A unifying theory. Progress in Neurobiology, 76(4), 236–256.
Deco, G., & Rolls, E. T. (2005b). Neurody-namics of biased competition and cooperation for attention: A model with spiking neurons. Journal of Neurophysiology, 94(1), 295–313.
Deco, G., Rolls, E. T., & Horwitz, B. (2004). “What” and “where” in visual working memory: A computational neurodynamical perspective for integrating FMRI and singleneuron data. Journal of Cognitive Neuroscience, 16(4), 683–701.
Deco, G., & Zihl, J. (2001a). A neurodynamical model of visual attention: Feedback enhancement of spatial resolution in a hierarchical system. Journal of Computational Neuroscience, 10(3), 231–253.
Deco, G., & Zihl, J. (2001b). Top-down selective visual attention: A neurodynamical approach. Visual Cognition, 8(1), 119–140.
Deco, G., & Zihl, J. (2004). A biased competition based neurodynamical model of visual neglect. Medical Engineering & Physics, 26(9), 733–743.
Dehaene, S., & Changeux, J. P. (1991). The Wisconsin card sorting test: Theoretical analysis and modeling in a neuronal network. Cerebral Cortex, 1(1), 62–79.
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.
Durstewitz, D., Kelc, M., & Gunturkun, O. (1999). A neurocomputational theory of the dopaminergic modulation of working memory functions. Journal of Neuroscience, 19(7), 2807–2822.
Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3(Suppl) 1184–1191.
Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake & P. Shah (Eds.), Models of working memory (pp. 102–134). New York: Cambridge University Press.
Gilbert, S. J., & Shallice, T. (2002). Task switching: A PDP model. Cognitive Psychology, 44(3), 297–337.
Hamker, F. H. (2003). The reentry hypothesis: Linking eye movements to visual perception. Journal of Vision, 3(11), 808–816.
Heinke, D., Deco, G., Zihl, J., & Humphreys, G. (2002). A computational neuroscience account of visual neglect. Neurocomputing, 44–46, 811–816.
Heinke, D., & Humphreys, G. W. (2003). Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). Psychological Review, 110(1), 29–87.
Heinke, D., & Humphreys, G. W. (2005). Computational models of visual selective attention: A review. In G. Houghton (Ed.), Connectionist models in psychology (pp. 273–312). London: Psychology Press.
Herd, S. A., Banich, M. T., & O’Reilly, R. C. (2006). Neural mechanisms of cognitive control: An integrative model of stroop task performance and FMRI data. Journal of Cognitive Neuroscience, 18(1), 22–32.
Houghton, G., & Tipper, S. P. (1996). Inhibitory mechanisms of neural and cognitive control: Applications to selective attention and sequential action. Brain and Cognition, 30(1), 20–43.
Humphreys, G. W., & Muller, H. J. (1993). SEarch via Recursive Rejection (SERR): A connectionist model of visual search. Cognitive Psychology, 25(1), 43–110.
Itti, L., & Baldi, P. (2005). A principled approach to detecting surprising events in video. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1, 631–637.
Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40(10–12), 1489–1506.
Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature reviews. Neuroscience, 2(3), 194–203.
Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.
James, W. (1890). Principle of Psychology. Dover Publications 1950, vol. 1.
Just, M. A., Carpenter, P. A., Keller, T. A., Emery, L., Zajac, H., & Thulborn, K. R. (2001). Interdependence of nonoverlapping cortical systems in dual cognitive tasks. Neuroimage, 14(2), 417–426.
Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4), 219–227.
Koechlin, E., Basso, G., Pietrini, P., Panzer, S., & Grafman, J. (1999). The role of the anterior prefrontal cortex in human cognition. Nature, 399, 148–151.
Kwak, H. W., & Egeth, H. (1992). Consequences of allocating attention to locations and to other attributes. Perception & Psychophysics, 51(5), 455–464.
Lee, D. K., Itti, L., Koch, C., & Braun, J. (1999). Attention activates winner-take-all competition among visual filters. Nature Neuroscience, 2(4), 375–381.
Lee, T. S. (1996). Image representation using 2D gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10), 959–971.
Logan, G. D. (2005). The time it takes to switch attention. Psychonomic Bulletin & Review, 12(4), 647–653.
Logan, G. D., & Gordon, R. D. (2001). Executive control of visual attention in dual-task situations. Psychological Review, 108(2), 393–434.
Lovett, M. C. (2002). Modeling selective attention: Not just another model of Stroop. Cognitive Systems Research, 3(1), 67–76.
Maia, T. V., & Cleeremans, A. (2005). Consciousness: Converging insights from connectionist modeling and neuroscience. Trends in Cognitive Sciences, 9(8), 397–404.
McClelland, J. L. (1979). On the time relations of mental processes: An examination of systems of processes in cascade. Psychological Review, 86, 287–330.
McClelland, J. L., & Rumelhart, D. E. (1981). An interative activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375–407.
Melara, R. D., & Algom, D. (2003). Driven by information: A tectonic theory of Stroop effects. Psychological Review, 110, 422–471.
Meyer, D. E., & Kieras, D. E. (1997). A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. Psychological Review, 104, 3–65.
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 21, 167–202.
Mozer, M. C., Shettel, M., & Vecera, S. (2006). Top-down control of visual attention: A rational account. In Y. Weiss, B. Schoelkopf, & J. Platt (Eds.), Neural information processing systems (pp. 923–930). Cambridge, MA: MIT Press.
Mozer, M. C., & Sitton, M. (1998). Computational modeling of spatial attention. In H. Pashler (Ed.), Attention (pp. 341–393). London: Psychology Press.
Navalpakkam, V., & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45(2), 205–231.
Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In R. J. Davidson, G. E. Schwartz, & D. Shapiro (Eds.), Consciousness and Self-regulation (Vol. 4, pp. 1–18). New York: Plenum Press.
Olshausen, B. A., Anderson, C. H., & Van Essen, D. C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. The Journal of Neuroscience, 13(11), 4700–4719.
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 91–94.
O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically-based computational model of working memory. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control. New York: Cambridge University Press.
O’Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the ming by simulating the brain. Cambridge, MA: MIT Press.
Parkhurst, D., Law, K., & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107–123.
Pashler, H. (1994). Dual-task interference in simple tasks: Data and theory. Psychological Bulletin, 116(2), 220–244.
Phaf, R. H., Van der Heijden, A. H., & Hudson, P. T. (1990). SLAM: A connectionist model for attention in visual selection tasks. Cognitive Psychology, 22(3), 273–341.
Posner, M. I., Cohen, Y., & Rafal, R. D. (1982). Neural systems control of spatial orienting. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences, 298(1089), 187–198.
Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition (pp. 55–85). Hillsdale, NJ: Erlbaum.
Reynolds, J. R., Braver, T. S., Brown, J. W., & Stigchel, S. (2006). Computational and neural mechanisms of task-switching. Neurocomputing, 69, 1332–1336.
Roelofs, A. (2000). Control of language: A computational account of the Stroop asymmetry. In Proceedings of the Third International Conference on Cognitive Modeling. Veenendaal, The Netherlands: Universal Press.
Rougier, N. P., Noelle, D., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and the flexibility of cognitive control: Rules without symbols. Proceedings of the National Academy of Sciences, 102(20), 7338–7343.
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1 and 2). Cambridge, MA: MIT Press.
Salvucci, D. D. (2005). A multitasking general executive for compound continuous tasks. Cognitive Science, 29, 457–492.
Schneider, D. W., & Logan, G. D. (2005). Modeling task switching without switching tasks: A short-term priming account of explicitly cued performance. Journal of Experimental Psychology: General, 34(3), 343–367.
Schneider, W., & Chein, J. M. (2003). Controlled and automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science, 27(3), 525–559.
Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1–66.
Servan-Schreiber, D., Bruno, R., Carter, C., & Cohen, J. (1998). Dopamine and the mechanisms of cognition: Part I. A neural network model predicting dopamine effects on selective attention. Biological Psychiatry, 43(10), 713–722.
Shipp, S. (2004). The brain circuitry of attention. Trends in Cognitive Sciences, 8(5), 223–230.
Sohn, M.-H., & Anderson, J. R. (2003). Stimulus-related priming during task switching. Memory & Cognition, 31(5), 775–780.
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.
Treisman, A. (1999). Feature binding, attention and object perception. In J. W. Humphreys & J. Duncan (Eds.), Attention space and action (pp. 91–111). Oxford, UK: Oxford University Press.
Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136.
Tsotsos, K. J., Culhane, M. S., Wai, K. W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995). Modeling visual attention via selective tuning. Artificial Intelligence, 78, 507–545.
Usher, M., & Cohen, J. D. (1999). Short term memory and selection processes in a frontallobe model.In D. Heinke, G. W. Humphries, & A. Olsen (Eds.), Connectionist models in cognitive neuroscience (pp. 78–91). The 5th Neural Computation and Psychology Workshop. Springer Verlag, University of Birmingham, UK.
van de Laar, P., Heskes, T., & Gielen, S. (1997). Task-dependent learning of attention. Neural Networks, 10(6), 981–992.
van der Heijden, A. H., & Bem, S. (1997). Successive approximations to an adequate model of attention. Consciousness and Cognition, 6(2–3), 413–428.
Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46, 681–692.
Ward, R. (1999). Interaction between perception and action systems: A model for selective action. In W. G. Humphreys, J. Duncan, & A. Treisman (Eds.), Attention, space and action –Studies in cognitive neuroscience (pp. 311–332). Oxford, UK: Oxford University Press.
Wolfe, C. D., & Bell, M. A. (2004). Working memory and inhibitory control in early childhood: Contributions from physiology, temperament, and language. Developmental Psychobiology, 44(1), 68–83.
Wolfe, J. (1994). Guided search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202–238.