Abstract
Previous research has developed a formal methods-based (cognitive-level) model of the Interacting Cognitive Subsystems central engine, with which we have simulated attentional capture in the context of Barnard’s key-distractor Attentional Blink task. This model captures core aspects of the allocation of human attention over time and as such should be applicable across a range of practical settings when human attentional limitations come into play. In addition, this model simulates human electrophysiological data, such as electroencephalogram recordings, which can be compared to real electrophysiological data recorded from human participants. We have used this model to evaluate the performance trade-offs that would arise from varying key parameters and applying either a constructive or a reactive approach to improving interactive systems in a stimulus rich environment. A strength of formal methods is that they are abstract and the resulting specifications of the operator are general purpose, ensuring that our findings are broadly applicable. Thus, we argue that new modelling techniques from computer science can also be employed in computational modelling of the mind. These would complement existing techniques, being specifically targeted at psychological level modelling, in which it is advantageous to directly represent the distribution of control.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Abeles M, Bergman H, Margalis E, Vaadia E (1993) Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J Neurophysiol 70: 1629–1638
Anderson JR (1993) Rules of the mind. Erlbaum, Hillsdale
Anderson AK (2005) Affective influences on the attentional dynamics supporting awareness. J Exp Psychol General 134(2): 258–281
Baeten JC, Middelburg CA, Middelburg K (2002) Process algebra with timing. Springer, New York
Barnard PJ (1999) Interacting cognitive subsystems: modelling working memory phenomena within a multi-processor architecture, Models of Working Memory: Mechanisms of active maintenance and executive control, pp 298–339
Barnard PJ, Scott S, Taylor J, May J, Knightley W (2004) Paying attention to meaning. Psychol Sci 15(3): 179–186
Barnard PJ, Bowman H (2003) Rendering information processing models of cognition and affect computationally explicit: distributed executive control and the deployment of attention. Cogn Sci Q 3(3): 297–328
Barnard PJ, Ramponi C, Battye G, Mackintosh B (2005) Anxiety and the deployment of visual attention over time. Vis Cogn 12(1): 181–211
Beverina F, Palmas G, Silvoni S, Piccione F, Giove S (2003) User adaptive BCIs: SSVEP and P300 based interfaces. Psychnology 1(4): 331–354
Bolognesi T, Brinksma E (1988) Introduction to the ISO specification language LOTOS. Comput Netw ISDN Syst 14910: 25–29
Bond AH (1999) Describing behavioural states using a system model of the primate brain. Am J Primatol 49: 315–338
Bowman H, Bryans JW, Derrick J (2001) Analysis of a multimedia stream using stochastic process algebra. Comput J 44(4): 230–245
Bowman H, Faconti G (1999) Analysing cognitive behaviour using LOTOS and Mexitl. Formal Aspects Comput 11: 132–159
Bowman H, Gomez RS (2006) Concurrency theory, calculi and automata for modelling untimed and timed concurrent systems. Springer, Berlin
Bowman H, Wyble B (2007) The simultaneous type, serial token model of temporal attention and working memory. Psychol Rev 114(1): 38–70
Carver CS, Scheier MF (1998) On the self-regulation of behaviour. Cambridge University Press, Cambridge
Chun MM, Potter MC (1995) A two-stage model for multiple target detection in rapid serial visual presentation. J Exp Psychol Hum Percept Perform 21(1): 109–127
Cilliers PJ, Van Der Kouwe AJW (1993) A VEP-based computer interfcae for C2-Quadriplegics. Engineering in Medicine and Biology Society, 1993. In: Proceedings of the 15th Annual International Conference of the IEEE, vol 15(3), pp 1263–1624
Cooper R, Fox J, Farringdon J, Shallice T (1996) A systematic methodology for cognitive modelling. Artif Intell 85: 3–44
Craston P, Wyble B, Chennu S, Bowman H (2008) The attentional blink reveals serial working memory encoding: evidence from virtual and human event-related potentials. J Cogn Neurosci (in press)
Donchin E (1981) Presidential address, 1980. Surprise!...Surprise?. Psychophysiology 18(5): 493–513
Duke DJ, Barnard PJ, Duce DA, May J (1998) Syndetic modelling. Hum Comput Interact 13(4): 337–393
Ehrig H, Fey W, Hansen H (1983) ACT ONE—an algebraic specification language with two levels of semantics, ADT
Elman JL, Bates EA, Johnson MH, Karmiloff-Smith A, Parisi D, Plunkett K (1996) Rethinking innateness: a connectionist perspective on development, a Bradford book. MIT Press, Cambridge
Erickson TD, Mattson ME (1981) From words to meaning: a semantic illusion. J Verbal Learn Verbal Behav 20: 540–551
Fodor JA, Pylyshyn ZW (1988) Connectionism and cognitive architecture: a critical analysis. Cognition 28: 3–71
Garavel H, Lang F, Mateescu F (2002) An overview of CADP 2001. EASST Newsl 4: 13–24
Garavel H, Viho C, Zendri M (2001) System design of a CC-NUMA multiprocessor architecture using formal specification, model-checking, co-simulation, and test generation. Springer Int J Softw Tools Technol Transf (STTT) 3(3): 314–331
Gomez ME, Santonja V (1998) Self-similiary in I/O workload: analysis and modeling. In: Workshop on Workload Characterization
Grill-Spector K, Kanwisher N (2005) Visual recognition: as soon as you know it is there, you know what it is. Psychol Sci 16(2): 152–160
Hoare CAR (1985) Communicating sequential processes. Prentice-Hall, London
Kieras DE, Meyer DE, Mueller S, Seymour T (1999) Insights into working memory from the perspective of the EPIC architecture for modelling skilled perceptual-motor and cognitive human performance, Models of Working Memory, Mechanisms of Active Maintenance and Executive Control. Cambridge University Press, New York, pp 183–223
Leventhal H (1979) A perceptual-motor processing model of emotion. In: Pilner P, Blankstein K, Spigel IM (eds) Perception of emotion in self and others, vol 5. Plenum, New York, pp 1–46
Levine SP, Huggins JE, BeMent SL, Kushwaha RK, Schuh LA, Rohde MM (2000) A direct brain interface based on event-related potentials. IEEE Trans Rehabil Eng 8(2): 180–185
Mackworth J (1963) The duration of the visual image. Can J Paychol 17(1): 62–68
Maki WS, Frigen K, Paulsen K (1997) Associative priming by targets and distractors during rapid serial presentation. J Exp Psychol Hum Percept Perform 23: 1014–1034
Meinicke P, Kaper M, Hoppe F, Heumann M, Ritter H (2003) Improving transfer rates in brain computer interfacing: a case study. Adv Neural Inform Process Syst 15: 1107–1114
Metz CE (1978) Basic principles of ROC analysis. Semin Nuclear Med 8: 283–298
Meyer DE, Kieras DE (1997) A computational theory of executive cognitive processes and multiple task performance: Part 1. Basic mechanisms. Psychol Rev 104: 3–65
Milner R (1989) Communication and concurrency. Prentice-Hall, Hemel Hempstead
Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge
O’Reilly RC, Munakata Y (2000) Computational explorations in cognitive neuroscience: understanding the mind by simulating the brain, a Bradford book. MIT Press, Cambidge
Raymond J, Shapiro K, Arnell KM (1992) Temporary suppression of visual processing in an RSVP Task: an attentional blink. J Exp Psychol Hum Percept Perform 18(3): 849–860
Rolls ET, Stringer SM (2001) A model of the interaction between mood and memory. Netw: Comput Neural Syst 12: 89–109
Rolls ET, Treves A (1998) Neural networks and brain function. Oxford University Press, Oxford
Rumelhart DE, McClelland JL, thePDP Research Group (1986) Parallel distributed processing, explorations in the microstructure of cognition. Volume 1: Foundations and Volume 2: Psychological and Biological Models, a Bradford book. MIT Press, Cambridge
Schmolesky M, Wang Y, Hanes D, Thompson K, Leutgeb S, Schall J, Leventhal AG (1998) Signal timing across the Macaque visual system. J Neurophysiopl 79(6): 3272–3278
Shapiro KL, Caldwell JI, Sorensen RE (1997) Personal names and the attentional blink: the cocktail party revisited. J Exp Psychol Hum Percept Perform 23: 504–514
Shapiro KL, Luck SJ (1999) The attentional blink: a front-end mechanism for fleeting memories. In: Fleeting memories, cognition of brief visual stimuli, A Bradford book. MIT Press, Boston, pp 95–118
Snodgrass JG, Corwin J (1988) Pragmatics of measuring recognition memory: applications to dementia and amnesia. J Exp Psychol Gen 117(1): 34–50
Squires NK, Squires KC, Hillyard SA (1975) Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalogr Clin Neurophysiol 38(4): 387–401
Su L, Bowman H, Barnard PJ (2007) Attentional capture by meaning: a multi-level modelling study. In: Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society, Austin, pp 1521–1526
Su L, Bowman H, Barnard PJ (2008) Performance of reactive interfaces in stimulus rich environments, applying formal methods and cognitive frameworks. In: The 2nd International Workshop on Formal Methods for Interactive Systems FMIS2007 (held in conjunction with HCI2007), Electronic Notes in Theoretical Computer Science, vol 208. Elsevier, Amsterdam, pp 95–111
Teasdale JD, Barnard PJ (1993) Affect, cognition and change: re-modelling depressive thought. Lawrence Erlbaum Associates, Hove
Vidal JJ (1973) Toward direct brain-computer communication. Ann Rev Biophys Bioeng 2: 157–180
Vogel EK, Luck SJ, Shapiro KL (1998) Electrophysiological evidence for a postperceptual locus of suppression during the attentional blink. J Exp Psychol Hum Percept Perform 24(6): 1656–1674
Wang M, Madhyastha T, Chan NH, Papadimitriou S, Faloutsos C (2002) Data mining meets performance evaluation: fast algorithms for modeling bursty traffic. In: 18th International Conference on Data Engineering
Wyble B, Bowman H (2005) Computational and experimental evaluation of the attentional blink: testing the simultaneous type serial token model. In: Bara BG, Barsalou LW, Bucciarelli M (eds) CogSci 2005, XXVII Annual Conference of the Cognitive Science Society. Cognitive Science Society, Cognitive Science Society through Lawrence Erlbaum, Austin, pp 2371–2376
Wyble B, Craston P, Bowman H (2006) Electrophysiological feedback in adaptive human–computer interfaces, Technical Report 8-06, Computing Laboratory, University of Kent, Canterbury, UK
Acknowledgements
We are indebted to anonymous reviewers for their comments on the previous version of this manuscript. We also thank Patrick Craston, Srivas Chennu and Dell Green for their contribution to the collection and analysis of the EEG data. The UK Engineering and Physical Sciences Research Council supported this research (grant number GR/S15075/01). The participation of Philip Barnard in this project was supported by the Medical Research Council under project code U.1055.02.003.00001.01.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Author information
Authors and Affiliations
Corresponding author
Additional information
A. Cerone, P. Curzon and D.A. Duce
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Su, L., Bowman, H., Barnard, P. et al. Process algebraic modelling of attentional capture and human electrophysiology in interactive systems. Form Asp Comp 21, 513–539 (2009). https://doi.org/10.1007/s00165-008-0094-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00165-008-0094-3