Skip to main content

Advertisement

SpringerLink
Log in

Accounting for Certain Mental Disorders Within a Comprehensive Cognitive Architecture

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

This paper explores how mental disorders of certain types might be explained based on mechanisms and processes of human motivation (including drives and goals) and action selection (as well as other related mechanisms and processes), within a generic, comprehensive computational cognitive architecture model. It is hypothesized that such mechanisms may capture the relative invariance within an individual in terms of behavioral inclinations (at different times and with regard to different situations, as well as the necessary variability of behaviors). The hypothesis results from the computational cognitive architecture CLARION. Several simulation tests have been conducted that demonstrate that the model is reasonable and captures some characteristics of certain mental disorders (such as certain types of addiction and obsessive-compulsive disorder). The work is a first step in showing the feasibility of integrating mental disorders modeling/simulation into a generic cognitive model (i.e., a cognitive architecture).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Note that the selection probabilities may be variable, determined through a psychological process known as “probability matching”, that is, the probability of selecting a component is determined based on the relative success ratio of that component.

  2. Note that a generalized notion of “drive” is adopted here, different from the stricter interpretations of drives (e.g., as physiological deficits that require to be reduced by corresponding behaviors; Hull, 1951). In our sense, drives denote internally felt needs of all kinds that likely may lead to corresponding behaviors, regardless of whether the needs are physiological or not, whether the needs may be reduced by the corresponding behaviors or not, or whether the needs are for end states or for processes. Therefore, it is a generalized notion that transcends controversies surrounding the stricter notions of drive.

  3. Note that, in terms of the gain s parameter, averaging of the two gain s parameters may be used to handle those drives that are in both BIS and BAS.

  4. Note that drive strengths actually could be a function of the above equation; in the simplest case, an identity function may be assumed, as shown above.

  5. The deficit of a drive was “decayed” at each step using a multiplicative factor (decay d  = 10%), when the agent was actively addressing the drive (i.e., when the agent’s goal corresponded to the drive). See Appendix for more discussions of this issue. The "decay" of the deficit of the corresponding drive was just a simplification for the sake of this particular simulation (decay of the deficit of the drive occurred only if the input state remained the same for this simulation).

References

  1. Bach J. Principles of synthetic intelligence psi: an architecture of motivated cognition. New York: Oxford University Press; 2009.

    Book  Google Scholar 

  2. Baker TB, Piper ME, McCarthy DE, Majeskie MR, Fiore MC. Addiction motivation reformulated: an affective processing model of negative reinforcement. Psychol Rev. 2004;111:33–51.

    Article  PubMed  Google Scholar 

  3. Cacioppo JT, Gardner WL, Berntson GG. The affect system has parallel and inte-grative processing components: Form follows function. J Pers Soc Psychol. 1999;76:839–55.

    Article  Google Scholar 

  4. Caprara GV, Cervone D. Personality: determinants, dynamics, and potentials. New York: Cambridge University Press; 2000.

    Google Scholar 

  5. Clark LA, Watson D. Temperament: a new paradigm for trait psychology. In: Pervin LA, John OP, editors. Handbook of personality: theory and research. 2nd ed. New York: Guilford Press; 1999. p. 399–423.

    Google Scholar 

  6. Deci E. Intrinsic motivation and personality. In: Staub E, editor. Personality: basic issues and current research. Englewood Cliffs: Prentice Hall; 1980. p. 35–80.

    Google Scholar 

  7. Depue RA, Collins PF. Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation and extraversion. Behav Brain Sci. 1999;22:491–569.

    PubMed  CAS  Google Scholar 

  8. Elliot A, Thrash T. Approach-avoidance motivation in personality: approach and avoidance temperaments and goals. J Pers Soc Psychol. 2002;82(5):804–18.

    Article  PubMed  Google Scholar 

  9. Gray JA. The neuropsychology of emotion and personality. In: Gray JA, Stahl SM, Iverson SD, Goodman EC, editors. Cognitive neurochemistry. New York, NY: Oxford University Press; 1987. p. 171–90.

    Google Scholar 

  10. Gutkin BS, Dehaene S, Changeux J. A neurocomputational hypothesis for nicotine addiction. PNAS. 2006;103(4):1106–11.

    Article  PubMed  CAS  Google Scholar 

  11. Helie S, Sun R. Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychol Rev. 2010;117(3):994–1024.

    Article  PubMed  Google Scholar 

  12. Huey ED, Zahn Roland, Krueger Frank, Moll Jorge, Kapogiannis Dimitrios, Wassermann EricM, Grafman Jordan. A psychological and neuroanatomical model of obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci. 2008;20:390–408.

    Article  PubMed  Google Scholar 

  13. Kubota S, Aihara K. Computational model of obsessive-compulsive disorder: a unified explanation of the treatments. Int J Bifurcation Chaos. 2004;14(1):263–77.

    Article  Google Scholar 

  14. Mazzoni G, Nelson T, editors. Metacognition and cognitive neuropsychology. Mahwah: Erlbaum; 1998.

    Google Scholar 

  15. McDougall W. An introduction to social psychology. London: Methuen & Co.; 1936.

    Google Scholar 

  16. McFarland D. Problems of animal behavior. Singapore: Longman Publishing; 1989.

    Google Scholar 

  17. Murray H. Explorations in personality. New York: Oxford University Press; 1938.

    Google Scholar 

  18. Newell A. Unified theories of cognition. Cambridge, MA: Harvard University Press; 1990.

    Google Scholar 

  19. Ownby RL. Computational model of obsessive-compulsive disorder: examination of etiologic hypothesis and treatment strategies. Depression and Anxiety. 1998;8(3):91–103.

    Article  PubMed  CAS  Google Scholar 

  20. Read SJ, Monroe BM, Brownstein AL, Yang Y, Chopra G, Miller LC. A neural network model of the structure and dynamics of human personality. Psychol Rev. 2010;117(1):61–92.

    Article  PubMed  Google Scholar 

  21. Reder L, editor. Implicit memory and metacognition. Mahwah, NJ: Erlbaum; 1996.

    Google Scholar 

  22. Redish AD. Addiction as a computational process gone awry. Science. 2004;306(5703):1944–7.

    Article  PubMed  CAS  Google Scholar 

  23. Rumelhart D, McClelland J, The PDP Research Group. Parallel distributed processing: explorations in the microstructures of cognition. Cambridge, MA: MIT Press; 1986.

    Google Scholar 

  24. Smillie LD, Pickering AD, Jackson CJ. The new reinforcement sensitivity theory: implications for personality measurement. Pers Soc Psychol Rev. 2006;10:320–35.

    Article  PubMed  Google Scholar 

  25. Sun R. Duality of the mind: a bottom-up approach toward cognition. Mahwah: Lawrence Erlbaum Associates; 2002.

    Google Scholar 

  26. Sun R (2003) A Tutorial on CLARION 5.0. Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute. http://www.cogsci.rpi.edu/rsun/sun.tutorial.pdf.

  27. Sun R, editor. The Cambridge handbook of computational psychology. New York: Cambridge University Press; 2008.

    Google Scholar 

  28. Sun R. Theoretical status of computational cognitive modeling. Cognit Syst Res. 2009;10(2):124–40.

    Article  Google Scholar 

  29. Sun R. Motivational representations within a computational cognitive architecture. Cognit Comput. 2009;1(1):91–103.

    Article  Google Scholar 

  30. Sun R, Merrill E, Peterson T. From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognit Sci. 2001;25:203–44.

    Article  Google Scholar 

  31. Sun R, Slusarz P, Terry C. The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychol Rev. 2005;112:159–92.

    Article  PubMed  Google Scholar 

  32. Sun R, Wilson N. Motivational processes within the perception-action cycle. In: Cutsuridis V, Hussain A, Taylor JG, editors. Perception-action cycle. Berlin: Springer; 2010.

    Google Scholar 

  33. Sun R, Zhang X. Top-down versus bottom-up learning in cognitive skill acquisition. Cognit Syst Res. 2004;5:63–89.

    Article  Google Scholar 

  34. Sun R, Zhang X. Accounting for a variety of reasoning data within a cognitive architecture. J Exp Theor Artif Intell. 2006;18:169–91.

    Article  CAS  Google Scholar 

  35. Sun R, Zhang X, Mathews R. Modeling meta-cognition in a cognitive architecture. Cognit Syst Res. 2006;7:327–38.

    Article  Google Scholar 

  36. Szechtman H, Woody Erik. Obsessive-compulsive disorder as a disturbance of security motivation. Psychol Rev. 2004;11(1):111–27.

    Article  Google Scholar 

  37. Toates F. Motivational systems. Cambridge: Cambridge University Press; 1986.

    Google Scholar 

  38. Tolman EC. Purposive behavior in animals and men. New York: Century; 1932.

    Google Scholar 

  39. Tyrell, T. (1993). Computational mechanisms for action selection. Ph.D Thesis, University of Edinburgh, Edinburgh.

  40. Watkins, C. (1989). Learning with delayed rewards. Ph.D Thesis, Cambridge University, Cambridge.

  41. Welsh JM, Jing L, Rodriguiz RM, Trotta NC, Peca J, Ding J-D, Feliciano C, Chen M, Adams JP, Luo J, Dudek SM, Weinberg RJ, Calakos N, Wetsel WC, Feng G. Cortico-striatal synaptic defects and OCD-like behaviors in SAPAP3-mutant mice. Nature. 2007;448:894–900.

    Article  Google Scholar 

  42. Wiers RW, Stacy AW. Handbook of implicit cognition and addiction. Thousand Oaks, CA: Sage Publications; 2006.

    Google Scholar 

  43. Wilson N, Sun R, Mathews R. Performance degradation under pressure. Neural Netw. 2009;22:502–8.

    Article  PubMed  Google Scholar 

  44. Wilson N, Sun R, Mathews R (2010) A motivationally based computational interpretation of social anxiety induced stereotype bias. In: Proceedings of the 2010 cognitive science society conference (pp. 1750–1755). Cognitive Science Society, Austin, Texas.

Download references

Acknowledgments

This work has been supported in part by the ARI grant W74V8H-05-K-0002 (to Ron Sun and Bob Mathews) and the ONR grant N00014-08-1-0068 (to Ron Sun). Thanks are due to Larry Reid and Paul Bello for their comments.

Author information

Authors and Affiliations

  1. Department of Cognitive Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Ron Sun & Nick Wilson

  2. Department of Psychology, Louisiana State University, Baton Rouge, LA, 70803, USA

    Robert Mathews

Authors
  1. Ron Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nick Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Mathews
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ron Sun.

Appendix

Appendix

Change of Drive Deficits

In general, we need a mechanism (i.e., the “deficit change module”, as discussed before) for reducing/increasing the deficit related to a drive that is affected by the current action under the current goal. The reduction/increase in deficits is outside the current specification of the MS in CLARION. This is because their causes are complex and varied; it may be physiologically determined sometimes (e.g., for “food deficit” usually), but may also be psychologically determined in a complex way some other times (e.g., for “dominance and power deficit”).

For instance, in a simplified simulation, one “winning” drive that gets to set the “winning” goal, will be impacted by subsequent actions; it may be gradually reduced. But in general, multiple drives may contribute to setting the “winning” goal (see the technical details in [26, 29]), and many of them may be impacted, if the actions performed lead to reducing (or increasing) the deficits related to these drives. (For example, eating solid food might lead to reducing both the food deficit as well as the water deficit.). Even other drives that did not contribute to setting the “winning” goal may also be impacted in some fashion, if the actions performed lead to reducing or increasing the deficits related to these drives.

Determining Avoidance Versus Approach Drives, Goals, and Behaviors

To decide whether a drive belongs to BIS or BAS, the following principles have been hypothesized:

  • This determination is based on seeking positive rewards versus avoiding punishments (i.e., negative rewards).

  • This determination does not involve complex reasoning, mental simulation, and so on because the processes of drives are reflexive and immediate [25].

  • Some drives come with intrinsic positive rewards (e.g., food, reproduction, fairness/vengeance, dominance, affiliation, achievement—essentially all the drives in the BAS), while others do not have related intrinsic positive reward (e.g., sleeping, avoiding danger, avoiding the unpleasant; so, mostly, they are for avoiding negative rewards).

Based on the above, we can justify one by one why each drive should be in BAS, BIS, or both (details omitted). See Table 2 for the result of this determination.

To determine whether a goal is approach or avoidance oriented, we base the decision on its associations to approach or avoidance drives. Specifically, we sum the strengths of a goal (as determined by the goal strength equation of CLARION) taken over all of the scenarios for approach and avoidance drives, respectively. If the sum of strengths for a given goal is higher when coupled with approach drives than with avoidance drives, then, it is an approach goal. If the sum of strengths for a given goal is higher when coupled with avoidance drives than with approach drives, then, it is an avoidance goal.

We define behaviors as being either approach or avoidance oriented, based upon associations with approach or avoidance goals. We accomplish this by summing the values of each behavior (as determined by the trained bottom level of the ACS of CLARION) taken over all of the scenarios for approach and avoidance goals, respectively. If the sum of values for a given behavior is higher when coupled with approach goals than with avoidance goals, then the behavior is an approach behavior. If the sum of values for a given behavior is higher when coupled with avoidance goals than with approach goals, then the behavior is an avoidance behavior.

Some Additional Data Used in the Simulations

Some additional data used in the simulations can be seen below in Tables 17, 18, 19, 20. See also [32] for other related details and justifications.

Table 17 The relevance of the drives to goals (for the sake of selecting a goal to pursue)
Full size table
Table 18 The drive-specific stimulus levels for the 15 scenarios (for simulation tests 1-3)
Full size table
Table 19 The drive-specific stimulus levels for some scenarios (for simulation test 4)
Full size table
Table 20 The drive-specific stimulus levels for some scenarios (for simulation test 5)
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, R., Wilson, N. & Mathews, R. Accounting for Certain Mental Disorders Within a Comprehensive Cognitive Architecture. Cogn Comput 3, 341–359 (2011). https://doi.org/10.1007/s12559-011-9099-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-011-9099-y

Keywords

Search

Navigation