Skip to main content
SpringerLink
Log in

ACT-R models of information foraging in geospatial intelligence tasks

  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

We describe the development of computational cognitive models that predict information selection behavior in simulated geospatial intelligence tasks. These map-based tasks require users to select layers that visualize different types of intelligence, and to revise probability estimates of attack by hypothetical insurgent groups. Our first model has vast amounts of task-specific declarative memory and selects information layers that provide maximum expected information gain. This first model exhibits layer selection sequences that are almost identical to a rational (Bayesian) model, but fails to predict the layer selection sequences of human participants’ performing the tasks. Our second model integrates instance-based learning with reinforcement learning and information foraging theory to predict the selection of information layers. The second model replicates the distribution of participants’ layer selection sequences well. We conclude with some limitations that our current ACT-R model has and the role of cognitive models in the intelligence analysis tasks.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Anderson JR, Lebiere C (1998) The atomic components of thought. Erlbaum, Mahwah

    Google Scholar 

  • Anderson JR, Schooler LJ (1991) Reflections of the environment in memory. Psychol Sci 2:396–408

    Article  Google Scholar 

  • Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y (2004) An integrated theory of the mind. Psychol Rev 111:1036–1060

    Article  Google Scholar 

  • Barto AG (1998) Reinforcement learning: an introduction. MIT press, Cambridge

    Google Scholar 

  • Betts RK (2007) Two faces of intelligence failure: September 11 and Iraq’s missing WMD. Polit Sci Q 122:585–606

    Article  Google Scholar 

  • Burns K, Greenwald H, Fine M (2014) Integrated cognitive-neuroscience architectures for understanding sensemaking (ICArUS): phase 1 challenge problem design and test specification (MITRE Technical Report 140410). Retrieved from http://www.mitre.org/sites/default/files/publications/pr_2189-icarus-phase-1-challenge-problem-design-and-test-specification.pdf

  • Fingar T (2011) Analysis in the US intelligence community: Missions, masters, and methods. Intelligence Analysis: Behavioral and Social Scientific Foundations, pp 3–27

  • Fu W-T, Gray WD (2006) Suboptimal tradeoffs in information seeking. Cogn Psychol 52:195–242

    Article  Google Scholar 

  • Fu W-T, Pirolli P (2007) SNIF-ACT: a cognitive model of user navigation on the World Wide Web. Hum-Comput Interact 22:355–412

    Google Scholar 

  • Fuglede B, Topsoe F (2004) Jensen-Shannon divergence and Hilbert space embedding. In: IEEE international symposium on information theory, pp 31–31

  • Gibson JJ (1977) The theory of affordances. In: Shaw R, Bransford J (eds) Perceiving, acting, and knowing. Towards an ecological psychology. Wiley, Hobboken

    Google Scholar 

  • Gonzalez C, Lerch JF, Lebiere C (2003) Instance-based learning in dynamic decision making. Cogn Sci 27:591–635

    Article  Google Scholar 

  • Heuer RJ (1999) Psychology of intelligence analysis. Center for the Study of Intelligence, Central Intelligence Agency, Washington, DC

    Google Scholar 

  • Kerr RJ (2008) The track record: CIA analysis from 1950 to 2000 Analyzing intelligence: origins, obstacles, and innovations, pp 35–54

  • Klein G (1999) Sources of power: how people make decisions. MIT press, Cambridge

    Google Scholar 

  • Klein G, Moon B, Hoffman RR (2006a) Making sense of sensemaking 1: alternative perspectives. Intell Syst IEEE 21:70–73

    Article  Google Scholar 

  • Klein G, Moon B, Hoffman RR (2006b) Making sense of sensemaking 2: a macrocognitive model. Intell Syst IEEE 21:88–92

    Article  Google Scholar 

  • Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:79–86

    Article  Google Scholar 

  • Lebiere C (1999) The dynamics of cognition: an ACT-R model of cognitive arithmetic. Kognitionswissenschaft 8:5–19

    Article  Google Scholar 

  • Lebiere C, Pirolli P, Thomson R, Paik J, Rutledge-Taylor M, Staszewski J, Anderson JR (2013) A functional model of sensemaking in a neurocognitive architecture. Comput Intell Neurosci 2013:5

    Article  Google Scholar 

  • Miller GA (1956) The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    Article  Google Scholar 

  • Nkambou R, Bourdeau J, Mizoguchi R (2010) Advances in intelligent tutoring systems, vol 308. Springer, New York

    Google Scholar 

  • Paik J, Pirolli P, Lebiere C, Rutledge-Taylor M (2012) Cognitive biases in a geospatial intelligence analysis task: an ACT-R model. In: 34th annual meeting of the Cognitive Science Society, Sapporo, Japan

  • Pirolli P (2007) Information foraging theory. In: Adaptive interation with information. Oxford University Press, New York

  • Pirolli P, Card S (1999) Information foraging. Psychol Rev 106:643

    Article  Google Scholar 

  • Pirolli P, Card S (2005) The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In: International conference on intelligence analysis, McLean, VA, pp 2–4

  • Puvathingal B, Hantual D (2012) Revisiting the psychology of intelligence analysis: from rational actors to adaptive thinkers. Am Psychol 67:199–210

    Article  Google Scholar 

  • Roll I, Baker RS, Aleven V, McLaren BM, Koedinger KR (2005) Modeling students’ metacognitive errors in two intelligent tutoring systems. In: User modeling 2005. Springer, New York, pp 367–376

  • Russell DM, Stefik MJ, Pirolli P, Card SK (1993) The cost structure of sensemaking. In: Proceedings of the INTERACT’93 conference on human factors in computing systems, Amsterdam. ACM, pp 269–276

  • Simon HA (1974) How big is a chunk? Science 183:482–488

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of the Interior (DOI) contract number D10PC20021. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained hereon are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI, or the U.S. Government. We thank Christian Lebiere, Robert Thomson, Matthew Rutledge-Taylor, James Staszewski, and John Anderson for their useful comments.

Author information

Authors and Affiliations

  1. UX Laboratory, LG Electronics, Seoul, Korea

    Jaehyon Paik

  2. Palo Alto Research Center, 3333 Coyote Hill Rd., Palo Alto, CA, 94304, USA

    Peter Pirolli

Authors
  1. Jaehyon Paik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Pirolli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaehyon Paik.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paik, J., Pirolli, P. ACT-R models of information foraging in geospatial intelligence tasks. Comput Math Organ Theory 21, 274–295 (2015). https://doi.org/10.1007/s10588-015-9185-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-015-9185-x

Keywords

Search

Navigation