Abstract
We examined performance in a cued detection task when a peripheral cue was either 50% or 100% valid, tested in separate experiments. We combined a Posner cueing task with a double factorial manipulation of stimulus salience. Unlike previous investigations in which participants responded to either target, we employed an AND decision task in which a target-present decision required there to be a target at both locations. When the cue was 50% valid, all the participants adopted parallel exhaustive processing to detect redundant targets with unlimited to limited capacity. When the cue was 100% valid, three participants, who performed this experiment first, adopted serial exhaustive processing. By contrast, the participants who first performed the 50% validity experiment continued to adopt parallel exhaustive processing. Capacity generally declined below a lower bound, suggesting extremely limited capacity. Our conclusion is that the validity of the cue affected processing strategy but participants could increase the relative efficiency of the parallel processing with practice.
Similar content being viewed by others
Notes
Note that the inferences regarding the multiple time bins along the SIC function based on 95% CI for SIC would result in an increase of family-wise error rate, i.e., the probability of rejecting a true null hypothesis (serial processing model). However, the current inferences were consistent with those from the Houpt and Townsend statistics.
Similar to the inferences of 95% CI for SIC, we should be cautious to draw conclusions from the 95% CI for C(t).
References
Altieri, N., Fific, M., Little, D. R., & Yang, C.-T. (2017). Historical foundations and a tutorial introduction to Systems Factorial Technology. In D. R. Little, N. Altieri, M. Fific, & C.-T. Yang (Eds.), Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. London: Academic press.
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4), 369–406. https://doi.org/10.1037/0033-295X.89.4.369.
Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39(2), 216–233. https://doi.org/10.1006/jmps.1995.1021.
Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372–400. https://doi.org/10.1006/jmps.1993.1023.
Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93(2), 154–179.
Blaha, L. M. (2017). 7—an examination of task demands on the elicited processing capacity. In Systems factorial technology (pp. 137–156). San Diego: Academic Press.
Blaha, L. M., Busey, T. A, & Townsend, J. (2009). An LDA approach to the neural correlates of configural learning.
Blunden, A. G., Wang, T., Griffiths, D. W., & Little, D. R. (2014). Logical-rules and the classification of integral dimensions: individual differences in the processing of arbitrary dimensions. Frontiers in Psychology, 5, 1531. https://doi.org/10.3389/fpsyg.2014.01531.
Broadbent, D. E. (1958). Perception and communication: Elmsford, NY, US: Pergamon Press.
Burbeck, S. L., & Luce, R. D. (1982). Evidence from auditory simple reaction times for both change and level detectors. Perception & Psychophysics, 32(2), 117–133. https://doi.org/10.3758/BF03204271.
Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research, 51(13), 1484–1525. https://doi.org/10.1016/j.visres.2011.04.012.
Carrasco, M., & McElree, B. (2001). Covert attention accelerates the rate of visual information processing. Proceedings of the National Academy of Sciences, 98(9), 5363–5367. https://doi.org/10.1073/pnas.081074098.
Cheal, M., & Lyon, D. R. (1991). Central and peripheral precuing of forced-choice discrimination. The Quarterly Journal of Experimental Psychology, 43, 859–880.
Cleland, B. G., Dubin, M. W., & Levick, W. R. (1971). Sustained and transient neurones in the cat's retina and lateral geniculate nucleus. The Journal of Physiology, 217(2), 473–496. https://doi.org/10.1113/jphysiol.1971.sp009581.
Colonius, H. (1986). Measuring channel dependence in separate activation models. Perception & Psychophysics, 40(4), 251–255. https://doi.org/10.3758/bf03211504.
Colonius, H. (1990). Possibly dependent probability summation of reaction time. Journal of Mathematical Psychology, 34(3), 253–275.
Colonius, H., & Townsend, J. T. (Eds.). (1997). Activation-state representation of models for the redundant-signals-effect. Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
Colonius, H., & Vorberg, D. (1994). Distribution inequalities for parallel models with unlimited capacity. Journal of Mathematical Psychology, 38(1), 35–58.
Donkin, C., Kary, A., Tahir, F., & Taylor, R. (2016). Resources masquerading as slots: flexible allocation of visual working memory. Cognitive Psychology, 85, 30–42. https://doi.org/10.1016/j.cogpsych.2016.01.002.
Donkin, C., Newell, B. R., Kalish, M., Dunn, J. C., & Nosofsky, R. M. (2015). Identifying strategy use in category learning tasks: a case for more diagnostic data and models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(4), 933–948. https://doi.org/10.1037/xlm0000083.
Donkin, C., & Nosofsky, R. M. (2012). The structure of short-term memory scanning: an investigation using response time distribution models. Psychonomic Bulletin & Review, 19(3), 363–394. https://doi.org/10.3758/s13423-012-0236-8.
Dunn, J. C. (2004). Remember-know: a matter of confidence. Psychological Review, 111(2), 524–542. https://doi.org/10.1037/0033-295X.111.2.524.
Dutilh, G., Krypotos, A.-M., & Wagenmakers, E.-J. (2011). Task-related versus stimulus-specific practice: a diffusion model account. Experimental Psychology, 58(6), 434–442. https://doi.org/10.1027/1618-3169/a000111.
Dutilh, G., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16(6), 1026–1036. https://doi.org/10.3758/16.6.1026.
Dzhafarov, E. N. (1999). Conditionally selective dependence of random variables on external factors. Journal of Mathematical Psychology, 43(1), 123–157.
Eidels, A., Houpt, J. W., Altieri, N., Pei, L., & Townsend, J. T. (2011). Nice guys finish fast and bad guys finish last: Facilitatory vs. inhibitory interaction in parallel systems. Journal of Mathematical Psychology, 55(2), 176–190. https://doi.org/10.1016/j.jmp.2010.11.003.
Fific, M., & Little, D. R. (2017). 2 - Stretching Mental Processes: An Overview of and Guide for SFT Applications. In D. R. Little, N. Altieri, M. Fific, & C.-T. Yang (Eds.), Systems Factorial Technology (pp. 27-51). San Diego: Academic Press.
Fific, M., Little, D. R., & Nosofsky, R. M. (2010). Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches. Psychological Review, 117(2), 309–348. https://doi.org/10.1037/a0018526.
Fific, M., Nosofsky, R. M., & Townsend, J. T. (2008). Information-processing architectures in multidimensional classification: a validation test of the systems factorial technology. Journal of Experimental Psychology: Human Perception and Performance, 34(2), 356–375. https://doi.org/10.1037/0096-1523.34.2.356.
Fox, E., & Houpt, J. W. (2016). The perceptual processing of fused multispectral imagery. Cognitive Research: Principles and Implications., 1. https://doi.org/10.1186/s41235-016-0030-7.
Giordano, A. M., McElree, B., & Carrasco, M. (2009). On the automaticity and flexibility of covert attention: a speed-accuracy trade-off analysis. Journal of Vision, 9(3).
Goldstone, R. L. (2000). Unitization during category learning. Journal of Experimental Psychology: Human Perception and Performance, 26(1), 86–112. https://doi.org/10.1037/0096-1523.26.1.86.
Gottlob, L. R., & Madden, D. J. (1999). Age differences in the strategic allocation of visual attention. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 54B(3), P165–P172.
Heathcote, A., Brown, S., & Mewhort, D. J. K. (2002). Quantile maximum likelihood estimation of response time distributions. Psychonomic Bulletin & Review, 9(2), 394–401. https://doi.org/10.3758/BF03196299.
Heathcote, A., Brown, S., Wagenmakers, E. J., & Eidels, A. (2010). Distribution-free tests of stochastic dominance for small samples. Journal of Mathematical Psychology, 54(5), 454–463. https://doi.org/10.1016/j.jmp.2010.06.005.
Hoffman, A. B., & Rehder, B. (2010). The costs of supervised classification: the effect of learning task on conceptual flexibility. Journal of Experimental Psychology: General, 139(2), 319–340. https://doi.org/10.1037/a0019042.
Houpt, J. W., Blaha, L. M., McIntire, J. P., Havig, P. R., & Townsend, J. T. (2013). Systems factorial technology with R. Behavior Research Methods, 46, 1–24. https://doi.org/10.3758/s13428-013-0377-3.
Houpt, J. W., & Townsend, J. T. (2010). The statistical properties of the survivor interaction contrast. Journal of Mathematical Psychology, 54(5), 446–453. https://doi.org/10.1016/j.jmp.2010.06.006.
Jonides, J. (1981). Voluntary versus automatic control over the mind’s eye’s movement. Attention and Performance, 187–203.
Kahneman, D. (1973). Attention and effort. Englewood Cliffs, N.J.: Prentice-Hall.
Kujala, J. V., & Dzhafarov, E. N. (2008). Testing for selectivity in the dependence of random variables on external factors. Journal of Mathematical Psychology, 52(2), 128–144. https://doi.org/10.1016/j.jmp.2008.01.008.
Lambert, A., Spencer, E., & Mohindra, N. (1987). Automaticity and the capture of attention by a peripheral display change. Current Psychology, 6(2), 136–147. https://doi.org/10.1007/BF02686618.
Little, D. R., Altieri, N., Fific, M., & Yang, C.-T. (2017a). Systems factorial technology: a theory driven methodology for the identification of perceptual and cognitive mechanisms: Academic Press.
Little, D. R., Eidels, A., Houpt, J. W., & Yang, C.-T. (2017b). Set size slope still does not distinguish parallel from serial search. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/S0140525X16000157.
Little, D. R., Nosofsky, R. M., Donkin, C., & Denton, S. E. (2013). Logical rules and the classification of integral-dimension stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(3), 801–820. https://doi.org/10.1037/a0029667.
Liu, C. C., & Watanabe, T. (2012). Accounting for speed–accuracy tradeoff in perceptual learning. Vision Research, 61, 107–114. https://doi.org/10.1016/j.visres.2011.09.007.
Livingstone, M., & Hubel, D. (1988). Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science, 240(4853), 740–749. https://doi.org/10.1126/science.3283936.
Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95(4), 492–527.
Logan, G. D. (1992). Shapes of reaction-time distributions and shapes of learning curves: a test of the instance theory of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 883–914. https://doi.org/10.1037/0278-7393.18.5.883.
Luce, D. (1986). Response times: their role in inferring elementary mental organization. New York: Oxford University Press.
Müller, H. J., & Rabbitt, P. M. (1989). Reflexive and voluntary orienting of visual attention: time course of activation and resistance to interruption. Journal of Experimental Psychology: Human Perception and Performance, 15(2), 315–330. https://doi.org/10.1037/0096-1523.15.2.315.
McCormick, P. A. (1997). Orienting attention without awareness. Journal of Experimental Psychology: Human Perception and Performance, 23(1), 168–180. https://doi.org/10.1037/0096-1523.23.1.168.
Miller, J. (1991). Channel interaction and the redundant-targets effect in bimodal divided attention. Journal of Experimental Psychology: Human Perception and Performance, 17(1), 160–169.
Nakayama, K., & Mackeben, M. (1989). Sustained and transient components of focal visual attention. Vision Research, 29(11), 1631–1647. https://doi.org/10.1016/0042-6989(89)90144-2.
Nosofsky, R. M., & Alfonso-reese, L. A. (1999). Effects of similarity and practice on speeded classification response times and accuracies: further tests of an exemplar-retrieval model. Memory & Cognition, 27(1), 78–93. https://doi.org/10.3758/BF03201215.
Nosofsky, R. M., Little, D. R., Donkin, C., & Fific, M. (2011). Short-term memory scanning viewed as exemplar-based categorization. Psychological Review, 118(2), 280–315. https://doi.org/10.1037/a0022494.
Pirolli, P. L., & Anderson, J. R. (1985). The role of practice in fact retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(1), 136–153. https://doi.org/10.1037/0278-7393.11.1.136.
Posner, M. I. (1980). Orienting of attention. The Quarterly Journal of Experimental Psychology, 32(1), 3–25.
Posner, M. I., Snyder, C. R., & Davidson, B. J. (1980). Attention and the detection of signals. Journal of Experimental Psychology: General, 109(2), 160–174.
Raab, D. H. (1962). Statistical facilitation of simple reaction times. Transactions of the New York Academy of Sciences, 24(5), 574–590.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59.
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356. https://doi.org/10.1111/1467-9280.00067.
Ratcliff, R., Thapar, A., & McKoon, G. (2006). Aging, practice, and perceptual tasks: a diffusion model analysis. Psychology and Aging, 21(2), 353–371.
Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime: user’s guide: Psychology Software Inc.
Sewell, D. K., & Smith, P. L. (2012). Attentional control in visual signal detection: effects of abrupt-onset and no-onset stimuli. Journal of Experimental Psychology: Human Perception and Performance, 38(4), 1043–1068. https://doi.org/10.1037/a0026591.
Sewell, D. K., & Smith, P. L. (2016). The psychology and psychobiology of simple decisions: speeded choice and its neural correlates. In M. Reuter & C. Montag (Eds.), Neuroeconomics (pp. 253–292). Berlin, Heidelberg: Springer Berlin Heidelberg.
Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567–593. https://doi.org/10.1037/0033-295X.102.3.567.
Taylor, D. A. (1976). Effect of identity in the multiletter matching task. Journal of Experimental Psychology: Human Perception and Performance, 2(3), 417–428.
Theeuwes, J., Kramer, A. F., Hahn, S., & Irwin, D. E. (1998). Our eyes do not always go where we want them to go: capture of the eyes by new objects. Psychological Science, 9(5), 379–385. https://doi.org/10.1111/1467-9280.00071.
Townsend, J. T. (1990). Serial vs. parallel processing: sometimes they look like Tweedledum and Tweedledee but they can (and should) be distinguished. Psychological Science, 1(1), 46–54. https://doi.org/10.1111/j.1467-9280.1990.tb00067.x.
Townsend, J. T., & Eidels, A. (2011). Workload capacity spaces: a unified methodology for response time measures of efficiency as workload is varied. Psychonomic Bulletin & Review, 18(4), 659–681. https://doi.org/10.3758/s13423-011-0106-9.
Townsend, J. T., & Nozawa, G. (1995). Spatio-temporal properties of elementary perception: an investigation of parallel, serial, and coactive theories. Journal of Mathematical Psychology, 39(4), 321–359.
Townsend, J. T., & Thomas, R. D. (1994). Stochastic dependencies in parallel and serial models: effects on systems factorial interactions. Journal of Mathematical Psychology, 38(1), 1–34.
Townsend, J. T., & Wenger, M. J. (2004). A theory of interactive parallel processing: new capacity measures and predictions for a response time inequality series. Psychological Review, 111(4), 1003–1035.
Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7(3), 424–465.
Wright, R. D., & Ward, L. M. (2008). Orienting of attention. New York, NY, US: Oxford University Press.
Yang, C.-T., Altieri, N., & Little, D. R. (2018). An examination of parallel versus coactive processing accounts of redundant-target audiovisual signal processing. Journal of Mathematical Psychology, 82, 138–158.
Yang, C.-T., Little, D. R., & Hsu, C.-C. (2014). The influence of cueing on attentional focus in perceptual decision making. Attention, Perception & Psychophysics, 76(8), 2256–2275.
Yantis, S., & Hillstrom, A. P. (1994). Stimulus-driven attentional capture: evidence from equiluminant visual objects. Journal of Experimental Psychology: Human Perception and Performance, 20(1), 95–107. https://doi.org/10.1037/0096-1523.20.1.95.
Yantis, S., & Jonides, J. (1990). Abrupt visual onsets and selective attention: voluntary versus automatic allocation. Journal of Experimental Psychology: Human Perception and Performance, 16(1), 121–134. https://doi.org/10.1037/0096-1523.16.1.121.
Funding
This work was funded by the National Science Council (NSC 102-2628-H-006-001-MY3 and 105-2410-H-006-020-MY2 to C.-T. Yang) and the National Cheng Kung University (NCKU Rising-Star Top-Notch Project Grant to C.-T. Yang) and the Australian Research Council (DP160102360 to Daniel R. Little).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Prior to the experiment, all the participants signed an informed consent form. The ethics approval for the study was obtained from the Ethics Committee of Department of Psychology at National Cheng Kung University, and the study was conducted in accordance with the approved guidelines and regulations.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, CT., Wang, CH., Chang, TY. et al. Cue-Driven Changes in Detection Strategies Reflect Trade-Offs in Strategic Efficiency. Comput Brain Behav 2, 109–127 (2019). https://doi.org/10.1007/s42113-019-00027-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42113-019-00027-0