Serial versus parallel search: A model comparison approach based on reaction time distributions

References

• Anderson, G. M., Heinke, D., & Humphreys, G. W. (2010). Featural guidance in conjunction search: The contrast between orientation and color. Journal of Experimental Psychology: Human Perception and Performance, 36(5), 1108–1127. doi:10.1037/a0017179.
   PubMed Web of Science ®Google Scholar
• Balota, D. A., & Yap, M. J. (2011). Moving beyond the mean in studies of mental chronometry: The power of response time distributional analyses. Current Directions in Psychological Science, 20, 160–166. doi:10.1177/0963721411408885.
   Web of Science ®Google Scholar
• Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406. doi: 10.1146/annurev-ecolsys-102209-144621
   Web of Science ®Google Scholar
• ter Braak, C. J. F. (2006). A Markov chain Monte Carlo version of the genetic algorithm Differential Evolution: Easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239–249. doi: 10.1007/s11222-006-8769-1
   Web of Science ®Google Scholar
• Bruce, N. D. B., & Tsotsos, J. K. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9, 1–24. Retrieved from http://www.journalofvision.org/content/9/3/5, doi: 10.1167/9.3.5.
   PubMed Web of Science ®Google Scholar
• Bundesen, C. (1990). A theory of visual attention. Psychological Review, 97(4), 523–547. doi:10.1037/0033-295X.97.4.523.
   PubMed Web of Science ®Google Scholar
• Busemeyer, J. R., & Diederich, A. (2010). Cognitive modelling. Thousand Oaks, CA: Sage.
   Google Scholar
• Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorials in Quantitative Methods for Psychology, 1(1), 4–10. doi: 10.20982/tqmp.01.1.p004
   Google Scholar
• Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222. doi: 10.1146/annurev.ne.18.030195.001205
   PubMed Web of Science ®Google Scholar
• Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433–458. doi: 10.1037/0033-295X.96.3.433
   PubMed Web of Science ®Google Scholar
• Duncan, J., Humphreys, G. W., & Ward, R. (1997). Competitive brain activity in visual attention. Current Opinion in Neurobiology, 7, 255–261. doi: 10.1016/S0959-4388(97)80014-1
   PubMed Web of Science ®Google Scholar
• Eckstein, M. P. (2011). Visual search: A retrospective. Journal of Vision, 11(5), 14–14. doi:10.1167/11.5.14.
   PubMed Web of Science ®Google Scholar
• Egeth, H., Jonides, J., & Wall, S. (1972). Parallel processing of multielement displays. Cognitive Psychology, 3, 674–698. doi:10.1016/0010-0285(72)90026-6.
   Web of Science ®Google Scholar
• Egly, R., Driver, J., & Rafal, R. D. (1994). Shifting visual attention between objects and locations: Evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General, 123(2), 161–177. doi: 10.1037/0096-3445.123.2.161
   PubMed Web of Science ®Google Scholar
• Heathcote, A., Brown, S. D., & Mewhort, D. J. (2002). Quantile maximum likelihood estimation of response time distributions. Psychonomic Bulletin and Review, 9, 394–401. doi: 10.3758/BF03196299
   PubMed Web of Science ®Google Scholar
• Heinke, D., & Backhaus, A. (2011). Modeling visual search with the Selective Attention for Identification model (VS-SAIM): A novel explanation for visual search asymmetries. Cognitive Computation, 3(1), 185–205. doi: 10.1007/s12559-010-9076-x
   PubMed Web of Science ®Google Scholar
• 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. doi: 10.1037/0033-295X.110.1.29
   PubMed Web of Science ®Google Scholar
• Hulleman, J., & Olivers, C. N. L. (2016). The impending demise of the item in visual search. Behaverial Brain Science. doi:10.1017/S0140525X15002794
   Web of Science ®Google Scholar
• Humphreys, G. W., & Müller, H. J. (1993). SEarch via recursive rejection (SERR): A connectionist model of visual search. Cognitive Psychology, 25, 43–110. doi: 10.1006/cogp.1993.1002
   Web of Science ®Google Scholar
• 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. doi: 10.1109/34.730558.
   Web of Science ®Google Scholar
• Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4, 219–227.
   PubMedGoogle Scholar
• Kristan, M., Leonardis, A., & Skočaj, D. (2011). Multivariate online kernel density estimation with Gaussian kernels. Pattern Recognition, 44(10), 2630–2642. doi: 10.1016/j.patcog.2011.03.019
   Web of Science ®Google Scholar
• Kwak, H., Dagenbach, D., & Egeth, H. (1991). Further evidence for a time-independent shift of the focus of attention. Perception and Psychophysics, 49(5), 473–480. doi: 10.3758/BF03212181
   PubMed Web of Science ®Google Scholar
• Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. doi: 10.1016/j.jesp.2013.03.013
   Web of Science ®Google Scholar
• Lin, Y-S., Heinke, D., & Humphreys, G. W. (2015). Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77(3), 985–1010. doi: 10.3758/s13414-014-0825-x
   PubMed Web of Science ®Google Scholar
• Maljkovic, V., & Nakayama, K. (2000). Priming of popout: III. A short-term implicit memorysystem beneficial for rapid target selection. Visual Cognition, 7, 571–595. doi: 10.1080/135062800407202
   Web of Science ®Google Scholar
• Mavritsaki, E., Heinke, D., Allen, H., Deco, G., & Humphreys, G. W. (2011). Bridging the gap between physiology and behavior: Evidence from the sSoTS model of human visual attention. Psychological Review, 118(1), 3–41. doi: 10.1037/a0021868
   PubMed Web of Science ®Google Scholar
• Mavritsaki, E., Heinke, D., Humphreys, G. W., & Deco, G. (2006). A computational model of visual marking using an interconnected network of spiking neurons: The spiking search over time & space model (sSoTS). Journal of Physiology-Paris, 100(1/2/3), 110–124. doi: 10.1016/j.jphysparis.2006.09.003
   PubMedGoogle Scholar
• Moran, R., Zehetleitner, M., Mueller, H. J., & Usher, M. (2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), 24–24. doi: 10.1167/13.8.24
   PubMed Web of Science ®Google Scholar
• Müller-Plath, G., & Elsner, K. (2007). Space-based and object-based capacity limitations in visual search. Visual Cognition, 15(5), 599–634. DOI:10.1080/13506280600845572.
   Web of Science ®Google Scholar
• Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7, 308–313. doi:10.1093/comjnl/7.4.308.
   Web of Science ®Google Scholar
• Palmer, E. M., Horowitz, T. S., Torralba, A., & Wolfe, J. M. (2011). What are the shapes of response time distributions in visual search? Journal of Experimental Psychology: Human Perception and Performance, 37, 58–71.
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. doi: 10.1037/0033-295X.85.2.59
   Web of Science ®Google Scholar
• Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall.
   Google Scholar
• Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. doi: 10.1023/A:1008202821328
   Web of Science ®Google Scholar
• Townsend, J. T., & Ashby, F. G. (1984). Measurement scales and statistics: The misconception misconceived. Psychological Bulletin, 96(2), 394–401. doi: 10.1037/0033-2909.96.2.394
   Web of Science ®Google Scholar
• 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, 321–359. doi: 10.1006/jmps.1995.1033.
   Web of Science ®Google Scholar
• Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136. doi: 10.1016/0010-0285(80)90005-5
   PubMed Web of Science ®Google Scholar
• Treisman, A., & Gormican, S. (1988). Feature analysis in early vision: Evidence from search asymmetries. Psychological Review, 95, 15–48. doi: 10.1037/0033-295X.95.1.15
   PubMed Web of Science ®Google Scholar
• Turner, B. M., & Sederberg, P. B. (2014). A generalized, likelihood-free method for posterior estimation. Psychonomic Bulletin & Review, 21(2), 227–250. doi: 10.3758/s13423-013-0530-0
   PubMed Web of Science ®Google Scholar
• Turner, B. M., Sederberg, P. B., Brown, S. D., & Steyvers, M. (2013). A method for efficiently sampling from distributions with correlated dimensions. Psychological Methods, 18, 368–384. doi: 10.1037/a0032222
   PubMed Web of Science ®Google Scholar
• Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550–592. doi: 10.1037/0033-295X.108.3.550
   PubMed Web of Science ®Google Scholar
• Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin and Review, 7, 424–465. doi: 10.3758/BF03214357
   PubMed Web of Science ®Google Scholar
• Wertheimer, M. (1923). Untersuchungen zur Lehre von der Gestalt. II. Psychologische Forschung, 4, 301–350. doi: 10.1007/BF00410640
   Google Scholar
• Wickelgren, W. A. (1977). Speed–accuracy tradeoff and information processing dynamics. Acta Psychologica, 41, 67–85. doi: 10.1016/0001-6918(77)90012-9
   Web of Science ®Google Scholar
• Wolfe, J. M. (1994). Guided search 2.0 a revised model of visual search. Psychonomic Bulletin & Review, 1(2), 202–238. doi: 10.3758/BF03200774
   PubMed Web of Science ®Google Scholar
• Wolfe, J. M. (1998). Visual search. In H. Pashler (Ed.), Attention (Vol. 1, pp. 13–73). London: University College London Press.
   Google Scholar
• Wolfe, J. M. (2007). Guided search 4.0. In W. D. Gray (Ed.), Integrated models of cognitive systems (cognitive models and architectures) (pp. 99–120). Oxford: Integrated Models of Cognitive Systems.
   Google Scholar
• Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: An alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception and Performance, 15, 419–433.
   PubMed Web of Science ®Google Scholar
• Wolfe, J. M., Palmer, E. M., & Horowitz, T. S. (2010). Reaction time distributions constrain models of visual search. Vision Research, 50(14), 1304–1311. doi: 10.1016/j.visres.2009.11.002
   PubMed Web of Science ®Google Scholar
• Woodgate, P. J., Strauss, S., Sami, S. A., & Heinke, D. (2015). Motor cortex guides selection of predictable movement targets. Behavioural Brain Research, 287, 238–246. doi: 10.1016/j.bbr.2015.03.057.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Heinke, D., Ivanoff, J., Klein, M. K., & Humphreys, G. W. (2011). Two components in IOR: Evidence for response bias and perceptual processing delays using the SAT methodology. Attention Perception & Psychophysics, 73(7), 2143–2159. doi: 10.3758/s13414-011-0181-z
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Humphreys, G. W., & Heinke, D. (2012). A biased-competition approach to spatial cueing: Combining empirical studies and computational modelling. Visual Cognition, 20(2), 170–210. doi: 10.1080/13506285.2012.655806
   Web of Science ®Google Scholar