Studying the dynamics of visual search behavior using RT hazard and micro-level speed–accuracy tradeoff functions: A role for recurrent object recognition and cognitive control processes
Thanks to the work of Anne Treisman and many others, the visual search paradigm has become one of the most popular paradigms in the study of visual attention. However, statistics like mean correct response time (RT) and percent error do not usually suffice to decide between the different search models that have been developed. Recently, to move beyond mean performance measures in visual search, RT histograms have been plotted, theoretical waiting time distributions have been fitted, and whole RT and error distributions have been simulated. Here we promote and illustrate the general application of discrete-time hazard analysis to response times, and of micro-level speed–accuracy tradeoff analysis to timed response accuracies. An exploratory analysis of published benchmark search data from feature, conjunction, and spatial configuration search tasks reveals new features of visual search behavior, such as a relatively flat hazard function in the right tail of the RT distributions for all tasks, a clear effect of set size on the shape of the RT distribution for the feature search task, and individual differences in the presence of a systematic pattern of early errors. Our findings suggest that the temporal dynamics of visual search behavior results from a decision process that is temporally modulated by concurrently active recurrent object recognition, learning, and cognitive control processes, next to attentional selection processes.
KeywordsVisual search Response times Discrete-time hazard analysis Individual differences Speed–accuracy tradeoff Event history analysis
We wish to thank Heinrich René Liesefeld, Hermann J. Müller, and an anonymous reviewer for their helpful comments on previous drafts. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer PA 2947/1-1 (to S.P.).
Open practices statement
This article presents a reanalysis of publicly available data. The R code for the descriptive and inferential event-history analyses is available from the first author upon request.
- Allison, P. D. (2010). Survival analysis using SAS: A practical guide, Second Edition. SAS Institute Inc., Cary, NC, USA.Google Scholar
- Grieben, R., Tekülve, J., Zibner, S. K. U., Schneegans, S., & Schöner, G. (2018). Sequences of discrete attentional shifts emerge from a neural dynamic architecture for conjunctive visual search that operates in continuous time. In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40thAnnual Conference of the Cognitive Science Society (pp. 429–434). Downloaded from http://mindmodeling.org/cogsci2018/papers/0099/index.html
- Li, K., Kadohisa, M., Kusunoki, M., Duncan, J., Bundesen, C., & Ditlevsen, S. (2018). Distinguishing between parallel and serial processing in visual attention from neurobiological data. bioRxiv preprint first posted online Aug. 2, 2018; 10.1101/383596.Google Scholar
- Liesefeld, H. R. (2018). Estimating the timing of cognitive operations with MEG/EEG latency measures: A primer, a brief tutorial, and an implementation of various methods. Frontiers in Neuroscience, 12, Article 765.Google Scholar
- Liesefeld, H. R., Moran, R., Usher, M., Müller, H. J., & Zehetleitner, M. (2016). Search efficiency as a function of target saliency: The transition from inefficient to efficient search and beyond. Journal of Experimental Psychology: Human Perception and Performance, 42 (6), 821–836.PubMedGoogle Scholar
- Liesefeld, H.R., & Müller, H.J. (2019). A theoretical attempt to revive the serial/parallel-search dichotomy. Attention, Perception, & Psychophysics. Advance online publication. https://doi.org/10.3758/s13414-019-01819-z
- Luce, R. D. (1986). Response times. Their role in inferring elementary mental organization. New York: Oxford University Press Inc.Google Scholar
- McElree, B., & Carrasco, M. (1999). The temporal dynamics of visual search: Evidence for parallel processing in feature and conjunction searches. JEP:HPP, 25 (6), 1517–1539.Google Scholar
- Pachella, R. G. (1974). The interpretation of reaction time in information processing research. In: B. Kantowitz (Ed.), Human information processing, 41–82. Potomac, MD: Erlbaum.Google Scholar
- R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
- Wenger, M. J., & Gibson, B. S. (2004). Using hazard functions to assess changes in processing capacity in an attentional cuing paradigm. JEP:HPP, 30 (4), 708–719.Google Scholar
- Wolfe, J. M. (2007). Guided search 4.0: Current progress with a model of visual search. In: W. D. Grey (Ed.), Integrated Models of Cognitive Systems, 99–119. New York, Oxford University Press, Inc.Google Scholar
- Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: An alternative to the feature integration model for visual search. JEP:HPP, 15 (3), 419–433.Google Scholar
- Willett, J. B., & Singer, J. D. (1995). It’s déjà vu all over again: Using multiple-spell discrete-time survival analysis. Journal of Educational and Behavioral Statistics, 20, 41–67.Google Scholar