Research Note—Competitive Brand Salience

References

• Aribarg A., Arora N. Inter-brand variant overlap: Impact on brand preference and portfolio profit. Marketing Sci. (2008) 27(3Link, Google Scholar
• Broniarczyk S. M., Hoyer W. D., McAlister L. Consumers' perceptions of the assortment offered in a grocery category: The impact of item reduction. J. Marketing Res. (1998) 35:166–176Crossref, Google Scholar
• Bullier J., Schall J. D., Morel A. Functional streams in Occipito-Frontal connections in the monkey. Behav. Brain Res. (1996) 76:89–97Crossref, Google Scholar
• Chib S. Marginal likelihood from the Gibbs output. J. Amer. Statist. Assoc. (1995) 90(432):1313–1321Crossref, Google Scholar
• Chib S., Hamilton B. H. Bayesian analysis of cross-section and clustered data treatment models. J. Econometrics (2000) 97:25–50Crossref, Google Scholar
• Damien P., Wakefield J., Walker S. Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables. J. Roy. Statist. Soc.: Ser. B (1999) 61:331–344Crossref, Google Scholar
• Duncan J., Humphreys G. W. Beyond the search surface: Visual search and attentional engagement. J. Experiment. Psych.: Human Perception Performance (1992) 18(2):578–588Crossref, Google Scholar
• Findlay J. M., Itti L., Rees G., Tsotsos J. K. Covert attention and saccadic eye movements. Neurobiology of Attention (2005) (Elsevier Academic Press, Amsterdam) 114–116Crossref, Google Scholar
• Gilchrist I. D., Harvey M. Evidence for a systematic component within scan paths in visual search. Visual Cognition (2006) 14(4–8):704–715Crossref, Google Scholar
• Herpen E. van, Pieters R. The variety of an assortment: An extension to the attribute-based approach. Marketing Sci. (2002) 21(3):331–341Link, Google Scholar
• Hoch S. J., Bradlow E. T., Wansink B. The variety of an assortment. Marketing Sci. (1999) 18(4):527–546Link, Google Scholar
• Hui K.-L. Product variety under brand influence: An empirical investigation of personal computer demand. Management Sci. (2004) 50(5):686–700Link, Google Scholar
• Itti L., Koch C. Computational modelling of visual attention. Nature Rev. Neuroscience (2001) 2(3):194–203Crossref, Google Scholar
• Keller K. L., Lehmann D. R. Brands and branding: Research findings and future priorities. Marketing Sci. (2006) 25(6):740–759Link, Google Scholar
• Koch C., Ullman S. Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology (1985) 4:219–227Google Scholar
• Lee T. S., Mumford D. Hierarchical Bayesian inference in the visual cortex. J. Optical Soc. America (2003) 20(7):1434–1448Crossref, Google Scholar
• Liechty J., Pieters R., Wedel M. Global and local covert visual attention: Evidence from a Bayesian Hidden Markov model. Psychometrika (2003) 68(4):519–541Crossref, Google Scholar
• Marr D.Vision: A Computation Investigation into the Human Representation and Processing of Visual Information (1982) (W. H. Freeman and Company, San Francisco) Google Scholar
• Monk T. H., Warm J. S. Search. Sustained Attention in Human Performance (1984) (Wiley, New York) 293–321Google Scholar
• Motter B. C., Holsapple J. W., Braun J., Koch C., Davis J. L. Separating attention from chance in active visual search. Visual Attention and Cortical Circuits (2001) (MIT Press, Cambridge, MA) 159–175Google Scholar
• Niebur E., Koch C., Parasuraman R. Computational architectures for attention. The Attentive Brain (1998) (MIT Press, Cambridge, MA) 163–186Google Scholar
• Pomplun M., Reingold E. M., Shen J., Williams D. E., Gleitman L. R., Joshi A. K. The area activation model of saccadic selectivity in visual search. Proc. Twenty Second Annual Conference Cognitive Science Society (2000) Philadelphia:543–548Google Scholar
• Ponsoda V., Scott D., Findlay J. M. A probability vector and transition matrix analysis of eye movements during visual search. Acta Psychologica (1995) 88:167–185Crossref, Google Scholar
• Robert C. P., Celeux G., Diebolt J. Bayesian estimation of Hidden Markov chains: A stochastic implementation. Statist. Probab. Lett. (1993) 16:77–83Crossref, Google Scholar
• Shapiro L. G., Stockman G. C.Computer Vision (2001) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
• Thompson K. G., Itti L., Rees G., Tsotsos J. K. Dissociation of selection from saccade programming. Neurobiology of Attention (2005) (Elsevier, San Diego) 124–129Crossref, Google Scholar
• van der Lans R., Pieters R., Wedel M. Eye-movement analysis of search effectiveness. J. Amer. Statist. Assoc. (2008) . ForthcomingGoogle Scholar
• Wedel M., Pieters R. Eye fixations on advertisements and memory for brands: A model and findings. Marketing Sci. (2000) 19:297–312Link, Google Scholar
• Wolfe J. M., Horowitz T. S. What attributes guide the deployment of visual attention and how do they do it? Nature Rev. Neuroscience (2004) 5(June):1–7Google Scholar
• Wolfe J. M., Yu K. P., Stewart M. I., Shorter A. D., Friedman-Hill S. R., Cave K. R. Limitations on the parallel guidance of visual search: Color×color and orientation×orientation conjunctions. J. Experiment. Psych.: Human Perception Performance (1990) 16(4):879–892Crossref, Google Scholar
• Yantis S., Egeth H. E. On the distinction between visual salience and stimulus-driven attentional capture. J. Experiment. Psych.: Human Perception Performance (1999) 25(3):661–676Crossref, Google Scholar