Cogprints

Gaze and informativeness during category learning: Evidence for an inverse relation

Vigo , Dr. Ronaldo and Zeigler, Derek and Halsey , Phillip (2013) Gaze and informativeness during category learning: Evidence for an inverse relation. [Journal (Paginated)]

Full text available as:

[img]
Preview
PDF
771Kb

Abstract

In what follows, we explore the general relationship between eye gaze during a category learning task and the information conveyed by each member of the learned category. To understand the nature of this relationship empirically, we used eye tracking during a novel object classification paradigm. Results suggest that the average fixation time per object during learning is inversely proportional to the amount of information that object conveys about its category. This inverse relationship may seem counterintuitive; however, objects that have a high information value are inherently more representative of their category. Therefore, their generality captures the essence of the category structure relative to less representative objects. As such, it takes relatively less time to process these objects than their less informative companions. We use a general information measure referred to as representational information theory (Vigo, 2011a, 2013a) to articulate and interpret the results from our experiment and compare its predictions to those of three models of prototypicality.

Item Type:Journal (Paginated)
Keywords:Category learning; Eye movements; Math modelling; Object-based attention; Representational information.
Subjects:Psychology > Cognitive Psychology
Psychology > Perceptual Cognitive Psychology
Psychology > Psychophysics
ID Code:9077
Deposited By: Zeigler , Derek
Deposited On:18 Nov 2013 21:03
Last Modified:18 Nov 2013 21:03

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Bourne, L. E. (1966). Human conceptual behavior. Boston, MA: Allyn & Bacon.

Cheng, S., & Liu, Y. (2012). Eye-tracking based adaptive user interface: implicit humancomputer interaction for preference indication. Journal on Multimodal User Interfaces, 5,

77�84.

Djamasbi, S., Siegel, M., Skorinko, J., & Tullis, T. (2011). Online Viewing and Aesthetic Preferences of Generation Y and the Baby Boom Generation: Testing User Web Site Experience Through Eye Tracking.International Journal of Electronic Commerce,15, 121�158.

Dreze, X., & Hussherr, F. X. (2003). Internet advertising: Is anybody watching? Journal of interactive marketing, 17, 8�23.

Estes, W. K. (1986). Array models for category learning. Cognitive psychology, 18, 500�549.

Estes, W. K. (1994). Classification and cognition (Oxford Psychology Series, No. 22). Oxford: Oxford University Press.

Furrer, S. D., & Younger, B. A. (2005). Beyond the distributional input? A developmental investigation of asymmetry in infants’ categorization of cats and dogs. Developmental Science, 8, 544�550. doi:10.1111/j.1467-7687.2005.00446.x

Garner, W. R. (1974). The processing of information and structure. New York, NY: Wiley.

Hayhoe, M., & Ballard, D. (2005). Eye movements in natural behavior. Trends in Cognitive Sciences, 9, 188�194. doi:10.1016/j.tics.2005.02.009

Henderson, J. M., & Hollingworth, A. (1999). High-level scene perception. Annual Review of Psychology, 50, 243�271. doi:10.1146/annurev.psych.50.1.243

Kowler, E. (1991). The role of visual and cognitive processes in the control of eye movement. In E. Kowler (Ed.),Eye Movements and Their Role in Visual and Cognitive Processes(pp. 1�70). Amsterdam: Elsevier.

Krajbich, I., & Rangel, A. (2011). A multi-alternative drift diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108, 13852�13857. doi:10.1073/pnas.1101328108

Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category

learning. Psychological Review, 99, 22�44. doi:10.1037/0033-295X.99.1.22

Kruschke, J. K. (2003). Attention in learning. Current Directions in Psychological Science, 12,

171�175. doi:10.1111/1467-8721.01254

Little, D. R., Nosofsky, R. M., & Denton, S. E. (2011). Response-time tests of logical-rule models of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 1�27. doi:10.1037/a0021330

Little, D. R., Nosofsky, R. M., Donkin, C., & Denton, S. E. (2012). Logical Rules and the Classification of Integral-Dimension Stimuli. Journal of experimental psychology. Learning, memory, and cognition, 39, 801�820.

Liversedge, S. P., & Findlay, J. M. (2000). Saccadic eye movements and cognition. Trends in Cognitive Sciences, 4, 6�14. doi:10.1016/S1364-6613(99)01418-7

Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309�332. doi:10.1037/0033-295X.111.2.309

Martinez-Conde, S., Macknik, S. L., & Hubel, D. H. (2004). The role of fixational eye movements in visual perception. Nature Reviews Neuroscience, 5, 229�240. doi:10.1038/nrn1348

Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207�238. doi:10.1037/0033-295X.85.3.207

Mele, M. L., & Federici, S. (2012). Gaze and eye-tracking solutions for psychological research. Cognitive processing, 13, 261�265.

Najemnik, J., & Geisler, W. S. (2009). Simple summation rule for optimal fixation selection in visual search. Vision Research, 49, 1286�1294. doi:10.1016/j.visres.2008.12.005

Nelson, J. D. (2005). Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain.Psychological Review, 112, 979�999. doi:10.1037/0033-295X.112.4.979

Nelson, J. D. & Cottrell, G. W. (2007). A probabilistic model of eye movements in concept formation. Neurocomputing, 70, 2256�2272. doi:10.1016/j.neucom.2006.02.026

Nelson, J. D., McKenzie, C. R. M. Cottrell, G. W., & Sejnowski, T. J. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21, 960� 969. doi:10.1177/0956797610372637

Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., Mckinley, S. C., & Glauthier, S. C. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961). Memory and Cognition, 24, 352�369.

Nosofsky, R. M. Palmeri, T. J., & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53�79. doi:10.1037/0033-295X.101.1.53

Nosofsky, R. M., & Zaki, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924�940. doi:10.1037/0278-7393.28.5.924.

Oakes, L. M., & Ribar, R. J. (2005). A comparison of infants’ categorization in paired and successive presentation familiarization tasks.Infancy, 7, 85�98. doi:10.1207/s15327078in0701_7

Ozanne, J. L. Brucks, M., & Grewal, D. (1992). A study of information search behavior during the categorization of new products. Journal of Consumer Research, 18, 452�463. doi:10.1086/ 209273

Palmeri, T. J., & Nosofsky, R. M. (1995). Recognition memory for exceptions to the category rule. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 548�568. doi:10.1037/0278-7393.21.3.548

Pan, B., Hembrooke, H. A., Gay, G. K., Granka, L. A., Feusner, M. K., & Newman, J. K.(2004, March). The determinants of web page viewing behavior: an eye-tracking study. In Proceedings of the 2004 symposium on Eye tracking research & applications (pp. 147�154). San Antonio, TX. ACM.

Quinn, P. C. (2004). Development of subordinate-level categorization in 3-to 7-month-old infants. Child Development, 75, 886�899. doi:10.1111/j.1467-8624.2004.00712.x

Quinn, P. C., & Bhatt, R. S. (1998). Visual pop-out in young infants: Convergent evidence and an extension. Infant Behavior and Development, 21, 273�288. doi:10.1016/S0163-6383(98)90006-6

Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372�422. doi:10.1037/0033-2909.124.3.372

Rayner, K., Smith, T. J., Malcolm, G. L., & Henderson, J. M. (2009). Eye movements and visual encoding during scene perception. Psychological Science, 20, 6�10. doi:10.1111/j.1467- 9280.2008.02243.x

Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1�41. doi:10.1016/j.cogpsych.2004.11.001

Reutskaja, E., Nagel, R., Camerer, C. F., & Rangel, A. (2011). Search dynamics in consumer choice under time pressure: An eye-tracking study. The American Economic Review, 101, 900�926.

Rolfs, M. (2009). Microsaccades: Small steps on a long way. Vision Research, 49, 2415�2441. doi:10.1016/j.visres.2009.08.010

Rosch, E. (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization (pp. 27�48). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Reprinted in E.Margolis & S. Laurence (Eds.). (1999), Concepts: Core readings (pp. 189�206). Cambridge, MA: MIT Press.

Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structures of categories. Cognitive Psychology, 7, 573�605. doi:10.1016/0010-0285(75)90024-9

Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1�42. doi:10.1037/ h0093825

Shepard, R. N. (1974). Representation of structure in similarity data: Problems and prospects. Psychometrika, 39, 373�421.

Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317�1323.

Smith, J. D., Murray, M. J., & Minda, J. P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 659�680. doi:10.1037/ 0278-7393.23.3.659

Vigo, R. (2009a). Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology, 53, 203�221.

Vigo, R. (2009b). Modal similarity. Journal of Experimental and Theoretical Artificial Intelligence, 21, 181�196.

Vigo, R., Allen, C. (2009c). How to reason without words: inference as categorization. Cognitive Processing, 10, 77�88.

Vigo, R. (2011a). Representational information: A new general notion and measure of information. Information Sciences, 181, 4847�4859.

Vigo, R. (2011). Towards a Law of Invariance in Human Conceptual Behavior. In Proceedings of the 33rd Annual Meeting of the Cognitive Science Society (pp. 2580�2585). Cognitive Science Society.

Vigo, R. (2013a). Meaning over uncertainty in Generalized Representational Information Theory (GRIT): A structure sensitive theory of information. Information, 4, 1�30.

Vigo, R. (2013b). The GIST of concepts. Cognition. doi:10.1016/j.cognition.2013.05.008

Vigo, R., & Basawaraj, B. (2013). Will the most informative object stand? Determining the impact of structural context on informativeness judgements. Journal of Cognitive Psychology, 1�19.(in press)

Viviani, P. (1990). Eye movements in visual search: cognitive, perceptual and motor control aspects. In E. Kowler (Ed.), Eye Movements and Their Role in Visual and Cognitive Processes (pp. 253�393). Amsterdam: Elsevier.

Yarbus, A. L. (1967). Eye-movements and vision. New York, NY: Plenum Press.

Metadata

Repository Staff Only: item control page