When is it time to move to the next raspberry bush? Foraging rules in human visual search.

Animals, including humans, engage in many forms of foraging behavior in which resources are collected from the world. This paper examines human foraging in a visual search context. A real-world analog would be berry picking. The selection of individual berries is not the most interesting problem in such a task. Of more interest is when does a forager leave one patch or berry bush for the next one? Marginal Value Theorem (MVT; Charnov, 1976) predicts that observers will leave a patch when the instantaneous yield from that patch drops below the average yield from the entire "field." Experiments 1, 2, 3, and 4 show that MVT gives a good description of human behavior for roughly uniform collections of patches. Experiments 5 and 6 show strong departures from MVT when patch quality varies and when visual information is degraded.

[1]  Valerie M. Beck,et al.  Simultaneous Control of Attention by Multiple Working Memory Representations , 2012, Psychological science.

[2]  Andreas Wilke,et al.  Fishing for the Right Words: Decision Rules for Human Foraging Behavior in Internal Search Tasks , 2009, Cogn. Sci..

[3]  Joel s. Brown,et al.  Foraging : behavior and ecology , 2007 .

[4]  Jeremy M. Wolfe,et al.  Approaches to Visual Search , 2014 .

[5]  P. Verghese Visual Search and Attention A Signal Detection Theory Approach , 2001, Neuron.

[6]  Thomas T. Hills Animal Foraging and the Evolution of Goal-Directed Cognition , 2006, Cogn. Sci..

[7]  J. Henderson,et al.  Looking back at Waldo: oculomotor inhibition of return does not prevent return fixations. , 2011, Journal of vision.

[8]  J. Wolfe Saved by a Log , 2012, Psychological science.

[9]  Thomas T. Hills,et al.  Optimal foraging in semantic memory. , 2012, Psychological review.

[10]  Edward Vul,et al.  A Bayesian Optimal Foraging Model of Human Visual Search , 2012, Psychological science.

[11]  A. Bond,et al.  Visual predators select for crypticity and polymorphism in virtual prey , 2002, Nature.

[12]  I. Rock,et al.  Perceptual organization and attention , 1992, Cognitive Psychology.

[13]  Michael C. Mozer,et al.  Experience-Guided Search: A Theory of Attentional Control , 2007, NIPS.

[14]  K. Nakayama,et al.  Situating visual search , 2011, Vision Research.

[15]  Ranxiao Frances Wang,et al.  Fruitful visual search: Inhibition of return in a virtual foraging task , 2005, Psychonomic Bulletin & Review.

[16]  P. Todd,et al.  Patch leaving in humans: can a generalist adapt its rules to dispersal of items across patches? , 2008, Animal Behaviour.

[17]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[18]  Naomi M. Kenner,et al.  How fast can you change your mind? The speed of top-down guidance in visual search , 2004, Vision Research.

[19]  P. Glimcher,et al.  Neuroeconomics: The Consilience of Brain and Decision , 2004, Science.

[20]  G. Zelinsky A theory of eye movements during target acquisition. , 2008, Psychological review.

[21]  Peter Pirolli,et al.  Information Foraging , 2009, Encyclopedia of Database Systems.

[22]  Peter Pirolli,et al.  Information foraging theory , 2007 .

[23]  Frank E. Ritter,et al.  The Rise of Cognitive Architectures , 2007, Integrated Models of Cognitive Systems.

[24]  Susan L. Franzel,et al.  Guided search: an alternative to the feature integration model for visual search. , 1989, Journal of experimental psychology. Human perception and performance.

[25]  Graham H. Pyke,et al.  Optimal Foraging: A Selective Review of Theory and Tests , 1977, The Quarterly Review of Biology.

[26]  U. Neisser VISUAL SEARCH. , 1964, Scientific American.

[27]  R. Klein,et al.  Inhibition of Return is a Foraging Facilitator in Visual Search , 1999 .

[28]  Krista A. Ehinger,et al.  Modelling search for people in 900 scenes: A combined source model of eye guidance , 2009 .

[29]  Stephen J. Boies,et al.  Components of attention. , 1971 .

[30]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[31]  E. Charnov Optimal foraging, the marginal value theorem. , 1976, Theoretical population biology.

[32]  Thomas L. Thornton,et al.  Parallel and serial processes in visual search. , 2007, Psychological review.

[33]  K. Nakayama,et al.  Priming of pop-out: I. Role of features , 1994, Memory & cognition.

[34]  Peter Pirolli,et al.  Computational models of information scent-following in a very large browsable text collection , 1997, CHI.

[35]  Iain D. Gilchrist,et al.  Visual search and foraging compared in a large-scale search task , 2008, Cognitive Processing.

[36]  Laurent Itti,et al.  A Bayesian model for efficient visual search and recognition , 2010, Vision Research.

[37]  G. Pierce,et al.  Eight Reasons Why Optimal Foraging Theory Is a Complete Waste of Time , 1987 .

[38]  Dragan Rangelov,et al.  Dimension-specific intertrial priming effects are task-specific: evidence for multiple weighting systems. , 2011, Journal of experimental psychology. Human perception and performance.

[39]  D. Meyer,et al.  Neuroeconomics: The Consilience of Brain and Decision , 2004 .

[40]  Miguel P Eckstein,et al.  Visual search: a retrospective. , 2011, Journal of vision.

[41]  R. Kliegl,et al.  Task-set switching and long-term memory retrieval. , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[42]  J. Townsend,et al.  The serial-parallel dilemma: A case study in a linkage of theory and method , 2004, Psychonomic bulletin & review.