Abstract
Interest in the modeling of visual attention and cognition is strong with the number of models growing quickly. It thus becomes important to try to consolidate what all this activity has demonstrated in terms of what principles may be abstracted from the collective experience that can guide future research. This is not a straightforward task; many have tried and have little to show for it. Here, a different view is presented, one that attempts to combine multiple perspectives on the problem. The novelty is that in contrast with the vast majority of past work, there is an explicit assertion that no single principle can capture the complexities of human attentional and cognitive behavior. There are several principles, each defined in a particular context, with interactions among them. Many previous authors have stated principles that in fact are more correctly considered as modeling philosophies or requirements and these will be so distinguished. The development of a model of human visual cognition is dependent on the choice of which experimental observations act as constraints during its development (Tsotsos 2014). Those constraints provide a means to select solutions among those potential ones that satisfy the principles. Here, we begin by proposing a set of elements that may be considered as the components of attention, without any claims or completeness or optimality, and these will act as the first level set of constraints on any modeling activity. A look at specific models of attention and cognitive architectures will reveal a variety of principles, philosophies and requirements that have been shown to be important. This will be followed by the introduction of the Survival Requirement as a replacement for any over-arching principle of optimality because there still is a need for a selection criterion for choosing among competing solutions. The presentation will conclude by considering how progress on open problems in neuroscience may be facilitated by considering this list of principles, philosophies and requirements.
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Notes
- 1.
Computer science, broadly defined, is the theory and practice of representing, processing, and using information and encompasses a body of knowledge concerning algorithms, communication, languages, software, and information systems. In a nice paper, Peter Denning (Denning 2007) claimed that it offers a powerful foundation for modeling complex phenomena such as cognition. The language of computation is the best language we have to date, he claims, for describing how information is encoded, stored, manipulated, and used by natural as well as synthetic systems. As such, the language of computation subsumes mathematics and formal logic which are nevertheless critical tools for expressing appropriate elements of theories.
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Acknowledgments
JKT gratefully acknowledges support from the following sources without which this research could not have been conducted: the Natural Sciences and Engineering Research Council of Canada; the Canada Research Chairs Program; the Air Force Office of Scientific Research, Air Force Material Command, USAF under Award No. FA9550-14-1-0393.
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Tsotsos, J.K. (2017). Attention and Cognition: Principles to Guide Modeling. In: Zhao, Q. (eds) Computational and Cognitive Neuroscience of Vision. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0213-7_12
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