Forty years after Feature Integration Theory

By Jeremy M. Wolfe. 2020

20 citations recorded by the source corpus.

Anne Treisman’s seminal paper on Feature Integration Theory (FIT) appeared 40 years ago (Treisman & Gelade, 1980). When she died in 2018, we wanted to honor her memory with a special issue of Attention, Perception, and Psychophysics and FIT seemed like a good organizing theme. At one level, that seems like an obvious choice. With over 13,000 citations in Google Scholar, the 1980 paper is her most cited work (though there are at least a dozen more with over 1000 citations). On the other hand, if you asked almost any of the researchers currently working on topics like visual search or texture segmentation about FIT, they would probably tell you that the model was wrong. To cite a personal example, I entered the discussion in 1989 with a paper entitled “Guided Search: An alternative to the Feature Integration model for visual search” (Wolfe, Cave, & Franzel, 1989). So, why are we rendering homage to this model, 40 years later? In the 1970’s, the statistician George Box coined the aphorism “All models are wrong but some are useful.” FIT has proven to be more than useful. It has shaped the discussion about visual search and much of the discussion about attention more generally for decades. You could not work in this area and ignore the idea of a two-stage system with a “preattentive” front end leading to a bottleneck into later attentive processes. You had to react to the idea that a few visual attributes like color and size could be processed in parallel in that front end. You had to take some position on the idea that attention was necessary if you were going to ‘bind’ those features into a representation – an ‘object file’ – that could be recognized as a salad, a chair, or whatever it might be. Some of us adopted Treisman’s ideas and modified them to accommodate newer data (That is certainly the story of Guided Search.). Others have actively rejected various components like a serial process of binding one object after another. However, all of us engaged with Treisman’s ideas. Given the importance of FIT, it seemed likely that we would get a reasonable response if we announced a special issue on the topic. We expected 30-40 submissions, yielding a special issue of about 20 papers. We received 80 submissions and the resulting crop of papers will be spread over two issues of the journal. The vagaries of the peer review process mean that some of these papers were accepted a year ago, while others were being revised. In this electronic era, papers appeared online as they became available. We are happy now to be able to gather them together in one place (Well, in two, successive places. Placement into the first or second issue was done on the basis of the data of final acceptance.). These papers advance our science and pay tribute to the impact of Anne Treisman. So, forty years after FIT appeared, what work did it inspire for this first of two special issues of Attention, Perception and Psychophysics? We start with an excellent piece of historical frame-setting. One might expect a review in which FIT is the seed from which a tree of research has grown (e.g. Quinlan, 2003). However, in Kristjansson and Egeth’s (DOI: https:// doi.org/10.3758/s13414-019-01803-7) paper, FIT is the fruit of earlier work. That fruit will become the seed that leads to so much work over the last decades but Kristjansson and Egeth do us an important service in showing how FIT emerged from other ideas. Hochstein (DOI: https://doi.org/10.3758/s13414019-01797-2) provides a second brief review to get us started, focusing in some detail on Treisman’s work on gist and ensemble processing, topics she took up later in her career (see Chong, DOI: https://doi.org/10.3758/s13414-01901827-z, in this issue). Moreover, he discusses her little known early work on binocular rivalry. After those reviews as preamble, I have tried to organize the issue roughly around the structure of FIT. FIT proposed a preattentive stage, followed by an attentive stage. Thus, the next papers deal with preattentive processing; what aspects of an object can be appreciated before attention is directed to that * Jeremy M Wolfe jwolfe@bwh.harvard.edu

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Food Science

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “Feature Integration Theory (FIT) provides a model of human visual attention for perceiving objects (see Wolfe, 2020 for an overview).”