Dynamic Modeling of Visual Search

In 1998/1999, three participants trained for up to 74-h-long sessions to find a target present on half the trials in visual displays of 1, 2, or 4 initially novel objects. There were four targets and four foils that never changed. Displays occurred simultaneously, or the objects occurred successively, or the four features of each object occurred successively. When successive, the SOAs were short (17, 33, or 50 ms), so the displays appeared simultaneous, making it likely that the search strategy was the same in all conditions. A 2004 publication examined only the simultaneous condition and found evidence suggesting serial search as well as some small amount of automatic attention to targets and occasional early or late termination of search. A 2021 publication examined only the displays with single objects, obtaining evidence for dynamic perception of features. These studies drew conclusions from modeling subtle aspects of the response time distributions; extending such modeling to all conditions would have been complex making it difficult to understand the main processes at work. Here, we present a simple way to extend the 2021 model to the conditions with multiple item displays. It is a hybrid model with parallel automatic processing of features from all display items, processing that finishes during the first comparison, combined with serial comparisons that terminate when a target is found, or when none is found. When objects occur sequentially, there is a tendency to compare first the first object presented that probability rising with SOA. This model gives a good qualitative account of the accuracy and median response times from all the conditions. This success suggests that a more complex model incorporating the dynamic processes of the 2021 model would provide an excellent quantitative account for the accuracy and response time distributions for all the conditions of this visual search study.