Enumerating the forest before the trees: The time courses of estimation-based and individuation-based numerical processi
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Enumerating the forest before the trees: The time courses of estimation-based and individuation-based numerical processing David Melcher 1,2 & Christoph Huber-Huber 1,3 & Andreas Wutz 4,5 Accepted: 1 September 2020 # The Author(s) 2020
Abstract Ensemble perception refers to the ability to report attributes of a group of objects, rather than focusing on only one or a few individuals. An everyday example of ensemble perception is the ability to estimate the numerosity of a large number of items. The time course of ensemble processing, including that of numerical estimation, remains a matter of debate, with some studies arguing for rapid, “preattentive” processing and other studies suggesting that ensemble perception improves with longer presentation durations. We used a forward-simultaneous masking procedure that effectively controls stimulus durations to directly measure the temporal dynamics of ensemble estimation and compared it with more precise enumeration of individual objects. Our main finding was that object individuation within the subitizing range (one to four items) took about 100–150 ms to reach its typical capacity limits, whereas estimation (six or more items) showed a temporal resolution of 50 ms or less. Estimation accuracy did not improve over time. Instead, there was an increasing tendency, with longer effective durations, to underestimate the number of targets for larger set sizes (11–35 items). Overall, the time course of enumeration for one or a few single items was dramatically different from that of estimating numerosity of six or more items. These results are consistent with the idea that the temporal resolution of ensemble processing may be as rapid as, or even faster than, individuation of individual items, and support a basic distinction between the mechanisms underlying exact enumeration of small sets (one to four items) from estimation. Keywords Object recognition . Scene perception . Temporal processing
Visual scenes are typically crowded and contain numerous objects. An example is a set table for a large dinner party, which might contain a series of plates, glasses, and silverware. Such a scene can be perceived in terms of individual objects, such as by fixating on a particular glass in order to grasp it. However, we are also able to quickly and effectively glean the overall meaning of the table and report the average color, size, * David Melcher [email protected] 1
Center for Mind/Brain Sciences and Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini 31, 38068 Rovereto, Italy
2
Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE
3
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
4
Center for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
5
Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
and shape of the plates or glasses and give a close, but often inexact, estimate of the number of place settings. Participants are ab
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