Because many natural scenes are composed of multiple objects that are similar and therefore highly redundant, it is more efficient for the visual system to compress these scenes by encoding information about summary statistics than to encode features of individual objects (for a review, see Alvarez, 2011). However, the task of rapidly combining numerous features into a coherent percept is an incredible computational challenge. Humans frequently encounter ensembles or groups of objects (e.g., crowds of people, peaches at a fruit stall, cars in traffic) and are able to quickly process them with ease. Together, these results indicate that summary statistical coding is not limited to visual processing and is an important auditory mechanism for extracting ensemble frequency information from sequences of sounds.
This precise ensemble coding occurred despite very limited representation of individual tones from the sequence: Listeners were poor at identifying specific individual member tones (Experiment 3) and at determining their positions in the sequence (Experiment 4). Their performance was severely reduced when only a subset of tones from a given sequence was presented (Experiment 2), which demonstrates that ensemble coding is based on a substantial number of the tones in a sequence.
We found that listeners could accurately estimate the mean frequency of a set of logarithmically spaced pure tones presented in a temporal sequence (Experiment 1). Here, we present direct evidence that ensemble coding is also important for auditory processing. In vision, humans use summary statistics (e.g., the average facial expression of a crowd) to efficiently perceive the gist of groups of features.
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