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A distinction can be drawn between models of crowding that rely on information loss, with crowded items either suppressed [ 11, 12] or lost [ 13 15], and change-based models such as averaging [ 16] and flanker substitution [ 20 22].
Pooling and nonpooling, together, make up the space of all possible models of crowding.
Pooling and nonpooling (simple substitution) together include all possible models of crowding.
These changes follow predictions from two change-based models of crowding.
Our results are also inconsistent with inhibition-based models of crowding [ 11, 12] and explanations based on masking [ 17, 29 31].
These results confirm predictions from change-based models of crowding, such as averaging [ 16], and establish crowding as a regularization process that simplifies the peripheral field by promoting consistent appearance among adjacent objects.
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Van den Berg et al. (2010) proposed a feature integration model of crowding based on population codes that exhibits substitution-like behavior for example, source confusion.
Capture those similarities in a model and it should be possible to predict how a crowd will behave.Existing models of crowd behaviour do just that.
Using computational models of crowds, it is possible to set up various scenarios and evaluate how the occupants move through the building.The trouble is that the software that is used to do this generally treats people like particles in a fluid, says Mr Wittasek.
We propose that a truly unified model of crowding must include a grouping stage.
Any model of crowding must receive the stimulus as input and produce the appropriate kind of observer response (e.g., a letter or a word).
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