Dataset for "Colour crowding explained as adaptive spatial integration"

Dataset for paper: Colour crowding explained as adaptive spatial integration
Please refer to the readme file for a key to the variables included in Matlab files.
Below an abstract of the work:
Crowding is the inability to recognize an object in clutter, classically considered a fundamental low-level bottleneck to object recognition. Recently, however, it has been suggested that crowding, like predictive phenomena such as serial dependence, may result from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions, such as crowding being greater for non-salient targets and, counter-intuitively, that flanker interference should be associated with higher precision in judgements, leading to lower overall error rate. Here we measured colour discrimination for targets flanked by stimuli of variable colour. The results verified both predictions, showing that while crowding can affect object recognition, it may be better understood not as a processing bottleneck, but as a consequence of mechanisms evolved to efficiently exploit the spatial redundancies of the natural world. Analyses of reaction times of judgments shows that the integration occurs at sensory, rather than decisional levels.