Publication | Open Access
When more is less: Extraction of summary statistics benefits from larger sets
105
Citations
29
References
2011
Year
Reaction Time MeasuresEngineeringData AggregationStatistical FoundationEntity SummarizationCognitionAttentionSocial SciencesText MiningAutomatic SummarizationEarly VisionSummary StatisticsAggregate FunctionInformation RetrievalData ScienceData MiningParallel MechanismCognitive NeuroscienceStatisticsSummary Statistics BenefitsLarger SetsCognitive ScienceKnowledge DiscoveryVision ResearchVisual ProcessingVisual FunctionVisual ReasoningEye TrackingStatistical Inference
Despite several processing limitations that have been identified in the visual system, research shows that statistical information about a set of objects could be perceived as accurately as the information about a single object. It has been suggested that extraction of summary statistics represents a different mode of visual processing, which employs a parallel mechanism free of capacity limitations. Here, we demonstrate, using reaction time measures, that increasing the number of stimuli in the set results in faster reaction times and better accuracy for estimating the mean tendency of a set. These results provide clear evidence that extraction of summary statistics relies on a distributed attention mode that operates across the whole display at once and that this process benefits from larger samples across which the summary statistics are calculated.
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