Exploring Adult Working Memory: Chunking and Object Tracking Through Overlapping Attributes
This study investigates adult working memory (WM) utilizing principles derived from infant memory research. It builds on findings from Feigenson and colleagues, suggesting that WM can group multiple objects into manageable chunks based on their shared attributes like size and shape. The experiment involved tracking objects represented in two buckets, revealing that WM can efficiently handle overlapping attributes, thus allowing individuals to keep track of more than three sets simultaneously, despite conventional limits. The findings underscore the complexity of WM in processing visual information.
Exploring Adult Working Memory: Chunking and Object Tracking Through Overlapping Attributes
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Presentation Transcript
SNKYO Nate R. and Arin T. A Study on Adult Working Memory
Background • Feigenson, Carey, and Hauser 2002 • Study involving crackers – suggested that infants cannot keep track of more than 3 objects in a chunk. • Each bucket represents a unit of memory or a chunk. In this case, chunks are delineated by the physical space between the buckets. X
Background • Feigenson & Halberda 2004 • Showed that WM is not limited to tracking 3 objects per se but 3 chunks of representation, each of which may contain up to 3 objects. • Space can be the divider of chunks. Suggests that instead of representing each individual, WM groups objects into smaller and more manageable chunks… • Units in WM can be individuals or chunks… but what about large sets? • Feigenson 2008 • In WM, what about larger number of things. How many boys and girls are in the lecture room? • Placed one type, 2 type, 3 types and 4 types of objects in two buckets
Sets in WM What if the four objects have overlapping attributes- size and shape? In other words, each item is a member of more than one “set” Differ in size, color, texture, shape, and category dimensions Overlap in shape and size dimensions. Overcome capacity limits?
2X2 Overlapping Sets • Hypothesis • By taking advantage of overlaps among object attributes, WM efficiently keeps track of more than 3 sets at the same time. • Two attributes examined here are size and shape • Four individual objects – two blocks and two balls, each of which can be either large or small in size
Methods Labeling Condition: As objects are dropped in a bucket, the subject says either the shape or size of each object Shape Size Probed Dimension: Subject is asked which bucket has more objects of one shape or size Shape Size
Results • Only the answer to the first question counts to eliminate bias • Congruent N=7 Incongruent N=4