Purchase this article with an account.
Timothy F. Brady, Talia Konkle, George A. Alvarez, Aude Oliva; Compression in visual short-term memory: Using statistical regularities to form more efficient memory representations. Journal of Vision 2008;8(6):199. doi: https://doi.org/10.1167/8.6.199.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
It is widely accepted that our visual systems are tuned to the statistics of input from the natural world, which suggests that our visual short-term memory may also take advantage of statistical regularities through efficient coding schemes. Previous work on VSTM capacity has typically used patches of color or simple features which are drawn from a uniform distribution, and estimated the capacity of VSTM for simple color patches to be ∼ 4 items (Luck & Vogel, 1997), and even fewer for more complex objects (Alvarez & Cavanagh, 2004). Here, we introduce covariance information between colors, and ask if VSTM can take advantage of the shared statistics to form a more efficient representation of the displays.
We presented observers with displays of eight objects, presented in pairs around the fixation point, and then probed a single object in an eight-alternative forced-choice test. The displays were constructed so that each of the eight possible colors appeared in every display, but the color they were next to was not random - each color had a high probability pair (e.g. red appeared with green 80% of time). In information theoretic terms, the displays with statistical regularities have lower entropy compared with uniform displays, and thus require less information to encode. We found that observers could successfully remember 5.5 colors on these displays, significantly higher than the 3.5 colors remembered when the displays were changed to be uniformly distributed in the last block. These results show that capacity estimates, measured in number of objects, actually increased when the displays had some statistical regularities, and that VSTM capacity is not a fixed number of items. We suggest that quantifying capacity in number of objects fails to capture factors such as object complexity or statistical information, and that information theoretic measures are better suited to characterizing VSTM.
This PDF is available to Subscribers Only