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Mark Schurgin, Jonathan Flombaum; Visual Working Memory Has Greater Tolerance Than Visual Long-Term Memory. Journal of Vision 2016;16(12):1062. doi: 10.1167/16.12.1062.
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© ARVO (1962-2015); The Authors (2016-present)
We propose that visual working memory (VWM) is designed to support the acquisition of appropriately constrained representations in visual long-term memory (VLTM). This novel hypothesis has implications for understanding apparent limitations in VWM, and the mechanisms that transition VWM to VLTM. The prediction is that VWM should have greater tolerance than VLTM; it should more readily recognize objects as the same despite inputs that differ considerably in appearance. We tested this prediction and further investigated the relationship between these systems in several experiments. In the experiments participants were exposed to two (or more) real-world objects in a trial. After a short delay, one of the objects was paired with a new object and the task was to indicate which was in the recently seen set. After 180 trials a long-term memory test probed the previously untested objects against foils in exactly the same way. We observed two especially notable results: In several experiments we injected the test images with varying amounts of noise by randomly scrambling pixels. VWM performance was unaffected, even by 75% noise. But VLTM performance suffered linearly as noise increased. In other experiments, VWM was unaffected when test objects were viewed at a new orientation (compared to exposure), but VLTM performance dropped significantly. Altogether, VWM was more tolerant: willing to recognize an object as the same despite inputs that differed considerably in appearance. These results suggest that what many interpret as noise in VWM may actually be a feature of tolerance. Several experiments manipulating load further support this perspective. The experiments also suggest a new perspective on VLTM, which is often thought of as highly tolerant, designed to accommodate recognition despite changes in viewing conditions. But compared to VWM it is relatively intolerant, suggesting that representations actually become more discriminating as they consolidate in VLTM.
Meeting abstract presented at VSS 2016
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