Limits...
No evidence for an item limit in change detection.

Keshvari S, van den Berg R, Ma WJ - PLoS Comput. Biol. (2013)

Bottom Line: Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models").Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes.In a rigorous comparison of five models, we found no evidence of an item limit.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.

ABSTRACT
Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models"). Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models"). Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color) in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

Show MeSH
Apparent guessing analysis.Apparent guessing rate as a function of set size as obtained from subject data (circles and error bars) and synthetic data generated by each model (shaded areas). Even though the VP model does not contain any “true” guesses, it still accounts best for the apparent guessing rate.
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pcbi-1002927-g005: Apparent guessing analysis.Apparent guessing rate as a function of set size as obtained from subject data (circles and error bars) and synthetic data generated by each model (shaded areas). Even though the VP model does not contain any “true” guesses, it still accounts best for the apparent guessing rate.

Mentions: We perform an analogous analysis for change detection here. We fitted, at each set size separately, a model in which subjects guess on a certain proportion of trials, and on other trials, respond like an EP observer. Free parameters, at each set size separately, are the guessing parameter, which we call apparent guessing rate (AGR), and the precision parameter of the EP observer. We found that AGR was significantly different from zero at every set size (t(9)>4.5, p<0.001) and increased with set size (Fig. 5; repeated-measures ANOVA, main effect of set size: F(3,27) = 21.1, p<0.001), reaching as much as 0.60±0.06 at set size 8.


No evidence for an item limit in change detection.

Keshvari S, van den Berg R, Ma WJ - PLoS Comput. Biol. (2013)

Apparent guessing analysis.Apparent guessing rate as a function of set size as obtained from subject data (circles and error bars) and synthetic data generated by each model (shaded areas). Even though the VP model does not contain any “true” guesses, it still accounts best for the apparent guessing rate.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585403&req=5

pcbi-1002927-g005: Apparent guessing analysis.Apparent guessing rate as a function of set size as obtained from subject data (circles and error bars) and synthetic data generated by each model (shaded areas). Even though the VP model does not contain any “true” guesses, it still accounts best for the apparent guessing rate.
Mentions: We perform an analogous analysis for change detection here. We fitted, at each set size separately, a model in which subjects guess on a certain proportion of trials, and on other trials, respond like an EP observer. Free parameters, at each set size separately, are the guessing parameter, which we call apparent guessing rate (AGR), and the precision parameter of the EP observer. We found that AGR was significantly different from zero at every set size (t(9)>4.5, p<0.001) and increased with set size (Fig. 5; repeated-measures ANOVA, main effect of set size: F(3,27) = 21.1, p<0.001), reaching as much as 0.60±0.06 at set size 8.

Bottom Line: Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models").Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes.In a rigorous comparison of five models, we found no evidence of an item limit.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.

ABSTRACT
Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models"). Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models"). Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color) in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

Show MeSH