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.

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Orientation change detection.(a) Observers reported whether one of the orientations changed between the first and second displays. (b) Hit and false-alarm rates as a function of set size. (c) Psychometric curves, showing the proportion of “change” reports as a function of the magnitude of change, for each set size (mean ± s.e.m across subjects). Magnitude of change was binned into 9° bins. The first point on each curve (at 0°) contains all trials in which no change occurred, and thus represents the false-alarm rate. Using the standard formula for K would return different estimates for different change magnitudes.
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pcbi-1002927-g002: Orientation change detection.(a) Observers reported whether one of the orientations changed between the first and second displays. (b) Hit and false-alarm rates as a function of set size. (c) Psychometric curves, showing the proportion of “change” reports as a function of the magnitude of change, for each set size (mean ± s.e.m across subjects). Magnitude of change was binned into 9° bins. The first point on each curve (at 0°) contains all trials in which no change occurred, and thus represents the false-alarm rate. Using the standard formula for K would return different estimates for different change magnitudes.

Mentions: We conducted an orientation change detection task in which we manipulated both set size and change magnitude (Fig. 2a). Consistent with earlier studies (e.g. [10], [15], [17]), we found that the ability of observers to detect a change decreased with set size, with hit rate H monotonically decreasing and false-alarm rate F monotonically increasing (Fig. 2b). Effects of set size were significant (repeated-measures ANOVA; hit rate: F(3,27) = 52.8, p<0.001; false alarm rate: F(3,27) = 82.0, p<0.001). The increase in F is inconsistent with the IP model, as this model would predict no dependence.


No evidence for an item limit in change detection.

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

Orientation change detection.(a) Observers reported whether one of the orientations changed between the first and second displays. (b) Hit and false-alarm rates as a function of set size. (c) Psychometric curves, showing the proportion of “change” reports as a function of the magnitude of change, for each set size (mean ± s.e.m across subjects). Magnitude of change was binned into 9° bins. The first point on each curve (at 0°) contains all trials in which no change occurred, and thus represents the false-alarm rate. Using the standard formula for K would return different estimates for different change magnitudes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002927-g002: Orientation change detection.(a) Observers reported whether one of the orientations changed between the first and second displays. (b) Hit and false-alarm rates as a function of set size. (c) Psychometric curves, showing the proportion of “change” reports as a function of the magnitude of change, for each set size (mean ± s.e.m across subjects). Magnitude of change was binned into 9° bins. The first point on each curve (at 0°) contains all trials in which no change occurred, and thus represents the false-alarm rate. Using the standard formula for K would return different estimates for different change magnitudes.
Mentions: We conducted an orientation change detection task in which we manipulated both set size and change magnitude (Fig. 2a). Consistent with earlier studies (e.g. [10], [15], [17]), we found that the ability of observers to detect a change decreased with set size, with hit rate H monotonically decreasing and false-alarm rate F monotonically increasing (Fig. 2b). Effects of set size were significant (repeated-measures ANOVA; hit rate: F(3,27) = 52.8, p<0.001; false alarm rate: F(3,27) = 82.0, p<0.001). The increase in F is inconsistent with the IP model, as this model would predict no dependence.

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