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
Models of change detection.Infinite-precision item limit (IP), slots plus averaging (SA), slots plus resources (SR), equal precision (EP), and variable precision (VP). The first three are item-limit models, the last two continuous-resource models. (a) Illustration of resource allocation in the models at set sizes 2 and 5, with a capacity of 3 slots/chunks for IP, SA, and SR. The VP model is distinct from the other models in that the amount of resource varies on a continuum without a hard upper bound. (b) Probability density functions over encoding precision in the VP model, for four set sizes. Parameters were taken from the best fit to the data of one human subject. Mean precision, indicated by a dashed line, is inversely proportional to set size. In the EP model, these distributions would be infinitely sharp (delta functions). (c) Decision process during change detection for each of the five models.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3585403&req=5

pcbi-1002927-g001: Models of change detection.Infinite-precision item limit (IP), slots plus averaging (SA), slots plus resources (SR), equal precision (EP), and variable precision (VP). The first three are item-limit models, the last two continuous-resource models. (a) Illustration of resource allocation in the models at set sizes 2 and 5, with a capacity of 3 slots/chunks for IP, SA, and SR. The VP model is distinct from the other models in that the amount of resource varies on a continuum without a hard upper bound. (b) Probability density functions over encoding precision in the VP model, for four set sizes. Parameters were taken from the best fit to the data of one human subject. Mean precision, indicated by a dashed line, is inversely proportional to set size. In the EP model, these distributions would be infinitely sharp (delta functions). (c) Decision process during change detection for each of the five models.

Mentions: We model a task in which the observer is presented with two displays, each containing N oriented stimuli and separated in time by a delay period. On each trial, there is a 50% probability that one stimulus changes orientation between the first and the second display. The change can be of any magnitude. Observers report whether or not a change occurred. We tested five models of this task, which differ in the way they conceptualize what memory resource consists of and how it is distributed across items (Fig. 1a).


No evidence for an item limit in change detection.

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

Models of change detection.Infinite-precision item limit (IP), slots plus averaging (SA), slots plus resources (SR), equal precision (EP), and variable precision (VP). The first three are item-limit models, the last two continuous-resource models. (a) Illustration of resource allocation in the models at set sizes 2 and 5, with a capacity of 3 slots/chunks for IP, SA, and SR. The VP model is distinct from the other models in that the amount of resource varies on a continuum without a hard upper bound. (b) Probability density functions over encoding precision in the VP model, for four set sizes. Parameters were taken from the best fit to the data of one human subject. Mean precision, indicated by a dashed line, is inversely proportional to set size. In the EP model, these distributions would be infinitely sharp (delta functions). (c) Decision process during change detection for each of the five models.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002927-g001: Models of change detection.Infinite-precision item limit (IP), slots plus averaging (SA), slots plus resources (SR), equal precision (EP), and variable precision (VP). The first three are item-limit models, the last two continuous-resource models. (a) Illustration of resource allocation in the models at set sizes 2 and 5, with a capacity of 3 slots/chunks for IP, SA, and SR. The VP model is distinct from the other models in that the amount of resource varies on a continuum without a hard upper bound. (b) Probability density functions over encoding precision in the VP model, for four set sizes. Parameters were taken from the best fit to the data of one human subject. Mean precision, indicated by a dashed line, is inversely proportional to set size. In the EP model, these distributions would be infinitely sharp (delta functions). (c) Decision process during change detection for each of the five models.
Mentions: We model a task in which the observer is presented with two displays, each containing N oriented stimuli and separated in time by a delay period. On each trial, there is a 50% probability that one stimulus changes orientation between the first and the second display. The change can be of any magnitude. Observers report whether or not a change occurred. We tested five models of this task, which differ in the way they conceptualize what memory resource consists of and how it is distributed across items (Fig. 1a).

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