Spatial Transposition Gradients in Visual Working Memory

Rerko, Laura; Oberauer, Klaus; Lin, Hsuan-Yu

doi:10.1080/17470218.2013.789543

Cited by 47 publications

(51 citation statements)

References 21 publications

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“…The IMM in its full version (and version bsc) makes one prediction that distinguishes it from the mixture model: Intrusions from nontargets should depend on their proximity to the target on the cue-feature dimension. This prediction has received support in some previous experiments (Bays, 2016;Rerko, Oberauer, & Lin, 2014), and again in the experiments reanalyzed here (in particular Experiment 2). At the same time, the recovery simulations for the HIMM show that the signal of distance-dependent nontarget intrusions is likely to be weak in data from experiments with conventional numbers of participants and trials, and this was also noticeable in Experiment 1.…”

Section: Discussionsupporting

confidence: 83%

Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory

et al. 2017

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The article presents Bayesian hierarchical modeling frameworks for two measurement models for visual working memory. The models can be applied to the distributions of responses on a circular feature dimension, as obtained with the continuous reproduction (a.k.a. delayed estimation) task. The first measurement model is a mixture model that describes the response distributions as a mixture of one (Zhang & Luck, 2008) or several (Bays, Catalao, & Husain, 2009) von-Mises distribution(s) and a uniform distribution. The second model is a novel, interference-based measurement model. We present parameter recovery simulations for both models, demonstrating that the hierarchical framework enables precise parameter estimates when a small number of trials are compensated by a large number of subjects. Simulations with the mixture model show that the Bayesian hierarchical framework minimizes the previously observed estimation bias for memory precision in conditions of low performance. Unbiased and reasonably precise parameter estimates can also be obtained from the interference measurement model, though some parameters of this model demand a relatively large amount of data for precise measurement. Both models are applied to two experimental data sets. Experiment 1 measures the effect of memory set size on the model parameters. Experiment 2 provides evidence for the assumption in the interference model that the target feature tends to be confused with features of those nontargets that are close to the target on the dimension used as retrieval cue.

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Section: Discussionsupporting

confidence: 83%

Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory

et al. 2017

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“…Such an effect has previously been described for spatial proximity when the target is cued by locations (Emrich and Ferber, 2012; Rerko et al, 2014; for a meta-analysis, see Bays, 2016). Here, we have experimentally confirmed the cue similarity effect independent of the feature dimension used for the cue, and quantitatively accounted for the effects in the population model.…”

Section: Discussionsupporting

confidence: 56%

Neural Architecture for Feature Binding in Visual Working Memory

2017

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Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system.SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual information by neurons that respond to the conjunction of features. We find clear evidence that nonspatial features are bound via space: we memorize directly where a color or an orientation appeared, but we memorize which color belonged with which orientation only indirectly by virtue of their shared location.

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“…This benchmark is qualified by Benchmark 4.1.2 (Appendix B). (Farrell & Lewandowsky, 2004 (Rerko et al, 2014) Benchmark 4.1.2. Fill-in effect in serial recall (C).…”

Section: Benchmark 41 Confusions Of Target Item With Other Items Inmentioning

confidence: 99%

Benchmarks for models of short-term and working memory.

Oberauer¹,

Lewandowsky²,

Awh³

et al. 2018

Psychological Bulletin

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Any mature field of research in psychology-such as short-term/working memory-is characterized by a wealth of empirical findings. It is currently unrealistic to expect a theory to explain them all; theorists must satisfice with explaining a subset of findings. The aim of the present article is to make the choice of that subset less arbitrary and idiosyncratic than is current practice. We propose criteria for identifying benchmark findings that every theory in a field should be able to explain: Benchmarks should be reproducible, generalize across materials and methodological variations, and be theoretically informative. We propose a set of benchmarks for theories and computational models of short-term and working memory. The benchmarks are described in as theory-neutral a way as possible, so that they can serve as empirical common ground for competing theoretical approaches. Benchmarks are rated on three levels according to their priority for explanation. Selection and ratings of the benchmarks is based on consensus among the authors, who jointly represent a broad range of theoretical perspectives on working memory, and they are supported by a survey among other experts on working memory. The article is accompanied by a web page providing an open forum for discussion and for submitting proposals for new benchmarks; and a repository for reference data sets for each benchmark. (PsycINFO Database Record

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Spatial Transposition Gradients in Visual Working Memory

Cited by 47 publications

References 21 publications

Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory

Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory

Neural Architecture for Feature Binding in Visual Working Memory

Benchmarks for models of short-term and working memory.

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