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Kartik Sreenivasan, Ainsley Temudo, Vahan Babushkin; The neural basis of binding errors in visual working memory. Journal of Vision 2018;18(10):116. https://doi.org/10.1167/18.10.116.
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Memory errors are a window into the capacity limits that famously constrain visual working memory (VWM). When subjects maintain multiple items in VWM and are asked to report a feature of one item, they sometimes mistakenly report the feature of another item. This is referred to as a binding error. Understanding the neurophysiology underlying binding errors can provide key insights into how features are bound together in VWM. One biophysiological model (Barbosa and Compte, 2015) suggests that object features are stored as pairs of bumps in individual attractor networks, and that features are bound via network oscillations. This model predicts that binding errors can result from disruptions in the oscillatory pattern of the network – specifically in the alpha/beta range (8-25 Hz). Our aim was to validate this model using magnetoencephalography (MEG) to measure network oscillations in a task designed to induce binding errors. On each trial, subjects briefly saw 3 circles and had to remember their colors and locations over a memory delay. After the delay, they were sequentially cued to report the location of each circle (via a central color cue). Using a maximum likelihood approach, we assigned each response a likelihood of being a binding error. Trials with likelihoods greater than 0.7 were considered binding error trials. We computed a phase preservation index (PPI) for each MEG sensor separately for trials with and without binding errors. PPI measures the consistency of the relationship in oscillatory phase across trials. Binding errors were associated with significantly reduced alpha (8-12 Hz) PPI during the memory delay in frontoparietal sensors. This pattern of reduced frontoparietal alpha phase consistency was specific to binding errors, as opposed to other VWM errors. This finding provides initial support for the idea that object features are bound via low-frequency network oscillations in VWM.
Meeting abstract presented at VSS 2018
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