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Studying the sources of errors in memory recall has proven invaluable for understanding the mechanisms of working memory (WM). While one-dimensional memory features (e.g. colour, orientation) can be analysed using existing mixture modelling toolboxes to separate the influence of imprecision, guessing, and misbinding (the tendency to confuse features that belong to different memoranda), such toolboxes are not currently available for two-dimensional spatial WM tasks.Here we present a method to isolate sources of spatial error in tasks where participants have to report the spatial location of an item in memory, using two-dimensional mixture models. The method recovers simulated parameters well, and is robust to the influence of response distributions and biases, and number of non-targets and trials. To demonstrate the model, we fit data from a complex spatial WM task, and show the recovered parameters correspond well with previous spatial WM findings, and with recovered parameters on a one-dimensional analogue of this task, suggesting convergent validity for this two-dimensional modelling approach. Because the extra dimension allows greater separation of memoranda and responses, spatial tasks turn out to be much better for separating misbinding from imprecision and guessing than one-dimensional tasks. Code for these models is freely available in the MemToolbox2D package and is integrated to work with the commonly used Matlab package MemToolbox.

Original publication

DOI

10.31234/osf.io/q57fm

Type

Journal article

Publication Date

30/07/2019