Year-end Sale % OFF 
Abstract
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., color, orientation) can be analyzed using existing mixture modeling 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, as well as number of nontargets 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 modeling 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.
Highlights
Working memory (WM) is typically measured by providing a person with a set of stimuli to remember and probing their memory after a delay of a few seconds
All the metrics correlated with multiple true parameters, so we present here only the metric most strongly correlated with each parameter
We have developed a 2D mixture modeling toolbox for tasks such as spatial WM tasks
Summary
The 1D mixture models split errors into three components (Bays et al, 2009) (Figure 1). The misbinding model has three sources of errors: imprecision, misbinding, and random guessing: P θ = αφκ θ − θ. Where P(θ ) is the probability of finding a response orientation θ, θ is the orientation of the target stimulus, φκ is the von Mises distribution (circular analogue of the Gaussian distribution), φi is the orientation of the nontarget stimulus i, m is the number of nontarget stimuli, and guessing is uniform over the entire circle (2π ). The parameters α, β, γ , and κ control the proportion of target responding, nontarget responding, guessing, and the concentration of the von Mises distribution, respectively. The spread of the distributions of target and nontarget responses is assumed to be the same. Β, and γ must sum to 1, α is not included as a free parameter in the fitting
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
