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Regular version of the site

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Yury Markov
Using the Statistical Mixture Model for Studying Object-Location Binding in Visual Working Memory
Psychology
(Bachelor’s programme)
2017
Research shows that object-location binding errors can occur in VWM indicating a failure to store bound representations rather than mere forgetting (Bays et al., 2009; Pertzov et. al. 2012). Here we investigated how categorical similarity between real-world objects influences the probability of object-location binding errors. Our observers memorized three objects (image database: Konkle et. al. 2010) presented for 3 seconds and located around an invisible circumference. After a 1-second delay they had to (1) locate one of those objects on the circumference (localization task), or (2) recognize an old object when paired with a new object (recognition task). On each trial, three encoded objects could be drawn from a same or different categories, providing two levels of categorical similarity. For the localization task, we used the mixture model (Zhang & Luck, 2008) with swap; Bays et al., 2009) to estimate the probabilities of correct and swapped object-location conjunctions, as well as the precision of localization and guess rate. We found that categorical similarity had no effect on localization precision and guess rate. However, the observers made more swaps when the encoded objects have been drawn from the same category. Importantly, there were no correlations between the probabilities of these binding errors and probabilities of false recognition in the recognition task, which suggests that the binding errors cannot be explained solely by poor memory for objects. Rather, remembering objects and binding them to locations appear to be distinct processes. We suggest that categorical similarity imposes additional demands on encoding and retaining a correct object at a correct location. The need for object-location binding impairs the precision of representing the locations in VWM.

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