Abstract：
:Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory. Here, we show that discrete attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of random diffusion. Model-based and model-free analyses of human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, and precision of working memory reports. Furthermore, attractor dynamics are adaptive. They increase in strength as noise increases with memory load and experiments in humans show these dynamics adapt to the statistics of the environment, such that memories drift towards contextually-predicted values. Together, our results suggest attractor dynamics mitigate errors in working memory by counteracting noise and integrating contextual information into memories.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Panichello MF,DePasquale B,Pillow JW,Buschman TJdoi
10.1038/s41467-019-11298-3subject
Has Abstractpub_date
2019-07-29 00:00:00pages
3366issue
1issn
2041-1723pii
10.1038/s41467-019-11298-3journal_volume
10pub_type
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