The cognitive neuroscience of visual short-term memory

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Highlights

  • Multivariate pattern analysis (MVPA) has revealed that elevated delay-period activity is neither specific to nor necessary for storage in short-term memory (STM).

  • MVPA can track the dynamics of mental coding, and its control, in STM.

  • Multivariate forward encoding models reveal specific role for alpha-band oscillations in STM.

  • When focus of attention and STM are unconfounded, neural activity patterns track the former.

  • MVPA supports models of synaptic weight-based storage of information in STM.

Our understanding of the neural bases of visual short-term memory (STM), the ability to mentally retain information over short periods of time, is being reshaped by two important developments: the application of methods from statistical machine learning, often a variant of multivariate pattern analysis (MVPA), to functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) data sets; and advances in our understanding of the physiology and functions of neuronal oscillations. One consequence is that many commonly observed physiological ‘signatures’ that have previously been interpreted as directly related to the retention of information in visual STM may require reinterpretation as more general, state-related changes that can accompany cognitive-task performance. Another is important refinements of theoretical models of visual STM.

Section snippets

Reconsidering the link between delay-period activity and ‘storage’

For decades, a governing assumption in STM research has been that the short-term retention of visual information is supported by regions that show elevated levels of activity during the delay period of STM tasks. Thus, for example, debates over the role of the prefrontal cortex (PFC) in STM and the related construct of working memory were framed in terms of whether or not its delay-period activity showed load-sensitivity  systematic variation of signal intensity as a function of memory set size 1

Event-related potential (ERP) correlates of STM

Another neural effect that has influenced models of visual STM capacity limitation is the contralateral delay activity (CDA), an ERP component that scales monotonically with STM load, but asymptotes at the psychophysically estimated capacity of an individual [34]. The CDA is widely interpreted as an index of the short-term retention of information (e.g., [35]), such that, for example, the presence of a CDA during visual search has been taken as evidence for ‘memory in search’ 36, 37], and the

Do distributed patterns of activity reflect STM or attention?

The multivariate methods reviewed here draw on two longstanding assumptions about STM. First, that stimulus representation is accomplished by anatomically distributed networks. Second, that the short-term retention of these representations is accomplished via elevated activity in these networks. Most often, however, STM tasks confound the focus of attention with the short-term retention, per se, of information. Recent studies have addressed this by first presenting two sample items, then

Conclusion

High-level cognition, including STM, emerges from dynamic, distributed neural interactions that unfold on multiple time scales. The adoption of methods that more closely align with these principles of brain function is leading to discoveries with important implications for cognitive models of STM and working memory (e.g., 51, 52]), and is informing ongoing research into such questions as the factors that underlie capacity limitations of visual STM 27•, 28•], and the relation between STM and

Conflict of interest statement

I declare that I have no conflict of interest.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgments

I thank Nathan Rose for helpful comments on this manuscript, and Adam Riggall for help with figures. The author was supported by National Institutes of Health grants MH064498 and MH095984.

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