Encoding: the keystone to efficient functioning of verbal short-term memory

Better Phonological Short-Term Memory Is Linked to Improved Cortical Memory Representations for Word Forms and Better Word Learning

Language learning relies on both short-term and long-term memory. Phonological short-term memory (pSTM) is thought to play an important role in the learning of novel word forms. However, language learners may differ in their ability to maintain word representations in pSTM during interfering auditory input. We used magnetoencephalography (MEG) to investigate how pSTM capacity in better and poorer pSTM groups is linked to language learning and the maintenance of pseudowords in pSTM. In particular, MEG was recorded while participants maintained pseudowords in pSTM by covert speech rehearsal, and while these brain representations were probed by presenting auditory pseudowords with first or third syllables matching or mismatching the rehearsed item. A control condition included identical stimuli but no rehearsal. Differences in response strength between matching and mismatching syllables were interpreted as the phonological mapping negativity (PMN). While PMN for the first syllable was found in both groups, it was observed for the third syllable only in the group with better pSTM. This suggests that individuals with better pSTM maintained representations of trisyllabic pseudowords more accurately during interference than individuals with poorer pSTM. Importantly, the group with better pSTM learned words faster in a paired-associate word learning task, linking the PMN findings to language learning.

Common cortical architectures for phonological working memory identified in individual brains

Phonological working memory is the capacity to briefly maintain and recall representations of sounds important for speech and language and is believed to be critical for language and reading acquisition. Whether phonological working memory is supported by fronto-parietal brain regions associated with short-term memory storage or perisylvian brain structures implicated in speech perception and production is unclear, perhaps due to variability in stimuli, task demands, and individuals. We used fMRI to assess neurophysiological responses while individuals performed two tasks with closely matched stimuli but divergent task demands-nonword repetition and nonword discrimination-at two levels of phonological working memory load. Using analyses designed to address intersubject variability, we found significant neural responses to the critical contrast of high vs. low phonological working memory load in both tasks in a set of regions closely resembling those involved in speech perception and production. Moreover, within those regions, the voxel-wise patterns of load-related activation were highly correlated between the two tasks. These results suggest that brain regions in the temporal and frontal lobes encapsulate the core neurocomputational components of phonological working memory; an architecture that becomes increasingly evident as neural responses are examined in successively finer-grained detail in individual participants.

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke.