Turning the mind’s eye inward: the interplay between selective attention and working memory

Historically, cognitive sciences have considered selective attention and working memory as largely separated cognitive functions. That is, selective attention as a concept is typically reserved for the processes that allow for the prioritization of specific sensory input, while working memory entails more central structures for maintaining (and operating on) temporary mental representations. However, over the last decades various observations have been reported that question such sharp distinction. Most importantly, information stored in working memory has been shown to modulate selective attention processing – and vice versa. At the theoretical level, these observations are paralleled by an increasingly dominant focus on working memory as (involving) the attended part of long-term memory, with some positions considering that working memory is equivalent to selective attention turned to long-term memory representations – or internal selective attention. This questions the existence of working memory as a dedicated cognitive function and raises the need for integrative accounts of working memory and attention. The next step will be to explore the precise implications of attentional accounts of WM for the understanding of specific aspects and characteristics of WM, such as serial order processing, its modality-specificity, its capacity limitations, its relation with executive functions, as well as the nature of attentional mechanisms involved. This research topic in Frontiers in Human Neuroscience aims at bringing together the latest insights and findings about the interplay between working memory and selective attention

Social working memory: neurocognitive networks and directions for future research.

Navigating the social world requires the ability to maintain and manipulate information about people's beliefs, traits, and mental states. We characterize this capacity as social working memory (SWM). To date, very little research has explored this phenomenon, in part because of the assumption that general working memory systems would support working memory for social information. Various lines of research, however, suggest that social cognitive processing relies on a neurocognitive network (i.e., the "mentalizing network") that is functionally distinct from, and considered antagonistic with, the canonical working memory network. Here, we review evidence suggesting that demanding social cognition requires SWM and that both the mentalizing and canonical working memory neurocognitive networks support SWM. The neural data run counter to the common finding of parametric decreases in mentalizing regions as a function of working memory demand and suggest that the mentalizing network can support demanding cognition, when it is demanding social cognition. Implications for individual differences in social cognition and pathologies of social cognition are discussed

Training working memory to reduce rumination

Cognitive symptoms of depression, such as rumination, have shown to be associated with deficits in working memory functioning. More precisely, the capacity to expel irrelevant negative information from working memory seems to be affected. Even though these associations have repeatedly been demonstrated, the nature and causal direction of this association is still unclear. Therefore, within an experimental design, we tried to manipulate working memory functioning of participants with heightened rumination scores in two similar experiments (n = 72 and n = 45) using a six day working memory training compared to active and passive control groups. Subsequently the effects on the processing of non-emotional and emotional information in working memory were monitored. In both experiments, performance during the training task significantly increased, but this performance gain did not transfer to the outcome working memory tasks or rumination and depression measures. Possible explanations for the failure to find transfer effects are discussed

Working memory load elicits attentional bias to threat

Anxious individuals tend to show attentional bias to threats and dangers; this is usually in-terpreted as a specific bias in threat-processing. However, they also tend to show general working memory and cognitive control impairments. We hypothesised that the lack of work-ing memory resources might contribute to attentional bias, by limiting anxious individuals’ ability to regulate their responses to emotional stimuli. If this is true, then loading working memory should elicit attentional bias to threat, even in non-anxious participants. We tested this hypothesis in two experiments, with participants unselected for anxiety. In Experiment 1, a phonological working memory load (remembering a string of digits) elicited an attentional bias to fear-conditioned Japanese words. In Experiment 2, a visuo-spatial working memory load (remembering a series of locations in a matrix of squares) elicited an attentional bias to emotional schematic faces. Results suggest that working memory and cognitive control may moderate the attentional bias to threat commonly observed in anxiety

What working memory is for

Glenberg focuses on conceptualizations that change from moment to moment, yet he dismisses the concept of working memory (sect. 4.3), which offers an account of temporary storage and on-line cognition. This commentary questions whether Glenberg's account adequately caters for observations of consistent data patterns in temporary storage of verbal and visuospatial information in healthy adults and in brain-damaged patients with deficits in temporary retention.</jats:p

Variable Rate Working Memories for Phonetic Categorization and Invariant Speech Perception

Speech can be understood at widely varying production rates. A working memory is described for short-term storage of temporal lists of input items. The working memory is a cooperative-competitive neural network that automatically adjusts its integration rate, or gain, to generate a short-term memory code for a list that is independent of item presentation rate. Such an invariant working memory model is used to simulate data of Repp (1980) concerning the changes of phonetic category boundaries as a function of their presentation rate. Thus the variability of categorical boundaries can be traced to the temporal in variance of the working memory code.Air Force Office of Scientific Research (F49620-92-J-0225, 90-0128); Defense Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100

It is better than you think: fluid intelligence across the lifespan

The growth and decline of fluid intelligence is associated with brain structural changes. For example, development of fluid IQ is associated with cortex thickness during the critical period between 6 to 12 years old. On the other end of the lifespan, poor performance in cognitive functioning is attributed to a decrease of frontal gray matter density in elderly populations. In particular, there is a sharp decline in fluid IQ scores after 65 years of age. There is substantial evidence that working memory and fluid intelligence (Gf) share neural substrates, such as the prefrontal and parietal cortices. However, very little research has examined whether the pattern of growth and decline in working memory mirrors that of fluid intelligence. For example, does the decline of working memory skills in elderly populations mirror fluid intelligence? Is the rate of working memory decline similar to the rate of growth

Training of Working Memory Impacts Neural Processing of Vocal Pitch Regulation

Working memory training can improve the performance of tasks that were not trained. Whether auditory-motor integration for voice control can benefit from working memory training, however, remains unclear. The present event-related potential (ERP) study examined the impact of working memory training on the auditory-motor processing of vocal pitch. Trained participants underwent adaptive working memory training using a digit span backwards paradigm, while control participants did not receive any training. Before and after training, both trained and control participants were exposed to frequency-altered auditory feedback while producing vocalizations. After training, trained participants exhibited significantly decreased N1 amplitudes and increased P2 amplitudes in response to pitch errors in voice auditory feedback. In addition, there was a significant positive correlation between the degree of improvement in working memory capacity and the post-pre difference in P2 amplitudes. Training-related changes in the vocal compensation, however, were not observed. There was no systematic change in either vocal or cortical responses for control participants. These findings provide evidence that working memory training impacts the cortical processing of feedback errors in vocal pitch regulation. This enhanced cortical processing may be the result of increased neural efficiency in the detection of pitch errors between the intended and actual feedback

Linking working memory and long-term memory: A computational model of the learning of new words

The nonword repetition (NWR) test has been shown to be a good predictor of children’s vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long-term memory (LTM) has been proposed. In this paper, we present a computational model of children’s vocabulary acquisition (EPAM-VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model’s behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM-VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM-VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM-VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity. Keywords: EPAM, working memory, long-term memory, nonword repetition, vocabulary acquisition, developmental change