Working memory and language: An overview of key topics

Given these obvious gaps in the research literature, we thus set out to compile this comprehensive handbook, with the goal in mind to fill up all these lacunae from previous research. Furthermore, we also aim for theoretical ingenuity and empirical robustness in our individual chapter reviews and devote independent sections to key areas of foundational theories, including working memory models and measures in cognitive psychology, as well as incorporating working memory within well-established linguistic theories and processing frameworks. As far as we know, much of these have not been done before. As such, we are hoping that the comprehensive coverage of key topics in all these essential areas in our handbook will benefit researchers and students not just from psychology and linguistics, but also readers from all other related fields of cognitive sciences to draw insights and inspirations from the chapters herein.

Unsupervised learning for robust working memory

Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it suffers from a fine-tuning of network connectivity, in particular, to form continuous attractors suggested for working memory encoding analog signals. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity targeting the slip of instant neural activity and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity with less sensitivity to learning parameters. However, for the maintenance of spatially structured persistent activity, differential plasticity could recover persistent activity, but its pattern can be irregular for different stimulus locations. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations, while it was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules.

Music With the Message in Mind

This chapter traces advances in the knowledge and understanding of how background music influences consumers’ learning and memory of brand and message elements in ads. Early empirical research in marketing focused on comparison of ad brand and message recall from ads with and without music, documenting the consistent distraction posed by background music. Research comparing multiple music backgrounds followed, linking greater distraction with lack of background fit with brand and message. Based on psychologists’ working memory models, studies revealed that background music distracts but also potentially serves as a cue to later recall of brand and message elements. Work in neuropsychology revealed automatic, preattentive brain responses to surprising changes in background music, which led researchers in marketing to quantify the increased distraction by backgrounds with more surprises from structural changes in the music. Building upon the contributions of Meyers’s (1956) seminal work, recent research unveiled differences in distraction and capacity to cue later brand and message recall between backgrounds evoking connotative or private images. The chapter concludes with directions for future research to further expand the knowledge and understanding of how music impacts advertising effectiveness.

Low-dimensional dynamics for working memory and time encoding

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Working memory is not fixed capacity: More active storage capacity for real-world objects than simple stimuli

Visual working memory is the cognitive system that holds visual information active to make it resistant to interference from new perceptual input. Information about simple stimuli – colors, orientations – is encoded into working memory rapidly: in under 100ms, working memory ‘fills up’, revealing a stark capacity limit. However, for real-world objects, the same behavioral limits do not hold: with increasing encoding time, people store more real-world objects and do so with more detail. This boost in performance for real-world objects is generally assumed to reflect the use of a separate episodic long-term memory system, rather than working memory. Here we show that this behavioral increase in capacity with real-world objects is not solely due to the use of separate episodic long-term memory systems. In particular, we show that this increase is a result of active storage in working memory, as shown by directly measuring neural activity during the delay period of a working memory task using EEG. These data challenge fixed capacity working memory models, and demonstrate that working memory and its capacity limitations are dependent upon our existing knowledge.

Modeling Working Memory to Identify Computational Correlates of Consciousness

Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and propose that three computational correlates of consciousness follow from the results of this work: itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically.