Flexible Working Memory Through Selective Gating and Attentional Tagging

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Exploring the dynamic interplay between learning and working memory within various cognitive contexts

Introduction

The intertwined relationship between reinforcement learning and working memory in the brain is a complex subject, widely studied across various domains in neuroscience. Research efforts have focused on identifying the specific brain areas responsible for these functions, understanding their contributions in accomplishing the related tasks, and exploring their adaptability under conditions such as cognitive impairment or aging.

Methods

Numerous models have been introduced to formulate either these two subsystems of reinforcement learning and working memory separately or their combination and relationship in executing cognitive tasks. This study adopts the RLWM model as a computational framework to analyze the behavioral parameters of subjects with varying cognitive abilities due to age or cognitive status. A related RLWM task is employed to assess a group of subjects across different age groups and cognitive abilities, as measured by the Montreal Cognitive Assessment tool (MoCA).

Results

Analysis reveals a decline in overall performance accuracy and speed with differing age groups (young vs. middle-aged). Significant differences are observed in model parameters such as learning rate, WM decay, and decision noise. Furthermore, among the middle-aged group, distinctions emerge between subjects categorized as normal vs. MCI based on MoCA scores, notably in speed, performance accuracy, and decision noise.

Affective valence does not reflect progress prediction errors in perceptual decisions

Affective valence and intensity form the core of our emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states, such that better/worse-than-expected discrepancies result in positive/negative affect. However, whether the same principle applies to progress prediction errors remains unclear. We empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto an expected drift-rate parameter. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings, suggesting that sequential sampling models provide a promising framework to model progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error.

Remember me - user-centered implementation of working memory architectures on an industrial robot

The present research is innovative as we followed a user-centered approach to implement and train two working memory architectures on an industrial RB-KAIROS + robot: GRU, a state-of-the-art architecture, and WorkMATe, a biologically-inspired alternative. Although user-centered approaches are essential to create a comfortable and safe HRI, they are still rare in industrial settings. Closing this research gap, we conducted two online user studies with large heterogeneous samples. The major aim of these studies was to evaluate the RB-KAIROS + robot's appearance, movements, and perceived memory functions before (User Study 1) and after the implementation and training of robot working memory (User Study 2). In User Study 1, we furthermore explored participants' ideas about robot memory and what aspects of the robot's movements participants found positive and what aspects they would change. The effects of participants' demographic background and attitudes were controlled for. In User Study 1, participants' overall evaluations of the robot were moderate. Participant age and negative attitudes toward robots led to more negative robot evaluations. According to exploratory analyses, these effects were driven by perceived low experience with robots. Participants expressed clear ideas of robot memory and precise suggestions for a safe, efficient, and comfortable robot navigation which are valuable for further research and development. In User Study 2, the implementation of WorkMATe and GRU led to more positive evaluations of perceived robot memory, but not of robot appearance and movements. Participants' robot evaluations were driven by their positive views of robots. Our results demonstrate that considering potential users' views can greatly contribute to an efficient and positively perceived robot navigation, while users' experience with robots is crucial for a positive HRI.

Working memory is supported by learning to represent items as actions

Working memory is typically described as a set of processes that allow for the maintenance and manipulation of information for proximal actions, yet the "action" portion of this construct is commonly overlooked. In contrast, neuroscience-informed theories of working memory have emphasized the hierarchical nature of memory representations, including both goals and sensory representations. These two representational domains are combined for the service of actions. Here, we tested whether, as it is commonly measured (i.e., with computer-based stimuli and button-based responses), working memory involved the planning of motor actions (i.e., specific button presses). Next, we examined the role of motor plan learning in successful working memory performance. Results showed that visual working memory performance was disrupted by unpredictable motor mappings, indicating a role for motor planning in working memory. Further, predictable motor mappings were in fact learned over the course of the experiment, thereby causing the measure of working memory to be partially a measure of participants' ability to learn arbitrary associations between visual stimuli and motor responses. Such learning was not highly specific to certain mappings; in sequences of short tasks, participants improved in their abilities to learn to represent items as actions in working memory. We discuss implications for working memory theories in light of hierarchical structure learning and ecological validity.

Systematic reviews of the acute effects of amphetamine on working memory and other cognitive performances in healthy individuals, with a focus on the potential influence of personality traits

OBJECTIVES

This research aimed to systematically review the acute effects of amphetamine (AMP), a dopamine-releasing agent, on working memory (WM) and other cognitive performances. The investigation also aimed to review the impact of personality traits on the subjective and objective effects of AMP and possible links between personality traits and effects of AMP.

METHODS

Previous double-blind controlled studies assessing the main effects of AMP on WM and other cognitive performances in healthy volunteers were systematically reviewed. An electronic search was performed in the PUBMED and SCOPUS databases. Narrative reviews of the influence of personality traits on the subjective and objective effects of AMP were included.

RESULTS

Nineteen WM studies were included in the current review. Seven studies found effects of AMP on spatial WM, but only one study found the effect of AMP on verbal WM. Thirty-seven independent studies on other aspects of cognitive performance were identified. Twenty-two reported effects of AMP on cognitive functions. Studies also showed that personality traits are associated with the subjective effects of AMP. However, few studies reported the impacts of personality traits on the objective (such as WM) effects of AMP.

CONCLUSION

Overall, findings indicate that AMP has mixed-effects on spatial WM and other cognitive functions, but it lacks effects on verbal WM. Although there are insufficient studies on objective measures, studies also indicated that the subjective effects of AMP administration are linked to between-person variations in personality traits.

Quiet Trajectories as Neural Building Blocks

Our concept of the neural mechanisms of working memory has recently undergone an upheaval, because of two transformative concepts: multivariate neural state trajectories and the activity-silent hypothesis. I will argue that putting these concepts together raises the difficult problem of "quiet trajectories," where future neural activity is not fully determined by current activity. However, this also promises new building blocks for neural computation.

Priority-based transformations of stimulus representation in visual working memory

How does the brain prioritize among the contents of working memory (WM) to appropriately guide behavior? Previous work, employing inverted encoding modeling (IEM) of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) datasets, has shown that unprioritized memory items (UMI) are actively represented in the brain, but in a “flipped”, or opposite, format compared to prioritized memory items (PMI). To acquire independent evidence for such a priority-based representational transformation, and to explore underlying mechanisms, we trained recurrent neural networks (RNNs) with a long short-term memory (LSTM) architecture to perform a 2-back WM task. Visualization of LSTM hidden layer activity using Principal Component Analysis (PCA) confirmed that stimulus representations undergo a representational transformation–consistent with a flip—while transitioning from the functional status of UMI to PMI. Demixed (d)PCA of the same data identified two representational trajectories, one each within a UMI subspace and a PMI subspace, both undergoing a reversal of stimulus coding axes. dPCA of data from an EEG dataset also provided evidence for priority-based transformations of the representational code, albeit with some differences. This type of transformation could allow for retention of unprioritized information in WM while preventing it from interfering with concurrent behavior. The results from this initial exploration suggest that the algorithmic details of how this transformation is carried out by RNNs, versus by the human brain, may differ.

The Predictive Dynamics of Happiness and Well-Being

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective

Reinforcement learning and working memory are two core processes of human cognition and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here, we show that the brain networks that support them actually overlap significantly and that they are less distinct cognitive processes than often assumed. We review literature demonstrating the benefits of considering each process to explain properties of the other and highlight recent work investigating their more complex interactions. We discuss how future research in both computational and cognitive sciences can benefit from one another, suggesting that a key missing piece for artificial agents to learn to behave with more human-like efficiency is taking working memory's role in learning seriously. This review highlights the risks of neglecting the interplay between different processes when studying human behavior (in particular when considering individual differences). We emphasize the importance of investigating these dynamics to build a comprehensive understanding of human cognition.

Understanding occipital and parietal contributions to visual working memory: Commentary on Xu (2020)

In her commentary, Xu (2020) admonishes the reader that "To have a full understanding of the cognitive mechanisms underlying VWM [visual working memory], both behavioral and neural evidence needs to be taken into account. This is a must, and not a choice, for any study that attempts to capture the nature of VWM" (p. 11). Although we don't disagree with this statement, our overall assessment of this commentary is that it, itself, fails to satisfy several "musts" and, consequently, does not pose a serious challenge for the sensory recruitment framework for understanding visual working memory. These "musts" include accurately characterizing the framework being critiqued, not favoring verbal models and intuition at the expense of formal quantitative models, and providing even-handed interpretation of the work of others. We'll conclude with a summary of how the sensory recruitment framework can be incorporated into a broader working model of visual working memory.

Spike frequency adaptation supports network computations on temporally dispersed information

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex - especially in higher areas of the human neocortex - moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

A Novel Poly-N-Epoxy Propyl Carbazole Based Memory Device

Generally, polymer-based memory devices store information in a manner distinct from that of silicon-based memory devices. Conventional silicon memory devices store charges as either zero or one for digital information, whereas most polymers store charges by the switching of electrical resistance. For the first time, this study reports that the novel conducting polymer Poly-N-Epoxy-Propyl Carbazole (PEPC) can offer effective memory storage behavior. In the current research, the electrical characterization of a single layer memory device (metal/polymer/metal) using PEPC, with or without doping of charge transfer complexes 7,7,8,8-tetra-cyanoquino-dimethane (TCNQ), was investigated. From the current–voltage characteristics, it was found that PEPC shows memory switching effects in both cases (with or without the TCNQ complex). However, in the presence of TCNQ, the PEPC performs faster memory switching at relatively lower voltage and, therefore, a higher ON and OFF ratio (I_ON/I_OFF ~ 100) was observed. The outcome of this study may help to further understand the memory switching effects of conducting polymer.

The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated

The Ouroboros Model has been proposed as a biologically-inspired comprehensive cognitive architecture for general intelligence, comprising natural and artificial manifestations. The approach addresses very diverse fundamental desiderata of research in natural cognition and also artificial intelligence, AI. Here, it is described how the postulated structures have met with supportive evidence over recent years. The associated hypothesized processes could remedy pressing problems plaguing many, and even the most powerful current implementations of AI, including in particular deep neural networks. Some selected recent findings from very different fields are summoned, which illustrate the status and substantiate the proposal.