Working memory serves as a crucial building block in cognitive systems due to its fundamental role in information processing and decision-making. Acting as a temporary storage and manipulation mechanism, working memory enables individuals to actively hold and manipulate relevant information essential for ongoing tasks. This cognitive function is pivotal for various complex processes, including problem-solving, language comprehension, and decision-making. This paper introduces a novel working memory model designed as a spiking recurrent neural network of excitatory and inhibitory neurons. The proposed model incorporates biological mechanisms that allows attractors to be active phasically, thereby reducing the energy budget associated with maintaining attractor states. The core innovation of the model lies in its ability to leverage phasic attractors for state-dependent computation in a probabilistic manner. By modulating the activity of attractors via synaptic delays, the model demonstrates context-sensitive information processing. The results showcase the efficiency gains achieved by the proposed phasic attractor mechanism and highlight the model’s capacity for flexible and adaptive information processing.

Performing endovascular medical interventions safely and efficiently requires a diverse set of skills that need to be practised in dedicated training sessions. Here, we used multimodal magnetic resonance (MR) imaging to determine the structural and functional plasticity and core skills associated with skill acquisition. A training group learned to perform a simulator-based endovascular procedure, while a control group performed a simpli-fied version of the task; multimodal MR images were acquired before and after training. Using a well-controlled interaction design, we found strong multimodal evidence for the role of the intraparietal sulcus (IPS) in endovas-cular skill acquisition that is in line with previous work implicating the structure in visuospatial transformations including simple visuo-motor and mental rotation tasks. Our results provide a unique window into the multi-modal nature of rapid structural and functional plasticity of the human brain while learning a multifaceted and complex clinical skill. Further, our results provide a detailed description of the plasticity process associated with endovascular skill acquisition and highlight specific facets of skills that could enhance current medical pedagogy and be useful to explicitly target during clinical resident training.

Humans can learn simple new tasks very quickly. This ability suggests that people can reuse previously learned procedural knowledge when it applies to a new context. We have proposed a modeling approach based on this idea and used it to create a model of the attentional blink (AB). The main idea of the skill-based approach is that models are not created from scratch but, instead, built up from reusable pieces of procedural knowledge (skills). This approach not only provides an explanation for the fast learning of simple tasks but also shows much promise to improve certain aspects of cognitive modeling (e.g., robustness and generalizability). We performed two experiments, in order to collect empirical support for the model's prediction that the AB will disappear when the two targets are consolidated as a single chunk. Firstly, we performed an unsuccessful replication of a study reporting that the AB disappears when participants are instructed to remember the targets as a syllable. However, a subsequent experiment using easily combinable stimuli supported the model's prediction and showed a strongly reduced AB in a large group of participants. This result suggests that it is possible to avoid the AB with the right consolidation strategy. The skill-based approach allowed relating this finding to a general cognitive process, thereby demonstrating that incorporating this approach can be very helpful to generalize the findings of cognitive models, which otherwise tends to be rather difficult.

Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students' tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student's work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.

During real-time language processing, people rely on linguistic and non-linguistic biases to anticipate upcoming linguistic input. One of these linguistic biases is known as the implicit causality bias, wherein language users anticipate that certain entities will be rementioned in the discourse based on the entity's particular role in an expressed causal event. For example, when language users encounter a sentence like "Elizabeth congratulated Tina…" during real-time language processing, they seemingly anticipate that the discourse will continue about Tina, the object referent, rather than Elizabeth, the subject referent. However, it is often unclear how these reference biases are acquired and how exactly they get used during real-time language processing. In order to investigate these questions, we developed a reference learning model within the PRIMs cognitive architecture that simulated the process of predicting upcoming discourse referents and their linguistic forms. Crucially, across the linguistic input the model was presented with, there were asymmetries with respect to how the discourse continued. By utilizing the learning mechanisms of the PRIMs architecture, the model was able to optimize its predictions, ultimately leading to biased model behavior. More specifically, following subject-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the subject referent, whereas following object-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the object referent. In a similar fashion, the model was more likely to predict that subject referent continuations would be in the form of a pronoun, whereas object referent continuations would be in the form of a proper name. These learned biases were also shown to generalize to novel contexts in which either the verb or the subject and object referents were new. The results of the present study demonstrate that seemingly complex linguistic behavior can be explained by cognitively plausible domain-general learning mechanisms. This study has implications for psycholinguistic accounts of predictive language processing and language learning, as well as for theories of implicit causality and reference processing.

Memristors are an electronic device whose resistance depends on the voltage history that has been applied to its two terminals. Despite its clear advantage as a computational element, a suitable transport model is lacking for the special class of interface-based memristors. Here, we adapt the widely-used Yakopcic compact model by including transport equations relevant to interface-type memristors. This model is able to reproduce the qualitative behaviour measured upon Nb-doped SrTiO$_3$ memristive devices. Our analysis demonstrates a direct correlation between the devices' characteristic parameters and those of our model. The model can clearly identify the charge transport mechanism in different resistive states thus facilitating evaluation of the relevant parameters pertaining to resistive switching in interface-based memristors. One clear application of our study is its ability to inform the design and fabrication of related memristive devices.

Memristive devices are a class of circuit elements that shows great promise as future building block for brain-inspired computing. One influential view in theoretical neuroscience sees the brain as a function-computing device: given input signals, the brain applies a function in order to generate new internal states and motor outputs. Therefore, being able to approximate functions is a fundamental axiom to build upon for future brain research and to derive more efficient computational machines. In this work we apply a novel supervised learning algorithm - based on controlling niobium-doped strontium titanate memristive synapses - to learning non-trivial multidimensional functions. By implementing our method into the spiking neural network simulator Nengo, we show that we are able to at least match the performance obtained when using ideal, linear synapses and - in doing so - that this kind of memristive device can be harnessed as computational substrate to move towards more efficient, brain-inspired computing.

This article presents a cognitive model of distraction and mind-wandering that combines and formalizes several existing theories. It assumes that task-related goals and opportunities for distraction are continuously in competition for mental resources. If the task-related goal does not need a particular resource at a particular moment, the likelihood that it is captured by a distraction is high. We applied this model to explain the results of three distraction experiments that differ from each other in a number of ways. The first experiment is a slow-paced mind-wandering study; the main result is that less mind-wandering occurs if subjects have to maintain an item in working memory. The second experiment is a working memory task in which mind-wandering is triggered by the presence of self-referential words in a secondary task; these words increase mental elaboration and reduce memory performance. The third experiment is a mental arithmetic/ memory/visual attention task, in which subjects became more distracted by a flanking (irrelevant) video as the task increased in complexity: as subjects need more time to think, they leave the visual resource vulnerable to distraction. Although these phenomena have been treated separately in the literature, we show that these phenomena can be explained by a single comprehensive model that is based on the assumption that distractions target unused cognitive resources.

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.

Motivation can counteract the effects of mental fatigue. However, the underlying mechanism by which motivation affects performance in mentally fatiguing tasks is obscure. In this paper, we propose goal competition as a paradigm to understand the role of motivation and built three models of mental fatigue studies to demonstrate the mechanism in a cognitive architecture named PRIMs. Each of these studies explored the impact of reward and mental fatigue on performance. Overall, performance decreased in nonreward conditions but remained stable in reward conditions. The comparisons between our models and empirical data showed that our models were able to capture human performance. We managed to model changes in performance levels by adjusting the value of the main task goals, which controls the competition with distractions. In all the tasks modeled, the best model fits were obtained by a linear decrease in goal activation, suggesting this is a general pattern. We discuss possible mechanisms for activation decrease, and the potential of goal competition to model motivation. (C) 2021 The Authors. Published by Elsevier Inc.

Due to the increasing complexity of diseases in the aging population and rapid progress in catheter-based technology, the demands on operators' skills in conducting endovascular interventions (EI) has increased dramatically, putting more emphasis on training. However, it is not well understood which factors influence learning and performance. In the present study, we examined the ability of EI naïve medical students to acquire basic catheter skills and the role of pre-existing cognitive ability and manual dexterity in predicting performance. Nineteen medical students practised an internal carotid artery angiography during a three-day training on an endovascular simulator. Prior to the training they completed a battery of tests. Skill acquisition was assessed using quantitative and clinical performance measures; the outcome measures from the test battery were used to predict the learning rate. The quantitative metrics indicated that participants' performance improved significantly across the training, but the clinical evaluation revealed that participants did not significantly improve on the more complex part of the procedure. Mental rotation ability (MRA) predicted quantitative, but not clinical performance. We suggest that MRA tests in combination with simulator sessions could be used to assess the trainee's early competence level and tailor the training to individual needs.

Preparing for the future during ongoing activities is an essential skill. Yet, it is currently unclear to what extent we can prepare for the future in parallel with another task. In two experiments, we investigated how characteristics of a present task influenced whether and when participants prepared for the future, as well as its usefulness. We focused on the influence of concurrent working memory load, assuming that working memory would interfere most strongly with preparation. In both experiments, participants performed a novel sequential dual-task paradigm, in which they could voluntary prepare for a second task while performing a first task. We identified task preparation by means of eye tracking, through detecting when participants switched their gaze from the first to the second task. The results showed that participants prepared productively, as evidenced by faster RTs on the second task, with only a small cost to the present task. The probability of preparation and its productiveness decreased with general increases in present task difficulty. In contrast to our prediction, we found some but no consistent support for influence of concurrent working memory load on preparation. Only for concurrent high working memory load (i.e., two items in memory), we observed strong interference with preparation. We conclude that preparation is affected by present task difficulty, potentially due to decreased opportunities for preparation and changes in multitasking strategy. Furthermore, the interference from holding two items may reflect that concurrent preparation is compromised when working memory integration is required by both processes.

There have been many studies attempting to disentangle the relation between motivation and mental fatigue. Mental fatigue occurs after performing a demanding task for a prolonged time, and many studies have suggested that motivation can counteract the negative effects of mental fatigue on task performance. To complicate matters, most mental fatigue studies looked exclusively at the effects of extrinsic motivation but not intrinsic motivation. Individuals are said to be extrinsically motivated when they perform a task to attain rewards and avoid punishments, while they are said to be intrinsically motivated when they do for the pleasure of doing the activity. To assess whether intrinsic motivation has similar effects as extrinsic motivation, we conducted an experiment using subjective, performance, and physiological measures (heart rate variability and pupillometry). In this experiment, 28 participants solved Sudoku puzzles on a computer for three hours, with a cat video playing in the corner of the screen. The experiment consisted of 14 blocks with two alternating conditions: low intrinsic motivation and high intrinsic motivation. The main results showed that irrespective of condition, participants reported becoming fatigued over time. They performed better, invested more mental effort physiologically, and were less distracted in high-level than in low-level motivation blocks. The results suggest that similarly to extrinsic motivation, time-on-task effects are modulated by the level of intrinsic motivation: With high intrinsic motivation, people can maintain their performance over time as they seem willing to invest more effort as time progresses than in low intrinsic motivation.

AbstractPeople can often learn new tasks quickly. This is hard to explain with cognitive models because they either need extensive task‐specific knowledge or a long training session. In this article, we try to solve this by proposing that task knowledge can be decomposed into skills. A skill is a task‐independent set of knowledge that can be reused for different tasks. As a demonstration, we created an attentional blink model from the general skills that we extracted from models of visual attention and working memory. The results suggest that this is a feasible modeling method, which could lead to more generalizable models.

Many everyday decisions require an accurate perception of how much time has passed since a previous event. Although humans estimate time intervals with a high degree of mean accuracy, the precision of estimations varies greatly between individuals. In situations in which accurate timing is rewarded but responding too early is punished, the optimal amount of risk is directly dependent on the precision of the timer. Previously, it was found that humans and rodents displayed near-optimal adjustment of their mean response time based on their individual precision and the level of punishment. It is as of yet unknown whether these strategies of optimality in interval timing are specific to the timing domain, or instead reflect an ability that generalizes to other sensorimotor modalities of decision making. Here, we address this by combining a temporal reproduction experiment and a distance estimation experiment with an identical reward scheme. We found that participants approached optimality in both tasks, but generally underadjusted their responses in the face of high risk. As this individual adjustment was consistent over modalities, these results can best be explained by assuming that the adjustment of behavior towards optimal performance is driven by a modality independent mechanism.

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns.

Throughout the day, we may sometimes catch ourselves in patterns of thought that we experience as rigid and difficult to disengage from. Such “sticky” thinking can be highly disruptive to ongoing tasks, and when it turns into rumination constitutes a vulnerability for mental disorders such as depression and anxiety. The main goal of the present study was to explore the stickiness dimension of thought, by investigating how stickiness is reflected in task performance and pupil size. To measure spontaneous thought processes, we asked participants to perform a sustained attention to response task (SART), in which we embedded the participant’s concerns to potentially increase the probability of observing sticky thinking. The results indicated that sticky thinking was most frequently experienced when participants were disengaged from the task. Such episodes of sticky thought could be discriminated from neutral and non-sticky thought by an increase in errors on infrequent no-go trials. Furthermore, we found that sticky thought was associated with smaller pupil responses during correct responding. These results demonstrate that participants can report on the stickiness of their thought, and that stickiness can be investigated using pupillometry. In addition, the results suggest that sticky thought may limit attention and exertion of cognitive control to the task.

Action and perception are optimized by exploiting temporal regularities, and it has been suggested that the attentional system prioritizes information that contains some form of structure. Indeed, Zhao, Al-Aidroos, and Turk-Browne (Psychological Science, 24(5), 667–677, 2013) found that attention was biased towards the location and low-level visual features of shapes that appeared with a regular order but were irrelevant for the main search task. Here, we investigate whether this bias also holds for irrelevant metrical temporal regularities. In six experiments, participants were asked to perform search tasks. In Experiments 1a–d, sequences of squares, each presented at one of four locations, appeared in between the search trials. Crucially, in one location, the square appeared with a regular rhythm, whereas the timing in the other locations was random. In Experiments 2a and 2b, a sequence of centrally presented colored circles was shown in between the search trials, of which one specific color appeared regularly. We expected that, if attention is automatically biased towards these temporal regularities, reaction times would be faster if the target matches the location (Experiments 1a–d) or color (Experiments 2a–b) of the regular stimulus. However, no reaction time benefit was observed for these targets, suggesting that there was no attentional bias towards the regularity. In addition, we found no evidence for attentional entrainment to the rhythmic stimulus. These results suggest that people do not use implicit rhythmic temporal regularities to guide their attention in the same way as they use order regularities.

Several studies have suggested that the centro-parietal positivity (CPP), an EEG potential occurring approximately 500 ms post-stimulus, reflects the accumulation of evidence for making a decision. Yet, most previous studies of the CPP focused exclusively on perceptual decisions with very simple stimuli. In this study, we examined how the dynamics of the CPP depended on the type of decision being made, and whether its slope was related to parameters of an accumulator model of decision making. We show initial evidence that memory- and perceptual decisions about carefully-controlled face stimuli exhibit similar dynamics, but offset by a time difference in decision onset. Importantly, the individual-trial slopes of the CPP are related to the accumulator model's drift parameter. These findings help to further understand the role of the CPP across different kinds of decisions.