Visuo-motor transformations in the intraparietal sulcus mediate the acquisition of endovascular medical skill.

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 simplified 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 endovascular 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 multimodal 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.

A cognitive modeling approach to learning and using reference biases in language.

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.

Testing the skill-based approach: Consolidation strategy impacts attentional blink performance.

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.

Mental rotation ability predicts the acquisition of basic endovascular skills.

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.

The effects of intrinsic motivation on mental fatigue.

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.

The art of planning ahead: When do we prepare for the future and when is it effective?

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 voluntarily prepare for a second task while performing a first task. We identified task preparation by means of eye tracking, by detecting when participants switched their gaze to information about the second task while performing the first task. The results showed that participants prepared, but also that there were large individual differences in how often they did so. When participants prepared, it was productive, as evidenced by faster RTs on the second task and only a small cost to the present task. The probability of preparation and its productiveness decreased with increases in the difficulty of the first task. In particular, we found that working memory load from the first task interfered with preparation. We conclude from our study that people can productively prepare for the future while performing an ongoing task, and that it is possible to track this preparation process empirically. In addition, we conclude that working memory resources play an important role in task preparation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Captivated by thought: "Sticky" thinking leaves traces of perceptual decoupling in task-evoked pupil size.

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.

A Skill-Based Approach to Modeling the Attentional Blink.

People 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.

Learning to Approximate Functions Using Nb-Doped SrTiO3 Memristors.

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.

Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making.

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.

The Role of Motivation as a Factor in Mental Fatigue.

OBJECTIVE: The aim of this study was to assess motivation as a factor in mental fatigue using subjective, performance, and physiological measures. BACKGROUND: Sustained performance on a mentally demanding task can decrease over time. This decrement has two possible causes: a decline in available resources, meaning that performance cannot be sustained, and decrement in motivation, meaning a decline in willingness to sustain performance. However, so far, few experimental paradigms have effectively and continuously manipulated motivation, which is essential to understand its effect on mental fatigue. METHOD: Twenty participants performed a working memory task with 14 blocks, which alternated between reward and nonreward for 2.5 hr. In the reward blocks, monetary rewards could be gained for good performance. Besides reaction time and accuracy, we used physiological measures (heart rate variability, pupil diameter, eyeblink, eye movements with a video distractor) and subjective measures of fatigue and mental effort. RESULTS: Participants reported becoming fatigued over time and invested more mental effort in the reward blocks. Even though they reported fatigue, their accuracy in the reward blocks remained constant but declined in the nonreward blocks. Furthermore, in the nonreward blocks, participants became more distractable, invested less cognitive effort, blinked more often, and made fewer saccades. These results showed an effect of motivation on mental fatigue. CONCLUSION: The evidence suggests that motivation is an important factor in explaining the effects of mental fatigue.

Using Cognitive Agents to Train Negotiation Skills.

Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be "reset," and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.

The wandering self: Tracking distracting self-generated thought in a cognitively demanding context.

We investigated how self-referential processing (SRP) affected self-generated thought in a complex working memory task (CWM) to test the predictions of a computational cognitive model. This model described self-generated thought as resulting from competition between task- and distracting processes, and predicted that self-generated thought interferes with rehearsal, reducing memory performance. SRP was hypothesized to influence this goal competition process by encouraging distracting self-generated thinking. We used a spatial CWM task to examine if SRP instigated such thoughts, and employed eye-tracking to examine rehearsal interference in eye-movement and self-generated thinking in pupil size. The results showed that SRP was associated with lower performance and higher rates of self-generated thought. Self-generated thought was associated with less rehearsal and we observed a smaller pupil size for mind wandering. We conclude that SRP can instigate self-generated thought and that goal competition provides a likely explanation for how self-generated thoughts arises in a demanding task.

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.

Driving and Multitasking: The Good, the Bad, and the Dangerous.

Previous research has shown that multitasking can have a positive or a negative influence on driving performance. The aim of this study was to determine how the interaction between driving circumstances and cognitive requirements of secondary tasks affect a driver's ability to control a car. We created a driving simulator paradigm where participants had to perform one of two scenarios: one with no traffic in the driver's lane, and one with substantial traffic in both lanes, some of which had to be overtaken. Four different secondary task conditions were combined with these driving scenarios. In both driving scenarios, using a tablet resulted in the worst, most dangerous, performance, while passively listening to the radio or answering questions for a radio quiz led to the best driving performance. Interestingly, driving as a single task did not produce better performance than driving in combination with one of the radio tasks, and even tended to be slightly worse. These results suggest that drivers switch to internally focused secondary tasks when nothing else is available during monotonous or repetitive driving environments. This mind wandering potentially has a stronger interference effect with driving than non-visual secondary tasks.

Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model.

Numerical inductive reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical inductive reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical inductive reasoning. The current question of interest is whether numerical inductive reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical inductive reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical inductive reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.

Is There Neural Evidence for an Evidence Accumulation Process in Memory Decisions?

Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role of additional cognitive processes like memory retrieval that are crucial in real-world decisions. In this study, we tried to find a neural signature of evidence accumulation during a recognition memory task. To do this, we applied a method we have successfully used to localize evidence accumulation in scalp EEG during a perceptual decision making task. This time, however, we applied it to intracranial EEG recordings, which provide a much higher spatial resolution. We identified several brain areas where activity ramps up over time, but these neural patterns do not appear to be modulated by behavioral variables such as the amount of available evidence or response time. This casts doubt on the idea of evidence accumulation as a general decision-making mechanism underlying different types of decisions.

Instance-Based Models of Metacognition in the Prisoner's Dilemma.

In this article, we examine the advantages of simple metacognitive capabilities in a repeated social dilemma. Two types of metacognitive agent were developed and compared with a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent (opponent perspective) takes the perspective of the opponent to anticipate the opponent's future actions and respond accordingly. The other metacognitive agent (modeler) predicts the opponent's next move based on the previous moves of the agent and the opponent. The modeler agent achieves better individual outcomes than a non-metacognitive agent and is more successful at encouraging cooperation. The opponent-perspective agent, by contrast, fails to achieve these outcomes because it lacks important information about the opponent. These simple agents provide insights regarding modeling of metacognition in more complex tasks.

Cognitive Modeling at ICCM: State of the Art and Future Directions.

The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.

Using data-driven model-brain mappings to constrain formal models of cognition.

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.