Strategy Switching Research Articles

Examining neuropsychological differences in adults afflicted by COVID-19 with and without anosmia

This study presents the outcomes of a research endeavor aimed at comparing the neuropsychological functioning of two distinct groups: one comprising individuals with both COVID-19 and anosmia, and the other without anosmia. A comprehensive neuropsychological assessment, involving ten tests, was administered to 60 participants, divided equally into two groups of 30. The group exhibiting anosmia displayed lower scores in lexical access, inhibitory control, strategy switching, visuoconstructive praxis, nonverbal memory, and verbal fluency functions. The effect sizes for these differences generally ranged from low to medium. Overall, the identified impairments strongly indicate deficits in executive functioning. Certain theories even propose that the frontal lobes may be the primary target of SARS-CoV-2 within the brain. To enhance our comprehension, future research could delve into the specific mechanisms through which the virus interacts with the prefrontal cortex, thereby illuminating the intricacies of COVID-19-related cognitive impairments.

Automatic Experimental Numerosity Generation and Numerical Training for Rodents.

Non-symbolic stimuli representing numerosities are invariably associated with continuous magnitudes, complicating the interpretation of experimental studies on numerosity perception. Although various algorithms for experimental numerosity generation have been proposed, they do not consider the quantifiable distribution of values of continuous magnitudes and the degree of numerosity-magnitudes association. Consequently, they cannot thoroughly exclude the possibility of magnitudes integration or strategy switch between different magnitudes in numerical stimulus perception. Here, we introduce a protocol for numerosity generation, animal training, and behavior outcomes analysis that takes the aforementioned issues into consideration. This protocol has been applied to rodents and is applicable to other animals in numerosity studies. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Algorithm for generating non-symbolic numerical stimuli Alternate Protocol: General algorithm for generating non-symbolic numerical stimuli Basic Protocol 2: Numerical training and testing for rodents.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks.

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, and that behaviour is marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Kidney disease, diabetes, and diameter stenosis predict Rotablation bailout in modified balloon application for severely calcified coronary lesions.

Modified balloon (MB) treatment in severely calcified coronary artery lesions is an established technique. However, some lesions require Rotablation (RA) as bailout strategy. This study aimed to assess predictors of switch from MB to RA and its impact on procedural and midterm outcomes. Four hundred and eighty-three consecutive patients were included undergoing MB treatment (n = 204) with a scoring or cutting balloon, or upfront RA treatment (n = 279) serving as control cohort. Strategy switch from MB to RA was performed in 19 of 204 patients. Procedural success was defined as successful stent implantation and TIMI III flow. In the MB cohort, median age was 72 [63-78] years, 75.5% were male and 42.1% had acute coronary syndrome. Procedure success was achieved in 89.4% of the switch group versus 98.4% of the MB only group (p < 0.001) and in 96.4% of the RA cohort. In the switch group, periprocedural complications (31.6% vs. 8.1% vs. 11.8%, p = 0.007), radiation dose (149 [126-252] vs. 59 [30-97] vs. 102 [59-156] Gcm2; p < 0.001) and contrast volume (250 [190-250] vs. 190 [150-250] vs. 195 [190--250] mL; p < 0.001) were significantly higher. Diabetes (OR 3.8, 95%CI 1.1-13.9, p = 0.042), chronic kidney disease stage 4 or 5 (OR 19.0, 95%CI 3.3-108.6, p < 0.001) and pronounced calcification resulting in higher angiographic diameter stenosis (OR 1.13, 95%CI 1.1-1.2, p = 0.001) independently predicted strategy switch. Midterm results were not affected by strategy switch regarding 1-year target lesion revascularization rates (86% vs. 89% vs. 89%; log-rank p = 0.95). Primary RA strategy might be considered in patients with severely calcified coronary artery lesions with high angiographic diameter stenosis, diabetes or impaired renal function due to increased periprocedural complication rates, radiation dose, and contrast volume following strategy switch.

A Theoretical Comparison of Alternative Male Mating Strategies in Cephalopods and Fishes.

We used computer simulations of growth, mating and death of cephalopods and fishes to explore the effect of different life-history strategies on the relative prevalence of alternative male mating strategies. Specifically, we investigated the consequences of single or multiple matings per lifetime, mating strategy switching, cannibalism, resource stochasticity, and altruism towards relatives. We found that a combination of single (semelparous) matings, cannibalism and an absence of mating strategy changes in one lifetime led to a more strictly partitioned parameter space, with a reduced region where the two mating strategies co-exist in similar numbers. Explicitly including Hamilton's rule in simulations of the social system of a Cichlid led to an increase of dominant males, at the expense of both sneakers and dwarf males ("super-sneakers"). Our predictions provide general bounds on the viable ratios of alternative male mating strategies with different life-histories, and under possibly rapidly changing ecological situations.

The benefits of memory control processes in working memory: Comparing effects of self-reported and instructed strategy use.

Working memory performance is often assumed to benefit from different maintenance control strategies such as rehearsal, refreshing, elaboration, and grouping. In studies assessing strategy self-reports, some strategies were indeed associated with better recall. Nevertheless, experimental studies assessing the effect of instructing maintenance strategies compared to a no-instruction baseline lend no evidence for the effectiveness of these strategies for working memory. Explanations for this contradiction could be that instruction implementation engenders dual-task costs or that strategy instructions reduce adaptive strategy switching. Across two experiments, we investigated the frequency and variability of strategy use with trial-wise self-reports in serial recall of word lists. Furthermore, we examined potential instruction costs by comparing performance in trials with self-reported versus instructed use of the same strategies. Self-reported strategy use varied from trial to trial, with elaboration and rehearsal being the most frequent. Self-reported elaboration was correlated with better performance than reading and rehearsal. For the most prevalent strategies-elaboration and rehearsal-there were no costs of instructed strategy implementation. Our results speak against dual-task costs and for the advantage of adaptively choosing one's own strategy from trial to trial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stochastic characterization of navigation strategies in an automated variant of the Barnes maze.

Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.

Trait Neuroticism is Associated with how Often People Switch Between Emotion Regulation Strategies Used to Manage Negative Emotions in Daily Life

Switching between different emotion regulation strategies may promote mental health by helping match strategy use to different situations. However, switching strategies very frequently might undermine any given regulation attempt. Individuals with high levels of self-reported neuroticism may have trouble finding the right balance of strategy switching versus persistence given increased negative emotionality and impulsivity; yet it is unclear whether this difficulty is characterized by too much switching, too little switching, or both. As such, we tested whether high or low rates of strategy switching within daily life was associated with trait neuroticism. We quantified how N = 89 college students switched between 20 strategies to regulate positive emotions and 20 strategies to regulate negative emotions when sampled three times daily for 10 days. We tested whether the linear or quadratic effects of strategy switching—when measured across all 20 positive emotion- or all 20 negative emotion-focused strategies, and within smaller classes of related strategies—were associated with neuroticism. We found that lower rates of switching amongst all strategies used to regulate negative emotions, and specifically amongst the adaptive engagement strategies, was associated with higher rates of neuroticism. Moderate switching amongst the aversive cognitive perseveration strategies, by contrast, was associated with higher neuroticism. Switching amongst strategies used to regulate positive emotions was not associated with neuroticism at the overall or class level. This pre-registered study suggests that neuroticism is associated with unique patterns of strategy switching in response to negative—but not necessarily positive—emotions in daily life.

Trait Social Anxiety Moderates the Relationship Between Emotion-Regulation Strategy Switching and State Anxiety in Daily Life

Difficulty knowing when to switch emotion-regulation (ER) strategies is theorized to be a key pathway to emotion dysregulation, but relatively few studies have empirically examined this. We applied a new order-based metric to quantify how 109 socially anxious people switched between 19 different ER strategies (or chose not to regulate at all) throughout a 5-week ecological-momentary-assessment (EMA) study that yielded 12,616 observations. We tested whether state- and trait-anxiety reports and their interaction predicted differences in ER strategy switching. Results indicated that people with relatively higher social-anxiety symptoms switch more often between ER strategies during periods of high average state anxiety but less often during periods of high variability in state anxiety than less socially anxious people. Interventions focused on helping socially anxious people learn how ER strategies are connected to variations in state anxiety might hold promise to increase adaptive ER-switching decisions. More broadly, expanding ER-switching interventions to consider the role of changing situations is an important next step.

Redirection Strategy Switching: Selective Redirection Controller for Dynamic Environment Adaptation.

In this paper, we present the Selective Redirection Controller (SRC), which selects the optimal redirection controller based on the physical and virtual environment in Redirected Walking (RDW). The primary advantage of SRC over existing controllers is its dynamic switching among four different redirection controllers (S2C, TAPF, ARC, and SRL) based on the user's environment, as opposed to using a single fixed controller throughout the experience. By switching between redirection controllers based on the context around the user, SRC aims to optimize the advantages of each redirection strategy. The SRC model is trained using reinforcement learning to dynamically and instantaneously switch redirection controllers based on the user's environment. We evaluated the performance of SRC against traditional redirection controllers through simulations and user studies conducted in various physical and virtual environments. The findings indicate that SRC reduces the number of resets significantly compared to traditional redirection controllers. Heat map visualization was utilized during the development process to analyze which redirection controller SRC chooses based on the different environments around the user. SRC alternates between redirection techniques based on the user's environment, maximizing the advantages of each strategy for a superior RDW experience.

Enhancing FRT capability of DFIG based on RC-crowbar considering the resonance and matching control strategies for different fault degrees

This paper compares the problems existing in the current fault ride-through (FRT) schemes of the doubly-fed induction generator (DFIG), and deeply analyzes the transient characteristics of DFIG during FRT. A comprehensive FRT scheme is proposed to improve the FRT capability by using an RC-crowbar and matching control strategy switching. The main innovation of this scheme lies in the consideration of resonance and other factors in the capacitance design of the RC-crowbar. To ensure that the DFIG still has good FRT capability after the RC-crowbar is cut off in advance in the later stage of FRT, an improved rotor side converter (RSC) control strategy is proposed for this situation. At the same time, to balance the reactive power on the stator side and accelerate the recovery of stator voltage throughout the entire FRT period, the control strategy of the grid side converter (GSC) is also improved. The comprehensive FRT scheme proposed in this paper can dynamically switch based on the degree of fault so that it still has good FRT ability when applied to different degrees of faults. Finally, the correctness and effectiveness of the proposed comprehensive FRT scheme were verified through simulation experiments.

A theory-informed emotion regulation variability index: Bray-Curtis dissimilarity.

Emotion regulation (ER) variability refers to how individuals vary their use of ER strategies across time. It helps individuals to meet contextual needs, underscoring its importance in well-being. The theoretical foundation of ER variability recognizes two constituent processes: strategy switching (e.g., moving from distraction to social sharing) and endorsement change (e.g., decreasing the intensity of both distraction and social sharing). ER variability is commonly operationalized as the SD between strategies per observation (between-strategy SD) or within a strategy across time (within-strategy SD). In this article, we show that these SD-based approaches cannot sufficiently capture strategy switching and endorsement change, leading to ER variability indices with poor validity. We propose Bray-Curtis dissimilarity, a measure used in ecology to quantify biodiversity variability, as a theory-informed ER variability index. First, we demonstrate how Bray-Curtis dissimilarity is more sensitive than SD-based approaches in detecting ER variability through two simulation studies. Second, assuming that higher ER variability is adaptive in daily life, we test the relation between ER variability and negative affect in three experience sampling method data sets (total N = [70, 95, 200], number of moment-level observations = [5,040, 6,329, 14,098]). At both the moment level and person level, higher Bray-Curtis dissimilarity predicted lower negative affect more consistently than SD-based indices. We conclude that Bray-Curtis dissimilarity may better capture moment-level within-person ER variability and could have implications for studying variability in other multivariate dynamic processes. The article is accompanied by an R tutorial and practical recommendations for using Bray-Curtis dissimilarity with experience sampling method data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

To switch or not to switch? Individual differences in executive function and emotion regulation flexibility.

Emotion regulation (ER) constitutes strategies that modulate the experience and expression of emotions. While past work has predominantly assumed that ER strategies are consistently adaptive (or maladaptive) across situations, recent research has begun to examine individual-difference factors that are associated with the flexible use of ER strategies in line with contextual demands (i.e., ER flexibility). Theoretical accounts maintain that the choice to use ER strategies in a given context is contingent on individual differences in executive function (EF), which refers to a collection of general-purpose regulatory operations. Based on a comprehensive battery of EF tasks, we investigated how the various EF facets (i.e., common EF, working-memory-specific, and shifting-specific factors) are related to the frequency of maintaining and switching ER strategies in response to stimuli that elicit varying levels of emotional intensity. Results indicated that individuals with higher EF demonstrated a more flexible pattern of ER strategy use across high- and low-intensity conditions. Specifically, better working-memory-specific ability (i.e., manipulating information within a mental workspace) was associated with a greater frequency of reappraisal-to-distraction strategy switching in high-intensity contexts. Furthermore, more proficient common EF (i.e., sustaining relevant goals in the face of competing goals and responses) corresponded to a higher propensity to maintain the use of reappraisal in low-intensity situations. The outcomes of this study offer a first glimpse of the cognitive factors underlying ER flexibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Kinetic model for asset allocation with strategy switching

The kinetic theory is employed to study the changes in the amount of wealth invested in two types of risky assets when investors switch their trading strategies. Investors in the market are assumed to possess nonnegative wealth and opt to allocate a portion of their wealth into two types of risky assets. A Boltzmann model, which includes strategy switching and interaction probability, is utilized to investigate the evolution of the amount of wealth invested in risky assets and the number of investors using fundamentalist and chartist strategies. We employ the asymptotic procedure method to derive the Fokker-Planck equations from the Boltzmann model. The resulting stationary solution of these equations sheds light on the effects of strategy switching on the evolution of the amount of wealth invested in risky assets, as well as how investor interactions influence the number of investors employing different strategies.

Learning Nash Equilibria in Large Populations With Constrained Strategy Switching

Learning Nash Equilibria in Large Populations With Constrained Strategy Switching

AGE DIFFERENCES IN EMOTION REGULATION AND EXECUTIVE FUNCTION

Abstract Age-related improvements in emotion regulation (ER) are thought to account for the relatively healthy levels of well-being observed in late adulthood. However, extant laboratory-based evidence on age differences in the effective implementation of ER strategies―such as reappraisal (i.e., reframing the meaning of an emotion-eliciting event) and distraction (i.e., redirecting attention away from emotional to non-emotional information)―remains equivocal (Isaacowitz, 2022). Moreover, developmental differences in ER monitoring (i.e., adjusting regulatory approach as needed, such as maintaining or switching strategies), and factors that underpin such age-related differences, are poorly understood. Therefore, we investigated how younger and older adults (N = 335) differ in the implementation and monitoring aspects of ER, and how executive function (EF)―a collection of higher-order control operations that underlie ER processes (Pruessner et al., 2020; Schmeichel &amp; Tang, 2015)―could explain such age-related differences in ER. Using a comprehensive battery of nine EF and two ER tasks, results indicated age-related decline for the implementation of reappraisal but not distraction. For monitoring, advancing age was associated with a less flexible pattern of strategy maintenance and switching (of reappraisal and distraction) across high- and low-intensity emotional contexts. Importantly, these age-related differences in ER implementation and monitoring were significantly mediated by declines in EF (βs = .25‒.29, ps &amp;lt; .007), thereby alluding to the role of EF as a potential mechanism. Our findings suggest that certain ER processes are more cognitively demanding than others, and that EF could be a viable target of intervention to improve such ER processes in late adulthood.

An implicit Z-bus based sequential power flow algorithm for VSC AC/DC systems

AC/DC power flow algorithms have been proposed and developed as essential analysis tools for the Voltage Source Converter (VSC) based AC/DC hybrid grid. However, prevailing AC/DC power flow algorithms, e.g., the Newton-Raphson based algorithm, suffer from limitations in terms of convergence, multiple solutions, and high computational complexity. To address these issues, this paper presents a novel sequential AC/DC power flow algorithm based on the implicit Z-bus (IZB) method. We firstly provide a general AC/DC power flow model in real vector space, which takes into account the interface data, internal connection and various control modes of VSC stations. Then, simple and compact IZB iterative mappings in real vector space are derived for the AC solver and DC solver. A sequential algorithmic architecture and data exchange mechanisms are designed considering losses calculation, capacity limits and control strategy switching. The impacts of tolerance settings, initial value selections and load levels on the convergence and accuracy of the algorithm are theoretically analysed. Notably, rigorous convergence analyses substantiate the superior attributes of the proposed algorithm over existing models, guaranteeing convergence to high-voltage solutions (feasible and stable solutions). Numerical tests are performed to compare the proposed algorithm with existing works in terms of accuracy, efficiency, scalability and reliability.

The effect of emotional motivation on strategy flexibility: the moderating role of task load

IntroductionResearch has demonstrated that cognitive flexibility is associated with academic achievement, with poorer cognitive flexibility being linked to poorer academic performance. Strategy conversion is an example of cognitive flexibility, which requires individuals to quickly and flexibly switch between strategies depending on the task at hand. Studies have investigated the impact of emotional motivation on cognitive flexibility, with varying results. Furthermore, research has indicated that a high task load increases psychological burden and reduces cognitive flexibility, but few studies have analyzed the impact of task load on the relationship between emotional motivation and cognitive flexibility. This study sought to investigate the effect of emotional motivation on cognitive flexibility based on strategy switching, and the moderating effect of task load.MethodsThree experiments were conducted. Experiment 1 used forced conversion paradigm with a moderate task load that required participants to estimate tasks using a given strategy. Experiment 2 employed matched conversion paradigm with a high task load, informing participants of the strategies to be used but without any clues, necessitating the selection and execution of appropriate strategies based on the question features. Experiment 3 adopted free conversion paradigm with a relatively low task load, allowing participants to freely choose and execute strategies without any correctness or error criteria.ResultsThe intensity and direction of emotional motivation have a complicated and fluctuating impact on the flexibility to utilize strategies. When the task workload is high, the intensity of emotional motivation has a significant impact on strategy utilization flexibility, with low approach motivation being more conducive to flexibility. Conversely, when the task workload is low, the direction of emotional motivation has a greater effect, with high avoidance motivation being more advantageous.DiscussionThis study demonstrated that high workload could bring about a low intensity advantage, while low workload could induce an avoidance direction advantage, suggesting that task load could moderate the impact of emotional motivation on arithmetic strategy utilization flexibility, and avoidance motivation is not always detrimental to cognitive flexibility.

Perseveration on cognitive strategies

To acquire and process information, performers can frequently rely on both internal and extended cognitive strategies. However, after becoming acquainted with two strategies, performers in previous studies exhibited a pronounced behavioral preference for just one strategy, which we refer to as perseveration. What is the origin of such perseveration? Previous research suggests that a prime reason for cognitive strategy choice is performance: Perseveration could reflect the preference for a superior strategy as determined by accurately monitoring each strategy’s performance. However, following our preregistered hypotheses, we conjectured that perseveration persisted even if the available strategies featured similar performances. Such persisting perseveration could be reasonable if costs related to decision making, performance monitoring, and strategy switching would be additionally taken into account on top of isolated strategy performances. Here, we used a calibration procedure to equalize performances of strategies as far as possible and tested whether perseveration persisted. In Experiment 1, performance adjustment of strategies succeeded in equating accuracy but not speed. Many participants perseverated on the faster strategy. In Experiment 2, calibration succeeded regarding both accuracy and speed. No substantial perseveration was detected, and residual perseveration was conceivably related to metacognitive performance evaluations. We conclude that perseveration on cognitive strategies is frequently rooted in performance: Performers willingly use multiple strategies for the same task if performance differences appear sufficiently small. Surprisingly, other possible reasons for perseveration like effort or switch cost avoidance, mental challenge seeking, satisficing, or episodic retrieval of previous stimulus-strategy-bindings, were less relevant in the present study.