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In the manual ball-and-beam task, participants have to control a ball that is rolling continuously on a long and hand-held beam. Since the task can be performed individually, in a solo action setting, as well as collaboratively, in a (dyadic) joint action setting, it allows us to investigate how joint performances arise from individual performances, which we investigate in a series of interrelated studies. Here we focused on individual skill acquisition on the ball-and-beam task in the solo action setting, with the goal to characterize the behavioral dynamics that arise from learning to couple (ball motion) perception and (beam motion) action. By moving a beam extremity up and down to manipulate the beam's inclination angle, the task's objective was to roll the ball as fast as and accurately as possible between two indicated targets on the beam. Based on research into reciprocal aiming tasks, we hypothesized that the emergent dynamics of the beam's inclination angle would be constrained by the size of the targets, such that large targets would evoke a continuous beam movement strategy, while small targets would lead to a discrete beam movement strategy. 16 participants individually practiced the task in two separate six-block sessions. Each block consisted of one trial per target-size condition (small, medium and large). Overall, the number of target hits increased over trials, due to a larger range of motion of the beam's inclination angle, a stronger correlation between the ball and beam motion and a smaller variability of the beam motion. Contrary to our expectations, target size did not appreciably affect the shape of the beam movement patterns. Instead, we found stable inter-individual differences in the movement strategies adopted that were uncorrelated with the number of target hits on a trial. We concluded that multiple movement strategies may lead to success on the task, while individual skill acquisition was characterized by the refinement of behavioral dynamics that emerged in an early stage of learning. We speculate that such differences in individual strategies on the task may affect the interpersonal coordination that arises in joint-action performances on the task.

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In the context of slow-fast behavioral variation, fast individuals are hypothesized to be those who prioritize speed over accuracy while slow individuals are those which do the opposite. Since energy metabolism is a critical component of neural and cognitive functioning, this predicts such differences in cognitive style to be reflected at the level of the brain. We tested this idea in honeybees by first classifying individuals into slow and fast cognitive phenotypes based on a learning assay and then measuring their brain respiration with high-resolution respirometry. Our results broadly show that inter-individual differences in cognition are reflected in differences in brain mass and accompanying energy use at the level of the brain and the whole animal. Larger brains had lower mass-specific energy usage and bees with larger brains had a higher metabolic rate. These differences in brain respiration and brain mass were, in turn, associated with cognitive differences, such that bees with larger brains were fast cognitive phenotypes whereas those with smaller brains were slow cognitive phenotypes. We discuss these results in the context of the role of energy in brain functioning and slow-fast decision making and speed accuracy trade-off.

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Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

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Aging leads to response slowing but the underpinning cognitive and neural mechanisms remain elusive. We modelled older and younger adults' response times (RT) from a flanker task with a diffusion drift model (DDM) and employed diffusion-weighted magnetic resonance imaging and spectroscopy to study neurobiological predictors of DDM components (drift-rate, boundary separation, non-decision time). Microstructural indices were derived from white matter pathways involved in visuo-perceptual and attention processing [optic radiation, inferior and superior longitudinal fasciculi (ILF, SLF), fornix]. Estimates of metabolite concentrations [N-acetyl aspartate (NAA), glutamate (Glx), and γ-aminobutyric acid (GABA), creatine (Cr), choline (Cho), myoinositol (mI)] were measured from occipital (OCC), anterior cingulate (ACC) and posterior parietal cortices (PPC). Age-related increases in RT, boundary separation, and non-decision time were observed with response conservatism acounting for RT slowing. Aging was associated with reductions in white matter microstructure (lower fractional anisotropy and restricted signal fraction, larger diffusivities) and in metabolites (NAA in ACC and PPC, Glx in ACC). Regression analyses identified brain regions involved in top-down (fornix, SLF, ACC, PPC) and bottom-up (ILF, optic radiation OCC) processing as predictors for DDM parameters and RT. Fornix FA was the strongest predictor for increases in boundary separation (beta = -0.8) and mediated the effects of age on RT. These findings demonstrate that response slowing in visual discrimination is driven by the adoption of a more conservative response strategy. Age-related fornix decline may result in noisier communication of contextual information from the hippocampus to anterior decision-making regions and thus contribute to the conservative response strategy shift.

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Despite severe everyday problems recognising faces, some individuals with developmental prosopagnosia (DP) can achieve typical accuracy scores on laboratory face recognition tests. To address this, studies sometimes also examine response times (RTs), which tend to be longer in DPs relative to control participants. In the present study, 24 potential (according to self-report) DPs and 110 age-matched controls completed the Cambridge Face and Bicycle Memory Tests, old new faces task, and a famous faces test. We used accuracy and the Balanced Integration Score (BIS), a measure that adjusts accuracy for RTs, to classify our sample at the group and individual levels. Subjective face recognition ability was assessed using the PI20 questionnaire and semi structured interviews. Fifteen DPs showed a major impairment using BIS compared with only five using accuracy alone. Logistic regression showed that a model incorporating the BIS measures was the most sensitive for classifying DP and showed highest area under the curve (AUC). Furthermore, larger between-group effect sizes were observed for a derived global (averaged) memory measure calculated using BIS versus accuracy alone. BIS is thus an extremely sensitive novel measure for attenuating speed-accuracy trade-offs that can otherwise mask impairment measured only by accuracy in DP.

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Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process.Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined.Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill.Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

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The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production. Processing information efficiently can confer benefits, but it also entails time costs. Breeding strategies, encompassing timing of breeding onset and nest-site selection, may be related to brain size. In this study, we aim to elucidate the relationship between brain size, breeding timing, nest-site choice, and breeding success in the red-backed shrike Lanius collurio. Our findings revealed that the timing of the first egg-laying date was associated with female head size, with larger-headed females tending to lay eggs later in the breeding season. Additionally, we observed that breeding success was positively correlated with increased nest concealment. However, this relationship was stronger in males with smaller heads. In turn, nest concealment was not related to head size but primarily influenced breeding onset. These results suggest that the choice of breeding strategy may be moderated by brain size, with differences between sexes. Larger-headed females may invest more time in selecting nesting sites, leading to delayed breeding onset, while larger-headed males may compensate for suboptimal nest concealment. Our study sheds light on the intricate interplay between brain size, breeding timing, nest-site preferences, and breeding success in passerine birds, underscoring the potential role of cognitive capacity in shaping individual decision-making processes.

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Alertness has been construed as one of three fundamental components of attention. When generated by a warning signal, phasic changes in alertness ubiquitously decrease reaction time. But how does it do so? Based on earlier findings, in 1975, Posner proposed a theory of phasic alertness with two postulates: (i) phasic alertness does not affect the accumulation of information; (ii) phasic alertness accelerates when a response based on the accumulating information will be generated. When targets are continuously presented, this theory predicts that alertness will reduce reaction at the expense of an increase in errors-that is, generate a speed-accuracy trade-off. Los and Schut, Cognitive Psychology, 57, 20-55, (2008), while endorsing Posner's theory, claimed to have failed to replicated the tell-tale trade-off reported by Posner et al. Memory and Cognition, 1, 2-12, (1973, Experiment 1). The primary goal of this commentary was to use all the data from Los and Schut to see if the predicted speed-accuracy trade-off would be verified or not. With the increased power, it was confirmed that the conditions that benefited the most in reaction time from alertness also had higher error rates. It is noteworthy that recent studies have generated replications and extensions of the methods and findings of Posner et al; thus, it appears that the empirical pattern predicted by Posner's theory of phasic alertness is relatively robust.

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Matrix reasoning tasks are among the most widely used measures of cognitive ability in the behavioral sciences, but the lack of matrix reasoning tests in the public domain complicates their use. Here, we present an extensive investigation and psychometric validation of the matrix reasoning item bank (MaRs-IB), an open-access set of matrix reasoning items. In a first study, we calibrate the psychometric functioning of the items in the MaRs-IB in a large sample of adult participants (N = 1501). Using additive multilevel item structure models, we establish that the MaRs-IB has many desirable psychometric properties: its items span a wide range of difficulty, possess medium-to-large levels of discrimination, and exhibit robust associations between item complexity and difficulty. However, we also find that item clones are not always psychometrically equivalent and cannot be assumed to be exchangeable. In a second study, we demonstrate how experimenters can use the estimated item parameters to design new matrix reasoning tests using optimal item assembly. Specifically, we design and validate two new sets of test forms in an independent sample of adults (N = 600). We find these new tests possess good reliability and convergent validity with an established measure of matrix reasoning. We hope that the materials and results made available here will encourage experimenters to use the MaRs-IB in their research.

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The speed-accuracy trade-off (SAT), whereby faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during the performance of almost all daily tasks. To date, computational modeling has implicated the latent decision variable of response caution (thresholds), the amount of evidence required for a decision to be made, in the SAT. Previous imaging has associated frontal regions, notably the left prefrontal cortex and the presupplementary motor area (pre-SMA), with the setting of such caution levels. In addition, causal brain stimulation studies, using transcranial direct current stimulation (tDCS), have indicated that while both of these regions are involved in the SAT, their role appears to be dissociable. tDCS efficacy to impact decision-making processes has previously been linked with neurochemical concentrations and cortical thickness of stimulated regions. However, to date, it is unknown whether these neurophysiological measures predict individual differences in the SAT, and brain stimulation effects on the SAT. Using ultra-high field (7T) imaging, here we report that instruction-based adjustments in caution are associated with both neurochemical excitability (the balance between GABA+ and glutamate) and cortical thickness across a range of frontal regions in both sexes. In addition, cortical thickness, but not neurochemical concentrations, was associated with the efficacy of left prefrontal and superior medial frontal cortex (SMFC) stimulation to modulate performance. Overall, our findings elucidate key neurophysiological predictors, frontal neural excitation, of individual differences in latent psychological processes and the efficacy of stimulation to modulate these.SIGNIFICANCE STATEMENT The speed-accuracy trade-off (SAT), faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during most daily tasks. The SAT is often investigated by explicitly instructing participants to prioritize speed or accuracy when responding to stimuli. Using ultra-high field (7T) magnetic resonance imaging (MRI), we found that individual differences in the extent to which participants adjust their decision strategies with instruction related to neurochemical excitability (ratio of GABA+ to glutamate) and cortical thickness in the frontal cortex. Moreover, brain stimulation to the left prefrontal cortex and the superior medial frontal cortex (SMFC) modulated performance, with the efficacy specifically related to cortical thickness. This work sheds new light on the neurophysiological basis of decision strategies and brain stimulation.

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Animals move smoothly and reliably in unpredictable environments. Models of sensorimotor control, drawing on control theory, have assumed that sensory information from the environment leads to actions, which then act back on the environment, creating a single, unidirectional perception-action loop. However, the sensorimotor loop contains internal delays in sensory and motor pathways, which can lead to unstable control. We show here that these delays can be compensated by internal feedback signals that flow backward, from motor toward sensory areas. This internal feedback is ubiquitous in neural sensorimotor systems, and we show how internal feedback compensates internal delays. This is accomplished by filtering out self-generated and other predictable changes so that unpredicted, actionable information can be rapidly transmitted toward action by the fastest components, effectively compressing the sensory input to more efficiently use feedforward pathways: Tracts of fast, giant neurons necessarily convey less accurate signals than tracts with many smaller neurons, but they are crucial for fast and accurate behavior. We use a mathematically tractable control model to show that internal feedback has an indispensable role in achieving state estimation, localization of function (how different parts of the cortex control different parts of the body), and attention, all of which are crucial for effective sensorimotor control. This control model can explain anatomical, physiological, and behavioral observations, including motor signals in the visual cortex, heterogeneous kinetics of sensory receptors, and the presence of giant cells in the cortex of humans as well as internal feedback patterns and unexplained heterogeneity in neural systems.

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Noninvasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), show promise in treating a range of psychiatric and neurologic conditions. However, optimization of such applications requires a better understanding of how tDCS alters cognition and behavior. Existing evidence implicates dopamine in tDCS alterations of brain activity and plasticity; however, there is as yet no causal evidence for a role of dopamine in tDCS effects on cognition and behavior. Here, in a preregistered, double-blinded study, we examined how pharmacologically manipulating dopamine altered the effect of tDCS on the speed-accuracy trade-off, which taps ubiquitous strategic operations. Cathodal tDCS was delivered over the left prefrontal cortex and the superior medial frontal cortex before participants (N = 62, 24 males, 38 females) completed a dot-motion task, making judgments on the direction of a field of moving dots under instructions to emphasize speed, accuracy, or both. We leveraged computational modeling to uncover how our interventions altered latent decisional processes driving the speed-accuracy trade-off. We show that dopamine in combination with tDCS (but not tDCS alone nor dopamine alone) not only impaired decision accuracy but also impaired discriminability, which suggests that these manipulations altered the encoding or representation of discriminative evidence. This is, to the best of our knowledge, the first direct evidence implicating dopamine in the way tDCS affects cognition and behavior.SIGNIFICANCE STATEMENT tDCS can improve cognitive and behavioral impairments in clinical conditions; however, a better understanding of its mechanisms is required to optimize future clinical applications. Here, using a pharmacological approach to manipulate brain dopamine levels in healthy adults, we demonstrate a role for dopamine in the effects of tDCS in the speed-accuracy trade-off, a strategic cognitive process ubiquitous in many contexts. In doing so, we provide direct evidence implicating dopamine in the way tDCS affects cognition and behavior.

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The recognition of sheep faces based on computer vision has improved the efficiency and effectiveness of individual sheep identification, providing technical support for the development of smart farming. However, current recognition models have problems such as large parameter sizes, slow recognition speed, and difficult deployment. Therefore, this paper proposes an efficient and fast basic module called Eblock and uses it to build a lightweight sheep face recognition model called SheepFaceNet, which achieves the best balance between speed and accuracy. SheepFaceNet includes two modules: SheepFaceNetDet for detection and SheepFaceNetRec for recognition. SheepFaceNetDet uses Eblock to construct the backbone network to enhance feature extraction capability and efficiency, designs a bidirectional FPN layer (BiFPN) to enhance geometric location ability, and optimizes the network structure, which affects inference speed, to achieve fast and accurate sheep face detection. SheepFaceNetRec uses Eblock to construct the feature extraction network, uses ECA channel attention to improve the effectiveness of feature extraction, and uses multi-scale feature fusion to achieve fast and accurate sheep face recognition. On our self-built sheep face dataset, SheepFaceNet recognized 387 sheep face images per second with an accuracy rate of 97.75%, achieving an advanced balance between speed and accuracy. This research is expected to further promote the application of deep-learning-based sheep face recognition methods in production.

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The speed, or vigor, of our movements can vary depending on circumstances. For instance, the promise of a reward leads to faster movements. Reward also leads us to move with a lower reaction time, suggesting that the process of action selection can also be invigorated by reward. It has been proposed that invigoration of action selection and of action execution might occur through a common mechanism, and thus these aspects of behavior might be coupled. To test this hypothesis, we asked participants to make reaching movements to "shoot" through a target at varying speeds to assess whether moving more quickly was also associated with more rapid action selection. We found that, when participants were required to move with a lower velocity, the speed of their action selection was also significantly slowed. This finding was recapitulated in a further dataset in which participants determined their own movement speed, but had to move slowly to stop their movement inside the target. By reanalyzing a previous dataset, we also found evidence for the converse relationship between action execution and action selection; when pressured to select actions more rapidly, people also executed movements with higher velocity. Our results establish that invigoration of action selection and action execution vary in tandem with one another, supporting the hypothesis of a common underlying mechanism.NEW & NOTEWORTHY We show that voluntary increases in the vigor of action execution lead action selection to also occur more rapidly. Conversely, hastening action selection by imposing a deadline to act also leads to increases in movement speed. These findings provide evidence that these two distinct aspects of behavior are modulated by a common underlying mechanism.

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Shooting errors have multi-faceted causes with contributing factors that include sensorimotor activity and cognitive failures. Empirical investigations often assess mental errors through threat identification, yet other cognitive failures could contribute to poor outcomes. The current study explored several possible sources of cognitive failures unrelated to threat identification with live fire exercises. Experiment 1 examined a national shooting competition to compare marksmanship accuracy, expertise, and planning in the likelihood of hitting no-shoot or unintended targets. Experts demonstrated an inverse speed/accuracy trade-off and fired upon fewer no-shoot targets than lesser skilled shooters, yet overall, greater opportunity to plan produced more no-shoot errors, thereby demonstrating an increase in cognitive errors. Experiment 2 replicated and extended this finding under conditions accounting for target type, location, and number. These findings further dissociate the roles of marksmanship and cognition in shooting errors while suggesting that marksmanship evaluations should be re-designed to better incorporate cognitive variables.

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The aim of this study was to examine whether target width and target distance influence the planning phase of a fencing lunge (early and anticipatory postural adjustments) as well as the execution phase of a fencing lunge. Eight elite female fencers participated in the study. The displacement of the center of foot pressure, muscle activity of the tibialis anterior, and kinematics of center of mass were recorded using force plates. The results show that target width and distance have no effect on early and anticipatory postural adjustments as well as the acceleration and velocity of the center of mass at the moment of foot-off. However, a greater target distance was associated with a greater max center of mass acceleration and velocity, and larger target width resulted in a greater max center of mass acceleration during lunging (p < 0.05). We suppose that the effect of task parameters on preparing a fencing lunge may be mitigated due to the specific technique adopted by expert fencers and the ballistic nature of a fencing lunge.

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Acquisition of a behavioral task is influenced by many factors. The relative timing of stimuli is such a factor and is especially relevant for tasks relying on short-term memory, like working memory paradigms, because of the constant evolution and decay of neuronal activity evoked by stimuli. Here, we assess two aspects of stimulus timing on the acquisition of an olfactory delayed nonmatch-to-sample (DNMS) task. We demonstrate that head-fixed male mice learn to perform the task more quickly when the initial training uses a shorter sample-test odor delay without detectable loss of generalizability. Unexpectedly, we observed a slower task acquisition when the odor-reward interval was shorter. The effect of early reward timing was accompanied by a shortening of reaction times and more frequent sporadic licking. Analysis of this result using a drift-diffusion model indicated that a primary consequence of early reward delivery is a lowered threshold to act, or a lower decision bound. Because an accurate performance with a lower decision bound requires greater discriminability in the sensory representations, this may underlie the slower learning rate with early reward arrival. Together, our results reflect the possible effects of stimulus timing on stimulus encoding and its consequence on the acquisition of a complex task.SIGNIFICANCE STATEMENT This study describes how head-fixed mice acquire a working memory task (olfactory delayed nonmatch-to-sample task). We simplified and optimized the stimulus timing, allowing robust and efficient training of head-fixed mice. Unexpectedly, we found that early reward timing leads to slower learning. Analysis of this data using a computational model (drift-diffusion model) revealed that the reward timing affects the behavioral threshold, or how quickly animals respond to a stimulus. But, to still be accurate with early reaction times, the sensory representation needs to become even more refined. This may explain the slower learning rate with early reward timing.

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In a simulation study, Stafford et al. (Behavior Research Methods, 52, 2142-2155, 2020) explored the effect of sample size on detecting group differences in ability in the presence of speed-accuracy trade-offs using the Drift Diffusion Model (DDM) and introduced an online tool to perform a power analysis. They found that the DDM approach was superior to analyzing the observed response times and response accuracies alone. In their simulation, they applied the EZ method to estimate the model parameters. In this article, we demonstrate that the EZ method, which cannot estimate the response bias parameter of the DDM, causes severe estimation bias for all parameters if the true response bias is not 0.5. Moreover, the bias patterns differ between EZ and the equivalent maximum likelihood estimation with z fixed at 0.5. This should be taken into consideration when using the otherwise excellent power analysis tool for experimental designs, in which z≠ 0.5 cannot be ruled out or even stipulate it.

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Condition-specific speed-accuracy trade-offs (SATs) are a pervasive issue in experimental psychology, because they sometimes render impossible an unambiguous interpretation of experimental effects on either mean response times (mean RT) or percentage of correct responses (PC). For between-participants designs, we have recently validated a measure (Balanced Integration Score, BIS) that integrates standardized mean RT and standardized PC and thereby controls for cross-group variation in SAT. Another related measure (Linear Integrated Speed-Accuracy Score, LISAS) did not fulfill this specific purpose in our previous simulation study. Given the widespread and seemingly interchangeable use of the two measures, we here illustrate the crucial differences between LISAS and BIS related to their respective choice of standardization variance. We also disconfirm the recently articulated hypothesis that the differences in the behavior of the two combined performance measures observed in our previous simulation study were due to our choice of a between-participants design and we demonstrate why a previous attempt to validate BIS (and LISAS) for within-participants designs has failed, pointing out several consequential issues in the respective simulations and analyses. In sum, the present study clarifies the differences between LISAS and BIS, demonstrates that the choice of the variance used for standardization is crucial, provides further guidance on the calculation and use of BIS, and refutes the claim that BIS is not useful for attenuating condition-specific SATs in within-participants designs.

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A key strategic decision one must make in virtually every task context concerns the speed accuracy trade-off (SAT). Experimentally, this ubiquitous phenomenon, whereby response speed and task accuracy are inversely related, is typically studied by explicitly instructing participants to adjust their strategy: by either focusing on speed, or on accuracy. Computational modelling has been applied to deconvolve the latent decision processes involved in the SAT, with considerable evidence suggesting that response caution (the amount of evidence needed for a decision to be reached) is a key variable in the setting of SAT strategy. Neuroimaging has implicated the prefrontal cortex, the pre-supplementary motor area (preSMA), and the striatum in the setting of response caution. In addition, brain stimulation has provided causal evidence for the involvement of the left prefrontal cortex and superior medial frontal cortex (SMFC, which includes the preSMA) in adjustments of response caution following explicit instructions, although stimulation of the two regions has dissociable effects. Here, in a double-blind and preregistered study we investigated the role of these two regions using an incidental manipulation of SAT strategy - via stimulus signal variability - which has previously been shown to influence decision confidence. We again found tDCS applied to both regions modulated response caution, and there was a dissociation: stimulating prefrontal cortex increased, and stimulating SMFC decreased, response caution. These findings provide further support for key, but dissociable, roles of these brain regions in decision strategies whether they are implemented explicitly or incidentally.

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