The power law repealed: the case for an exponential law of practice

The Curve of Learning With and Without Instructions

In skill acquisition, instructing individuals the stimulus-response mappings indicating how to perform and act, yields better performance. Additionally, performance is helped by repeated practice. Whether providing instructions and repeated practice interact to achieve optimal performance remains debated. This paper addresses that question by analyzing the learning curves of individuals learning stimulus-response mappings of varying complexity. We particularly focus on the question whether instructions lead to improved performance in the longer run. Via evidence accumulation modeling, we find no evidence for this assertion. Instructions seem to provide individuals with a head start, leading to better initial performance in the early stages of learning, without long-lasting effects on behavior. We discuss the results in light of related studies that do report long-lasting effects of instructions, and propose that the complexity of a skill determines whether long-lasting benefits of initial instructions exist.

Analysis of learning the bimanual control of (tele)operating joint space controlled robotic arms with 4 degrees of freedom using the two-timescales power law of learning

Training costs for operators of robotic arms in forestry and construction are high. A systematic analysis of skill development can help to make training more efficient. This research focuses on motor skill development by investigating the bimanual control of a four-DoF robotic arm. The two-time scale power law of learning was used to identify difficulties in control learning. Ten participants acquired the control of the robotic arm in a simulator over ten sessions within seven weeks. Eight movement targets were presented in each of six blocks per session, comprising 432 robotic arm movements. The results suggest that learning varies for each joystick axis, with control of the elbow joint showing the highest learning gain. The base and shoulder joints showed similar learning gains. The wrist joint showed mixed results in terms of use or disuse. Performance increased with retention, suggesting that a longer period of consolidation aided skill acquisition.Practitioner summary: A shortage of skilled operators, costly, and extensive training of heavy machine operators in robotic arm control requires to revisit control skill learning. This study showed that focus of training ought to be shifted to specific joints and training requires to emphasise longer resting periods between training sessions.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

Automated discovery of symbolic laws governing skill acquisition from naturally occurring data

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues of unobservable cognitive states and an algorithmic explosion in searching. A deep learning model is initially employed to determine the learner's cognitive state and assess the feature importance. Symbolic regression algorithms are then used to parse the neural network model into algebraic equations. Experimental results show that the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings.

What Does It Take to Play the Piano? Cognito-Motor Functions Underlying Motor Learning in Older Adults

The acquisition of skills, such as learning to play a musical instrument, involves various phases that make specific demands on the learner. Knowledge of the cognitive and motor contributions during learning phases can be helpful in developing effective and targeted interventions for healthy aging. Eighty-six healthy older participants underwent an extensive cognitive, motoric, and musical test battery. Within one session, one piano-related and one music-independent movement sequence were both learned. We tested the associations between skill performance and cognito-motor abilities with Bayesian mixed models accounting for individual learning rates. Results showed that performance was positively associated with all cognito-motor abilities. Learning a piano-related task was characterized by relatively strong initial associations between performance and abilities. These associations then weakened considerably before increasing exponentially from the second trial onwards, approaching a plateau. Similar performance-ability relationships were detected in the course of learning a music-unrelated motor task. Positive performance-ability associations emphasize the potential of learning new skills to produce positive cognitive and motor transfer effects. Consistent high-performance tasks that demand maximum effort from the participants could be very effective. However, interventions should be sufficiently long so that the transfer potential can be fully exploited.

Reduced categorical learning of faces in dyslexia

The perception of phonological categories in dyslexia is less refined than in typically developing (TD) individuals. Traditionally, this characteristic was considered unique to phonology, yet many studies showed non-phonological perceptual difficulties. Importantly, measuring the dynamics of cortical adaptation, associated with category acquisition, revealed a broadly distributed faster decay of cortical adaptation. Taken together, these observations suggest that the acquisition of perceptual categories in dyslexia may be slower across modalities. To test this, we tested adult individuals with developmental dyslexia (IDDs) and TDs on learning of two unknown faces, yielding face-specific categorization. Initial accuracy was similar in the two groups, yet practice-induced increase in accuracy was significantly larger in TDs. Modeling the learning process (using Drift Diffusion Model) revealed that TDs' steeper learning results from a larger increase in their effective face-specific signal. We propose that IDDs' slower item-specific categorical learning of unknown faces indicates that slower categorical learning in dyslexia is a core, domain-general difficulty.

The time course of stimulus-specific perceptual learning

Practice on perceptual tasks can lead to long-lasting, stimulus-specific improvements. Rapid stimulus-specific learning, assessed 24 hours after practice, has been found with just 105 practice trials in a face identification task. However, a much longer time course for stimulus-specific learning has been found in other tasks. Here, we examined 1) whether rapid stimulus-specific learning occurs for unfamiliar, non-face stimuli in a texture identification task; 2) the effects of varying practice across a range from just 21 trials up to 840 trials; and 3) if rapid, stimulus-specific learning persists over a 1-week, as well as a 1-day, interval. Observers performed a texture identification task in two sessions separated by one day (Experiment 1) or 1 week (Experiment 2). Observers received varying amounts of practice (21, 63, 105, or 840 training trials) in session 1 and completed 840 trials in session 2. In session 2, one-half of the observers in each group performed the task with the same textures as in session 1, and one-half switched to novel textures (same vs. novel conditions). In both experiments we found that stimulus-specific learning - defined as the difference in response accuracy in the same and novel conditions - increased as a linear function of the log number of session 1 training trials and was statistically significant after approximately 100 training trials. The effects of stimulus novelty did not differ across experiments. These results support the idea that stimulus-specific learning in our task arises gradually and continuously through practice, perhaps concurrently with general learning.

Skill decay in surgeons deployed on military operations: a systematic review

INTRODUCTION

Decay of surgical skills due to paucity of opportunity to operate is a potential threat to patients being cared for by the Defence Medical Services while on operational deployment. Our aim was to review the literature regarding skill decay in the trained surgeon in order to understand how it may affect clinical performance and patient outcomes. We also wished to survey the likely causes of such decay and possible means of mitigation.

METHODS

A systematic review of the literature was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Study bias assessment was also undertaken. Content summaries for the papers included study design and methodology, participant level of experience, measures and magnitude of effect, duration of no practice, and study limitations.

RESULTS

Five papers met the selection criteria. There were insufficient quantitative data on the impact of surgical skill decay on patient outcome, surgeon performance or mitigation strategies, and a meaningful quantitative synthesis could not be undertaken.

CONCLUSIONS

This systematic review of the literature found very little specific evidence confirming or refuting surgical skill decay in trained surgeons, with measurement of decay hampered by the lack of an accepted methodology. Studying this in the deployed setting may offer a firmer evidence base from which to generate policy. Potential mitigation strategies are discussed. PROSPERO registration number ID260846.

Protocol for two models of behavioral transition from action to no-action when facing prolonged uncontrollable experience in mice

Uncontrollability could lead to behavioral adjustment or even giving up when facing repeated failure. Here, we detail a protocol to study the behavioral transition from action to no-action induced by prolonged uncontrollable experiences in mice. We describe the behavioral devices, video analysis, and the exponential learning curve fitting for mathematical assessment. We perform further validation experiments evaluating locomotor, social, and anxiety-/depression-like behaviors. This approach helps study neural mechanisms underlying adaptive decision-making when facing repeated failure. For complete details on the use and execution of this protocol, please refer to Li et al.1.

Comparison of online, offline, and hybrid hypotheses of motor sequence learning using a quantitative model that incorporate reactive inhibition

Two hypotheses have been advanced for when motor sequence learning occurs: offline between bouts of practice or online concurrently with practice. A third possibility is that learning occurs both online and offline. A complication for differentiating between those hypotheses is a process known as reactive inhibition, whereby performance worsens over consecutively executed sequences, but dissipates during breaks. We advance a new quantitative modeling framework that incorporates reactive inhibition and in which the three learning accounts can be implemented. Our results show that reactive inhibition plays a far larger role in performance than is appreciated in the literature. Across four groups of participants in which break times and correct sequences per trial were varied, the best overall fits were provided by a hybrid model. The version of the offline model that does not account for reactive inhibition, which is widely assumed in the literature, had the worst fits. We discuss implications for extant hypotheses and directions for future research.

Initial development of skill with a reversed bicycle and a case series of experienced riders

Riding a bicycle is considered a durable skill that cannot be forgotten. Here, novice participants practiced riding a reversed bicycle, in which a reversing gear inverted the handlebar's rotation. Although learning to ride the reversed bicycle was possible, it was slow, highly variable, implicit, and followed an S-shape pattern. In the initial learning phase, failed attempts to ride the normal bicycle indicated strong interference between the two bicycle skills. While additional practice decreased this interference effect, a subset of learners could not ride either bicycle after eight sessions of practice. Experienced riders who performed extensive practice could switch bicycles without failed attempts and exhibited similar performance (i.e., similar handlebar oscillations) on both bicycles. However, their performance on the normal bicycle was worse than that of the novice bicycle riders at baseline. In conclusion, "unlearning" of the normal bicycle skill precedes the initial learning of the reversed bicycle skill, and a signature of such unlearning is still present following extensive practice.

How do humans learn about the reliability of automation?

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults

BACKGROUND

Walking speed and energy economy tend to decline with age. Lower-limb exoskeletons have demonstrated potential to improve either measure, but primarily in studies conducted on healthy younger adults. Promising techniques like optimization of exoskeleton assistance have yet to be tested with older populations, while speed and energy consumption have yet to be simultaneously optimized for any population.

METHODS

We investigated the effectiveness of human-in-the-loop optimization of ankle exoskeletons with older adults. Ten healthy adults > 65 years of age (5 females; mean age: 72 ± 3 yrs) participated in approximately 240 min of training and optimization with tethered ankle exoskeletons on a self-paced treadmill. Multi-objective human-in-the-loop optimization was used to identify assistive ankle plantarflexion torque patterns that simultaneously improved self-selected walking speed and metabolic rate. The effects of optimized exoskeleton assistance were evaluated in separate trials.

RESULTS

Optimized exoskeleton assistance improved walking performance for older adults. Both objectives were simultaneously improved; self-selected walking speed increased by 8% (0.10 m/s; p = 0.001) and metabolic rate decreased by 19% (p = 0.007), resulting in a 25% decrease in energetic cost of transport (p = 8e-4) compared to walking with exoskeletons applying zero torque. Compared to younger participants in studies optimizing a single objective, our participants required lower exoskeleton torques, experienced smaller improvements in energy use, and required more time for motor adaptation.

CONCLUSIONS

Our results confirm that exoskeleton assistance can improve walking performance for older adults and show that multiple objectives can be simultaneously addressed through human-in-the-loop optimization.

A novel task and methods to evaluate inter-individual variation in audio-visual associative learning

Learning audio-visual associations is foundational to a number of real-world skills, such as reading acquisition or social communication. Characterizing individual differences in such learning has therefore been of interest to researchers in the field. Here, we present a novel audio-visual associative learning task designed to efficiently capture inter-individual differences in learning, with the added feature of using non-linguistic stimuli, so as to unconfound language and reading proficiency of the learner from their more domain-general learning capability. By fitting trial-by-trial performance in our novel learning task using simple-to-use statistical tools, we demonstrate the expected inter-individual variability in learning rate as well as high precision in its estimation. We further demonstrate that such measured learning rate is linked to working memory performance in Italian-speaking (N = 58) and French-speaking (N = 51) adults. Finally, we investigate the extent to which learning rate in our task, which measures cross-modal audio-visual associations while mitigating familiarity confounds, predicts reading ability across participants with different linguistic backgrounds. The present work thus introduces a novel non-linguistic audio-visual associative learning task that can be used across languages. In doing so, it brings a new tool to researchers in the various domains that rely on multi-sensory integration from reading to social cognition or socio-emotional learning.

Kinesthetic vs. visual focus: No evidence for effects of practice modality in representation types after action imagery practice and action execution practice

Action-imagery practice (AIP) is assumed to result in partly different action representations than action-execution practice (AEP). The present study investigated whether focusing on either kinesthetic or visual aspects of a task during practice amplifies or diminishes such differences between AIP and AEP. In ten sessions, four groups, using either AIP or AEP with either kinesthetic or visual focus, practiced a twelve-element sequence in a unimanual serial reaction time task. Tests involved the practice sequence, a mirror sequence, and a different sequence, each performed with the practice and transfer hand. In AIP and AEP, in both hands, reaction times (RTs) were shorter in the practice sequence than in the different sequence, indicating effector-independent visual-spatial sequence representations. Further, RTs were shorter in the practice hand than in the transfer hand in the practice sequence (but not in the different sequence), indicating effector-dependent representations in AEP and AIP. Although the representation types did not differ, learning effects were stronger in AEP than in AIP. Thus, although to a lower extent than in AEP, effector-dependent representations can be acquired using AIP. Contrary to the expectations, the focus manipulation did not have an impact on the acquired representation types. Hence, modality instructions in AIP may not have such a strong impact as commonly assumed, at least in implicit sequence learning.

Effects of short-term hand immobilization on anticipatory mechanism for tool use

The short-term immobilization of a limb such as the right arm can impair sensorimotor mechanisms, which in turn reduces motor control of this arm. However, it is not known whether immobilization also impairs the anticipatory mechanism for tool use without actual enactment. In two experiments, we asked participants to judge how they would use a tool in a particular environment (e.g., "Take the pencil to write on a sheet of paper"). Prior to this tool-use judgment task, some participants had been immobilized (right arm) for 24 h. Results revealed that compared to controls, immobilized participants performed more poorly on the tool-use judgment task (accuracy and response time) as well as in a manual dexterity task. As our tool-use judgment task involved anticipating the expected perceptual effect of using a tool to achieve an environmental goal (e.g., writing on a sheet of paper), our data are discussed in line with theories of motor control (e.g., ideomotor theory) that emphasize the expected perceptual consequences of the action.

Robust within-session modulations of IAT scores may reveal novel dynamics of rapid change

The Implicit Association Test (IAT) is employed in the domain of social psychology as a measure of implicit evaluation. Participants in this task complete blocks of trials where they are asked to respond to categories and attributes (e.g., types of faces and types of words). Reaction times in different blocks sharing certain response combinations are averaged and then subtracted from blocks with other response combinations and then normalized, the result of which is taken as a measure indicating implicit evaluation toward or away from the given categories. One assumption of this approach is stationarity of response time distributions, or at a minimum, that temporal dynamics in response times are not theoretically relevant. Here we test these assumptions, examine the extent to which response times change within the IAT blocks and, if so, how trajectories of change are meaningful in relation to external measures. Using multiple data sets we demonstrate within-session changes in IAT scores. Further, we demonstrate that dissociable components in the trajectories of IAT performance may be linked to theoretically distinct processes of cognitive biases as well as behaviors. The present work presents evidence that IAT performance changes within the task, while future work is needed to fully assess the implications of these temporal dynamics.

Information-Theoretic Neural Decoding Reproduces Several Laws of Human Behavior

Human response times conform to several regularities including the Hick-Hyman law, the power law of practice, speed-accuracy trade-offs, and the Stroop effect. Each of these has been thoroughly modeled in isolation, but no account describes these phenomena as predictions of a unified framework. We provide such a framework and show that the phenomena arise as decoding times in a simple neural rate code with an entropy stopping threshold. Whereas traditional information-theoretic encoding systems exploit task statistics to optimize encoding strategies, we move this optimization to the decoder, treating it as a Bayesian ideal observer that can track transmission statistics as prior information during decoding. Our approach allays prominent concerns that applying information-theoretic perspectives to modeling brain and behavior requires complex encoding schemes that are incommensurate with neural encoding.

A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice

Persistence in the face of failure helps to overcome challenges. But the ability to adjust behavior or even give up when the task is uncontrollable has advantages. How the mammalian brain switches behavior when facing uncontrollability remains an open question. We generated two mouse models of behavioral transition from action to no-action during exposure to a prolonged experience with an uncontrollable outcome. The transition was not caused by pain desensitization or muscle fatigue and was not a depression-/learned-helplessness-like behavior. Noradrenergic neurons projecting to GABAergic neurons within the orbitofrontal cortex (OFC) are key regulators of this behavior. Fiber photometry, microdialysis, mini-two-photon microscopy, and tetrode/optrode in vivo recording in freely behaving mice revealed that the reduction of norepinephrine and downregulation of alpha 1 receptor in the OFC reduced the number and activity of GABAergic neurons necessary for driving action behavior resulting in behavioral transition. These findings define a circuit governing behavioral switch in response to prolonged uncontrollability.

Disentangling Different Aspects of Change in Tests with the D-Diffusion Model

Diffusion-based item response theory models are measurement models that link parameters of the diffusion model (drift rate, boundary separation) to latent traits of test takers. Similar to standard latent trait models, they assume the invariance of the test takers' latent traits during a test. Previous research, however, suggests that traits change as test takers learn or decrease their effort. In this paper, we combine the diffusion-based item response theory model with a latent growth curve model. In the model, the latent traits of each test taker are allowed to change during the test until a stable level is reached. As different change processes are assumed for different traits, different aspects of change can be separated. We discuss different versions of the model that make different assumptions about the form (linear versus quadratic) and rate (fixed versus individual-specific) of change. In order to fit the model to data, we propose a Bayes estimator. Parameter recovery is investigated in a simulation study. The study suggests that parameter recovery is good under certain conditions. We illustrate the application of the model to data measuring visuo-spatial perspective-taking.

An assessment of learning rates in habitual prospective memory

Most laboratory research in the field of prospective memory has focused on newly formed (episodic) intentions that are carried out in the experimental context once or only a small number of times. However, many naturalistic prospective memories are carried out many times and these types of (habitual) intentions have been studied much less in the laboratory. In the current study, our aim was to extend prior work examining habitual intentions in laboratory prospective memory paradigms. Participants formed a typical prospective memory intention and then completed an ongoing task in which the intention could be executed up to 63 times. We examined changes in performance across trials in three traditionally important prospective memory metrics: cue detection, task interference, and cue interference. Across repeated performance of the prospective memory task, we observed an increase in cue detection, elimination of task interference, and elimination of cue interference. These results provide key insights into the operation of learning mechanisms in prospective memory paradigms and promote theory development by showing that many of the resource-demanding processes that are theorized to be necessary for successful prospective memory play much less of a role when intentions are repeatedly completed.

Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants' threshold of when enough evidence is present to act.

Strength training as a dynamical model of motor learning

This paper outlines a framework for strength training as a dynamical model of perceptual-motor learning. We show, with emphasis on fixed-point attractor dynamics, that strength training can be mapped to the general dynamical principles of motor learning that arise from the constraints on action, including the distribution of practice/training. The time scales of the respective dynamics of performance change (increment and decrement) in discrete strength training and motor learning tasks reveal superposition of exponential functions in fixed-point dynamics, but distinctive attractor and parameter dynamics in oscillatory limit cycle and more continuous tasks, together with unique timescales to process influences (including practice, learning, strength, fitness, fatigue, warm-up decrement). Increments and decrements of strength can be viewed within a dynamical model of change in motor performance that reflects the integration of practice and training processes at multiple levels of learning and skill development.

Learning spatial frequency identification through reweighted decoding

Perceptual learning, the improvement of perceptual judgments with practice, occurs in many visual tasks. There are, however, relatively fewer studies examining perceptual learning in spatial frequency judgments. In addition, perceptual learning has generally been studied in two-alternative tasks, occasionally in n-alternative tasks, and infrequently in identification. Recently, perceptual learning was found in an orientation identification task (eight-alternatives) and was well accounted for by a new identification integrated reweighting theory (I-IRT) (Liu et al., submitted). Here, we examined perceptual learning in a similar eight-alternative spatial frequency absolute identification task in two different training protocols, finding learning in the majority but not all observers. We fit the I-IRT to the spatial frequency learning data and discuss possible model explanations for variations in learning.

Repetition learning is neither a continuous nor an implicit process

Learning advances through repetition. A classic paradigm for studying this process is the Hebb repetition effect: Immediate serial recall performance improves for lists presented repeatedly as compared to nonrepeated lists. Learning in the Hebb paradigm has been described as a slow but continuous accumulation of long-term memory traces over repetitions [e.g., Page & Norris, Phil. Trans. R. Soc. B 364, 3737-3753 (2009)]. Furthermore, it has been argued that Hebb repetition learning requires no awareness of the repetition, thereby being an instance of implicit learning [e.g., Guérard et al., Mem. Cogn. 39, 1012-1022 (2011); McKelvie,  J. Gen. Psychol. 114, 75-88 (1987)]. While these assumptions match the data from a group-level perspective, another picture emerges when analyzing data on the individual level. We used a Bayesian hierarchical mixture modeling approach to describe individual learning curves. In two preregistered experiments, using a visual and a verbal Hebb repetition task, we demonstrate that 1) individual learning curves show an abrupt onset followed by rapid growth, with a variable time for the onset of learning across individuals, and that 2) learning onset was preceded by, or coincided with, participants becoming aware of the repetition. These results imply that repetition learning is not implicit and that the appearance of a slow and gradual accumulation of knowledge is an artifact of averaging over individual learning curves.

Time constraints may pace the ontogeny of movement behaviour

During early development, juvenile animals need to acquire a diverse behavioural repertoire to interact with their environment. The ontogeny of animal behaviour, is paced by the motivation to improve, e.g. internal clocks, and limited by external constraints, e.g. weather conditions. We here evaluate how naive Egyptian vultures (Neophron percnopterus) improve in locomotor performance, measured as daily maximum displacement, prior to their first migration under three different time constraint regimes: we compared wild hatched vultures, migrating one month after fledging, with captive-hatched vultures, released in spring four months or in winter nine months before migration. We found that the time until migration paced the development of movement behaviour: wild birds rapidly increased displacement distances within the first two weeks after fledging, while spring and winter released vultures delayed movement increases by two and four months, respectively. Under relaxed time constraints captive-hatched vultures displayed diverse functional forms of performance enhancements and therefore great variability in individual ontogeny of movement behaviour. While weather conditions in winter could limit flight movements, some birds indeed moved immediately after their release, indicating that weather may not be limiting. Our findings promote the idea that relaxed ecological constraints could uncover hidden phenotypic flexibility in ontogeny, which could present a greater potential for adaptability under environmental change than currently expected.

Strategically managing learning during perceptual decision making

Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.

Mind the gap: Distributed practice enhances performance in a MOBA game

Understanding how humans master complex skills has the potential for wide-reaching societal benefit. Research has shown that one important aspect of effective skill learning is the temporal distribution of practice episodes (i.e., distributed practice). Using a large observational sample of players (n = 162,417) drawn from a competitive and popular online game (League of Legends), we analysed the relationship between practice distribution and performance through time. We compared groups of players who exhibited different play schedules using data slicing and machine learning techniques, to show that players who cluster gameplay into shorter time frames ultimately achieve lower performance levels than those who space their games across longer time windows. Additionally, we found that the timing of intensive play periods does not affect final performance-it is the overall amount of spacing that matters. These results extend some of the key findings in the literature on practice and learning to an ecologically valid environment with huge n. We discuss our work in relation to recent studies that have examined practice effects using Big Data and suggest solutions for salient confounds.

Comparing models of learning and relearning in large-scale cognitive training data sets

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.

Accounting for retest effects in cognitive testing with the Bayesian double exponential model via intensive measurement burst designs

Monitoring early changes in cognitive performance is useful for studying cognitive aging as well as for detecting early markers of neurodegenerative diseases. Repeated evaluation of cognition via a measurement burst design can accomplish this goal. In such design participants complete brief evaluations of cognition, multiple times per day for several days, and ideally, repeat the process once or twice a year. However, long-term cognitive change in such repeated assessments can be masked by short-term within-person variability and retest learning (practice) effects. In this paper, we show how a Bayesian double exponential model can account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. We also highlight how this approach allows for the inclusion of person-level predictors and draw intuitive inferences on cognitive change with Bayesian posterior probabilities. We use older adults’ performance on cognitive tasks of processing speed and spatial working memory to demonstrate how individual differences in peak performance and change can be related to predictors of aging such as biological age and mild cognitive impairment status.

Using position rather than color at the traffic light - Covariation learning-based deviation from instructions in attention deficit/hyperactivity disorder

Based on instructions people can form task representations that shield relevant from seemingly irrelevant information. It has been documented that instructions can tie people to a particular way of performing a task despite that in principle a more efficient way could be learned and used. Since task shielding can lead to persistence of inefficient variants of task performance, it is relevant to test whether individuals with attention deficit/hyperactivity disorder (ADHD) - characterized by less task shielding - are more likely and quicker to escape a suboptimal instructed variant of performing a task. The paradigm used in this online study builds on the observation that in many environments different covarying features could be used to determine the appropriate response. For instance, as they approach a traffic light, drivers and pedestrians monitor the color (instructed stimulus feature) and/or the position of the signal (covarying stimulus feature, more efficient in case of reduced color sight). Similarly, we instructed participants to respond to the color of a stimulus without mentioning that color covaried with the position of the stimulus. In order to assess whether with practice participants would use the non-instructed feature position to an increasing extent, we compared reaction times and error rates for standard trials to trials in which color was either ambiguous or did not match the usual covariation. Results showed that the covariation learning task can be administered online to adult participants with and without ADHD. Performance differences suggested that with practice ADHD participants (n = 43 out of a total N = 245) might increase attention to non-instructed stimulus features. Yet, they used the non-instructed covarying stimulus feature to a similar extent as other participants. Together the results suggest that participants with ADHD do not lag behind in abandoning instructed task processing in favor of a learned alternative strategy.

Repeated series learning revisited with a novel prediction on the reduced effect of item frequency in dyslexia

Developmental dyslexia, a difficulty with acquiring fluent reading, has also been characterized by reduced short-term memory (STM) capacity, which is often operationalized with span tasks. The low performance of individuals with dyslexia (IDDs) in such tasks is commonly attributed to poor phonological memory. However, we suggest an alternative explanation based on the observation that many times the items that are used in spans tasks are high-frequency items (e.g., digit words). We suggest that IDDs do not enjoy the benefit of item frequency to the same extent as controls, and thus their performance in span tasks is especially hampered. On the contrary, learning of repeated sequences was shown to be largely independent of item frequency, and therefore this type of learning may be unimpaired in dyslexia. To test both predictions, we used the Hebb-learning paradigm. We found that IDDs’ performance is especially poor compared to controls’ when high-frequency items are used, and that their repeated series learning does not differ from that of controls. Taken together with existing literature, our findings suggest that impaired learning of repeated series is not a core characteristic of dyslexia, and that the reports on reduced STM in dyslexia may to a large extent be explained by reduced benefit of item frequency.

Ready to Learn: Incidental Exposure Fosters Category Learning

Our knowledge of the world is populated with categories such as dogs, cups, and chairs. Such categories shape how we perceive, remember, and reason about their members. Much of our exposure to the entities we come to categorize occurs incidentally as we experience and interact with them in our everyday lives, with limited access to explicit teaching. This research investigated whether incidental exposure contributes to building category knowledge by rendering people "ready to learn"-allowing them to rapidly capitalize on brief access to explicit teaching. Across five experiments (N = 438 adults), we found that incidental exposure did produce a ready-to-learn effect, even when learners showed no evidence of robust category learning during exposure. Importantly, this readiness to learn occurred only when categories possessed a rich structure in which many features were correlated within categories. These findings offer a window into how our everyday experiences may contribute to building category knowledge.

Perceptual learning is robust to manipulations of valence and arousal in childhood and adulthood

Despite clear links between affective processes in many areas of cognition and perception, the influence of affective valence and arousal on low-level perceptual learning have remained largely unexplored. Such influences could have the potential to disrupt or enhance learning that would have long-term consequences for young learners. The current study manipulated 8- to 11-year-old children's and young adults' mood using video clips (to induce a positive mood) or a psychosocial stressor (to induce a negative mood). Each participant then completed one session of a low-level visual learning task (visual texture paradigm). Using novel computational methods, we did not observe evidence for the modulation of visual perceptual learning by manipulations of emotional arousal or valence in either children or adults. The majority of results supported a model of perceptual learning that is overwhelmingly constrained to the task itself and independent from external factors such as variations in learners' affect.

Optimal Learning Under Time Constraints: Empirical and Simulated Trade-offs Between Depth and Breadth of Study

Learners are often constrained by their available study time, typically having to make a trade-off between depth and breadth of learning. Classic experimental paradigms in memory research treat all items as equally important, but this is unlikely the case in reality. Rather, information varies in importance, and people vary in their ability to distinguish what is more or less important. We test the impact of this trade-off in the study of Graduate Record Examination (GRE)-synonym word pairs. In our empirical Study 1, we split our stimuli set, with some items (focal) being afforded more rounds of retrieval practice than other items (non-focal). All conditions had the same total number of trials (i.e., constant study time), but differed in the number of focal words (breadth) and repetitions (depth). The conditions differed significantly in both mean performance and variance on the day-delayed test. Using this empirical data as a base, we then conducted a simulation (Study 2) modeling depth-breadth trade-offs under various conditions of learner forecasting accuracy and test coverage. In Study 2, we found that a medium-depth medium-breadth strategy was appropriate for most of the learning situations covered by our simulation, but that learners with a well-calibrated understanding of importance may benefit from a more targeted high-depth, low-breadth approach. Our results highlight the complexity of navigating the depth-breadth trade-off. Models of learning strategy optimization will need to account for learner forecasting sensitivity, which itself is likely an interaction between relatively stable individual differences and shifting contextual factors.

Maximizing the Potential of Digital Games for Understanding Skill Acquisition

Gaming is a domain of profound skill development. Players’ digital traces create data that track the development of skill from novice to expert levels. We argue that existing work, although promising, has yet to take advantage of the potential of game data for understanding skill acquisition, and that to realize this potential, future studies can use the fit of formal learning curves to individual data as a theoretical anchor. Learning-curve analysis allows learning rate, initial performance, and asymptotic performance to be separated out, and so can serve as a tool for reconciling the multiple factors that may affect learning. We review existing research on skill development using data from digital games, showing how such work can confirm, challenge, and extend existing claims about the psychology of expertise. Learning-curve analysis provides the foundation for direct experiments on the factors that affect skill development, which are necessary for a cross-domain cognitive theory of skill. We conclude by making recommendations for, and noting obstacles to, experimental studies of skill development in digital games.

General principles of brain electromagnetic rhythmic oscillations and implications for neuroplasticity

Neuro-plasticity describes the ability of the brain in achieving novel functions, either by transforming its internal connectivity, or by changing the elements of which it is made, meaning that, only those changes, that affect both structural and functional aspects of the system, can be defined as "plastic." The concept of plasticity can be applied to molecular as well as to environmental events that can be recognized as the basic mechanism by which our brain reacts to the internal and external stimuli. When considering brain plasticity within a clinical context-that is the process linked with changes of brain functions following a lesion- the term "reorganization" is somewhat synonymous, referring to the specific types of structural/functional modifications observed as axonal sprouting, long-term synaptic potentiation/inhibition or to the plasticity related genomic responses. Furthermore, brain rewires during maturation, and aging thus maintaining a remarkable learning capacity, allowing it to acquire a wide range of skills, from motor actions to complex abstract reasoning, in a lifelong expression. In this review, the contribution on the "neuroplasticity" topic coming from advanced analysis of EEG rhythms is put forward.

Assessing the functions underlying learning using by-trial and by-participant models: Evidence from two visual perceptual learning paradigms

Inferred mechanisms of learning, such as those involved in improvements resulting from perceptual training, are reliant on (and reflect) the functional forms that models of learning take. However, previous investigations of the functional forms of perceptual learning have been limited in ways that are incompatible with the known mechanisms of learning. For instance, previous work has overwhelmingly aggregated learning data across learning participants, learning trials, or both. Here we approach the study of the functional form of perceptual learning on the by-person and by-trial levels at which the mechanisms of learning are expected to act. Each participant completed one of two visual perceptual learning tasks over the course of two days, with the first 75% of task performance using a single reference stimulus (i.e., "training") and the last 25% using an orthogonal reference stimulus (to test generalization). Five learning functions, coming from either the exponential or the power family, were fit to each participant's data. The exponential family was uniformly supported by Bayesian Information Criteria (BIC) model comparisons. The simplest exponential function was the best fit to learning on a texture oddball detection task, while a Weibull (augmented exponential) function tended to be the best fit to learning on a dot-motion discrimination task. The support for the exponential family corroborated previous by-person investigations of the functional form of learning, while the novel evidence supporting the Weibull learning model has implications for both the analysis and the mechanistic bases of the learning.

The Effect of Noisy Galvanic Vestibular Stimulation on Learning of Functional Mobility and Manual Control Nulling Sensorimotor Tasks

Galvanic vestibular stimulation (GVS) is a non-invasive method of electrically stimulating the vestibular system. We investigated whether the application of GVS can alter the learning of new functional mobility and manual control tasks and whether learning can be retained following GVS application. In a between-subjects experiment design, 36 healthy subjects performed repeated trials, capturing the learning of either (a) a functional mobility task, navigating an obstacle course on a compliant surface with degraded visual cues or (b) a manual control task, using a joystick to null self-roll tilt against a pseudo-random disturbance while seated in the dark. In the “learning” phase of trials, bilateral, bipolar GVS was applied continuously. The GVS waveform also differed between subjects in each task group: (1) white noisy galvanic vestibular stimulation (nGVS) at 0.3 mA (2) high-level random GVS at 0.7 mA (selected from pilot testing as destabilizing, but not painful), or (3) with the absence of stimulation (i.e., sham). Following the “learning” trials, all subjects were blindly transitioned to sham GVS, upon which they immediately completed another series of trials to assess any aftereffects. In the functional mobility task, we found nGVS significantly improved task learning (p = 0.03, mean learning metric 171% more than the sham group). Further, improvements in learning the functional mobility task with nGVS were retained, even once the GVS application was stopped. The benefits in learning with nGVS were not observed in the manual control task. High level GVS tended to inhibit learning in both tasks, but not significantly so. Even once the high-level stimulation was stopped, the impaired performance remained. Improvements in learning with nGVS may be due to increased information throughput resulting from stochastic resonance. The benefit of nGVS for functional mobility, but not manual control nulling, may be due to the multisensory (e.g., visual and proprioceptive), strategic, motor coordination, or spatial awareness aspects of the former task. Learning improvements with nGVS have the potential to benefit individuals who perform functional mobility tasks, such as astronauts, firefighters, high performance athletes, and soldiers.

Conscious awareness of motor fluidity improves performance and decreases cognitive effort in sequence learning

Motor skill learning is improved when participants are instructed to judge after each trial whether their performed movements have reached maximal fluidity. Consequently, the conscious awareness of this maximal fluidity can be classified as a genuine learning factor for motor sequences. However, it is unknown whether this effect of conscious awareness on motor learning could be mediated by the increased cognitive effort that may accompany such judgment making. The main aim of this study was to test this hypothesis in comparing two groups with, and without, the conscious awareness of the maximal fluidity. To assess the possible involvement of cognitive effort, we have recorded the pupillary dilation to the task, which is well-known to increase in proportion to cognitive effort. Results confirmed that conscious awareness indeed improved motor sequence learning of the trained sequence specifically. Pupil dilation was smaller during trained than during novel sequence performance, indicating that sequence learning decreased the cognitive cost of sequence execution. However, we found that in the group that had to judge on their maximal fluidity, pupil dilation during sequence production was smaller than in the control group, indicating that the motor improvement induced by the fluidity judgment does not involve additional cognitive effort. We discuss these results in the context of motor learning and cognitive effort theories.

Cognitive & motor skill transfer across speeds: A video game study

We examined the detailed behavioral characteristics of transfer of skill and the ability of the adaptive control of thought rational (ACT-R) architecture to account for this with its new Controller module. We employed a simple action video game called Auto Orbit and investigated the control tuning of timing skills across speed perturbations of the environment. In Auto Orbit, players needed to learn to alternate turn and shot actions to blow and burst balloons under time constraints imposed by balloon resets and deflations. Cognitive and motor skill transfer was assessed both in terms of game performance and in terms of the details of their motor actions. We found that skill transfer across speeds necessitated the recalibration of action timing skills. In addition, we found that acquiring skill in Auto Orbit involved a progressive decrease in variability of behavior. Finally, we found that players with higher skill levels tended to be less variable in terms of action chunking and action timing. These findings further shed light on the complex cognitive and motor mechanisms of skill transfer across speeds in complex task environments.

How adaptation, training, and customization contribute to benefits from exoskeleton assistance

Exoskeletons can enhance human mobility, but we still know little about why they are effective. For example, we do not know the relative importance of training, how much is required, or what type is most effective; how people adapt with the device; or the relative benefits of customizing assistance. We conducted experiments in which naïve users learned to walk with ankle exoskeletons under one of three training regimens characterized by different levels of variation in device behavior. Assistance was also customized for one group. After moderate-variation training, the benefits of customized assistance were large; metabolic rate was reduced by 39% compared with walking with the exoskeleton turned off. Training contributed about half of this benefit and customization about one-quarter; a generic controller reduced energy cost by 10% before training and 31% afterward. Training required much more exposure than typical of exoskeleton studies, about 109 minutes of assisted walking. Type of training also had a strong effect; the low-variation group required twice as long as the moderate-variation group to become expert, and the high-variation group never acquired this level of expertise. Curiously, all users adapted in a way that resulted in less mechanical power from the exoskeleton as they gained expertise. Customizing assistance required less time than training for all parameters except peak torque magnitude, which grew slowly over the study, suggesting a longer time scale adaptation in the person. These results underscore the importance of training to the benefits of exoskeleton assistance and suggest the topic deserves more attention.

Speeding-up while growing-up: Synchronous functional development of motor and non-motor processes across childhood and adolescence

Describing the maturation of information processing in children is fundamental for developmental science. Although non-linear changes in reaction times have been well-documented, direct measurement of the development of the different processing components is lacking. In this study, electromyography was used to quantify the maturation of premotor and motor processes on a sample of 114 children (6–14 years-old) and 15 adults. Using a model-based approach, we show that the development of these two components is well-described by an exponential decrease in duration, with the decay rate being equal for the two components. These findings provide the first unbiased evidence in favour of the common developmental rate of nonmotor and motor processes by directly confronting rates of development of different processing components within the same task. This common developmental rate contrasts with the differential physical maturation of region-specific cerebral gray and white matter. Tentative paths of interpretation are proposed in the discussion.

Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise

Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.

Why human factors science is demonstrably necessary: historical and evolutionary foundations

We review the theoretical foundation for the need for human factors science. Over the past 2.8 million years, humans and tools have co-evolved. However, in the last century, technology is introduced at a rate that exceeds human evolution. The proliferation of computers and, more recently, robots, introduces new cognitive demands, as the human is required to be a monitor rather than a direct controller. The usage of robots and artificial intelligence is only expected to increase, and the present COVID-19 pandemic may prove to be catalytic in this regard. One way to improve overall system performance is to 'adapt the human to the machine' via task procedures, operator training, operator selection, a Procrustean mandate. Using classic research examples, we demonstrate that Procrustean methods can improve performance only to a limited extent. For a viable future, therefore, technology must adapt to the human, which underwrites the necessity of human factors science. Practitioner Summary: Various research articles have reported that the science of Human Factors is of vital importance in improving human-machine systems. However, what is lacking is a fundamental historical outline of why Human Factors is important. This article provides such a foundation, using arguments ranging from pre-history to post-COVID.

Hierarchical Inference in Sound Change: Words, Sounds, and Frequency of Use

This paper aims examines the role of hierarchical inference in sound change. Through hierarchical inference, a language learner can distribute credit for a pronunciation between the intended phone and the larger units in which it is embedded, such as triphones, morphemes, words and larger syntactic constructions and collocations. In this way, hierarchical inference resolves the longstanding debate about the unit of sound change: it is not necessary for change to affect only sounds, or only words. Instead, both can be assigned their proper amount of credit for a particular pronunciation of a phone. Hierarchical inference is shown to generate novel predictions for the emergence of stable variation. Under standard assumptions about linguistic generalization, it also generates a counterintuitive prediction of a U-shaped frequency effect in an advanced articulatorily-motivated sound change. Once the change has progressed far enough for the phone to become associated with the reduced pronunciation, novel words will be more reduced than existing words that, for any reason, have become associated with the unreduced variant. Avoiding this prediction requires learners to not consider novel words to be representative of the experienced lexicon. Instead, learners should generalize to novel words from other words that are likely to exhibit similar behavior: rare words, and the words that occur in similar contexts. Directions for future work are outlined.

Overview of Machine Learning Process Modelling

Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner's (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their 'capacity to learn' based on the amount of available or desired data.

The S-Shaped Performance Curve Prevails in Practicing Juggling

The development of performance, such as learning a new motor skill, can be represented in a performance curve. The shape of the performance curve is both of theoretical and practical relevance. Here, the author studied the interday performance of juggling over a period of 17 days in 112 college students. The results showed that 60% of participants followed an S-shaped performance curve with the inflection date on the 11th day, followed by a decelerated (20%), accelerated (14%), and linear curve (6%). As expected, except on Day 1, male participants performed at least 33% better than female participants on each practice day. Also as expected, learning performance was found to depend on the type of performance curve with the best learning performance exhibited by the linear group. The results further revealed that pooling all participants’ performance together without considering the percentage of each underlying type of performance curve would lead to biased, nonrepresentative results. Given the variety of the observed performance curves and the dominance of the S-shaped performance curve among them, coaches should continuously monitor the shape of an individual’s performance curve.