Estimation of word frequency in continuous and discrete tasks.

ABSTRACTPreviously, we have shown that discrete and continuous rapid aiming tasks are governed by distinct visuomotor control mechanisms by assessing the combined visual illusion effects on the perceived and effective index of difficulty (ID). All participants were perceptually biased by the combined visual illusion before they performed the rapid aiming tasks. In the current study, the authors manipulated the order of performing perceptual and motor tasks to examine whether perceptual or motor experience with the illusory visual target would influence the subsequent perceived and effective ID in discrete and continuous tapping tasks. The results supported our hypothesis showing that perceptual experience with the illusory visual target in the discrete condition reduced the effective ID in the subsequent discrete tapping task, and motor experience with the illusory visual target in the continuous condition reduced the illusion effects on the perceived ID in the subsequent perceptual judgment task. The study demonstrates the coinfluence of perception and action, and suggests that perception and action influence one another with different magnitude depending on the spatial frame of reference used to perform the perceptuomotor task.

Introduction: Dual-task studies have demonstrated that walking is attention-demanding for younger adults. However, numerous studies have attributed this to task type rather than the amount of required to accomplish the task. This study examined four tasks: two discrete (i.e., short intervals of attention) and two continuous (i.e., sustained attention) to determine whether greater attentional demands result in greater dual-task costs due to an overloaded processing capacity.Methods: Nineteen young adults (21.5 ± 3.6 years, 13 females) completed simple reaction time (SRT) and go/no-go (GNG) discrete cognitive tasks and n-back (NBK) and double number sequence (DNS) continuous cognitive tasks with or without self-paced walking. Prefrontal cerebral hemodynamics were measured using functional near-infrared spectroscopy (fNIRS) and performance was measured using response time, accuracy, and gait speed.Results: Repeated measures ANOVAs revealed decreased accuracy with increasing cognitive demands (p = 0.001) and increased dual-task accuracy costs (p < 0.001). Response times were faster during the single compared to dual-tasks during the SRT (p = 0.005) and NBK (p = 0.004). DNS gait speed was also slower in the dual compared to single task (p < 0.001). Neural findings revealed marginally significant interactions between dual-task walking and walking alone in the DNS (p = 0.06) and dual -task walking compared to the NBK cognitive task alone (p = 0.05).Conclusion: Neural findings suggest a trend towards increased PFC activation during continuous tasks. Cognitive and motor measures revealed worse performance during the discrete compared to continuous tasks. Future studies should consider examining different attentional demands of motor tasks.

We investigated variations in continuous and discrete flight demands in a simulated flight mission employing a novel physiological measure of invested effort, Peripheral Arterial Tone (PAT). Twelve male subjects performed a computer-simulated agricultural flight task. They were required to fly over a specific lane of a field (continuous task) and change lanes in response to flags, which were presented at varying intervals (discrete task). The difficulty of the flight task was manipulated by changing the plane control (single-vs. dual-axis control), while the difficulty associated with the discrete events was manipulated by varying the amount of lateral change signalled by the flag (no change vs. 1.5 or 3 lanes of change). PAT amplitude values were lower in the difficult level of the continuous task and was further attenuated following the appearance of the flag when a change in the flight position was required.

A large portion of the rehabilitation experience after stroke relies on implicit learning. However, our understanding of how best to facilitate motor learning after stroke is limited by a paucity of research that has explored the interaction between explicit information and implicit learning across various task domains. Previously we reported that the delivery of explicit instructions disrupted implicit motor learning after stroke that involved the sensorimotor cortical areas or basal ganglia. The purpose of this study was to determine the robustness of these findings by determining whether they could be replicated with 2 motor tasks, one discrete and one continuous, employed by the same group of participants. Ten individuals with stroke in the sensorimotor cortical areas (SMC), 10 with stroke in the basal ganglia (BG), and 10 age-matched healthy controls (HC) participated in this study. Each completed 3 days of practice of both a discrete implicit motor task (the serial reaction time task) and a continuous motor task (the continuous tracking task); all returned on a fourth day for retention tests. By random designation, participants were divided into either the explicit information (EI) or no explicit information (No-EI) groups. Consistent with previous results, we found that the response to explicit information after stroke was uniformly negative regardless of task or lesion location; both stroke groups demonstrated an interference effect of explicit information while the healthy control group did not. Strengthening these findings is the fact that the interference effect of explicit information was not task dependent. This point is particularly important for rehabilitation scientists as they instruct clients during various therapeutic tasks after stroke. Our data suggest that certain forms of explicit information delivered before task practice may not be as useful for learning as discovering the solution to the motor task with practice alone, and this is regardless of the type of task being learned.

Saccades constitute a major source of artifacts and confounds in brain imaging studies. Whereas some artifacts can be removed by omitting segments of data, saccadic artifacts cannot be typically eliminated by this method because of their high occurrence rate even during fixation (1-3 per second). Some saccadic artifacts can be alleviated by offline-correction algorithms, but these methods leave nonnegligible residuals and cannot mitigate the saccade-related visual activity. Here, we propose a novel yet simple approach for diminishing saccadic artifacts and confounds through experimental design. We suggest that specific tasks can lead to substantially less saccade occurrences around the time of stimulus presentation, starting from slightly before its onset and lasting for a few hundred milliseconds. In three experiments, we compared the frequency and size of saccades in a variety of tasks. Results of Experiment 1 showed that a foveal change-detection task reduced the number and sizes of saccades, relative to a parafoveal orientation-discrimination task. Experiment 2 replicated this finding with a parafoveal object recognition task. Experiment 3 showed that both foveal and parafoveal continuous change detection tasks induced fewer and smaller saccades than a discrete orientation-discrimination task. We conclude that adding a foveal or a parafoveal continuous task reduces saccades' number and size. This would lead to better artifact correction and enable the omission of contaminated data segments. This study may be the first step toward developing saccade-free experimental designs.

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor μ, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation.

Previous studies manipulating redundancy in tracking experiments used discrete rather than continuous tracking tasks. A methodology is presented for manipulating redundancy in continuous input tracks in order to compare the influence of redundancy on continuous tracking performance to that found previously using discrete tracking tasks. Three orders of redundancy were introduced in continuous tracks. Within each order of redundancy, five different amounts of redundancy were used by manipulating the sequential probabilities of the tracks' maximum amplitude values. The analysis of response lags and error data indicated that Ss did not respond as predicted to the conditional probabilities of amplitude heights existing in the tracks, even when the probabilities were relatively high, but rather responded to different aspects of the tracks. These results were interpreted in terms of the unique characteristics of the continuous input and the postulated sampling strategy employed by Ss.

Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instances and complex problems that require different low-level approaches. However, ensemble methods are relatively less popular in reinforcement learning owing to the high sample complexity and computational expense involved in obtaining a diverse ensemble. We present a novel training and model selection framework for model-free reinforcement algorithms that use ensembles of policies obtained from a single training run. These policies are diverse in nature and are learned through directed perturbation of the model parameters at regular intervals. We show that learning and selecting an adequately diverse set of policies is required for a good ensemble while extreme diversity can prove detrimental to overall performance. Selection of an adequately diverse set of policies is done through our novel policy selection framework. We evaluate our approach on challenging discrete and continuous control tasks and also discuss various ensembling strategies. Our framework is substantially sample efficient, computationally inexpensive and is seen to outperform state-of-the-art (SOTA) scores in Atari 2600 and Mujoco.

Previous evidence has shown that word frequencies calculated from corpora based on film and television subtitles can readily account for reading performance, since the language used in subtitles greatly approximates everyday language. The present study examines this issue in a society with increased exposure to subtitle reading. We compiled SUBTLEX-GR, a subtitled-based corpus consisting of more than 27 million Modern Greek words, and tested to what extent subtitle-based frequency estimates and those taken from a written corpus of Modern Greek account for the lexical decision performance of young Greek adults who are exposed to subtitle reading on a daily basis. Results showed that SUBTLEX-GR frequency estimates effectively accounted for participants’ reading performance in two different visual word recognition experiments. More importantly, different analyses showed that frequencies estimated from a subtitle corpus explained the obtained results significantly better than traditional frequencies derived from written corpora.

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns a specific functional role to each muscle coupling by characterising its task-relevance. Crucially, our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’ in different ways: a) irrespective of the task at hand but also b) redundantly or c) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (sharing the same task information) or synergistic (representing different aspects of the task). To demonstrate these types of interactions in real data, we apply the framework to large-scale datasets of human movements and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar (redundant) and complementary (synergistic) task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

Singapore English, shaped by unique historical and sociocultural factors, diverges significantly from American and British English varieties. Despite its centrality in Singapore’s communication landscape, comprehensive word frequency estimates for this dialect have been lacking, limiting psycholinguistic research that accurately reflects local language processing. Dialectal variation fundamentally influences cognitive mechanisms, and neglecting these differences can distort theoretical models of language processing. This study addresses this gap by constructing word frequency (WF) and contextual diversity (CD) estimates from the National Speech Corpus (NSC), a large-scale speech corpus of Singapore English. We analyzed three NSC subparts comprising over 10,000 transcripts and 30 + million words to generate frequency estimates for 50,000 words, including both standard and uniquely Singaporean terms. To validate these measures, we used lexical decision data from the Auditory English Lexicon Project, featuring Singaporean participants who responded to stimuli across different accent conditions. Linear and binomial mixed-effect models compared NSC-based estimates against SUBTLEX-US and SUBTLEX-UK in predicting reaction times and accuracy. NSC estimates produced the lowest Akaike information criterion (AIC) values across both British and Singaporean accents, indicating superior fit to local language processing patterns. These results demonstrate that WF and CD effects are shaped by specific language experiences and highlight the necessity of dialect-appropriate psycholinguistic tools. This database advances cognitive science by enabling more accurate modeling of dialect-sensitive language processing, representing one of the largest psycholinguistic resources available for a less-studied English variety, and underscores the importance of developing similar resources for other English varieties worldwide.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13428-026-03012-1.

The accuracy and response latency of absolute frequency judgments were measured as a function of test lag (the number of intervening items between presentations of a test item) in a continuous memory task. Frequency was varied from one to three presentations in Experiments 1 and 2 and from one to five presentations in Experiment 3. The proportion of correct responses decreased as frequency increased, and correct mean response time tended to increase with frequency. Both accuracy and correct mean response time were found to be largely a function of the most recent test lag. The lag-latency functions were best described by piecewise linear functions, with the breakpoint occurring between lags 1 and 2. Continuous frequency estimation was also shown to improve with extended practice. The implications of the results are discussed with reference to trace strength, numerical-inference, and multiple-trace theories of frequency discrimination.

Bouncing a ball with a racket is a hybrid rhythmic-discrete motor task, combining continuous rhythmic racket movements with discrete impact events. Rhythmicity is exceptionally important in motor learning, because it underlies fundamental movements such as walking. Studies suggested that rhythmic and discrete movements are governed by different control mechanisms at different levels of the Central Nervous System. The aim of this study is to evaluate the effect of fixed/fading haptic guidance on learning to bounce a ball to a desired apex in virtual reality with varying gravity. Changing gravity changes dominance of rhythmic versus discrete control: The higher the value of gravity, the more rhythmic the task; lower values reduce the bouncing frequency and increase dwell times, eventually leading to a repetitive discrete task that requires initiation and termination, resembling target-oriented reaching. Although motor learning in the ball-bouncing task with varying gravity has been studied, the effect of haptic guidance on learning such a hybrid rhythmic-discrete motor task has not been addressed. We performed an experiment with thirty healthy subjects and found that the most effective training condition depended on the degree of rhythmicity: Haptic guidance seems to hamper learning of continuous rhythmic tasks, but it seems to promote learning for repetitive tasks that resemble discrete movements.

We present SUBTLEX-CY, a new word frequency database created from a 32-million-word corpus of Welsh television subtitles. An experiment comprising a lexical decision task examined SUBTLEX-CY frequency estimates against words with inconsistent frequencies in a much smaller Welsh corpus that is often used by researchers, the Cronfa Electroneg o’r Gymraeg (CEG), and three other Welsh word frequency databases. Words were selected that were classified as low frequency (LF) in SUBTLEX-CY and high frequency (HF) in CEG and compared with words that were classified as medium frequency (MF) in both SUBTLEX-CY and CEG. Reaction time analyses showed that HF words in CEG were responded to more slowly compared to MF words, suggesting that SUBTLEX-CY corpus provides a more reliable estimate of Welsh word frequencies. The new Welsh word frequency database that also includes part-of-speech, contextual diversity, and other lexical information is freely available for research purposes on the Open Science Framework repository at https://osf.io/9gkqm/.

Megastudies with processing efficiency measures for thousands of words allow researchers to assess the quality of the word features they are using. In this article, we analyse reading aloud and lexical decision reaction times and accuracy rates for 2,336 words to assess the influence of subjective frequency and age of acquisition on performance. Specifically, we compare newly presented word frequency measures with the existing frequency norms of Kucera and Francis (1967), HAL (Burgess & Livesay, 1998), Brysbaert and New (2009), and Zeno, Ivens, Millard, and Duvvuri (1995). We show that the use of the Kucera and Francis word frequency measure accounts for much less variance than the other word frequencies, which leaves more variance to be "explained" by familiarity ratings and age-of-acquisition ratings. We argue that subjective frequency ratings are no longer needed if researchers have good objective word frequency counts. The effect of age of acquisition remains significant and has an effect size that is of practical relevance, although it is substantially smaller than that of the first phoneme in naming and the objective word frequency in lexical decision. Thus, our results suggest that models of word processing need to utilize these recently developed frequency estimates during training or setting baseline activation levels in the lexicon.