Rate Of Information Processing Research Articles

Novelty Seeking Differences in Temporal Dynamics for Novelty and Appropriateness Processing of Creative Information: An ERP Investigation

ABSTRACTNovelty seeking has been found to affect creative performance, but its impact on the temporal dynamics of creative information processing remains unclear. Creative information is identified by two key indicators—novelty and appropriateness. To explore the effect of novelty seeking on the temporal processing of novelty and appropriateness, a revised alternative uses task (AUT) was conducted with 29 high novelty‐seeking (HNS) and 31 low novelty‐seeking (LNS) individuals. Event‐related potentials (ERPs) and spontaneous blink rate (EBR) were used to measure temporal dynamics and infer physiological mechanisms. (1) For novelty processing, HNS individuals performed quicker information processing (shorter N1/P2 latency) and had a greater capacity to recognize semantic distance (higher P600 peak amplitude). (2) For appropriate processing, HNS individuals also had a faster information processing rate (shorter N1/P2 latency). (3) HNS individuals had higher baseline EBR and showed enhancements in cognitive speed based on real‐time EBR. In contrast, the LNS group did not show the same improvement even with increased attention allocation. These findings expand the application of the Novelty Seeking Model (NGM) in creative information processing. Additionally, the results of EBR suggest that dopamine might be the critical physiological mechanism through which novelty‐seeking influences creative information processing.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature.

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200°C. Based on the advanced functions under light stimulus, the constructed 5×3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200°C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

Frequency encoded tristate Pauli X-gate using SOA assisted photonic band gap crystal

The quantum optical tristate Pauli-X gate can broadly be used due to its very fast speed of operation up to THz order, very fast information processing rate, and a wide domain of application. This Pauli X-gate is also important as it can undergo inversion operation. In this proposed scheme, the quantum tristate Pauli-X logic gate has been constructed by a closely packed square lattice of 2D air photonic band gap crystal (PhCs) composed of GaAsInP-dopped rods. The photonic band gap crystal-based tristate Pauli-X gate is performed also as an all-optical reversible quantum logic gate which has the character of low-powered output, very fast speed and a densely packed design structure. This photonic crystal-based structure is also verified by simulation experiment that this proposed scheme performs as the path of two input signals are cross-exchanged to one another at the output, retaining the path of the other input signal same. In this paper, the frequency encoding technique is used in all the designs to establish the logic of all-optical tristate Pauli X-gate towards the need of obtaining a three-input-three-output inversion system. The frequency encoding technique and the photonic crystal-based three Semiconductor Optical Amplifiers (pc-SOAs) are used here in all the schemes to establish the logic of the tristate Pauli-X gate scheme. Tristate Pauli-X gates have been designed and analyzed by Finite-Difference Time-Domain (FDTD) and Plane Wave Expansion (PWE) techniques. The Plane Wave Expansion (PWE) technique is used here in each system for determining the frequency range of band gap structure of transverse electric (TE) mode in the units of μm-1. All of these devices perform within the frequency range 0.629818≤ω2πc≤0.888037 and 1.17551≤ω2πc ≤ 1.34921 μm-1. The wafer dimension of each photonic band gap crystal-based device is 19.20 μm (length) × 16.20 μm (width) providing a very fast response time and very fast information handling (bit rate) capacity, which is found good for these THz Photonic crystal devices. Simulation results show that the proposed gate provides a response period of 0.291 ps and a bit rate of 3.44 Tbps, operating with a low input power of 3.45 mW. The reversible gate exhibits a response period of 0.289 ps and a bit rate of 3.46 Tbps, operating also with a low input power of 2.70 mW. Additionally, the alternative Pauli-X gate demonstrates a response time of 0.291 ps and a bit rate of 3.44 Tbps, operating with a low input power of 2.83 mW.

Resting-State Alpha Activity in the Frontal and Occipital Lobes and Assessment of Cognitive Impairment in Depression Patients.

Major depressive disorder (MDD) becomes one of the psychiatric disorders characteristic of a combination of cognitive, emotional, and somatic symptoms. Additionally, cognitive impairment has the most significant impact on functional results. However, the evaluation of cognitive level is still based on various subjective questionnaires as there is no objective standard assessment yet. This research focuses on resting-state alpha activity to identify cognition in MDD patients using electroencephalography (EEG) signals. Ninety-two subjects were recruited: 44 patients with MDD and 48 healthy individuals as controls. Functional outcome and cognition were assessed using standardized instruments, and the EEG resting state signal of open and closed eyes was recorded. The comparison and correlation of cognitive levels with alpha power in the bilateral frontal region, bilateral central region, bilateral occipital region, and middle line was evaluated. The relative alpha power in MDD group was significantly lower than that in the control group (P < 0.05). Through correlation analysis, it was shown that the bilateral frontal and occipital alpha power of MDD patients in the closed-eyes state was positively correlated with information processing rate, verbal learning, working memory, and attention retention. The alpha power of the bilateral frontal region in the open-eyes state was positively correlated with information processing rate, working memory, and attention retention (P < 0.05). The research indicates that the changes in frontal and occipital alpha activities may be a promising neurophysiological indicator of cognitive level to diagnose and treat response prediction.

Impact of Business Intelligence on Company Performance: A System Dynamics Approach

Abstract Research background Businesses struggle with operational optimisation and seek a solution by implementing Business Intelligence (BI) to boost sales. But, due to the lack of research that use actual data from real-world situations; the impetus of this research is to exploit BI parameters to enhance company performance. Purpose This research aims to develop a System Dynamics (SD) based model to assess whether a Japanese company which manufactures Printed Circuit Boards (PCB) should invest in BI to improve its operations based on the rate of information processing, thereby leading to increased financial performance. Research methodology The authors requested financial statements for three years (2019 to 2021) from the management of the PCB company, followed by validation based on subject experts’ assessments. The model was developed and simulated step-by-step with consideration of the SD approach involving problem identification, model prototyping, trouble shooting and error analysis. Results If BI was used to process data at a rate of 40%, the endogenous factors considered in this study would increase the company’s net profit and accumulated earnings by 25.77% and 48.28%, respectively. Novelty The research is unique in the sense that the model was developed based on inter-relationships between the variables, and the data is based on a real-life situation. Furthermore, the methodology could be applied with the necessary modifications to industries such as service, media, and education.

A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.

Traditional measurement models assume that all item responses correlate with each other only through their underlying latent variables. This conditional independence assumption has been extended in joint models of responses and response times (RTs), implying that an item has the same item characteristics fors all respondents regardless of levels of latent ability/trait and speed. However, previous studies have shown that this assumption is violated in various types of tests and questionnaires and there are substantial interactions between respondents and items that cannot be captured by person- and item-effect parameters in psychometric models with the conditional independence assumption. To study the existence and potential cognitive sources of conditional dependence and utilize it to extract diagnostic information for respondents and items, we propose a diffusion item response theory model integrated with the latent space of variations in information processing rate of within-individual measurement processes. Respondents and items are mapped onto the latent space, and their distances represent conditional dependence and unexplained interactions. We provide three empirical applications to illustrate (1) how to use an estimated latent space to inform conditional dependence and its relation to person and item measures, (2) how to derive diagnostic feedback personalized for respondents, and (3) how to validate estimated results with an external measure. We also provide a simulation study to support that the proposed approach can accurately recover its parameters and detect conditional dependence underlying data.

Artificial Intelligence and Creative Thinking, the Future of Idea Generation

Artificial intelligence has been the most successful modern technology created in recent years. Its development began in the 1960s and was based on simulating the human brain. The scientific community's interest has focused on developing machine-learning neural networks and their potential applications in music, art, architecture, and other fields. AI is increasingly capable of generating creative ideas and assisting humans in decision-making processes. With advanced algorithms, machine learning, and natural language processing, AI has the potential to revolutionize creative thinking. In this article, there is discussed whether AI can generate creative ideas and how expressive and creative these ideas can be. AI technologies can be used in creative industries such as content creation, information analysis, information retrieval, and processing. The purpose of new technologies is to automate specific processes, simplify them, maintain accuracy, and reduce production costs. In some cases, they enable us to complete tasks or solve problems that were previously impossible. AI is a set of codes, methods, algorithms, and other data that allow a computer system to imitate human behavior and make human-like decisions. AI systems widely use data compression and storage capabilities, which enable them to process information by observing human actions and behaviors. To generate creative ideas, AI requires a different level of training and expertise, a faster rate of information processing, and high-quality analysis. Advances in AI largely depend on data relevance. Human creative thinking often uses imagination to generate original ideas that may not follow general rules. Creative tasks usually require originality of thought and innovation, which can be challenging to replicate with AI systems. Artificial intelligence has shown mixed results in both original creative and automated work processes. However, it is increasingly being associated with human creativity and original thinking. Over the decades, numerous studies have been conducted on the use of artificial intelligence in the creative field. Keywords: Creative idea, digital technologies, artificial intelligence, machine learning language (NLP).

Development of methods for managing energy consumption and energy efficiency in a common system

&lt;span lang="EN-US"&gt;The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-to-machine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.&lt;/span&gt;

Improving the processing rate of VCSEL-based reservoir computing through adopting variable polarization information injection

A novel information injection method is proposed for improving the information processing rate in reservoir computing (RC) systems based on vertical-cavity surface-emitting lasers (VCSELs), and its effectiveness is numerically verified via Santa-Fe chaotic time series prediction task and waveform recognition task. For VCSEL-based RC systems, conventional information injection (CII) method is usually adopted, namely the polarization direction of the injection light loaded by the information is along either X-polarization component (X-PC) or Y-polarization component (Y-PC) of VCSEL. In this work, the polarization direction of the injection light loaded by the information is set at 45-degree deviated from the X-PC of the VCSEL, which is named as variable polarization information injection (VPII) method. By utilizing the VCSEL-based RC to deal with two different scenarios of single task and two tasks in parallel, the numerical results demonstrate that after adopting VPII to replace CII, the processing rate can be doubled while the system performance maintains the same level.

How speed impacts threat assessment in lethal force decisions

Despite the importance of being both fast and accurate in lethal force decisions, there is little empirical evidence to identify how speed impacts threat-related decisions and perception. Two experiments used speeded and unspeeded manipulations to determine how the speed imperative impacted threat assessments. Experiment 1 used drift diffusion modeling to quantify decision parameters, including rate of information processing, decision threshold, bias, and non-decisional processes. Speeded conditions reduced the information threshold needed to make decisions and shortened non-decisional processes, yet this manipulation had no impact on the rate of information processing or starting bias. Experiment 2 explored perceptual differences in threat assessment. Participants confidently made threat assessments despite only 30 ms exposure to stimuli with little impact on their subjective threat ratings based on exposure duration. Taken together, these results document the influence of speed on decision-making parameters of threat assessments while demonstrating little impact on threat perception.

Nonlinear Computational Edge Detection Metalens

AbstractOptical image processing and computing systems provide supreme information processing rates by utilizing parallel optical architectures. Existing optical analog processing techniques require multiple devices for projecting images and executing computations. In addition, those devices are typically limited to linear operations due to the time‐invariant optical responses of the building materials. In this work, a single metalens with an illumination intensity dependent coherent transfer function (CTF) is proposed and experimentally demonstrated, which performs varying computed imaging without requiring any additional optical components. The metalens consisting of nanoantenna structures with a static geometric phase and a nonlinear metallic quantum well layer offering an intensity‐dependent dynamic phase results in a continuously tunable CTF. The approach allows for a weighted summation of two designed functions based on the metalens design, which potentially enables all optical computations of complex functions. The nonlinear metalens may lead to important applications in optical neural networks and parallel analog computing.

Optical neuromorphic computing based on a large-scale laterally coupled laser array

We propose a neuromorphic photonic time-delayed reservoir computing (RC) scheme based on a large-scale laterally coupled laser array with optical feedback and injection. Here, the feasibility of this RC system for dealing with a complex task-image recognition is proved numerically. By the offline pre-processing using randomly initialized convolutional neural network, two common datasets in the visual domain, i.e., MNIST and Fashion-MNIST, are employed to evaluate the performance of the proposed RC. After enlarging the laser array size and optimizing the parameters, our RC system achieves identification accuracy of 98.2% for the MNIST dataset and of 89.9% for the Fashion-MNIST dataset. Since the feedback delay time and information mask period of each laser in the array are coincident and maintained at 0.2 ns, the information processing rate of the RC system reaches 5 Gbps. Besides, the potential situations of parameter mismatch are also analyzed.

High-Speed Reservoir Computing Based on Circular-Side Hexagonal Resonator Microlaser with Optical Feedback

In the current environment of the explosive growth in the amount of information, the demand for efficient information-processing methods has become increasingly urgent. We propose and numerically investigate a delay-based high-speed reservoir computing (RC) using a circular-side hexagonal resonator (CSHR) microlaser with optical feedback and injection. In this RC system, a smaller time interval can be obtained between virtual nodes, and a higher information processing rate (Rinf) can also be achieved, due to the ultra-short photon lifetime and wide bandwidth of the CSHR microlaser. The performance of the RC system was tested with three benchmark tasks (Santa-Fe chaotic time series prediction task, the 10th order Nonlinear Auto Regressive Moving Average task and Nonlinear channel equalization task). The results show that the system achieves high-accuracy prediction, even with a small number of virtual nodes (25), and is more feasible, with lower requirements for arbitrary waveform generators at the same rate. Significantly, at the high rate of 10 Gbps, low error predictions can be achieved over a large parameter space (e.g., frequency detuning in the interval 80 GHz, injected strength in the range of 0.9 variation and 2% range for feedback strength). Interestingly, it has the potential to achieve Rinf of 25 Gbps under technical advancements. Additionally, its shorter external cavity length and cubic micron scale size make it an excellent choice for large-scale photonic integration reservoir computing.

Implementation of the approaches of the "Lean region" in the development of the system for assessing the quality of education of the Khanty-Mansi autonomous okrug – Ugra

Introduction. Leather-made technologies are becoming increasingly distributed not only in the production sector of Russian enterprises, but also in the system of state and municipal management, proving their effectiveness and effectiveness. The article is attempted to summarize the idea of ​​the implementation of leaning production in the regional management system and the rationale for the integrated system of "leaning" management on the example of an education quality assessment system of the Khanty-Mansi Autonomous okrug Ugra.&#x0D; The purpose: is to analyze the implementation of the concept of a "lean region" in the quality management system of education of the of the Khanty-Mansi Autonomous okrug Ugra.&#x0D; Methods: The article uses such general scientific research methods as a generalization, analysis and synthesis, concretization.&#x0D; The results are determined by the main directions for the implementation of the concept of "leaning region" in the system of government quality management of the provision of services of the social sphere. The regional methodology for a comprehensive assessment of the quality of educational results of students of the Khanty-Mansi Autonomous okrug Ugra is analyzed from the position of the implementation of the proceeding principles identified at theoretical stage.&#x0D; The conclusions by the author substantiates the need for a comprehensive approach to the implementation of leaning technologies into the system of social services quality management, including such components such as an increase in the rate of information processing, eliminating a number of "typical" losses, management on the principles of "continuous improvement systems", as well as the development of "foresight systems.

Darwinian standard model of physics obtains general relativity

A Darwinian perspective of the standard model of physics (SMP) quantum fields (QFs) is proposed, called the physics-cell (PC) approach. Because Darwinian evolution is not deterministic, the PC approach allows for the violation of the charge-parity-time symmetry. In the PC approach, the SMP laws are contained in the PCs which receive and emit QFs through the PCs’ outer surface which is necessarily constrained by Bekenstein’s surface-information limit. The establishment of gauge invariance-compatible communication protocol-agreements between the PCs obtains an average correlation of QFs that is equivalent to an asymmetric metric tensor with the symmetric component being equivalent to general relativity and the anti-symmetric component being very small but still large enough to allow for enough ex-nihilo mass-creation to explain dark matter. Based on experimental data, the PC minimum-size is 1.5cdot 10^{-31} m which is similar to the scale at which the grand unified theory force convergence occurs. Plus, the cosmological constant energy density is equal to the energy density of the discreteness-correction QF alterations that constitute the dark energy and are caused by the finiteness of the PC time-step which equals 5.0cdot 10^{-40} s, hence obtaining a PC maximum information processing rate of 6.6cdot 10^{47} qubit/s. Moreover, the PC approach obtains that the minimum mass for black holes is 2.1 cdot 10^{9} larger than the maximum mass for which the no-hiding theorem can apply and that the maximum capacity for quantum computers is about 29.0 cdot 10^{12} qubit.

Optimization of photonic reservoir computing using short internal cavity laser

With the advent of the high-speed information age and the explosive growth of the information, higher requirements have been placed on the information processing speed. In recent years, the delay-based reservoir computing (RC) systems have been extensively investigated. Meanwhile, the information processing rate is improved mainly around the replacement of nonlinear nodes in the system. Nevertheless, as the most commonly used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB turns more mature, so that the size of DFB can be fabricated in a range of 100 μm–1 mm when it still generates laser, and the photon lifetime of the laser will also change. The shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process the information at a higher rate in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter into each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. According to this, we study the relationship between the internal cavity length (120 μm–900 μm) and the information processing rate of the RC system. In addition, the influences of different internal cavity lengths on the parameter space of the RC system are analyzed. The results show that when the internal cavity length is in a range from 120 μm to 171 μm, the system can achieve 20-Gbps low-error information processing. It is worth noting that when the internal cavity length decreases from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (NMSE) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with NMSE no more than 0.1 is improved by nearly 40% for the 10&lt;sup&gt;th&lt;/sup&gt; order nonlinear auto-regressive moving average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve a high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.

Work sharing as a metric and productivity indicator for administrative workflows

A mathematical method was developed to generate an indicator on the productivity of administrative complex workflows. The indicator can roughly and steadily reveal the definition of productivity as a state of endosectoral (among internal sector agents) and exosectoral (among external sector agents) administrative services sharing. The indicator can be used by public and private managers to measure human resource efficiency in proportion to work requests/inputs of all administrative services occurring in a given workflow. Defining administrative workflow events as a nonlinear dynamics that assume a random ordered or disordered growth rate of information processing, a method has been proposed for large-scale administrative systems defined as structures of hybrid system variables (continuous or discrete), iterated and composed of fixed-point attracted events at which for all possible metric spaces solutions, the modeling of variables from Lyapunov exponential stability point of view allows the projection of system performance to be oriented, that is in other words, the relationship between the number of agents and the number of administrative services within an administrative workflow environment. This definition differs from traditional or differentiated key performance indicators (KPI), such as working hours, medical certificate, per capita productivity, among others and the proposed methodology is only suitable for large-scale administrative activities that have a wide range of activities as well as the number of agents that perform them. For system management purposes, it is possible to view the possibility of administrating system discretization, computerization and monitoring in order to be able to predict and validate the exponential function as a valid indicator of nonlinear systems where the results were defined by the amount of work sharing effect.

High-speed photonic reservoir computer based on a delayed Fano laser under electrical modulation.

We propose and numerically demonstrate a high-speed photonic reservoir computing (RC) system using a compact Fano laser (FL) with optical feedback under electrical modulation. Benefiting from its insensitivity to external feedback, an FL has a wider dynamic steady-state region compared with a conventional Fabry-Perot laser, which significantly extends the ranges of desirable RC implementation. Interestingly, we observe two separate regions of good RC performances corresponding to two scenarios of the dynamic steady state of the FL, respectively. Moreover, the robust RC performance versus the feedback phase can be achieved in one of the steady-state regions, where the laser is not destabilized for lower external reflectivity. Owing to the ultra-short photon lifetime in the FL, the information processing rate of our proposed RC system may reach 10 Gbps. More importantly, as a specific type of microscopic laser, the FL offers potential applications to RC-based integrated neuromorphic photonic systems.

Beyond information rate, the capacity of cognitive control predicts response criteria in perceptual decision-making

Recent studies indicate that higher capacity of cognitive control (CCC) represents higher processing efficiency (i.e., high accuracy with fast speed). However, the speed-accuracy tradeoff (SAT) exists ubiquitously in decision-making, and little is known about whether and how the CCC is associated with SAT and whether the CCC-SAT relationship would be affected by changes in information entropy. In this study, fifty-nine college students performed a majority function task in which accuracy and response speed were equally emphasized. A Bayesian-based hierarchical drift diffusion modeling method was used to estimate three parameters of boundary separation, drift rate, and nondecision time for each participant in this task. In addition, the CCC of each participant was estimated. The results showed that the CCC was positively correlated with the SAT represented by jointly increasing accuracy and reaction time (RT), which was modulated by the change in task-relevant information entropy. Multiple mediation analyses indicated that drift rate served as the key mediator in the positive CCC-accuracy relationship while boundary separation played the major mediating role in the positive CCC-RT relationship. These findings suggest that the CCC reflects not only the rate of information processing but also decision strategies for achieving current goals.

The Age-Related Changes in Speed of Visual Perception, Visual Verbal and Visuomotor Performance, and Nonverbal Intelligence During Early School Years.

Speed of sensory information processing has long been recognized as an important characteristic of global intelligence, though few studies have concurrently investigated the contribution of different types of information processing to nonverbal IQ in children, nor looked at whether chronological age vs. months of early schooling plays a larger role. Thus, this study investigated the speed of visual information processing in three tasks including a simple visual inspection time (IT) task, a visual-verbal processing task using Rapid Automatic Naming (RAN) of objects as an accepted preschool predictor of reading, and a visuomotor processing task using a game-like iPad application, (the “SLURP” task) that requires writing like skills, in association with nonverbal IQ (Raven’s Coloured Progressive Matrices) in children (n = 100) aged 5–7 years old. Our results indicate that the rate and accuracy of information processing for all three tasks develop with age, but that only RAN and SLURP rates show significant improvement with years of schooling. RAN and SLURP also correlated significantly with nonverbal IQ scores, but not with IT. Regression analyses demonstrate that months of formal schooling provide additional contributions to the speed of dual-task visual-verbal (RAN) and visuomotor performance and Raven’s scores supporting the domain-specific hypothesis of processing speed development for specific skills as they contribute to global measures such as nonverbal IQ. Finally, RAN and SLURP are likely to be useful measures for the early identification of young children with lower intelligence and potentially poor reading.