Parallel Information Processing Research Articles

Mobile Technologies in Teaching Mathematics

Purpose. The article discusses the use of mobile technologies in teaching mathematics to engineering students. The relevance of the paper is due to the need to introduce new methods and means of teaching in connection with the increasing integration of mobile technologies into all areas of life, the interests and characteristics of the new, networked generation of students, the limited possibilities of using innovative methods and means of teaching mathematics due to the conduct of classes outside the computer lab.Materials and methods. The peculiarity of this study is that the integration of mobile technologies into the process of teaching mathematics is considered as a system of three components: technological, informational and social. The analysis of these components made it possible to obtain answers to research questions: where and how mobile devices should be used when studying mathematics; what mobile applications will provide the most effective learning of the discipline; how to organize the educational process using mobile technologies to increase interest and motivation in learning mathematics.Results. The main reasons for the lack of interest in studying mathematics and low motivation include the high level of abstraction of the discipline and the characteristics of the modern, networked generation of students. Analysis of scientific sources and our own observations made it possible to identify such characteristics as problems with understanding complex and long text, the ability to easily read from a small screen and process information in parallel, the choice of sound and images instead of text, the desire for short-term learning and instant results, and attitude towards mobile devices as vital items. Taking into account the interests and wishes of students, and relying on the didactic capabilities of mobile technologies, the paper proposes mobile applications for studying various topics of a mathematical course, assignment options that help increase motivation, develop group work skills and digital literacy. The importance and effectiveness of using social services for visualizing mathematical concepts and calculation schemes, as well as the WhatsApp messenger for instant messaging between students and receiving timely individual and group consultation from a lecturer, is shown. An important characteristic of the messenger is the presence of video communication.Conclusion. The analysis of scientific literature, the study of the requirements of employers and the interests of the modern, networked generation of students, indicated the need to integrate mobile technologies into the process of teaching mathematics. Working with different groups in terms of both the level of mathematical knowledge and the level of proficiency in mobile technologies made it possible to identify and outline ways to solve the main issues of using mobile technologies in teaching mathematics. With limited time to study mathematics, it is difficult for a lecturer to allocate additional time to present the capabilities and effectiveness of mobile applications. A solution to this issue may be to include a section “Mobile Technologies” in a course related to the study of information and communication technologies. It is noted that group work also contributes to a more effective study of the capabilities and techniques of working with mobile applications.

Predictive coding with spiking neurons and feedforward gist signaling.

Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.

Integrating human learning and reinforcement learning: A novel approach to agent training

Off-policy reinforcement learning (RL) algorithms are known for improving sample efficiency by employing prior experiences in experience replay memory. However, most existing off-policy RL algorithms are bottlenecked by the slow convergence speed and demand of a large number of interaction samples. In contrast, on-policy RL algorithms converge fast and continuously generate new samples. However, their success heavily depends on the accuracy of the generated samples. To address these challenges, a novel RL framework called HI-FER (Human-learning Inspired Frequent Experience Replay) is proposed by mimicking the process of human learning. HI-FER employs a parallelized experience replay and repetitive training framework to expedite the convergence rate of off-policy algorithms, which imitates the function of the human brain’s parallel information processing and repetitive learning. Additionally, a periodic network reset strategy and dynamic memory updating are leveraged by imitating the forgetting mechanism of humans to prevent overfitting triggered by repetitive updating on limited experiences. Extensive comparison experiments and ablation studies are performed on benchmark environments to evaluate the proposed method. The empirical results demonstrate that HI-FER outperforms the baselines in terms of sample efficiency on state-based (14% improvements) and image-based (51% improvements) tasks from DMControl. Project website and code: https://github.com/Arya87/HI-FER.

The Interaction of Linguistic and Visual Cues for the Processing of Case in Russian by Russian-German Bilinguals: An Eye Tracking Study.

Modulation of visual attention in the Visual World Paradigm relies on parallel processing of linguistic and visual information. Previous studies have argued that the human linguistic capacity includes an aspect of anticipation of upcoming material. Such anticipation can be triggered by both lexical and grammatical/morphosyntactic cues. In this study, we investigated the relationship between comprehension and prediction by testing how subtle changes in visual representations can affect the processing of grammatical case cues in Russian by Russian-German bilingual children (n = 49, age 8-13). The linguistic manipulation followed previous designs, contrasting SVO and OVS sentences, where the first NP (NP1) was marked with nominative or accusative case, respectively. Three types of visual displays were compared: (i) individual referents (potential agent/theme); (ii) pairs of referents (NP1 + potential agent/theme); and (iii) events (representing interactions between the referents). Participants were significantly more sensitive to the case manipulation when presented with events compared to the other two types of visual display. This suggests that they were able to quickly integrate the thematic role information signaled by grammatical case in the event representations. However, they were less likely to use the case information to anticipate upcoming arguments when the target pictures represented individual referents or pairs of noninteracting referents. We hypothesize that the process of argument anticipation is mediated by the activation of syntactic templates (SVO or OSV, depending on the case marking on NP1). The relatively weak anticipation effect observed may be attributed to the absence, or weak representation, of the noncanonical OVS template in the bilingual children's long-term memory.

Machine Learning Technique Reveals Intrinsic EEG Connectivity Characteristics of Patients With Mild Stroke During Cognitive Task Performing

Although convergent evidence has shown that patients with mild stroke (MS) are commonly accompanied by post-stroke cognitive and/or memory impairment, only disproportionate attention was paid compared to severe stroke. To promote post-stroke management for early intervention in MS-related cognitive impairment, a feasible and convenient method for MS detection is therefore favorable. A data-driven classification framework combined with quantitative graph theoretical analysis was introduced in this work, aiming to provide a comprehensive appreciation of MS-related brain network alterations. EEG functional connectivity (FC) was constructed from 45 patients with MS and 45 healthy participants during two cognitive tasks (i.e., visual and auditory oddball) and set as input for the classification model and graph theoretical analysis. As expected, patients showed significantly reduced behavioral performance in both tasks. Furthermore, we achieved a satisfactory classification accuracy of 88.9% with a decision fusion strategy from classification models of both tasks. The spatio-spectral characteristics of the discriminative FC revealed complex topological distributions in both tasks. Moreover, significantly decreased global efficiency was found, suggesting a MS-related disruption in parallel information processing. Overall, these results demonstrated the potential of FC as salient biomarkers for detecting MS, and extended our understanding of the underlying MS-related neural mechanisms during cognitive processing.

Acoustic metamaterials for realizing a scalable multiple phi-bit unitary transformation

The analogy between acoustic modes in nonlinear metamaterials and quantum computing platforms constituted of correlated two-level systems opens new frontiers in information science. We use an inductive procedure to demonstrate scalable initialization of and scalable unitary transformations on superpositions of states of multiple correlated logical phi-bits, classical nonlinear acoustic analog of qubits. A multiple phi-bit state representation as a complex vector in a high-dimensional, exponentially scaling Hilbert space is shown to correspond with the state of logical phi-bits represented in a low-dimensional linearly scaling physical space of an externally driven acoustic metamaterial. Manipulation of the phi-bits in the physical space enables the implementation of a non-trivial multiple phi-bit unitary transformation that scales exponentially. This scalable transformation operates in parallel on the components of the multiple phi-bit complex state vector, requiring only a single physical action on the metamaterial. This work demonstrates that acoustic metamaterials offer a viable path toward achieving massively parallel information processing capabilities that can challenge current quantum computing paradigms.

Superionic Silver Halide Solid Electrolyte: Dielectric Property and Iontronic Memtransistor Application for Bioinspired Computing

AbstractTechnology like high‐level parallel information processing and storage in the brain remains a dream to the researchers using conventional solid‐state electronics. Here, a robust thin film bilayer superionic dielectric of poly(ethylene oxide) (PEO) and rubidium silver iodide (RbAg4I5) is developed to fabricate solid‐state iontronic synaptic memtransistors, which can serve as the basic building blocks for the hardware‐implementation of neuromorphic computing. X‐ray photoelectron spectroscopy and impedance measurements precisely confirm the stoichiometric composition of RbAg4I5 and dielectric nature combining with a PEO layer, respectively. The superionic bilayer PEO/RbAg4I5 gating effectively modulates the channel conductance analogously and displays memtransistor functionality. Interestingly, the transfer curves depict a colossal hysteresis yielding negative differential transconductance of peak‐to‐valley ratio up to 5 × 103 after the gate‐controlled resistive switching. Systematic electrical characterizations reveal a variety of synaptic behaviors, including the inhibitory postsynaptic current, paired‐pulse depression, and potentiation/depression curve. Finally, an artificial neural network for off‐chip digit recognition is simulated to assess the performance of the device for the neuromorphic application and achieved a test accuracy of 95.94% on the Modified National Institute of Standards and Technology dataset.

Parallel processing of value-related information during multi-attribute decisions.

When choosing between options with multiple attributes, do we integrate all attributes into a unified measure for comparison, or does the comparison also occur at the level of each attribute, involving parallel processes that can dynamically influence each other? What happens when independent sensory features all carry information about the same decision factor, such as reward value? To investigate these questions, we asked human participants to perform a two-alternative forced choice reaching task in which the reward value of a target was indicated by two visual attributes-its brightness ("bottom-up," BU feature) and its orientation ("top-down," TD feature). If decisions always occur after the integration of both features, there should be no difference in the reaction time (RT) regardless of the attribute combinations that drove the choice. Counter to that prediction, RT distributions depended on the attribute combinations of given targets and the choices made by participants. RTs were shortest when both attributes were congruent or when the choice was based on the bottom-up feature, and longer when the attributes were in conflict (favoring opposite options). In conflict trials, nearly two-thirds of participants made faster decisions when choosing the option favored by the bottom-up feature than when choosing the top-down-favored option. We also observed mid-reach changes-of-mind in a subset of conflict trials, mostly changing from the bottom-up to the top-down-favored target. These data suggest that multi-attribute value-based decisions are better explained by a distributed process including competition among different features than by a competition based on a single, integrated estimate of value.NEW & NOTEWORTHY We show that during value-based decisions, humans do not always use all reward-related information to make their choice, but instead can "jump the gun" using partial information. In particular, when different sources of information were in conflict, early decisions were mostly based on fast bottom-up information, and sometimes followed by corrective changes-of-mind based on slower top-down information. This supports parallel decision processes among different information sources, as opposed to a single integrated "common currency."

Learning parallel and hierarchical mechanisms for edge detection.

Edge detection is one of the fundamental components of advanced computer vision tasks, and it is essential to preserve computational resources while ensuring a certain level of performance. In this paper, we propose a lightweight edge detection network called the Parallel and Hierarchical Network (PHNet), which draws inspiration from the parallel processing and hierarchical processing mechanisms of visual information in the visual cortex neurons and is implemented via a convolutional neural network (CNN). Specifically, we designed an encoding network with parallel and hierarchical processing based on the visual information transmission pathway of the "retina-LGN-V1" and meticulously modeled the receptive fields of the cells involved in the pathway. Empirical evaluation demonstrates that, despite a minimal parameter count of only 0.2 M, the proposed model achieves a remarkable ODS score of 0.781 on the BSDS500 dataset and ODS score of 0.863 on the MBDD dataset. These results underscore the efficacy of the proposed network in attaining superior edge detection performance at a low computational cost. Moreover, we believe that this study, which combines computational vision and biological vision, can provide new insights into edge detection model research.

Time-Frequency Signatures of Electronic Coherence of Colloidal CdSe Quantum Dot Dimer Assemblies Probed at Room Temperature by Two-Dimensional Electronic Spectroscopy.

Electronic coherence signatures can be directly identified in the time-frequency maps measured in two-dimensional electronic spectroscopy (2DES). Here, we demonstrate the theory and discuss the advantages of this approach via the detailed application to the fast-femtosecond beatings of a wide variety of electronic coherences in ensemble dimers of quantum dots (QDs), assembled from QDs of 3 nm in diameter, with 8% size dispersion in diameter. The observed and computed results can be consistently characterized directly in the time-frequency domain by probing the polarization in the 2DES setup. The experimental and computed time-frequency maps are found in very good agreement, and several electronic coherences are characterized at room temperature in solution, before the extensive dephasing due to the size dispersion begins. As compared to the frequency-frequency maps that are commonly used in 2DES, the time-frequency maps allow exploiting electronic coherences without additional post-processing and with fewer 2DES measurements. Towards quantum technology applications, we also report on the modeling of the time-frequency photocurrent response of these electronic coherences, which paves the way to integrating QD devices with classical architectures, thereby enhancing the quantum advantage of such technologies for parallel information processing at room temperature.

MFIRA: Multimodal Fusion Intent Recognition Algorithm for AR Chemistry Experiments

The current virtual system for secondary school experiments poses several issues, such as limited methods of operation for students and an inability of the system to comprehend the users’ operational intentions, resulting in a greater operational burden for students and hindering the goal of the experimental practice. However, many traditional multimodal fusion algorithms rely solely on individual modalities for the analysis of users’ experimental intentions, failing to fully utilize the intention information for each modality. To rectify these issues, we present a new multimodal fusion algorithm, MFIRA, which intersects and blends intention probabilities between channels by executing parallel processing of multimodal information at the intention layer. Additionally, we developed an augmented reality (AR) virtual experiment platform based on the Hololens 2, which enables students to conduct experiments using speech, gestures, and vision. Employing the MFIRA algorithm, the system captures users’ experimental intent and navigates or rectifies errors to guide students through their experiments. The experimental results indicate that the MFIRA algorithm boasts a 97.3% accuracy rate in terms of interpreting users’ experimental intent. Compared to existing experimental platforms, this system is considerably more interactive and immersive for students and is highly applicable in secondary school experimental chemistry classrooms.

Implementing Logic Gates by DNA Crystal Engineering.

DNA self-assembly computation is attractive for its potential to perform massively parallel information processing at the molecular level while at the same time maintaining its natural biocompatibility. It has been extensively studied at the individual molecule level, but not as much as ensembles in 3D. Here, the feasibility of implementing logic gates, the basic computation operations, in large ensembles: macroscopic, engineered 3D DNA crystals is demonstrated. The building blocks are the recently developed DNA double crossover-like (DXL) motifs. They can associate with each other via sticky-end cohesion. Common logic gates are realized by encoding the inputs within the sticky ends of the motifs. The outputs are demonstrated through the formation of macroscopic crystals that can be easily observed. This study points to a new direction of construction of complex 3D crystal architectures and DNA-based biosensors with easy readouts.

Menstrually-related migraine shapes the structural similarity network integration of brain.

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

Mechanisms in Emotional Information Processing in Individuals with Major Depressive Disorder: An Event-Related Potential Study of an Information Processing Model.

Individuals with major depressive disorder have a cognitive bias toward emotional stimuli, which influences the quality and speed of emotional information processing. This study aimed to understand the factors underlying this bias by identifying when it occurs during information processing using an information processing model. A total of 57 participants-19 each [ (16 (84.21%) females and 3 (15.79%) males per group)], for the first-episode MDD (FMDD), recurrent episodes MDD (RMDD), and healthy controls (HCs) - matched for sex and hand preference, completed event-related potentials (ERP) to perform psychological function and a choice response time task. Results revealed that recurrent episodes major depressive disorder participants had decreased N2b and P3b amplitudes but increased contingent negative variation during the processing of happy and neutral facial stimuli, relative to their counterparts. Both recurrent episodes major depressive disorder and first-episode major depressive disorder participants used a parallel information processing strategy for happy information at P3a latency, while healthy controls used a linear information processing strategy. The use of a parallel processing strategy among individuals with major depressive disorder may have led to impaired "happy" information processes, possibly explaining why individuals with major depressive disorder are less efficient than healthy controls. The results suggest the possibility that biases related to the processing of "happy" information among individuals with major depressive disorder may be related to a tendency for these individuals to engage in superficial decision-making. Future research is needed to examine the processes contributing to people with major depressive disorder having challenges with inhibition-facilitation of emotional stimuli.

Функциональное формирование нейроморфного резервуарного вычислительного элемента на базе мемристивного метаматериала

A neural network structure is designed based on the ability of a certain class of calculators to recombine internal resources in order to produce neuromorphic elements to solve applied problems. This approach is rooted in a composite material with controlled local conductivity to form volumetric inhomogeneities capable of responding to and influencing external electrostatic effects. Such compounds aggregate into stable clusters suitable for modelling the processes that occur during information processing in natural neuronal entities. The use of conductive transitions between substrate-formed neuromorphic clusters as a learning structure makes it possible to increase the reliability of the neural network system. Long-term, non-volatile storage of information about the elements of the training sample in variable structures is possible. The basic approach to information conversion is to manage the electrostatic influence as it passes through the layered structures formed. The response to the input is not formed by propagating the signal through conductive elements with variable conductivity, but by passing the energy impact through a limited volume of metamaterial. Thus, a massively parallel processing of information can be achieved with the implementation of a mechanism for combining the opinions of independent neural network clusters that influence the final decision. Furthermore, this method of spreading effects in such an environment greatly simplifies the process of adding elements to the neural network. The lack of direct electrical interconnection facilitates the easy addition of new computational elements without significant rearrangement of the conductive media. Networks of this type are capable of significant growth without loss of experience. The input conversion process using modified delta coding prevents premature wear and tear on reconfigurable network elements. The manner in which information is presented and the manner in which neural network computing is organised enabled the creation of limited autonomous oscillations within the volume of the calculator to maintain circulating memory and the ability to gradually accumulate network experience for its subsequent recording in configurable elements. The identified features resulted in the application of this kind of calculators in the task of developing radio frequency management plans for the organisation of stable communication in a complex electromagnetic environment.

수직구조 3단자 인공 시냅스 소자

Recently, synaptic devices have the advantages of being able to process information in parallel. However, nondestructive weight control is limited in 2-terminal synaptic devices because reading and writing are conducted in a common electrode. Hence, 3-terminal synaptic devices with separately reading and writing processes are currently emerging for not only nondestructive weight control without data loss but also a very short vertical channel length. Since the length of the vertical channel is determined as the thickness of the active layer, nanometer range channel length can be achieved. According to the nano-scaled channel length, vertical 3-terminal artificial device can be operated with low voltage and energy consumption. In this review paper, vertical 3-terminal artificial synaptic devices were classified by electric field transmission or ion migration into organic semiconductor. According to a recent study, vertical 3-terminal artificial synapse was able to simulate a biological synapse even with a low driving voltage of up to 10 μV and a current density of MAcm−2 level. This review article gives an overview of vertical 3-terminal artificial synaptic devices, and suggest a practical strategy to achieve the massive data processing with high speed and low power consumption.

Proton-Gated Synaptic Transistors, Based on an Electron-Beam Patterned Nafion Electrolyte.

Neuromorphic processors using artificial neural networks are the center of attention for energy-efficient analog computing. Artificial synapses act as building blocks in such neural networks for parallel information processing and data storage. Herein we describe the fabrication of a proton-gated synaptic transistor using a Nafion electrolyte thin film, which is patterned by electron-beam lithography (EBL). The device has an active channel of indium-zinc-oxide (IZO) between the source and drain electrodes, which shows Ohmic behavior with a conductance level on the order of 100 μS. Under voltage applications to the gate electrode, the channel conductance is changed due to the injection and extraction of protons between the IZO channel and the Nafion electrolyte, emulating various synaptic functions with short-term and long-term plasticity. When positive (negative) gate voltage pulses are consecutively applied, the device exhibits long-term potentiation (depression) at the same number of steps as the number of input pulses. Based on these characteristics, an artificial neural network using this transistor shows ∼84% image recognition accuracy for handwritten digits. The subject transistor also successfully mimics paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. Finally, dynamical pattern image memorization is demonstrated in a 5 × 5 array of these synaptic transistors. The results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.

Minimum complexity integrated photonic architecture for delay-based reservoir computing.

Reservoir computing is an analog bio-inspired computation scheme for efficiently processing time-dependent signals, the photonic implementations of which promise a combination of massive parallel information processing, low power consumption, and high-speed operation. However, most of these implementations, especially for the case of time-delay reservoir computing, require extensive multi-dimensional parameter optimization to find the optimal combination of parameters for a given task. We propose a novel, largely passive integrated photonic TDRC scheme based on an asymmetric Mach-Zehnder interferometer in a self-feedback configuration, where the nonlinearity is provided by the photodetector, and with only one tunable parameter in the form of a phase shifting element that, as a result of our configuration, allows also to tune the feedback strength, consequently tuning the memory capacity in a lossless manner. Through numerical simulations, we show that the proposed scheme achieves good performance -when compared to other integrated photonic architectures- on the temporal bitwise XOR task and various time series prediction tasks, while greatly reducing hardware and operational complexity.

Cryptographic Algorithm Based on Hybrid One-Dimensional Cellular Automata

The theory and application of cellular automata (CA) for a stream cipher-based encryption principle are presented in this study. Certain fundamental transformations are developed based on CA theory regarding decentralized computation for modeling different system’s behavior. The changes governing state transitions rely on simple evolution rules, which can easily be translated into functions using logic operators. A class of linear hybrid cellular automata (LHCA) based on rules 90 and 150 is used to implement these functions. Symmetric key systems theory is the foundation of the suggested algorithm. The algorithm functions use the proprieties provided by the LHCA evolution in order to convert plain text into cipher text and vice versa, in each case starting from the same initial state of the system and performing the same number of steps for each operation. Cellular automata’s parallel information processing property, in addition to their regular and dynamical structure, makes hardware implementation of such schemes best suited for VLSI implementation. Testing of the proposed algorithm was performed by developing both software and hardware solutions. Hardware implementation of the presented cryptosystem was developed using VHDL hardware description language and a FPGA device (XILINX Spartan3E XC3S500E). Design and software simulations have been carried out using the C# programming language.

Improved CoCoSo Method Based on Frank Softmax Aggregation Operators for T-Spherical Fuzzy Multiple Attribute Group Decision-Making

In this article, a novel CoCoSo (Combined compromise solution) method based on Frank operational laws and softmax function is investigated to handle multiple attribute group decision-making problems for T-spherical fuzzy sets. We extend Frank operations in T-spherical fuzzy environment and develop a series of aggregation operators, including T-spherical fuzzy Frank softmax (T-SFFS) average and geometric operators, and their weighted forms, i.e., T-SFFS weighted averaging (T-SFFSWA) and T-SFFS weighted geometric (T-SFFSWG) operators. Some of their basic properties and particular cases are discussed. Meanwhile, the monotonicity of proposed operators is also analyzed, and it is discussed that how they indicate the decision-makers’ optimistic and pessimistic decision attitudes with risk preference. Furthermore, a novel CoCoSo method based on Hamming distance measure is proposed, which considers both decision-maker’s decision attitude and attribute priority, and a multiple attribute group decision-making framework with two independent and parallel T-spherical fuzzy information processing processes are designed. Lastly, a real case of spent power battery recycling technology (SPBRT) selection is presented to show the practicability of the proposed method. Also sensitivity and comparative analyses are carried out to prove the reliability, effectiveness, and superiority of our proposed method.