Human Processes Research Articles

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms, and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Video kills the radio star: Copyright and the human versus artificial creativity war

AbstractThis article contributes to the dynamic debate surrounding the intersection of artificial intelligence (AI) and copyright law, offering a fresh perspective that builds upon interdisciplinary analyses. Focusing on the cognitive processes underpinning creativity in both human and AI contexts, the study draws a detailed parallel between Vincent Van Gogh's iconic “Starry Night” and its AI‐generated counterpart generated through DeepDream technology. Central to the investigation is the application of psychological and neuroscientific theories to understand and compare the creative processes in humans and AI. Based on such exercise, the article first examines whether art generated with AI, devoid of human emotions and motivations yet capable of mimicking human creative cognitive processes, qualifies for copyright protection. The analysis suggests that the similarities between human and AI creativity, particularly in their cognitive structuring, could render the work “original” according to different jurisdictional standards and interpretation of copyright law. Second, the article investigates whether AI infringes copyright if protected material is used for its training and processing. This question becomes particularly relevant in light of recent legal actions against AI‐artwork generators in California, which raise issues of potential infringement by AI using latent diffusion techniques on existing artworks. The discussion provides an original perspective that can advance the ongoing debate on the use of copyrighted material for AI training. The paper aims to contribute to the ongoing debate about AI and copyright by challenging the traditional human‐centric view of authorship in copyright law. The article argues for a nuanced understanding that acknowledges the complex nature of creativity, transcending the binary division between human and artificial sources. This approach is critical in redefining legal frameworks, ensuring they are adaptive to the evolving landscape of AI capabilities. At the same time, the article addresses the implications of AI drawing inspiration from existing art, recognizing the need to balance different stakeholders' interests when drawing policy considerations. Ultimately, the goal is to provide a layered perspective that not only deepens the legal discourse but also respects and fosters the coexistence and mutual advancement of both human and artificial creativity in the digital age, in line with the purpose of copyright.

Insights into the relationship between eye movements and personality traits in restricted visual fields

Previous studies have suggested behavioral patterns, such as visual attention and eye movements, relate to individual personality traits. However, these studies mainly focused on free visual tasks, and the impact of visual field restriction remains inadequately understood. The primary objective of this study is to elucidate the patterns of conscious eye movements induced by visual field restriction and to examine how these patterns relate to individual personality traits. Building on previous research, we aim to gain new insights through two behavioral experiments, unraveling the intricate relationship between visual behaviors and individual personality traits. As a result, both Experiment 1 and Experiment 2 revealed differences in eye movements during free observation and visual field restriction. Particularly, simulation results based on the analyzed data showed clear distinctions in eye movements between free observation and visual field restriction conditions. This suggests that eye movements during free observation involve a mixture of conscious and unconscious eye movements. Furthermore, we observed significant correlations between conscious eye movements and personality traits, with more pronounced effects in the visual field restriction condition used in Experiment 2 compared to Experiment 1. These analytical findings provide a novel perspective on human cognitive processes through visual perception.

Solving Table-based fact verification with mixture of logical reasoning

Table-based fact verification (TFV) is a binary classification task that requires understanding and reasoning about both table and text. This task poses many challenges, such as table parsing, text comprehension, and numerical reasoning. However, existing methods tend to depend solely on pre-trained models for tables, treating all types of reasoning equally and disregarding the importance of identifying logic types in inference process. In this regard, we propose MoETFV, an efficient and explanatory approach to solving TFV, which is based on a Mixture-of-Experts (MoE) framework. This approach can detect the underlying logic types of statements and leverage multiple independent experts to emulate diverse logical reasoning. It consists of one shared expert for general semantic understanding and several specific experts with distinct responsibilities for different logical inferences. Moreover, the practical applications of the MoE method in TFV are thoroughly investigated. This model doesn’t necessitate any table pre-trained models, and aligns closely with human cognitive processes in addressing such issues. Experimental results demonstrate the innovation and feasibility of the proposed approach.

What Can Language Models Tell Us About Human Cognition?

Language models are a rapidly developing field of artificial intelligence with enormous potential to improve our understanding of human cognition. However, many popular language models are cognitively implausible on multiple fronts. For language models to offer plausible insights into human cognitive processing, they should implement a transparent and cognitively plausible learning mechanism, train on a quantity of text that is achievable in a human’s lifetime of language exposure, and not assume to represent all of word meaning. When care is taken to create plausible language models within these constraints, they can be a powerful tool in uncovering the nature and scope of how language shapes semantic knowledge. The distributional relationships between words, which humans represent in memory as linguistic distributional knowledge, allow people to represent and process semantic information flexibly, robustly, and efficiently.

Building a Supportive Learning Environment: The Role of Psychology in Increasing Student Motivation and Engagement

A conducive and supportive learning environment is essential in increasing student motivation and engagement. Psychology, as a science that studies human behavior and mental processes, has an important role in understanding and creating an effective learning environment. The aim of this research is to understand how psychology can play a role in building a supportive learning environment, as well as to explore effective strategies in increasing student motivation and engagement in it. In this study, research was conducted using a qualitative method with a literature study approach as a data collection technique. After the data is collected, data analysis is carried out through three stages, namely data reduction, data presentation, and conclusion drawing. The findings of this research show that psychology plays an important role in understanding students' needs and characteristics, building positive relationships between teachers and students, and creating a conducive learning atmosphere. Strategies for building a supportive learning environment based on psychological understanding include providing meaningful and relevant learning, giving students opportunities to learn actively and independently, providing positive and constructive feedback, building positive relationships between teachers and students and creating a learning atmosphere. conducive and safe

Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual–semantic gap, steering the model towards enhanced performance in Zero-Shot Instance Retrieval. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. This work not only benchmarks the cognition alignment of AI but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.

Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development

This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.

Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development

This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.

Design of Music Emotion Analysis and Creation Aid System Based on Machine Learning

In human-centric contexts, visual vision plays an increasingly important role in decision making, learning, communication, and situation awareness. Music is a language that conveys emotion to all creatures, including plants and animals. These emotional characteristics are included in the emotion found in music, along with other feelings conveyed via melodies, rhythms, and composer-highlighted moments. Human interaction, communication, decision-making, and cognitive processes are all heavily influenced by emotion. However, Subjective music emotion classification is made more difficult by pure machine classification due to its objectivity; additionally, emotion classification has not reached the same level of accuracy as style classification due to the complex and poorly understood mechanisms underlying how humans perceive music and generate emotions. This manuscript proposes a Multi-Scale Adaptive Graph Neural Network (MSAGNN)optimized with the Remora Optimization Algorithm (ROA) for predicting music emotion MEA-MSAGNN-ROA. Initially data is taken from Music Video Emotion (MVE) dataset Afterward the data is fed to Attribute-based Neural Collaborative Filtering (AbNCF) based pre-processing process. The outcome from the pre-processing data is transferred to the MSAGNN. The music emotions are successfully classified by using MSAGNN are exciting, fear, sad and relaxation. The ROA is used to optimize the weight parameter of MSAGNN. The proposed MEA-MSAGNN-ROA is applied in Python working platform. Performance parameters, like accuracy, precision, sensitivity, F1-score, ROC and recall analyzed to compute proposed approach. The proposed MEA-MSAGNN-ROA approach yields improved results in terms of accuracy (22.46%, 38.58%, 21.74%), sensitivity (21.97%, 33.88%, 25.52%), and precision. reduced computation times of 21.86%, 36.76%, 28.95%, and 81.46%, 95.97%, 86.77%.The proposed MEA-MSAGNN-ROA method is compared with the existing methods such as MEA-CNN, MEA-BPNN, and MEA-FNN, respectively. From the result it is concludes that the proposed MEA-MSAGNN-ROA method based accuracy is higher than the existing methods.

478 Single-Neuron and Columnar Computations in the Human Prefrontal Cortex Underlying Transformations From Conscious Visual Perception to Speech Production

INTRODUCTION: The basic functional unit of the mammalian neocortex is the cortical column. It consists of a vast diversity of circuit elements and interconnected layers supporting a range of computations for cognitive functions. While much progress has been made in our understanding of its functional architecture in animal models, its role in complex, uniquely human cognitive processes remain unknown. METHODS: We utilized a new columnar recording approach in humans participating of cognitive tasks, while undergoing awake neurosurgical procedures. The novelty of this approach hinged on utilizing state-of-the-art Neuropixels probes for our recordings, a silicon-based electrophysiology recording electrodes with high channel count and recording site density. This technology allows us to measure neural activity with single cell resolution throughout the neocortical column and its layers, and at a scale that is far beyond the capabilities of current clinically-approved devices. The participants performed a naturalistic visual priming-based sentence production task that provided them with pictorial representations of events that had to be articulated in specific form and order. RESULTS: We observed distinct spatiotemporal activity dynamics throughout the cortical column in the language-dominant prefrontal cortex that encoded a preparatory mode for speech generation, as well as visual perception and sentence construction processing stages during natural speech. Furthermore, we observed the emergence of this columnar activity pattern with optimal visual inspection of presented images (by high resolution eye and pupil tracking) and associated improved performance during the task. CONCLUSIONS: These results highlight a functional organization of the cortical column in the human prefrontal cortex subserving sensory-motor transformations for visual conscious perception and speech production, with profound implications for future brain machine interfaces that aim to restore complex human cogntive functions.

Balancing Act: Exploring the Interplay Between Human Judgment and Artificial Intelligence in Problem-solving, Creativity, and Decision-making

This study explores the repercussions of excessive reliance on Artificial Intelligence (AI) on human cognitive processes, specifically targeting problem-solving, creativity, and decision-making. Employing qualitative semi-structured interviews and Interpretative Phenomenological Analysis (IPA), it delves into the nuanced challenges and risks stemming from an overemphasis on AI. The research illuminates a nuanced landscape: while AI streamlines problem-solving tasks and provides valuable support, there’s a crucial need to safeguard human judgment and intuition. In the realm of creativity, divergent viewpoints emerge, underscoring concerns regarding AI’s potential limitations and advocating for a harmonious interplay between AI-generated suggestions and individual creative thought. Regarding decision-making, participants recognize AI’s utility but underscore the necessity of blending AI insights with critical thinking and consideration of unique circumstances. They caution against complacency, advocating for a judicious equilibrium between AI guidance and individual expertise. This study innovates by providing multifaceted insights into the complexities of AI-human interaction, uncovering nuanced perspectives on its impacts across problem-solving, creativity, and decision-making domains. By bridging this gap, it advances understanding of how AI integration influences cognitive processes, offering practical implications for fostering a balanced approach. Its innovative methodology combines qualitative interviews and IPA, offering rich, nuanced data that provide a deeper understanding of the subject matter. This research serves as a beacon for promoting awareness of the risks associated with overreliance on AI, advocating for a mindful integration that upholds human agency while leveraging AI capabilities effectively.

Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons for option errors to choose the optimal path of the GoT and ultimately infer the correct answer. By progressively integrating intricate clues, our method effectively reduces the difficulty of multi-choice reasoning and provides a novel solution for DC-MCQ. Extensive experiments on the CICERO and CICERO_v2 datasets validate the significant improvement of our approach on DC-MCQ task. On zero-shot setting, our model outperform the best baseline by 17.67% in terms of F1 score for the multi-choice task. Most strikingly, our GPT3.5-based ReX-GoT framework achieves a remarkable 39.44% increase in F1 score.

The evolution of artificial intelligence: problems and prospects of rational cognition

Objective: This article undertakes a comprehensive exploration of the constructivist paradigm in artificial intelligence (AI) development, aiming to uncover how constructivist perspectives shape our understanding of AI. It delves into the evolution of AI thought, emphasizing the significance of constructivist epistemology in comprehending AI's philosophical and cognitive dimensions.
 Method: The study employs a variety of philosophical methodologies, including historical-philosophical analysis, comparative analysis of philosophical teachings, and a system-structural dialectical approach. These methods facilitate an in-depth examination of AI's conceptual intricacies within a constructivist framework, focusing on the relationship between artificial and natural intelligence and the epistemological implications of AI.
 Results: The investigation reveals that the main challenge in AI research is the absence of clear problem-solving rules, highlighting the current limitations of human self-knowledge in logical and emotional intelligence. It showcases AI's vast capabilities, from extensive knowledge bases to real-time processing, and emphasizes AI's role in enhancing human cognitive processes.
 Conclusions: Artificial intelligence, as a construct of human intellect, mirrors the capacity for design and creativity inherent in human thought. The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further our grasp of AI and its cognitive potential.

Sequential model based on human cognitive processing to robot acceptance.

Robots have tremendous potential, and have recently been introduced not only for simple operations in factories, but also in workplaces where customer service communication is required. However, communication robots have not always been accepted. This study proposes a three-stage (first contact, interaction, and decision) model for robot acceptance based on the human cognitive process flow to design preferred robots and clarifies the elements of the robot and the processes that affect robot acceptance decision-making. Unlike previous robot acceptance models, the current model focuses on a sequential account of how people decide to accept, considering the interaction (or carry-over) effect between impressions established at each stage. According to the model, this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed during the interaction with robot (politeness of its conversation and behavior) on robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior, the greater the acceptance rate. Importantly, there was no interaction between these two factors. The results indicating that the impressions of the first contact and interaction are additively processed suggest that we should accumulate findings that improving the appearance of the robot and making its communication behavior more human-like in politeness will lead to a more acceptable robot design.

Integrating human factors engineering into your pediatric radiology practice.

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.

Crafting emotional engagement and immersive experiences: Comprehensive scale development for and validation of hospitality marketing storytelling involvement

AbstractThis study develops and validates a scale for gauging consumer involvement in storytelling (to create memorable and emotionally resonant experiences) in hospitality marketing through four phases: qualitative inquiry, construct definition, item refinement, and nomological validation. Validation involves 24 in‐depth video interviews and consecutive online surveys. The hospitality marketing storytelling involvement scale, with four dimensions—contextual cues, emotional engagement, mental cognition, and immersive experience—comprising 14 items, precisely measures consumer involvement. The scale integrates narrative transportation theory and the elaboration likelihood model to enhance understanding of consumer engagement with fundamental human cognitive and emotional processes. The validated scale offers a valuable tool for marketers to precisely assess consumer involvement and strategically leverage storytelling to evoke emotions, foster brand loyalty, and judge campaign effectiveness. In diverse marketing contexts that elicit emotions, resonate with individuals, and foster immersive experiences, the scale demonstrates remarkable adaptability. Ultimately, it can help marketers craft compelling narratives, enhance brand perception, and strengthen consumer relationships, thereby contributing to emotional connections, optimizing strategies, and enhancing consumer engagement effectiveness.

TEKNIK PENGAMBILAN KEPUTUSAN DALAM BERORGANISASI

Decision making is a human cognitive process that results in selecting beliefs or actions among several available alternative options, either rationally or irrationally. In organizations, decision making is a factor that really determines the success of the organization in achieving its stated goals. Decision making is also one of the functions of a leader, therefore every leader must be aware of his strategic position and at the same time strive to be able to make every decision correctly and with quality. Apart from having to master various decision-making techniques, he must also be able to implement them correctly. Because in the end it is the subordinates who will implement every decision taken, the involvement of subordinates in every decision-making process must always be sought. In reality, the color of the decisions taken by a leader will really depend on the style he adopts, but every leader needs to realize that the situation faced is not always the same for everyone. This is where a leader is important in making decisions.

A machine reading comprehension framework for recognizing emotion cause in conversations

Recognizing Emotion Cause in Conversations (RECC) is a key issue in modeling human cognitive processes, involving Conversational Causal Emotion Entailment task (C2E2) and Conversational Causal Span Extraction task (C2SE). Previous emotion cause extraction research has been concentrated at the clause level, detecting if the cause is in the text, not describing the underlying causes in texts well. In order to address this issue, we suggest a novel approach that can recognize emotion cause spans. These spans can represent or imply the causes for controlling emotions. In this paper, we use a Machine Reading Comprehension framework to Recognize the Emotion Cause in Conversations (MRC-RECC), at both the span level and clause level simultaneously. Specifically, we use two types of queries to build the associations between the two different subtasks: emotion causal entailment task and emotion causal span extraction task. Our framework can recognize emotion cause more effectively by using joint learning to make these two tasks complement each other. Experiments demonstrate that our MRC-RECC provides state-of-the-art performances, which can reason more emotion causes in conversation texts. The code can be found at https://github.com/Guangzidetiaoyue/MRC-RECCON.

State-Space Compression for Efficient Policy Learning in Crude Oil Scheduling

The imperative for swift and intelligent decision making in production scheduling has intensified in recent years. Deep reinforcement learning, akin to human cognitive processes, has heralded advancements in complex decision making and has found applicability in the production scheduling domain. Yet, its deployment in industrial settings is marred by large state spaces, protracted training times, and challenging convergence, necessitating a more efficacious approach. Addressing these concerns, this paper introduces an innovative, accelerated deep reinforcement learning framework—VSCS (Variational Autoencoder for State Compression in Soft Actor–Critic). The framework adeptly employs a variational autoencoder (VAE) to condense the expansive high-dimensional state space into a tractable low-dimensional feature space, subsequently leveraging these features to refine policy learning and augment the policy network’s performance and training efficacy. Furthermore, a novel methodology to ascertain the optimal dimensionality of these low-dimensional features is presented, integrating feature reconstruction similarity with visual analysis to facilitate informed dimensionality selection. This approach, rigorously validated within the realm of crude oil scheduling, demonstrates significant improvements over traditional methods. Notably, the convergence rate of the proposed VSCS method shows a remarkable increase of 77.5%, coupled with an 89.3% enhancement in the reward and punishment values. Furthermore, this method substantiates the robustness and appropriateness of the chosen feature dimensions.