Human Information Processing System Research Articles

Retracted: Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm.

[This retracts the article DOI: 10.1155/2021/3669204.].

Retracted: Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm.

[This retracts the article DOI: 10.1155/2021/1110503.].

4.6 Assessing and Addressing Digital Distraction, Misinformation, and Disarray

Presentation attendees will: 1) understand the concepts of attentional literacy and digital disarray; 2) recognize social media’s capacity to powerfully engage human information processing systems; and 3) take advantage of opportunities to promote attentional literacy and support a healthy information culture.

Human-mimetic binaural ear design and sound source direction estimation for task realization of musculoskeletal humanoids

Human-like environment recognition by musculoskeletal humanoids is important for task realization in real complex environments and for use as dummies for test subjects. Humans integrate various sensory information to perceive their surroundings, and hearing is particularly useful for recognizing objects out of view or out of touch. In this research, we aim to realize human-like auditory environmental recognition and task realization for musculoskeletal humanoids by equipping them with a human-like auditory processing system. Humans realize sound-based environmental recognition by estimating directions of the sound sources and detecting environmental sounds based on changes in the time and frequency domain of incoming sounds and the integration of auditory information in the central nervous system. We propose a human mimetic auditory information processing system, which consists of three components: the human mimetic binaural ear unit, which mimics human ear structure and characteristics, the sound source direction estimation system, and the environmental sound detection system, which mimics processing in the central nervous system. We apply it to Musashi, a human mimetic musculoskeletal humanoid, and have it perform tasks that require sound information outside of view in real noisy environments to confirm the usefulness of the proposed methods.

Driver's Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks.

The visual attention system is the gateway to the human information processing system, and emotion is an important part of the human perceptual system. In this paper, the driver’s visual attention characteristics and the influences of typical driving emotions on those were explored through analyzing driver’s fixation time and identification accuracy to different visual cognitive tasks during driving. The results showed that: the increasing complexity of the cognitive object led to the improvement of visual identification speed. The memory and recall process increased drivers’ fixation time to cognitive objects, and the recall accuracy decreased with the increase in time interval. The increase in the number of cognitive objects resulted in the driver improving the visual identification speed for the cognitive object at the end of the sequence consciously. The results also showed that: the visual cognitive efficiency was improved in the emotional states of anger and contempt, and was decreased in the emotional states of surprise, fear, anxiety, helplessness and pleasure, and the emotional state of relief had no significant effect on the visual cognitive efficiency. The findings reveal the driver’s visual information processing mechanism to a certain extent, which are of great significance to understand the inner micro-psychology of driver’s cognition.

Human motion recognition information processing system based on LSTM Recurrent Neural Network Algorithm

Human motion recognition information processing system based on LSTM Recurrent Neural Network Algorithm

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm.

With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm.

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.

UNDERSTANDING THE ASPECTS OF LEARNING AND LEARNERS: A CONCEPTUAL ANALYSIS

The main focus of this research is to understand the meaning of learning and the nature of a learner. The analysis elaborates who learners are, where learners can be found, when learners and how learning happens. Besides, the article also explicates intelligence, information processing and motivation in learning. The discussion of the article explains that learning happens in any circumstances throughout human development and generates some changes in cognition and behavior. Furthermore, the learning process begins when humans face external stimuli through human interaction. The stimuli continue to be processed in the brain to activate thinking process hence it is called cognitive development. In the next discussion, we put forward that learning requires both logical-reasoning and emotional intelligence. Logical-reasoning is related to information processing in the brain. It is a process where human mind is being active when receiving, storing, and retrieving information hence new knowledge is gained after processed in human information processing system. While, emotional Intelligence is social intelligence which connects between learners’ emotional, social, and cognitive wellbeing. In the last discussion, we suggest that the effectiveness of learning needs good motivation which is influenced by the internal encouragement of human behavior, expectancy-value theory, and goal setting.

Neural Encoding for Human Visual Cortex With Deep Neural Networks Learning “What” and “Where”

Neural encoding, a crucial aspect to understand the human brain information processing system, aims to establish a quantitative relationship between the stimuli and the evoked brain activities. In the field of visual neuroscience, with the ability to explain how neurons in the primary visual cortex work, population receptive field (pRF) models have enjoyed high popularity and made reliable progress in recent years. However, existing models rely on either the inflexible prior assumptions about pRF or the clumsy parameter estimation methods, severely limiting the expressiveness and interpretability. In this article, we propose a novel neural encoding framework by learning “what” and “where” with deep neural networks. It involves two separate aspects: 1) the spatial characteristic (“where”) and 2) feature selection (“what”) of neuron populations in the visual cortex. Specifically, our approach first encodes visual stimuli into hierarchically intermediate features through a pretrained deep neural network (DNN), then converts DNN features into refined features with the channel attention and spatial receptive field (RF) to learn “where”, and finally regresses refined features simultaneously onto voxel activities to learn “what”. The sparsity regularization and smoothness regularization are adopted in our modeling approach so that the crucial RF can be estimated automatically without prior assumptions about shapes. Furthermore, an attempt is made to extend the voxel-wise modeling approach to multi-voxel joint encoding models, and we show that it is conducive to rescuing voxels with poor signal-to-noise characteristics. Extensive empirical results demonstrate that the method developed herein provides an effective strategy to establish neural encoding for the human visual cortex, with the weaker prior constraints but the higher encoding performance.

Errors and Action Monitoring: Errare Humanum Est Sed Corrigere Possibile.

It was recognized long ago by Seneca through his famous “errare humanum est.” that the human information processing system is intrinsically fallible. What is newer is the fact that, at least in sensorimotor information processing realized under time pressure, errors are largely dealt with by several (psycho)physiological-specific mechanisms: prevention, detection, inhibition, correction, and, if these mechanisms finally fail, strategic behavioral adjustments following errors. In this article, we review several datasets from laboratory experiments, showing that the human information processing system is well equipped not only to detect and correct errors when they occur but also to detect, inhibit, and correct them even before they fully develop. We argue that these (psycho)physiological mechanisms are important to consider when the brain works in everyday settings in order to render work systems more resilient to human errors and, thus, safer.

A Review on the Interface Design of Automotive Head-Up Displays for Communicating Safety-Related Information

Automotive head-up displays (HUDs) have the advantage of allowing drivers to keep their eyes forward while driving by superimposing visual information on top of the drivers’ forward field of view (...

Modeling Team Cognition in Emergency Response via Naturalistic Observation of Team Interactions

Emergency responders work collectively as an ad hoc team to save lives and infrastructures at risk, despite their varying experience, knowledge, cultural backgrounds, and difficult working conditions with high-levels of uncertainty and timepressure. Cognition, in particular, has gained attention as a key construct to consider in collective response efforts in emergency management. Team cognition, however, has not been fully appreciated or adequately addressed in the field of emergency response (Bigley & Roberts, 2001). The interactionist perspective (or interactive team cognition) effectively captures team cognition in heterogeneous and dynamic teams prevalent in the real-world (Cooke & Gorman, 2009; Cooke, Gorman, Myers, & Duran, 2013). Although researchers in the emergency response discipline appreciate the value of viewing team cognition as interaction (Comfort, 2007; Bergeron & Cooren, 2012; Wolbers & Boersma, 2013), an associated empirical or interventional attempt using this perspective remains scarce. Tracing the scarcity of literature back to lack of context-specific theorizing efforts (Moon, Peres, & Sasangohar, 2017), an observation-based, theory-building approach is utilized here to address this gap. The naturalistic observational study presented here is an initial effort to explore team cognition for an incident management team (IMT) as an interactive system. An IMT is an ad hoc team of command-level responders. Interestingly, an IMT is a team of functional sub-teams or sections (i.e., Command, Planning, Operations, Logistics, and Finance/ Administration). Within each sub-team there is also a team of functional units. This naturalistic observational study was conducted at a high-fidelity simulator replicating a generic IMT facility, i.e., the emergency operations training center (EOTC), College Station, TX. Interactions were observed and coded in terms of who initiated the interaction and with whom, which technology was being used, and what was communicated and for what purpose. The purpose of this study is to develop a theoretical interactionist model of team cognition in emergency response, to inform future interventional attempts to improve team decision-making. To do so, this study views a Plans team as a cognitive system capable of managing information through interdependent, nonlinear, and dynamic interactive behaviors for perceiving (P), diagnosing (D), and adapting (A) to the changes in the status of critical elements (Adapted from Moon et al., 2017). The proposed P·D·A model posits the following three premises: (1) a Plans team is a cognitive system where its team cognition is interactions of team members to complete a cognitive task; (2) team cognition for each of the three sub-teams of a Plans team is tied to the context-specific cognitive tasks of perceiving (P), diagnosing (D), and adapting (A) to the changes in the status of critical elements; and (3) team cognition for a Plans team is manifested as nonlinear, interdependent, and dynamic interactions within and among P, D, and A of the three sub-teams of the Plans team. Preliminary results from a content analysis of transcribed and coded interactions suggest that an Info/Intel unit, a Situation unit, and a Section Chief unit can be hypothesized to be critical contributors of team cognition for a Plans team in terms of P, D, and A, respectively. These hypotheses can be represented with network centrality measures as follows: Hypothesis 1. An Info/Intel unit has high in-degree and out-degree centrality with non-Plans teams. Hypothesis 2. A Situation unit has high betweenness centrality within a Plans team. Hypothesis 3. A Section Chief unit has high in-degree and out-degree centrality within a Plans team, and high betweenness centrality between the Plans team and non-Plans teams. The proposed P·D·A model illustrates the benefits of viewing team cognition as interaction within and among a team of teams, for context-specific tasks of P, D, and A. Most importantly, the model effectively captures the nonlinear, interdependent, and dynamic nature of team cognition as interaction in a multiteam system, or MTS (Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005; Bienefeld & Grote, 2014), embedded in complex socio-technical systems, STS (Vicente, 2002). As the information processing model views an individual as a cognitive system or a human information processing system (Wickens, 1992), the P·D·A model views a team as a cognitive system capable of managing information. The interactionist perspective on team cognition helps the P·D·A model to realize its potential to extend an individual cognition model to a team level. The interactionist perspective is “compatible with the view of human-machine system as a unitary system” (Cooke & Gorman, 2009, p. 28). In addition to the theoretical and practical implications, this study has methodological implications. Measuring interactive team cognition with network-based metrics (currently in progress) will open a new chapter. The need of incorporating a network perspective into team cognition in emergency response is in line with the literature (Wolbers & Boersma, 2013; Steigenberger, 2016). As a future work, the P·D·A model will be further developed with network and content analysis and validated through interviews with Subject Matter Experts (SMEs) involved in Hurricane Harvey.

Overcoming Older Adult Barriers to Learning Through an Understanding of Perspectives on Human Information Processing.

The importance to older adults of being able to learn to perform activities related to leisure, work, or daily living for maintaining independence and a good quality of life is unquestionable. An appreciation of the challenges that older adults face during learning, as well as insights into ways to help them overcome these challenges, whether through design or instruction, can be obtained through a basic understanding of human information processing. The purpose of this article is to facilitate this understanding within the context of older adult learning. The article begins with an overview of the human information-processing system and cognitive limitations and capabilities associated with aging. Critical components of the information-processing system are then revisited in greater detail from the perspective of older adult information-processing capabilities. Concepts are illustrated through examples to demonstrate how older adults could make more efficient use of their information-processing resources during learning.

Brain reflections: A circuit-based framework for understanding information processing and cognitive control.

Here, I propose a view of the architecture of the human information processing system, and of how it can be adapted to changing task demands (which is the hallmark of cognitive control). This view is informed by an interpretation of brain activity as reflecting the excitability level of neural representations, encoding not only stimuli and temporal contexts, but also action plans and task goals. The proposed cognitive architecture includes three types of circuits: open circuits, involved in feed-forward processing such as that connecting stimuli with responses and characterized by brief, transient brain activity; and two types of closed circuits, positive feedback circuits (characterized by sustained, high-frequency oscillatory activity), which help select and maintain representations, and negative feedback circuits (characterized by brief, low-frequency oscillatory bursts), which are instead associated with changes in representations. Feed-forward activity is primarily responsible for the spread of activation along the information processing system. Oscillatory activity, instead, controls this spread. Sustained oscillatory activity due to both local cortical circuits (gamma) and longer corticothalamic circuits (alpha and beta) allows for the selection of individuated representations. Through the interaction of these circuits, it also allows for the preservation of representations across different temporal spans (sensory and working memory) and their spread across the brain. In contrast, brief bursts of oscillatory activity, generated by novel and/or conflicting information, lead to the interruption of sustained oscillatory activity and promote the generation of new representations. I discuss how this framework can account for a number of psychological and behavioral phenomena.

An experimental approach to understanding design problem structuring strategies

This study has developed an experimental approach to understanding the effectiveness of problem structuring strategies such as decomposition modes and control strategies. Participants were randomly recruited to individually complete a design problem in a lab, and their design outcomes, including sketches and retrospective verbal protocols, were collected. The effectiveness of problem structuring was represented as the ratio of output to input of the human information processing system: the output was the quality of design outcomes, and the input was participants' mental effort normalised by the complexity of problem structuring. The case study has shown that the participants who applied the explicit decomposition mode and the breadth-first strategy had significantly higher effectiveness scores than those who applied the implicit mode and the depth-first strategy. The results suggest that effective problem structuring strategies should be encouraged in design idea generation and design education.

Multisensory integration in action control.

The integration of multisensory information is an essential mechanism in perception and action control. Research in multisensory integration is concerned with how the information from the different sensory modalities, such as the senses of vision, hearing, smell, taste, touch, and proprioception, are integrated to a coherent representation of objects (for an overview, see e.g., Calvert et al., 2004). The combination of information from the different senses is central for action control. For instance, when you grasp for a rubber duck, you can see its size, feel its compliance and hear the sound it produces. Moreover, identical physical properties of an object can be provided by different senses. You can both see and feel the size of the rubber duck. Even when you grasp for the rubber duck with a tool (e.g., with tongs), the information from the proximal hand, from the effective part of the distal tool and from the eyes are integrated in a manner to act successfully (for limitations of this integration see Sutter et al., 2013). Over the recent decade a surge of interest in multisensory integration and action control has been witnessed, especially in connection with the idea of a statistically optimized integration of multiple sensory sources. The human information processing system is assumed to adjust moment-by-moment the relative contribution of each sense's estimate to a multisensory task. The sense's contribution depends on its variance, so that the total variance of the multisensory estimate is lower than that for each sense alone. Accordingly, the validity of a statistically optimized multisensory integration has been demonstrated by extensive empirical research (e.g., Ernst and Banks, 2002; Alais and Burr, 2004; Reuschel et al., 2010), also in applied setting such as tool-use (e.g., Takahashi et al., 2009; in the present research topic: Takahashi and Watt, 2014). For this perspective to mature it will be helpful to delve deeper into the multisensory information processing mechanisms and their neural correlates, asking about the range and constraints of these mechanisms, about its localization and involved networks. The contributions to the present research topic range from how information from different senses and action control are linked and modulated by object affordances (Garrido-Vasquez and Schubo, 2014), by task-irrelevant information (Juravle et al., 2013; Wendker et al., 2014; for a review see Wesslein et al., 2014), by temporal and spatial coupling within and between senses (Cameron et al., 2014; Mueller and Fiehler, 2014; Rieger et al., 2014; Sugano et al., 2014) to childhood development of multisensory mechanisms (Jovanovic and Drewing, 2014). Correspondences between the information from different senses play an important role for multisensory integration. Integration does, for instance, not take place when vision and touch are spatially separated (e.g., Gepshtein et al., 2005). However, cognitive approaches on action effect control assume that information from different senses is still coded and represented within the same cognitive domain, when the information concerns the same action (e.g., Musseler, 1999; Hommel et al., 2001). The present research topic also addresses the corresponding issue of modality-specific action control (Boutin et al., 2013; Grunwald et al., 2014). Overall, the present research topic broadens our view on how multisensory mechanisms add to action control. We thank all authors and all reviewers for their valuable contributions.

Verbal Decision Analysis: Foundations and Trends

The primary goal of research in multiple criteria decision analysis is to develop tools to help people make more reasonable decisions. In many cases, the development of such tools requires the combination of knowledge derived from such areas as applied mathematics, cognitive psychology, and organizational behavior. Verbal Decision Analysis (VDA) is an example of such a combination. It is based on valid mathematical principles, takes into account peculiarities of human information processing system, and fits the decision process into existing organizational environments. The basic underpinnings of Verbal Decision Analysis are demonstrated by early VDA methods, such as ZAPROS and ORCLASS. New trends in their later modifications are discussed. Published applications of VDA methods are presented to support the findings.

Intra- and intermodal integration of discrepant visual and proprioceptive action effects

Integration of discrepant visual and proprioceptive action effects puts high demands on the human information processing system. The present study aimed to examine the integration mechanisms for the motor (Exp. 1) and visual modality (Exp. 2). According to theories of common coding, we assumed that visual as well as proprioceptive information is represented within the same cognitive domain and is therefore likely to affect each other (multisensory cross talk). Thus, apart from the often-confirmed visual dominance in multisensory integration, we asked about intra- and intermodal recall of either proprioceptive or visual information and whether there were any differences between the motor and visual modality. In a replication paradigm, we perturbed the relation between hand movements and cursor movements. The task required the (intra- vs. intermodal) replication of an initially performed (seen) hand (cursor) movement in a subsequent motor (visual) replication phase. First, mechanisms of integration were found to be dependent on the output modality. Visual action effects interfered the motor modality, but proprioceptive action effects did not have any effects on the visual modality. Second, however, intermodal integration was more susceptible to interference, and this was found to be independent from the output modality. Third, for the motor modality, the locus of perturbation (perturbation of cursor amplitude or perturbation of hand amplitude) was irrelevant, but for the visual modality, perturbation of hand amplitudes reduced the cross talk. Tool use is one field of application of these kinds of results, since the optimized integration of conflicting action effects is a precondition for using tools successfully.

Limitations of distal effect anticipation when using tools

Modern technologies progressively create workplaces in which the execution of movements and the observation of their consequences are spatially separated. Challenging workplaces in which users act via technical equipment in a distant space include aviation, applied medical engineering and virtual reality. When using a tool, proprioceptive/tactile feedback from the moving hand (proximal action effect) and visual feedback from the moving effect point of the tool, such as the moving cursor on a display (the distal action effect) often do not correspond or are even in conflict. If proximal and distal feedback were equally important for controlling actions with tools, this discrepancy would be a constant source of interference. The human information processing system solves this problem by favoring the intended distal action effects while attenuating or ignoring proximal action effects. The study presents an overview of experiments aiming at the underlying motor and cognitive processes and the limitations of visual predominance in tool actions. The main findings are, that when transformations are in effect the awareness of one's own actions is quite low. This seems to be advantageous when using tools, as it allows for wide range of flexible sensorimotor adaptations and – may be more important – it evokes the feeling of being in control. Thus, the attenuation of perceiving one's own proximal action effects is an important precondition for using tools successfully. However, the ability to integrate discordant perception-action feedback has limits, especially, but not only, with complex transformations. When feature overlap between vision and proprioception is low, and when the existence of a transformation is obvious proximal action effects come to the fore and dominate action control in tool actions. In conclusion action–effect control plays an important role in understanding the constraints of the acquisition and application of tool transformations.