Model Of Human Information Processing Research Articles

Development of Remote Construction System of Modularized Rail Structure for Inexperienced Operators

Remotely controlled robots are required to operate in human-inaccessible environments, such as the decommissioning of nuclear power plants. In our previous studies, we proposed a modularized rail structure for such environments. In long-term decommissioning work at a nuclear power plant, robots are controlled by inexperienced operators who have limited knowledge of the design details of the robots. In this study, we developed a remote construction system for supporting inexperienced operators by transmitting the status information of the constructor robot to the operator during the remote construction of a modularized rail structure. The proposed remote construction system was designed based on a human information-processing model. Experiments were conducted using the developed remote construction system. The proposed method achieved a success count of inexperienced operators equivalent to that of skilled operators. Furthermore, a questionnaire administered to participants showed that their cognitive, psychological, and physical loads during construction were sufficiently low during the experiment.

Phase-encoded fMRI tracks down brainstorms of natural language processing with subsecond precision.

Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured real-time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross-language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as "brainstorms" on brain "weather" maps. The paths of hemodynamic traveling waves provide direct evidence for dual-stream models of the visual and auditory systems as well as logistics models for crossmodal and cross-language processing. Specifically, we have tracked down the step-by-step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain-general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. PRACTITIONER POINTS: Phase-encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real-time overt natural language tasks. Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing. This study unlocks the potential of applying rapid phase-encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high-order cognitive processes.

Computational analysis of Finnish nonfinite clauses

Abstract Finnish nonfinite clauses constitute a complex grammatical class with a seemingly chaotic mix of verbal and nominal properties. Thirteen nonfinite constructions, their selection, control, thematic role assignment, nonfinite agreement, embedded subjects, and syntactic status were targeted for analysis. An analysis is proposed which derives their syntactic and semantic properties by relying on a computational model of human information processing. The model analyzes Finnish nonfinite constructions as truncated clauses with one functional layer above the verb phrase. Research methods from naturalistic cognitive science and computational linguistics are considered as potentially useful tools for linguistics.

Neurosynaptic-like behavior of Ce-doped BaTiO3 ferroelectric thin film diodes for visual recognition applications

Brain-like neuromorphic computing networks based on the human brain information processing model are gradually breaking down the memory barriers caused by traditional computing frameworks. The brain-like neural system consists of electronic synapses and neurons. The multiple ferroelectric polarization switching modulated by the external electric field is well suited to simulate artificial neural synaptic weights. Therefore, ferroelectric diodes' (FDs) synapses have great advantages in building highly reliable and energy-efficient artificial neural networks. In this paper, we demonstrate the FDs synapse, which is based on rare-earth metal-doped BaTiO3 ferroelectric dielectric layer materials. This performs short-term and long-term synaptic plasticity behaviors by modulating synaptic weights using pulsed stimuli to polarize or flip ferroelectric films. In addition, convolutional neural networks were constructed on the MNIST dataset and the Fashion-MNIST dataset to check the feasibility of the device in simulating bio-visual recognition. The results expand the application of FDs' devices in the intersection of artificial intelligence and bioscience.

When Do Supervisors Punish Unethical Pro-organizational behaviors: the Role of Identity and Goal

Given that unethical pro-organizational behavior (UPB) violates moral standards but benefits the organization at the same time, responses to this behavior could be equivocal although it is supposed to be punished. Previous research, however, has centered on antecedents of UPB, less is known about its consequences, especially how subordinates’ UPB is responded by supervisors. Drawing on the human information processing model, this paper aims to explain when supervisors perceive subordinate UPB in a negative way, and further engage in negative leading behaviors as punishments for UPB. Results of a multi-wave, multiple-source survey suggest that subordinates’ UPB are most negatively related to supervisors’ trust when supervisors’ moral identity is prominent and their goals are congruent with the group. Furthermore, results show that reduced trust ultimately elicits abusive supervisor behavior. These findings extend knowledge on consequences of UPB and provide important insights into supervisors’ leading behavior from a bottom-up perspective.

Emphasizing on the Neglected Parts of Risk Management Process in Occupational Health and Safety Management, Using the European Statistics for Accidents at Work Codification, Communication – Human Information Processing Model and Management Standards

Occupational Health and Safety (OHS) management has been one of the first areas of implementation of Risk Management process. However, due to the extended application of OHS management in all organization and the requirements for compliance with relevant legislation, focus for standardization was set mostly on the technical aspects (risk identification and risk evaluation), whereas other aspects (risk treatment, risk communication, monitoring and reporting) were left to be treated ad hoc, intuitively or following regulated requirements. According to the Risk Management process, as described by the standard ISO 31000:2018, these aspects are equally important for effective management of risks. This paper aims to present the whole Risk Management process for OHS risks, emphasizing on these neglected aspects and presenting documented tools to effectively incorporate them in the structured application of OHS management. The importance of defining the scope, context and criteria of OHS management, as well as the selection of certain risk treatment strategy for each risk is emphasized. Use of ESAW (European Statistics for Accidents at Work) taxonomy for risk identification is proposed, an evaluation scheme for measures is presented, the C-HIP model is proposed to be integrated for communication, recording registry information is discussed and ISO 45001:2018 PDCA structure is proposed for continuous monitoring and review.

Exploring students' cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity

AbstractBackgroundProblem‐solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE‐log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem‐solving activities are rarely investigated.ObjectivesIn this study, we examined how the theory‐informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior‐knowledge.MethodsA high‐frequency temporal data was collected with a camera, an electroencephalogram, and an eye‐tracker from 40 computer science students (bachelor and master studies) in the context of a code‐debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective.ResultsOur findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code‐debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants.ImplicationsExtending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem‐solving skills.

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential.

Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source of distraction. The study compares the driver’s cognitive responses to fixations on billboards with fixations on the vehicle dashboard. The measured eye-fixation related potential (EFRP) shows that the P1 components are similar; however, the subsequent N1 and P2 components differ. In addition, an EEG motor response is observed when the driver makes an adjustment of driving speed when prompted by speed limit signs. The experimental results demonstrate that the proposed measurement system is a valid tool in assessing driver cognition and suggests the cognitive level of engagement to the billboard is likely to be a precursor to driver distraction. The experimental results are compared with the human information processing model found in the literature.

Cognitive Modelling for User Interface Design in HCI: A Comparative Analysis on Cognitive Models

This research aims to elaborate the cognition in the field of human-computer interaction, also acknowledges the cognitive modeling and human behavior processes. Cognitive modeling is a field of Human-Computer Interaction (HCI) which is used to design more efficient human interactive systems. It is used to model the interactive system in such a way that analysts can determine methods that users will interact with the system and also be used to understand the different processes of cognitive human behaviors. Hierarchal task analysis is a task to goal-based model, in which analyst selects the actions and tasks to perform. GOMS is a cognitive knowledge of the human information processing model in HCI that describes the user's cognitive architecture based on four components. The linguistic and grammatical model is a syntactical model in which languages and syntax are designed for the user for system communication in an interactive system. Cognitive human behavior processes are also described to understand the mutual coordination of cognition processes and cognitivemodels in designing an interactive system. Problem-solving is a cognitive process of the human mind to search for a problem and explore the possible solutions for that problem. Decision making is also a cognitive process of human behaviour in which human chooses an action from other alternatives based on certain criteria.

Prescribing cascades in persons with Alzheimer's disease: engaging patients, caregivers, and providers in a qualitative evaluation of print educational materials.

Introduction:Prescribing cascades occur when the side effect of a drug is misinterpreted as a new medical condition, and a second drug is prescribed to address the side effect. Persons with Alzheimer’s disease (AD) are at increased risk of prescribing cascades due to greater multimorbidity, polypharmacy, and complexity of care. The objective of this study was to evaluate educational materials about prescribing cascades in persons with AD, and elicit input on their use in a future trial.Methods:We interviewed community-dwelling adults with either an AD diagnosis or a prescription drug used to treat AD (n = 12), caregivers of patients meeting the same criteria (n = 14), and providers (n = 15). We coded interview transcripts and organized themes according to the communication–human information processing model. We revised the materials based on the interviews, and surveyed participating caregivers and providers for their reactions to the revised materials.Results:Analysis of patients’, caregivers’, and providers’ comments suggest: (a) Providers had conflicting views about the messaging of materials; (b) Caregivers were likely to read letters addressed to patients; (c) Providers were likely to ignore letters, but were receptive to patient/caregiver-initiated conversations; (d) Patients and caregivers had difficulty understanding prescribing cascades; (e) Providers worried that mailed materials would undermine trust; (f) Participants had mixed views on how materials might affect the clinical encounter; (g) Participants felt that materials would improve patient/caregiver engagement. When surveyed, most providers found the revised materials informative and actionable, and most caregivers found them understandable and useful.Conclusions:This evaluation of educational materials about prescribing cascades in patients with AD provides strong support for engaging caregivers to communicate with providers about prescribing cascades. By giving patients and caregivers a basic description of the prescribing cascade concept, our educational materials may help them prepare for a conversation with the provider, who can then tailor the discussion of the possible cascade to the specific needs of the individual patient and caregiver. However, evidence on whether materials can stimulate such conversations awaits testing in a future trial.Lay summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Prescribing cascades occur when the side effect of a medication is misinterpreted as a new medical condition, and a second medication is prescribed to treat the side effect. Persons with Alzheimer’s disease (AD) are at increased risk of prescribing cascades because they often have more medical conditions, more medications, and more complex care. The goal of this study was to evaluate mailed educational materials about prescribing cascades in persons with AD, and get input on their use in a future study. We interviewed 12 adults with AD, or prescribed a medication to treat AD, 14 caregivers of persons with AD, and 15 providers. We reviewed the interview transcripts to identify important findings about our educational materials. We edited the materials based on the interviews, and sent participating caregivers and providers a questionnaire to get their reactions to the new materials. Important findings from the interviews suggest: (a) Providers had conflicting views about the recommendations given; (b) Caregivers were likely to read letters addressed to patients; (c) Providers were likely to ignore letters, but were receptive to patients/caregivers introducing the topic; (d) Patients and caregivers had difficulty understanding prescribing cascades; (e) Providers worried mailed materials would undermine trust; (f) Participants had mixed views on how materials might affect a doctor’s appointment; (g) Participants felt strongly that materials would improve patient/caregiver engagement. When surveyed, almost all providers found the revised materials informative and actionable; and most caregivers found them understandable and useful. These findings provide strong support for engaging caregivers to communicate with providers about prescribing cascades. The educational materials may help patients and caregivers prepare for a conversation with the provider, who can then tailor the discussion of the possible cascade to the specific needs of the individual patient and caregiver. However, evidence on whether materials can stimulate such conversations awaits testing in a future study.

Personally relevant online advertisements: Effects of demographic targeting on visual attention and brand evaluation.

Global investments in online advertising rise quickly but internet users often avoid looking at ads due to established banner blindness. Demographic targeting is expected to overcome this tendency by attracting users’ attention to more self-relevant ad content. However, little is known about the effect of demographically targeted versus non-targeted ads on users’ actual attention allocation during exposure to webpages. The present study aimed to further fill this empirical gap by clarifying whether demographic targeting attracts visual attention and to exploratively examine whether it also affects brand attitude and website evaluation, as suggested by previous studies. Eye tracking data revealed that demographic targeting can have medium- to large-sized effects on several eye movement parameters when internet users are in a free-viewing mode. In contrast, demographic targeting did not influence brand attitude and website evaluation. We conclude that attention for personally relevant advertisement can be strong. However, attention, although being a necessary condition for subsequent judgment formation according to the model of human information processing, is not sufficient to elicit positive effects at the level of subjective judgments.

Automation-Adoption Approach Maps Human/System Interaction

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191408, “Automating Directional Drilling: Technology-Adoption Staircase Mapping Levels of Human Interaction,” by Robert Wylie, SPE, xnDrilling; Kevin McClard, Performance Drilling Technologies; and John de Wardt, SPE, De Wardt and Company, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. This paper describes the progress of directional-drilling-automation systems along the cognitive functions and levels of automation as defined by the Levels of Automation Taxonomy (LOAT) hierarchy introduced by the Drilling Systems Automation Roadmap Industry Initiative. LOAT is based on incremental automation of the four cognitive functions of interaction. Levels of human/system interaction are described on a nine-point scale ranging from fully manual, to levels of system support for the human, to levels of automation overseen by the human, to full automation. The LOAT Hierarchy LOAT is a powerful tool for mapping the transition from a purely manual process, to the degree of automation that any system can achieve in the early transition phase, through a timeline to full automation. The transition levels from manual to autonomous were likened to the four cognitive functions that occur in both human interaction with machinery and automated interaction with machinery. These four functions, based on a staged model of human information processing, were translated into equivalent sequential functions applicable to both human processing and automated processing. These functions are Information acquisition Information analysis Decision and action selection Action implementation Automation is often perceived as automation of the action-sequence execution, the implementation of the decision for action by a robot or robotic mechanism. In reality, it is easier and more reliable to apply degrees of automation to the information-acquisition and information-analysis cognitive functions of the control process before decision-making and certainly before action-sequence execution. In a first phase of automation implementation, the automated system acquires the data and undertakes an analysis of its information. The output from this process in many automated systems is typically in the form of alarms designed to attract the attention of the human overseers. However, challenges are inherent in this process: Does the human receiving the alarm believe the alarm is real and relevant, and, if so, does the human react to the alarm effectively by making a good decision and then implementing the appropriate action as a consequence of that decision? Drilling case studies often describe human indecision and failure to act in a timely manner as a cause of catastrophic events. The human/machine interface is the point at which the system displays to the human the situation and the human reacts and provides input through control devices. In a purely manual mode, systems simply display data that the human analyzes and uses to decide on and implement action. In staged automated modes, the system is designed to advance the automated collection and processing of data. Initially, the data are presented to the human operator to prompt a decision and implementation of an action. In the next stage, the system provides advice on which action should be taken.

Influence of human-machine interactions and task demand on automation selection and use

A seminal work by Sheridan and Verplank depicted 10 levels of automation, ranging from no automation to an automation that acts completely autonomously without human support. These levels of automation were later complemented with a four-stage model of human information processing. Next, human-machine cooperation centred models and associated cooperation modes were introduced. The objective of the experiment was to test which human-machine theorie describe automation use better. The participants were asked to choose repeatedly between four automation types (i.e. no automation, warning, co-action, function delegation) to complete three multi-attribute task battery tasks. The results showed that the participants favour the selection of automation types offering the best human-machine interactions quality rather that the most effective automation type. Contrary to human-machine cooperation models, technology centred models could not predict accurately automation selection. The most advanced automation was not the most selected.Practitioner Summary: The experiment dealt with how people select different automation types to complete the multi-attribute task battery that emulates recreational aircraft pilot tasks. Automation performance was not the main criteria that explain automation use, as people tend to select an automation type based on the quality of the human-machine cooperation.

Comparison of Visual Sampling Patterns Under Simulated L2 and L0 Systems

Automated driving systems (ADS) partially or fully perform or assist with primary driving functions. According to SAE J3016 (SAE, 2016), ADS can subsume driving tasks traditionally reserved for humans, ranging from L0 (no automation) to L5 (full automation), creating varying degrees of driver interaction and responsibility. However, the literature on human-automation interaction indicates that human operators may perform at a suboptimal level when interacting with automated support systems (Parasuraman & Riley, 1997), reducing the net benefit that automation can bring while also simultaneously increasing the potential for unforeseen human errors. Yamani and Horrey (in press) proposed a theoretical framework of human-automation interaction building upon a human information-processing model (Wickens, Hollands, Banbury, & Parasuraman, 2013) that accounts for human performance when interacting with varying types and levels of automation (Parasuraman, Sheridan, & Wickens, 2000). Following the model by Yamani and Horrey (in press), we hypothesized that when the ADS is perceived to be reliable, drivers engaging with such systems (e.g. L2) would exhibit eye movements no better or worse than the drivers engaged with manual or L0 driving since the drivers allocate their reserved or spare resources to other driving-irrelevant activities such as mind wandering or task irrelevant thoughts (Yanko & Spalek, 2014). The current driving simulator study compared young drivers’ eye movements across four unique scenarios in either L0 or L2 driving systems. We asked participants to complete a three-phased skill-based training program (RAPT-3; see Unverricht, Samuel, & Yamani for review) proven effective to improve young drivers’ ability to anticipate latent hazards, immediately followed by the evaluation of their eye movements in either L0 or L2 systems using a head-mounted eye tracker and a driving simulator. Participants in the L2 condition were instructed that the system detects and mitigates existing and latent threats on the forward roadway while maintaining appropriate speed and lateral positioning for the duration of the drive. To ensure similarity between both systems, L2 participants were required to position their hands on the steering wheel and feet above the pedal. No hazards materialized in any of the four driving scenarios. Data showed similar breadths of eye movements for the drivers of the L2 and L0 systems both horizontally [M = 36.5 vs. 36.3 pixels; L2 and L0, respectively] and vertically [M = 26.9 vs. 34.5 pixels] and no difference in mean fixation durations [M = 367 vs. 333 ms for L2 and L0 conditions]. However, data indicated substantial differences between L0 and L2 conditions for number of fixations, with L2 drivers fixating less frequently than L0 drivers, [M = 687 vs. 796 fixations, t (22) = 2.53, B10 = 3.23]. The results imply that L2 drivers may sample information from the forward roadway less often than L0 drivers, suggesting the mobilization of spare resources for non-driving related tasks. Future research should examine the relationship between conveyed system reliability and attention allocation for drivers of ADS with different automation levels. In summary, the current results support Yamani and Horrey’s model and offer potential implications for the design of autonomous systems and the NHTSA automation guidelines to consider the perceived reliability of lower level ADS towards ascribing the role of the driver when the driving task is either partially or fully automated.

A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving

Automated driving systems have begun to allow drivers the ability to operate vehicles with reduced driver control. However, the literature in Human Factors indicates that automated systems of different types, purposes, and characteristics can often be used by human drivers counterproductively. This paper introduces an integrative model of human-automation interaction based on attentional resources and allocation policy to guide the systematic research on issues related to automated driving. The model builds upon a human information-processing model (Wickens et al., 2013) and focuses on the effective allocation of attentional resources to different perceptual, cognitive, and response stages when interacting with varying types and levels of automation (Parasuraman et al., 2000). The closed-loop mechanism allows drivers to evaluate joint human-machine performance and modulate the allocation policy, influenced by other factors. The model accounts for complacency and automation bias, and offers guidance of systematic research on drivers' attentional state during automated driving.

A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving

Automated driving systems have begun to allow drivers the ability to operate vehicles with reduced driver control. However, the literature in Human Factors indicates that automated systems of different types, purposes, and characteristics can often be used by human drivers counterproductively. This paper introduces an integrative model of human-automation interaction based on attentional resources and allocation policy to guide the systematic research on issues related to automated driving. The model builds upon a human information-processing model (Wickens et al., 2013) and focuses on the effective allocation of attentional resources to different perceptual, cognitive, and response stages when interacting with varying types and levels of automation (Parasuraman et al., 2000). The closed-loop mechanism allows drivers to evaluate joint human-machine performance and modulate the allocation policy, influenced by other factors. The model accounts for complacency and automation bias, and offers guidance of systematic research on drivers' attentional state during automated driving.

The role of unconscious information processing in the acquisition and learning of instructional messages

This review investigates how the unconscious information processing can create satisfactory learning outcomes, and can be used to ameliorate the challenges of teaching students to regulate their learning processes. The search for the ideal model of human information processing as regards achievement of teaching and learning objectives is a recurring issue in educational studies. Although conscious processes are more conducive to the objectives than unconscious processes, they do not necessarily result in satisfactory learning of instructional messages. Studies reviewed here show that the unconscious can play a facilitatory role in the encoding, storage, retrieval processes of information and learning activities, even when the conscious fails to achieve this. To take advantages of both conscious and unconscious processes, relevant literature suggests educators utilizing visual teaching aids that optimize use of working memory capacity and allow students to form mental imageries, thereby facilitating the comprehension of instructional messages.

Toward a Model of Human Information Processing for Decision-Making and Skill Acquisition in Laparoscopic Colorectal Surgery

To create a human information-processing model for laparoscopic surgery based on already established literature and primary research to enhance laparoscopic surgical education in this context. We reviewed the literature for information-processing models most relevant to laparoscopic surgery. Our review highlighted the necessity for a model that accounts for dynamic environments, perception, allocation of attention resources between the actions of both hands of an operator, and skill acquisition and retention. The results of the literature review were augmented through intraoperative observations of 7 colorectal surgical procedures, supported by laparoscopic video analysis of 12 colorectal procedures. The Wickens human information-processing model was selected as the most relevant theoretical model to which we make adaptions for this specific application. We expanded the perception subsystem of the model to involve all aspects of perception during laparoscopic surgery. We extended the decision-making system to include dynamic decision-making to account for case/patient-specific and surgeon-specific deviations. The response subsystem now includes dual-task performance and nontechnical skills, such as intraoperative communication. The memory subsystem is expanded to include skill acquisition and retention. Surgical decision-making during laparoscopic surgery is the result of a highly complex series of processes influenced not only by the operator's knowledge, but also patient anatomy and interaction with the surgical team. Newer developments in simulation-based education must focus on the theoretically supported elements and events that underpin skill acquisition and affect the cognitive abilities of novice surgeons. The proposed human information-processing model builds on established literature regarding information processing, accounting for a dynamic environment of laparoscopic surgery. This revised model may be used as a foundation for a model describing robotic surgery.

Searching for serial refreshing in working memory: Using response times to track the content of the focus of attention over time.

One popular idea is that, to support the maintenance of a set of elements over brief periods of time, the focus of attention rotates among the different elements, thereby serially refreshing the content of working memory (WM). In the research reported here, probe letters were presented between to-be-remembered letters, and response times to these probes were used to infer the status of the different items in WM. If the focus of attention cycles from one item to the next, its content should be different at different points in time, and this should be reflected in a change in the response time patterns over time. Across a set of four experiments, we demonstrated a striking pattern of invariance in the response time patterns over time, suggesting either that the content of the focus of attention did not change over time or that response times cannot be used to infer the content of the focus of attention. We discuss how this pattern constrains models of WM, attention, and human information processing.

The Effect of Sign Saturation on Driver Speed Limit Compliance in School Zones

School zone sign saturation can be defined as the density of the school zones around a reference school zone. This study investigated the effect of school zone sign saturation on vehicle speed and compliance to school zone speed limit. Based on the models of human information processing, the presence of too many school zone signs in a given area could lead to inattention or lack of attention to those signs leading to a non-compliance of the speed limit in school zones. To investigate the effect of sign saturation, vehicle speed data was collected in four school zones. Results found that the vehicle speed is significantly lower and compliance is significantly higher in high saturation school zones.