Applying psychological science to the CCTV review process: a review of cognitive and ergonomic literature

As CCTV cameras are used more and more often to increase security in communities, police are spending a larger proportion of their resources, including time, in processing CCTV images when investigating crimes that have occurred (Levesley & Martin, 2005; Nichols, 2001). As with all tasks, there are ways to approach this task that will facilitate performance and other approaches that will degrade performance, either by increasing errors or by unnecessarily prolonging the process. A clearer understanding of psychological factors influencing the effectiveness of footage review will facilitate future training in best practice with respect to the review of CCTV footage. The goal of this report is to provide such understanding by reviewing research on footage review, research on related tasks that require similar skills, and experimental laboratory research about the cognitive skills underpinning the task. The report is organised to address five challenges to effectiveness of CCTV review: the effects of the degraded nature of CCTV footage, distractions and interrupts, the length of the task, inappropriate mindset, and variability in people’s abilities and experience. Recommendations for optimising CCTV footage review include (1) doing a cognitive task analysis to increase understanding of the ways in which performance might be limited, (2) exploiting technology advances to maximise the perceptual quality of the footage (3) training people to improve the flexibility of their mindset as they perceive and interpret the images seen, (4) monitoring performance either on an ongoing basis, by using psychophysiological measures of alertness, or periodically, by testing screeners’ ability to find evidence in footage developed for such testing, and (5) evaluating the relevance of possible selection tests to screen effective from ineffective screener

Improving expertise-sensitive help systems

Given the complexity and functionality of today’s software, task-specific, system-suggested help could be beneficial for users. Although system-suggested help assists users in completing their tasks quickly, user response to unsolicited advice from their applications has been lukewarm. One such problem is lack of knowledge of system-suggested help about the user’s expertise with the task they are currently doing. This thesis examines the possibility of improving system-suggested help by adding knowledge about user expertise into the help system and eventually designing an expertise-sensitive help system. An expertise-sensitive help system would detect user expertise dynamically and regularly so that systems could recommend help overtly to novices, subtly to average and poor users, and not at all to experts. This thesis makes several advances in this area through a series of four experiments. In the first experiment, we show that users respond differently to help interruptions depending on their expertise with a task. Having established that user response to helpful interruptions varies with expertise level, in the second experiment we create a four-level classifier of task expertise with an accuracy of 90%. To present helpful interruptions differently to novice, poor, and average users, we need to design three interrupting notifications that vary in their attentional draw. In experiment three, we investigate a number of options and choose three icons. Finally, in experiment four, we integrate the expertise model and three interrupting notifications into an expertise-sensitive system-suggested help program, and investigate the user response. Together, these four experiments show that users value helpful interruptions when their expertise with a task is low, and that an expertise-sensitive help system that presents helpful interruptions with attentional draw that matches user expertise is effective and valuable.&#8195

Embodied interaction with visualization and spatial navigation in time-sensitive scenarios

Paraphrasing the theory of embodied cognition, all aspects of our cognition are determined primarily by the contextual information and the means of physical interaction with data and information. In hybrid human-machine systems involving complex decision making, continuously maintaining a high level of attention while employing a deep understanding concerning the task performed as well as its context are essential. Utilizing embodied interaction to interact with machines has the potential to promote thinking and learning according to the theory of embodied cognition proposed by Lakoff. Additionally, the hybrid human-machine system utilizing natural and intuitive communication channels (e.g., gestures, speech, and body stances) should afford an array of cognitive benefits outstripping the more static forms of interaction (e.g., computer keyboard). This research proposes such a computational framework based on a Bayesian approach; this framework infers operator\u27s focus of attention based on the physical expressions of the operators. Specifically, this work aims to assess the effect of embodied interaction on attention during the solution of complex, time-sensitive, spatial navigational problems. Toward the goal of assessing the level of operator\u27s attention, we present a method linking the operator\u27s interaction utility, inference, and reasoning. The level of attention was inferred through networks coined Bayesian Attentional Networks (BANs). BANs are structures describing cause-effect relationships between operator\u27s attention, physical actions and decision-making. The proposed framework also generated a representative BAN, called the Consensus (Majority) Model (CMM); the CMM consists of an iteratively derived and agreed graph among candidate BANs obtained by experts and by the automatic learning process. Finally, the best combinations of interaction modalities and feedback were determined by the use of particular utility functions. This methodology was applied to a spatial navigational scenario; wherein, the operators interacted with dynamic images through a series of decision making processes. Real-world experiments were conducted to assess the framework\u27s ability to infer the operator\u27s levels of attention. Users were instructed to complete a series of spatial-navigational tasks using an assigned pairing of an interaction modality out of five categories (vision-based gesture, glove-based gesture, speech, feet, or body balance) and a feedback modality out of two (visual-based or auditory-based). Experimental results have confirmed that physical expressions are a determining factor in the quality of the solutions in a spatial navigational problem. Moreover, it was found that the combination of foot gestures with visual feedback resulted in the best task performance (p\u3c .001). Results have also shown that embodied interaction-based multimodal interface decreased execution errors that occurred in the cyber-physical scenarios (p \u3c .001). Therefore we conclude that appropriate use of interaction and feedback modalities allows the operators maintain their focus of attention, reduce errors, and enhance task performance in solving the decision making problems

The Role of Age in Technology-induced Workplace Stress

Recent research shows that such Information and Communication Technologies (ICTs) as instant messengers can cause workplace interruptions, which lead to stress for employees and substantial productivity losses for U.S. organizations. Since the introduction of ICTs, workplace interruptions have evolved in both frequency and nature from irregular phone calls to a continuous stream of e-mail notifications and other electronic interruptions, mediated through a large number of technological devices that constantly beep and buzz. This trend of an increasing frequency of workplace interruptions closely relates to another workplace trend: the graying of the workforce, implying that the U.S. workforce is aging at an increased rate. Since older people are particularly vulnerable to interruptions, the interdependencies inherent in these two workplace trends need to be better understood. Accordingly, this dissertation aims to understand whether, how, and why technology-mediated (T-M) interruptions impact stress and task performance differently for older compared to younger adults. To examine these questions, this research applies two complementary theoretical frames that explain interruptions\u27 influence on older and younger adults\u27 cognition. First, the Person-Environment Fit perspective suggests that T-M interruptions may lessen the fit between the mental resources available for performing a task and those required, thereby inducing workplace stress and, in turn, reducing individual task performance. Second, the Inhibitory Deficit Theory of Cognitive Aging holds that older peoples\u27 ability to actively disregard distracting stimuli is impaired. Thus, more T-M interruptions may \u27steal\u27 resources from the processing of task-related content in older adults. In combining these theories with user characteristics and technology features, this research develops an integrative model of ICTs, aging, stress, and task performance. We propose that older people are more distracted by T-M interruptions than younger, thereby experiencing greater mental workload and, in turn, more stress and lower performance. We test the model through a laboratory experiment that integrates the manipulation of ICT features with objective measures of stress and task performance, unlike the subjective measures commonly used. Experimental manipulations include the frequency with which interruptions appear as well as such interruption design characteristics as color codes. Outcome measures include actual performance in terms of the number of task elements solved, as well as the change in stress hormones found in saliva, a state-of-the art physiological measure of stress. In developing and testing the model, we help to clarify the role of age in technostress. This research also sheds more light on the mental processes that connect ICTs to stress and performance, and it has begun to open the black box of the ICT features linked to these outcomes. For managers, we provide guidance on assisting older employees in realizing their full potential for contributing to firm success. This research further advises systems designers on such issues as user involvement

Modeling User Perception of Interaction Opportunities for Effective Teamwork

This paper presents a model of collaborative decision-making for groups that involve people and computer agents. The model distinguishes between actions relating to participantspsila commitment to the group and actions relating to their individual tasks, uses this distinction to decompose group decision making into smaller problems that can be solved efficiently. It allows computer agents to reason about the benefits of their actions on a collaboration and the ways in which human participants perceive these benefits. The model was tested in a setting in which computer agents need to decide whether to interrupt people to obtain potentially valuable information. Results show that the magnitude of the benefit of interruption to the collaboration is a major factor influencing the likelihood that people will accept interruption requests. They further establish that peoplepsilas perceived type of their partners (whether humans or computers) significantly affected their perceptions of the usefulness of interruptions when the benefit of the interruption is not clear-cut. These results imply that system designers need to consider not only the possible benefits of interruptions to collaborative human-computer teams but also the way that such benefits are perceived by people.Engineering and Applied Science

Multi-format Notifications for Multi-tasking

Abstract. We studied people's perception of and response to a set of visual and auditory notifications issued in a multi-task environment. Primary findings show that participants' reactive preference ratings of notifications delivered in various contexts during experimentation appear to contradict their reflective, overall ratings of the notification formats when elicited independently of contextual information, indicating a potential difficulty in people's abilities to articulate their preferences in the absence of context. We also found people to vary considerably in their preferences for different notification formats delivered in different contexts, such taht simple approaches to selecting notification delivery formats will be dissatisfying to users a substantial portion of the time. These findings can inform the designs of future systems: rather than target the general user alone, they should strive to better understand each user individually

On the neural computation of utility: implications from studies of brain stimulation reward

1. Like other vertebrates, from goldfish to humans, rats will work in order to deliver electrical stimulation to certain brain sites. Although the stimulation produces no evident physiological benefit, it is sought out avidly, as if it were a biologically significant resource. Thus, it has long been thought that the rewarding stimulation activates neural circuitry involved in the evaluation and selection of goals. 2. Computing the utility of goal objects involves a tightly integrated set of perceptual, cognitive, and motivational mechanisms. I argue that rewarding electrical brain stimulation engages only a subset of these mechanisms. If so, comparison of the ways in which the utility of electrical brain stimulation and natural reinforcers are computed may highlight operating principles and isolate components of the computational mechanisms. 3. In the view proposed here, information about goal objects and consummatory acts is processed, in parallel, in three different channels. 3.1. Perceptual processing indicates what and where the goal object is. 3.2. A stopwatch-like interval timer predicts when or how often the goal object will be available. 3.3. Under the influence of information about the current physiological state, an evaluative channel returns a subjective weighting of strength variables such as the concentration of a sucrose solution or the temperature of an air current. 3.4. The output of these channels is recorded in multidimensional records that include 3.4.1. information of perceptual origin about amount and kind (e.g., food, water,or salt) 3.4.2. information from the timer about rate and delay 3.4.3. a subjective assessment of intensity provided by the evaluative channel 4. This chapter addresses the relationships between brain stimulation reward (BSR), the perceptual, interval timing, and evaluative channels, and the variants of utility proposed by Kahneman and his coworkers on the basis of their studies of evaluation and choice in human subjects. 4.1. It is argued that the output of the evaluative channel can be manifested in experience as pleasure or suffering but that awareness is not necessary in order for this signal to influence action. 5. The neural signal injected by rewarding electrical stimulation is portrayed as providing meaningful information about rate, delay and intensity but not about amount or kind. This proposal is used to account for 5.1. competition and summation between BSR and natural rewards 5.2. differential effects of physiological feedback on the utility of BSR and natural rewards 5.3. matching of behavioral allocation to the relative rates and intensities of BSR 5.4. differences in the elasticity of demand for BSR and food in a closed economy 5.5. the high substitutability of BSR for food and water in an open economy 6. The powerful aftereffect of BSR that potentiates efforts to obtain additional stimulation is related to expectancy

Reasoning about ideal interruptible moments: A soft computing implementation of an interruption classifier in free-form task environments

Current trends in society and technology make the concept of interruption a central human computer interaction problem. In this work, a novel soft computing implementation for an Interruption Classifier was designed, developed and evaluated that draws from a user model and real-time observations of the user\u27s actions as s/he works on computer-based tasks to determine ideal times to interact with the user. This research is timely as the number of interruptions people experience daily has grown considerably over the last decade. Thus, systems are needed to manage interruptions by reasoning about ideal timings of interactions. This research shows: (1) the classifier incorporates a user model in its’ reasoning process. Most of the research in this area has focused on task-based contextual information when designing systems that reason about interruptions; (2) the classifier performed at 96% accuracy in experimental test scenarios and significantly out-performed other comparable systems; (3) the classifier is implemented using an advanced machine learning technology—an Adaptive Neural-Fuzzy Inference System—this is unique since all other systems use Bayesian Networks or other machine learning tools; (4) the classifier does not require any direct user involvement—in other systems, users must provide interruption annotations while reviewing video sessions so the system can learn; and (5) a promising direction for reasoning about interruptions for free-form tasks–this is largely an unsolved problem

Peripheral Notifications: Effects of Feature Combination and Task Interference

Visual notifications are integral to interactive computing systems. The design of visual notifications entails two main considerations: first, visual notifications should be noticeable, as they usually aim to attract a user`s attention to a location away from their main task; second, their noticeability has to be moderated to prevent user distraction and annoyance. Although notifications have been around for a long time on standard desktop environments, new computing environments such as large screens add new factors that have to be taken into account when designing notifications. With large displays, much of the content is in the user's visual periphery, where human capacity to notice visual effects is diminished. One design strategy for enhancing noticeability is to combine visual features, such as motion and colour. Yet little is known about how feature combinations affect noticeability across the visual field, or about how peripheral noticeability changes when a user is working on an attention-demanding task. We addressed these questions by conducting two studies. We conducted a laboratory study that tested people's ability to detect popout targets that used combinations of three visual variables. After determining that the noticeability of feature combinations were approximately equal to the better of the individual features, we designed an experiment to investigate peripheral noticeability and distraction when a user is focusing on a primary task. Our results suggest that there can be interference between the demands of primary tasks and the visual features in the notifications. Furthermore, primary task performance is adversely affected by motion effects in the peripheral notifications. Our studies contribute to a better understanding of how visual features operate when used as peripheral notifications. We provide new insights, both in terms of combining features, and interactions with primary tasks