Action-related Information Research Articles

Physical effort modulates perceptual awareness judgment independent of level of processing

Recent studies have emphasized the association between action and perceptual awareness, suggesting that action-related information can contribute to perceptual awareness. Given that the Level of Processing (LoP) hypothesis proposes that the emergence of awareness depends on the level of stimulus processing, the current study examines whether action impacts perceptual awareness across different processing levels. In Experiment 1, participants identified target stimuli’s color (low-level task) or category (high-level task) via mouse clicks, followed by visual awareness ratings. Experiment 2 replicated the tasks using hand-grip dynamometers. Results from Experiment 1 support the LoP theory, showing a more gradual emergence of awareness for low-level features and a more dichotomous emergence for high-level features. In Experiment 2, higher reported visual awareness ratings were observed at greater physical effort, regardless of task type. These results suggest that action-related information influences reported awareness of stimuli in the same way at low- and high-level stimulus processing.

Sensitivity and specificity of the action observation network to kinematics, target object, and gesture meaning.

Hierarchical models have been proposed to explain how the brain encodes actions, whereby different areas represent different features, such as gesture kinematics, target object, action goal, and meaning. The visual processing of action-related information is distributed over a well-known network of brain regions spanning separate anatomical areas, attuned to specific stimulus properties, and referred to as action observation network (AON). To determine the brain organization of these features, we measured representational geometries during the observation of a large set of transitive and intransitive gestures in two independent functional magnetic resonance imaging experiments. We provided evidence for a partial dissociation between kinematics, object characteristics, and action meaning in the occipito-parietal, ventro-temporal, and lateral occipito-temporal cortex, respectively. Importantly, most of the AON showed low specificity to all the explored features, and representational spaces sharing similar information content were spread across the cortex without being anatomically adjacent. Overall, our results support the notion that the AON relies on overlapping and distributed coding and may act as a unique representational space instead of mapping features in a modular and segregated manner.

Reciprocal interactions among parietal and occipito-temporal representations support everyday object-directed actions

Everyday interactions with common manipulable objects require the integration of conceptual knowledge about objects and actions with real-time sensory information about the position, orientation and volumetric structure of the grasp target. The ability to successfully interact with everyday objects involves analysis of visual form and shape, surface texture, material properties, conceptual attributes such as identity, function and typical context, and visuomotor processing supporting hand transport, grasp form, and object manipulation. Functionally separable brain regions across the dorsal and ventral visual pathways support the processing of these different object properties and, in cohort, are necessary for functional object use. Object-directed grasps display end-state-comfort: they anticipate in form and force the shape and material properties of the grasp target, and how the object will be manipulated after it is grasped. End-state-comfort is the default for everyday interactions with manipulable objects and implies integration of information across the ventral and dorsal visual pathways. We propose a model of how visuomotor and action representations in parietal cortex interact with object representations in ventral and lateral occipito-temporal cortex. One pathway, from the supramarginal gyrus to the middle and inferior temporal gyrus, supports the integration of action-related information, including hand and limb position (supramarginal gyrus) with conceptual attributes and an appreciation of the action goal (middle temporal gyrus). A second pathway, from posterior IPS to the fusiform gyrus and collateral sulcus supports the integration of grasp parameters (IPS) with the surface texture and material properties (e.g., weight distribution) of the grasp target. Reciprocal interactions among these regions are part of a broader network of regions that support everyday functional object interactions.

Learnable Feature Augmentation Framework for Temporal Action Localization.

Temporal action localization (TAL) has drawn much attention in recent years, however, the performance of previous methods is still far from satisfactory due to the lack of annotated untrimmed video data. To deal with this issue, we propose to improve the utilization of current data through feature augmentation. Given an input video, we first extract video features with pre-trained video encoders, and then randomly mask various semantic contents of video features to consider different views of video features. To avoid damaging important action-related semantic information, we further develop a learnable feature augmentation framework to generate better views of videos. In particular, a Mask-based Feature Augmentation Module (MFAM) is proposed. The MFAM has three advantages: 1) it captures the temporal and semantic relationships of original video features, 2) it generates masked features with indispensable action-related information, and 3) it randomly recycles some masked information to ensure diversity. Finally, we input the masked features and the original features into shared action detectors respectively, and perform action classification and localization jointly for model learning. The proposed framework can improve the robustness and generalization of action detectors by learning more and better views of videos. In the testing stage, the MFAM can be removed, which does not bring extra computational costs. Extensive experiments are conducted on four TAL benchmark datasets. Our proposed framework significantly improves different TAL models and achieves the state-of-the-art performances.

Residual Neural Networks for Human Action Recognition from RGB-D Videos

Recently, the RGB-D based Human Action Recognition (HAR) has gained significant research attention due to the provision of complimentary information by different data modalities. However, the current models have experienced still unsatisfactory results due to several problems including noises and view point variations between different actions. To sort out these problems, this paper proposes two new action descriptors namely Modified Depth Motion Map (MDMM) and Spherical Redundant Joint Descriptor (SRJD). MDMM eliminates the noises from depth maps and preserves only the action related information. Further SRJD ensures resilience against view point variations and reduces the misclassifications between different actions with similar view properties. Further, to maximize the recognition accuracy, standard deep learning algorithm called as Residual Neural Network (ResNet) is used to train the system through the features extracted from MDMM and SRJD. Simulation experiments prove that the multiple data modalities are better than single data modality. The proposed approach was tested on two public datasets namely NTURGB+D dataset and UTD-MHAD dataset. The testing results declare that the proposed approach is superior to the earlier HAR methods. On an average, the proposed system gained an accuracy of 90.0442% and 92.3850% at Cross-subject and Cross-view validations respectively.

Decoding Reach Direction in Early "Visual" Cortex of Congenitally Blind Individuals.

Motor actions, such as reaching or grasping, can be decoded from fMRI activity of early visual cortex (EVC) in sighted humans. This effect can depend on vision or visual imagery, or alternatively, could be driven by mechanisms independent of visual experience. Here, we show that the actions of reaching in different directions can be reliably decoded from fMRI activity of EVC in congenitally blind humans (both sexes). Thus, neither visual experience nor visual imagery is necessary for EVC to represent action-related information. We also demonstrate that, within EVC of blind humans, the accuracy of reach direction decoding is highest in areas typically representing foveal vision and gradually decreases in areas typically representing peripheral vision. We propose that this might indicate the existence of a predictive, hard-wired mechanism of aligning action and visual spaces. This mechanism might send action-related information primarily to the high-resolution foveal visual areas, which are critical for guiding and online correction of motor actions. Finally, we show that, beyond EVC, the decoding of reach direction in blind humans is most accurate in dorsal stream areas known to be critical for visuo-spatial and visuo-motor integration in the sighted. Thus, these areas can develop space and action representations even in the lifelong absence of vision. Overall, our findings in congenitally blind humans match previous research on the action system in the sighted, and suggest that the development of action representations in the human brain might be largely independent of visual experience.SIGNIFICANCE STATEMENT Early visual cortex (EVC) was traditionally thought to process only visual signals from the retina. Recent studies proved this account incomplete, and showed EVC involvement in many activities not directly related to incoming visual information, such as memory, sound, or action processing. Is EVC involved in these activities because of visual imagery? Here, we show robust reach direction representation in EVC of humans born blind. This demonstrates that EVC can represent actions independently of vision and visual imagery. Beyond EVC, we found that reach direction representation in blind humans is strongest in dorsal brain areas, critical for action processing in the sighted. This suggests that the development of action representations in the human brain is largely independent of visual experience.

Anticipation of Appetitive Operant Action Induces Sustained Dopamine Release in the Nucleus Accumbens.

The mesolimbic dopamine system is implicated in signaling reward-related information as well as in actions that generate rewarding outcomes. These implications are commonly investigated in either pavlovian or operant reinforcement paradigms, where only the latter requires instrumental action. To parse contributions of reward- and action-related information to dopamine signals, we directly compared the two paradigms: male rats underwent either pavlovian or operant conditioning while dopamine release was measured in the nucleus accumbens, a brain region central for processing this information. Task conditions were identical with the exception of the operant-lever response requirement. Rats in both groups released the same quantity of dopamine at the onset of the reward-predictive cue. However, only the operant-conditioning group showed a subsequent, sustained plateau in dopamine concentration throughout the entire 5 s cue presentation (preceding the required action). This dopamine ramp was unaffected by probabilistic reward delivery, occurred exclusively before operant actions, and was not related to task performance or task acquisition as it persisted throughout the 2 week daily behavioral training. Instead, the ramp flexibly increased in duration with longer cue presentation, seemingly modulating the initial cue-onset-triggered dopamine release, that is, the reward prediction error (RPE) signal, as both signal amplitude and sustainment diminished when reward timing was made more predictable. Thus, our findings suggest that RPE and action components of dopamine release can be differentiated temporally into phasic and ramping/sustained signals, respectively, where the latter depends on the former and presumably reflects the anticipation or incentivization of appetitive action, conceptually akin to motivation.SIGNIFICANCE STATEMENT It is unclear whether the components of dopamine signals that are related to reward-associated information and reward-driven approach behavior can be separated. Most studies investigating the dopamine system use either pavlovian or operant conditioning, which both involve the delivery of reward and necessitate appetitive approach behavior. Thus, used exclusively, neither paradigm can disentangle the contributions of these components to dopamine release. However, by combining both paradigms in the same study, we find that anticipation of a reward-driven operant action induces a modulation of reward-prediction-associated dopamine release, producing so-called dopamine ramps. Therefore, our findings provide new insight into dopamine ramps and suggest that dopamine signals integrate reward and appetitive action in a temporally distinguishable, yet dependent, manner.

Reducing the Individual Carbon Impact of Video Streaming: A Seven-Week Intervention Using Information, Goal Setting, and Feedback

This online intervention study examined whether system- and action-related information alone, together with goal setting, or together with goal setting and feedback helps people change their video streaming activities in a pro-environmental way. The participants (N = 92) documented their video streaming activities for one week prior to the intervention (week 1), three weeks after the onset of the intervention (weeks 2–4), and in a follow-up period two weeks later (week 7). A reduction of greenhouse gas emissions associated with video streaming was observed over the course of the intervention, together with reduced streaming durations and lowered resolution settings across all groups. There were no differences between the groups. It appears that as regards video streaming, information combined with self-monitoring has considerable potential to change individual behaviour and its associated ecological impact.

Encouraging pro-environmental consumption behaviour amongst Gen-Y consumers: right messages to different consumers?

PurposeMedia information is often considered critical to foster pro-environmental consumption behaviours (PECB). However, empirical evidence was inconsistent about the impact of environmental information exposure on the behaviours. Thus, the study aims to clarify that impact by investigating three different types of environmental information. Moreover, the study posited that consumers who hold different environment-related values may be receptive to and influenced differently by these types of information.Design/methodology/approachSurvey data were collected from 259 Gen-Y consumers in urban areas. The data were analysed for measure reliabilities and validities before hypotheses were tested using ordinary least squares (OLS) regressions.FindingsThe results revealed that the environmental problem-, action- and effectiveness-related information exposure affects pro-environmental personal norms (PNs) which then influence the behaviours. More importantly, the influences of action-related information and effectiveness-related information on PNs were moderated differently by egoistic values held by consumers.Originality/valueThe study contributes to the literature by highlighting the importance of information-value congruence in designing effective media campaigns.

Exploiting the Nature of Repetitive Actions for Their Effective and Efficient Recognition

In the field of human action recognition (HAR), the recognition of actions with large duration is hindered by the memorization capacity limitations of the standard probabilistic and recurrent neural network (R-NN) approaches that are used for temporal sequence modeling. The simplest remedy is to employ methods that reduce the input sequence length, by performing window sampling, pooling, or key-frame extraction. However, due to the nature of the frame selection criteria or the employed pooling operations, the majority of these approaches do not guarantee that the useful, discriminative information is preserved. In this work, we focus on the case of repetitive actions. In such actions, a discriminative, core execution motif is maintained throughout each repetition, with slight variations in execution style and duration. Additionally, scene appearance may change as a consequence of the action. We exploit those two key observations on the nature of repetitive actions to build a compact and efficient representation of long actions by maintaining the discriminative sample information and removing redundant information which is due to task repetitiveness. We show that by partitioning an input sequence based on repetition and by treating each repetition as a discrete sample, HAR models can achieve an increase of up to 4% in action recognition accuracy. Additionally, we investigate the relation between the dataset and action set attributes with this strategy and explore the conditions under which the utilization of repetitiveness for input sequence sampling, is a useful preprocessing step in HAR. Finally, we suggest deep NN design directions that enable the effective exploitation of the distinctive action-related information found in repetitiveness, and evaluate them with a simple deep architecture that follows these principles.

The link between number and action in human infants

Humans' inborn ability to represent and manipulate numerical quantities is supported by the parietal cortex, which is also involved in a variety of spatial and motor abilities. While the behavioral links between numerical and spatial information have been extensively studied, little is known about the connection between number and action. Some studies in adults have shown a series of interference effects when simultaneously processing numerical and action information. We investigated the origins of this link by testing forty infants (7- to 9-month-old) in one of two experimental conditions: one group was habituated to congruent number-hand pairings, where the larger the number, the more open the hand-shape associated; the second group was habituated to incongruent number-hand pairings, where the larger the number, the more close the hand-shape associated. In test trials, both groups of infants were presented with congruent and incongruent pairings. We found that only infants habituated to congruency showed a significantly higher looking time to the test trial depicting incongruent pairings. These findings show for the first time that infants spontaneously associate magnitude-related changes across the dimensions of number and action-related information, thus offering support to the existence of an early, preverbal number-action link in the human mind.

Skeleton-based Action Recognition with Two-Branch Graph Convolutional Networks

Graph convolution is a popular technique for action recognition based on human skeleton data. Due to the fact that human skeleton data can be treated as a graph in three dimensions, Graph Convolutional Networks (GCN) represent the input data as a graph structure to perform the recognition task, and thus numerous approaches based on GCN to recognize actions have achieved great results. In spite of the input data being structured as a four-dimensional tensor in GCN, it still can not fully exploit the contained rich action-related information. Therefore, we proposed a new model that the three-dimensional skeleton data is put into both the Convolutional Neural Network (CNN) and the Graph Convolutional Neural Network branches to perform feature extraction in the spatiotemporal dimension separately, then the output information is fused for prediction. Given the richness of time-domain information characteristics, feature extraction is enhanced by increasing the model’s depth. After the pooling layer and the fully connected layer, we concatenate the outputs at the ends of the graph data stream and the convolution data stream to obtain the network’s final output. Finally, the prediction results can be obtained via the SoftMax layer. On the Kinetic 400 dataset, our suggested model outperforms Benchmark STGCN in terms of accuracy. The experiment results indicate that the proposed novel model successfully increases the generalization ability and classification performance for action recognition.

Perceptual decision confidence is sensitive to forgone physical effort expenditure

Contemporary theoretical accounts of metacognition propose that action-related information is used in the computation of perceptual decision confidence. We investigated whether the amount of expended physical effort, or the ‘motoric sunk cost’ of a decision, influences perceptual decision confidence judgements in humans. In particular, we examined whether people feel more confident in decisions which required more effort to report. Forty-two participants performed a luminance discrimination task that involved identifying which of two flickering grayscale squares was brightest. Participants reported their choice by squeezing hand-held dynamometers. Across trials, the effort required to report a decision was varied across three levels (low, medium, high). Critically, participants were only aware of the required effort level on each trial once they had initiated their motor response, meaning that the varying effort requirements could not influence their initial decisions. Following each decision, participants rated their confidence in their choice. We found that participants were more confident in decisions that required greater effort to report. This suggests that humans are sensitive to motoric sunk costs and supports contemporary models of metacognition in which actions inform the computation of decision confidence.

Neurophysiological correlates of perception\u2013action binding in the somatosensory system

Action control requires precisely and flexibly linking sensory input and motor output. This is true for both, visuo-motor and somatosensory-motor integration. However, while perception–action integration has been extensively investigated for the visual modality, data on how somatosensory and action-related information is associated are scarce. We use the Theory of Event Coding (TEC) as a framework to investigate perception–action integration in the somatosensory-motor domain. Based on studies examining the neural mechanisms underlying stimulus–response binding in the visuo-motor domain, the current study investigates binding mechanisms in the somatosensory-motor domain using EEG signal decomposition and source localization analyses. The present study clearly demonstrates binding between somatosensory stimulus and response features. Importantly, repetition benefits but no repetition costs are evident in the somatosensory modality, which differs from findings in the visual domain. EEG signal decomposition indicates that response selection mechanisms, rather than stimulus-related processes, account for the behavioral binding effects. This modulation is associated with activation differences in the left superior parietal cortex (BA 7), an important relay of sensorimotor integration.

Ambulatory searching task reveals importance of somatosensation for lower-limb amputees

The contribution of somatosensation to locomotor deficits in below-knee amputees (BKAs) has not been fully explored. Unilateral disruption of plantar sensation causes able-bodied individuals to adopt locomotor characteristics that resemble those of unilateral BKAs, suggesting that restoring somatosensation may improve locomotion for amputees. In prior studies, we demonstrated that electrically stimulating the residual nerves of amputees elicited somatosensory percepts that were felt as occurring in the missing foot. Subsequently, we developed a sensory neuroprosthesis that modulated stimulation-evoked sensation in response to interactions between the prosthesis and the environment. To characterize the impact of the sensory neuroprosthesis on locomotion, we created a novel ambulatory searching task. The task involved walking on a horizontal ladder while blindfolded, which engaged plantar sensation while minimizing visual compensation. We first compared the performance of six BKAs to 14 able-bodied controls. Able-bodied individuals demonstrated higher foot placement accuracy than BKAs, indicating that the ladder test was sensitive enough to detect locomotor deficits. When three of the original six BKAs used the sensory neuroprosthesis, the tradeoff between speed and accuracy significantly improved for two of them. This study advanced our understanding of how cutaneous plantar sensation can be used to acquire action-related information during challenging locomotor tasks.

Binding actions and emotions in the infant’s brain

ABSTRACT The first year represents an exceptional time of development during which important social skills emerge, like action and emotion understanding. However, to date, no study explored the neural underpinnings of infants’ ability to bind emotion- to action-related information. To assess this issue, we measured EEG activity while 6-month-old infants observed the same action performed by an actress displaying three different emotional expressions (happiness, anger and neutral). Results have shown that actions embedded in an emotional context (happiness and anger) elicited larger early negativity at parieto-occipital sites compared to a neutral context. This finding suggests that already at 6 months of age, infants use information coming from facial expressions to detect the saliency and relevance of others’ actions.

Action-related information trumps system information: Influencing consumers’ intention to reduce food waste

In order to substantially reduce food waste at the household level, it is essential to change consumer behavior. Informing consumers about the food waste issue is a promising means of bringing about behavior change: research confirms that information can increase food waste reduction behavior. However, it has yet to be determined what kind of information is most effective and exactly how that information affects consumer food waste behavior. This study compares the effects of system vs. action-related information (i.e., knowing what impacts specific actions entail vs. knowing how specific actions can help to accomplish a goal) on behavioral intention towards food waste. That is, the study focuses on the effect of information on the role of food waste in the food system versus information on actions that can be taken to avoid it. Moreover, an adapted model of the Theory of Planned Behavior is used to assess how these information effects are mediated by consumers’ attitude, norms, and perceived behavioral control. Results from an online experiment with a between-subjects design (N = 2248) show that action-related information significantly increases respondents’ intention to reduce food waste while system information has no significant effect. The change in behavioral intention in the action-related information group is ascribed to greater personal norm activation, more favorable attitudes towards food waste reduction, and higher perceived behavioral control of food waste behaviors. Even though system information does not significantly increase intention to reduce food waste, it results in more favorable attitudes towards food waste reduction. The findings provide insights for policy makers and NGOs on what type of information to consider when designing effective food waste reduction campaigns targeted at consumers, with action-related information supporting the opportunity for consumer behavior change.

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

Children with developmental coordination disorder show altered functional connectivity compared to peers

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder that affects a child’s ability to learn motor skills and participate in self-care, educational, and leisure activities. The cause of DCD is unknown, but evidence suggests that children with DCD have atypical brain structure and function. Resting-state MRI assesses functional connectivity by identifying brain regions that have parallel activation during rest. As only a few studies have examined functional connectivity in this population, our objective was to compare whole-brain resting-state functional connectivity of children with DCD and typically-developing children. Using Independent Component Analysis (ICA), we compared functional connectivity of 8–12 year old children with DCD (N = 35) and typically-developing children (N = 23) across 19 networks, controlling for age and sex. Children with DCD demonstrate altered functional connectivity between the sensorimotor network and the posterior cingulate cortex (PCC), precuneus, and the posterior middle temporal gyrus (pMTG) (p < 0.0001). Previous evidence suggests the PCC acts as a link between functionally distinct networks. Our results indicate that ineffective communication between the sensorimotor network and the PCC might play a role in inefficient motor learning seen in DCD. The pMTG acts as hub for action-related information and processing, and its involvement could explain some of the functional difficulties seen in DCD. This study increases our understanding of the neurological differences that characterize this common motor disorder.

Adherence Assessment of Medication Errors Reporting System at the Public Hospital in Riyadh, Saudi Arabia

International Journal of Pharmacology and Clinical Sciences,2019,8,2,105-109.DOI:10.5530/ijpcs.2019.8.18Published:June 2019Type:Research ArticleAuthors:Yousef Ahmed Alomi, Nisreen Al-Shubaar, Nadia Lubad, and Fatimah Ali Albusalih Author(s) affiliations:Yousef Ahmed Alomi1,* , Nisreen Al-Shubaar2, Nadia Lubad2, Fatimah Ali Albusalih3 1The Former General Manager of General Administration of Pharmaceutical Care, Former Head, National Clinical Pharmacy and Pharmacy Practice, Former Head, Pharmacy R and D Administration, Ministry of Health, Riyadh, SAUDI ARABIA. 2King Salman Hospital, Ministry of Health, Riyadh, SAUDI ARABIA. 3College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (University of Dammam) Dammam, SAUDI ARABIA. Abstract:Objective: Medication errors have noteworthy implications in the field of patient safety. Error detection through a dynamic supervision and an efficient error reporting system unveils medication errors and boosts safe practices. The overall goal of this study is to explore the pharmacist’s adherence to medication errors reporting system in adults and pediatrics at the public hospital in Riyadh, Saudi Arabia. Methods: This article describes 9 months retrospective cohort study in year of 2015. A retrospective study was conducted on all inpatients at a 300-bed hospital where all medication procedures in each ward were monitored by a clinical pharmacist. The study was conducted at the Public Hospital in Riyadh, Saudi Arabia. The hospital had medication safety officer with medication safety committee. The program led by trained pharmacist and delivered basic medication safety education programs to all health professional. The medication error report consisted of patient demographic information, qualification of committing mistakes, time of errors occurs, type of medication errors, reasons for medication errors, medications stages involved and errors outcome. Results: The total number of reports were 805. The most common adherence documentation of error involving medications were cost related information (100%), error related information with an average (90.59%), patient related information (50.37%) and drug related information (49.39%). While the action related information (0.57%) was harmless. The most common completed of error related information were causes of medication errors 775 (96.3%) and type of medication errors 770 (95.6%) followed by outcome of medication errors 764 (94.9%) and medication process stages involved 711 (88.3%). While the patient’s demographic information completed only (50%) in reports. Conclusion: Despite the medication error reporting is consider new at the public hospital, the number of reporting was high. The system needs more determination to follow completeness medication errors reporting system and that by development of an electronic reporting system, program awareness and positive documentation system feedback changes in pharmacy practice. Keywords:Adherence, Medication errors, Ministry of Health, Public, Riyadh, Saudi ArabiaView:PDF (323.67 KB) PDFClick here to download the PDF file. Images Medication Error Reports Compliance