Subjective Judgment Research Articles

Contractor selection by using multi-criteria decision-making for Egyptian road maintenance

Egypt has witnessed a revolution in all fields and the establishment of giant projects, which require the selection of the appropriate contractor for their implementation. The major factor for evaluating contractors is the tender price of the project. The lowest price is not always a guarantee of the quality or time of a building project. Recently, there has been a shift away from the “lowest-price-wins” concept and a subjective judgment towards multi-criteria decision-making of selecting the project's contractors aiming to help owners in making decisions. The manuscript presents a multi-criteria decision-making strategy to optimize the selection of contractors for construction projects. The calculation of this strategy is divided into two phases. The first phase is designed to identify contractor prequalification criteria and utilize the AHP approach based on current Egyptian practice to calculate their weight. The second phase provides two techniques: (1) technique for order preference by similarity to ideal solution (TOPSIS) and (2) vlsekriterijumska optimization I kompromisno resenje (VIKOR) to optimize the selection of the available contractors for construction projects. Contractor 2 has the highest prices and lower values in technical capability, economic status, and management capability, it emerges as the top-ranked contractor in both methods due to its exceptional expertise and safety standards. These elevated levels of expertise and safety, coupled with the higher weights assigned by decision-makers to these criteria, contribute significantly to Contractor 2's superior ranking. A real-life case study improvement and refurbishment project is examined to demonstrate the value and highlight capabilities.

ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings

Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments. Our model encodes scatterplots in the parametric space of a Gaussian Mixture Model and uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups quantify visual cluster patterns in scatterplots. It improves on existing VQMs, first, by better estimating human judgments on two-Gaussian cluster patterns and, second, by giving higher accuracy when ranking general cluster patterns in scatterplots. We use it to analyze kinship data for genome-wide association studies, in which experts rely on the visual analysis of large sets of scatterplots. We make the benchmark datasets and the new VQM available for practical use and further improvements.

Fast and Precise Detection of Dense Soybean Seedlings Images Based on Airborne Edge Device

During the growth stage of soybean seedlings, it is crucial to quickly and precisely identify them for emergence rate assessment and field management. Traditional manual counting methods have some limitations in scenarios with large-scale and high-efficiency requirements, such as being time-consuming, labor-intensive, and prone to human error (such as subjective judgment and visual fatigue). To address these issues, this study proposes a rapid detection method suitable for airborne edge devices and large-scale dense soybean seedling field images. For the dense small target images captured by the Unmanned Aerial Vehicle (UAV), the YOLOv5s model is used as the improvement benchmark in the technical solution. GhostNetV2 is selected as the backbone feature extraction network. In the feature fusion stage, an attention mechanism—Efficient Channel Attention (ECA)—and a Bidirectional Feature Pyramid Network (BiFPN) have been introduced to ensure the model prioritizes the regions of interest. Addressing the challenge of small-scale soybean seedlings in UAV images, the model’s input size is set to 1280 × 1280 pixels. Simultaneously, Performance-aware Approximation of Global Channel Pruning for Multitask CNNs (PAGCP) pruning technology is employed to meet the requirements of mobile or embedded devices. The experimental results show that the identification accuracy of the improved YOLOv5s model reached 92.1%. Compared with the baseline model, its model size and total parameters were reduced by 76.65% and 79.55%, respectively. Beyond these quantitative evaluations, this study also conducted field experiments to verify the detection performance of the improved model in various scenarios. By introducing innovative model structures and technologies, the study aims to effectively detect dense small target features in UAV images and provide a feasible solution for assessing the number of soybean seedlings. In the future, this detection method can also be extended to similar crops.

Equitable Machine Learning for Hypoglycaemia Risk Management.

We developed a machine learning (ML) model for the detection of patients with high risk of hypoglycaemic events during their hospital stay to improve the detection and management of hypoglycaemia. Our model was trained on data from a regional local health care district in Australia. The model was found to have good predictive performance in the general case (AUC 0.837). We conducted subgroup analysis to ensure that the model performed in a way that did not disadvantage population subgroups, in this case based on gender or indigenous status. We found that our specific problem domain assisted us in reducing unwanted bias within the model, because it did not rely on practice patterns or subjective judgements for the outcome measure. With careful analysis for equity there is great potential for ML models to automate the detection of high-risk cohorts and automate mitigation strategies to reduce preventable errors.

The challenges of preserving our modernist public parks

Among the projects of the 20th century modern landscape architecture, there are some works that deserve to be pre-served as an integral part of the garden heritage. A world-wide inventory and evaluation of this landscape heritage is currently underway to identify those works that best represent the landscape architecture of the period.How can these works be restored in a way that meets today's ecological and social sustainability requirements without losing their original character? In the intensively used public parks presented in this article, complete reconstruction is generally not feasible, but the scale of the transformation is a central issue. International experi-ence shows how unclear the criteria for aspects of select-ing works to be preserved. For the time being, it is still often a subjective judgement, even within the professional community, whether the intervention used in the renova-tion process changes the composition of the original work.The renewal of any historic park and garden from any era must address sustainability, usually primarily from an ecological perspective. However, the contemporary regeneration of public parks and urban open spaces from the modern era, which are still intensively used today, is further complicated by the need for contemporary recrea-tional functions to be incorporated during the renovation and the application of universal design principles, which present additional challenges to integrate. Social sustain-ability is a strong requirement at these renovations.In this article, we analyse four modern public parks that were been renewed in the last 25 years or are in the process revitalization. In our country today, there is still no professional consensus as to which modern public parks can be considered valuable and worth preserv-ing for the future - if not through complete reconstruc-tion, then by carefully preserving the basic concept and compositional principles. The guiding principle in the selection of the public parks presented in this article was that they should be of different periods and have different recreational functions, with different challenges in their restoration.

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis

BackgroundIn clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques.MethodsIn this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer’s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy.ResultsOur ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively.ConclusionsOur algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

CKTN: Commonsense knowledge transfer network for human activity understanding

In most human activity understanding research, various deep learning networks are well designed to fit the character distribution of different human activities in images. After being fed with enough training data, the networks can perceive related patterns passively. Such abilities originate from the stimulation of training data, but these networks lack the ability to perform subjective judgment. When lacking sufficient training data, networks have difficulty perceiving valuable information, which leads to poor performance. However, human beings can infer human activities in complex situations through subjective judgment with their knowledge. On this point, we propose the commonsense knowledge transfer network (CKTN), which is empowered with human cognitive mechanisms and is able to understand human activities by using prior commonsense knowledge. More specifically, we study the structural combination rules of human bodies and explore the co-occurrence relationship between different body parts and objects. Then, we construct the part action cooccurrence probability matrix and human skeleton knowledge matrix, which work as prior knowledge, guiding our network to absorb and reason what the human activities in images are. Compared with traditional deep learning methods, our network realizes the unification of both subjective cognition and passive perception abilities. Experiments show the effectiveness of this method.

Organizational responses to scandals: how effective is the European Commission?

How public organizations respond to scandals is important as it affects their reputation and legitimacy. Yet very little research has been conducted on this topic. Drawing on insights from the literature on corporate reputation management, this article questions how effectively the European Commission has responded to recent scandals. It does this by creating an original analytical framework composed of five indicators: speed of response; openness of response; application of relevant rules; tone of response; and engagement with reform. These indicators are then used to evaluate two cases of Commission scandal: the first, a cash-for-influence case involving a Commissioner from a small Member State; and the second, concerning a former Commission President. While no clear pattern emerges across the two cases, the article provides evidence of a mix of good practice and room for improvement in the Commission’s handling of scandals. The article also argues that even in the most bureaucratic of public organizations the management of scandals involves not only rule-following, but also subjective judgements by organizational leaders.

Determinants of office rents: the role of physical and locational characteristics in Colombo

PurposeThis paper examines rent determinants and their relationship with commercial office property rents.Design/methodology/approachThe method adopted in this study differs from that of previous studies on this topic. Firstly, based on the survey of the viewpoints of experts, Relative Importance Index (RII) analysis was used to identify rent determinants and to rank and ensure their relevance and validity in the Sri Lankan context. Secondly, sampling of data related to 115 office properties collected from property tenants and landlords located within the central built-up area of Colombo City was conducted using a multi-methods approach to carry out an objective hedonic analysis of office rents.FindingsThis research utilizes RII and hedonic models to provide insights into determinants and relationships. Both analyses confirm that the three top drivers of commercial office rent are distance from the major town center, availability of parking space and the condition of the property. In addition to these three factors, hedonic models reveal that the age of the property and the availability of a conference hall also play a relevant role in explaining office rents. Given the disparities in the findings of the two methods, further examination was able to confirm that factors such as distance from the major town center, parking availability, age of the property, presence of a conference hall, building condition, floor size, business type and type of building are likely to influence commercial office rent. These findings reflect elements such as the quality, newness and better facilities of different office properties.Practical implicationsThis systematic study and analysis of office rent for the guidance of real estate investors can support sound investment decisions, potentially leading to more financially sound property development, reduced public debt levels and improved public-private financing. Further, the research findings offer valuable insights to real estate investors, developers and planners regarding location decisions for office development quality enhancements in future office developments.Originality/valueThis research provides fresh insights into the local scale office market, an area where limited evidence currently exists. Further, the methodology adopted provides evidence that hedonic analysis, supported by a multi-method approach, can mitigate the subjective judgments made by professionals.

OS-FCM: A semi-supervised clustering approach to investigating consumers’ usage patterns of contactless shopping-delivery (S-D) channel

Investigating the factors that contribute to consumers’ usage patterns of contactless shopping-delivery (S-D) channel is important for e-commerce retailers and logistics operators. However, in the data as collected by a questionnaire survey, distinct usage patterns are not always easily observed due to the existence of outliers, which places a challenge for the investigation. Hence, this study develops a labelling approach, named OS-FCM, to determine the usage patterns. As a semi-supervised clustering approach, OS-FCM integrates One-Class Support Vector Machine and Fuzzy C-Means, which enables it to make a trade-off between subjective judgement and statistical evidence when identifying patterns and to effectively recognize the patterns of outliers. The dataset with the usage patterns is used to train Random Forest, upon which Shapley Additive exPlanations (SHAP) approach is applied to measure the contributions of each factor. The data as collected by a questionnaire survey in Singapore are used for a case study. By using OS-FCM, the participants are clustered into five patterns with each pattern involving three variables, namely, current usage frequency, current behavioral habit, and future continuance intention. The contributions of each potential factor to the patterns are measured by SHAP values. In the case study, two regular patterns and three outlier patterns are identified. It is found that consumers’ past behavior stands out to be the most important factor to the pattern formation from an aggregated perspective. Factors such as fear appeal and behavior maintenance/change motivations exert differentiated impacts on the formation of the five patterns. Besides, as compared to commonly used clustering methods, OS-FCM has better clustering performance and the patterns determined by OS-FCM are more reasonable and rational, which provides a solid basis for better understanding the usage patterns.

Visual assessment of fashion merchandising based on scene saliency

PurposeFashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.Design/methodology/approachTo develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.FindingsIn comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.Originality/valueThe study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

Methodology for Assessing Procurement Systems: the Case of Public Procurement Bid Disputes Management in KenyaP.

Purpose: This study sought to assess the efficacy of bid disputes management mechanism in Kenya. Theoretical Framework: Transparency in public procurement proceedings is a concern for most stakeholders. Allowing aggrieved bidders to challenge decisions of procuring entities is considered a critical development in the space of public procurement. The effectiveness and efficiency of bid disputes management mechanism should therefore be a concern for procuring entities, bidders, and development partners especially. This aspect is yet to be explored extensively in Africa. Design/Methodology/Approach: The Methodology for Assessing Procurement Systems (MAPS) as propagated by the Organisation for Economic Co-operation and Development (OECD) helps evaluate the quality of public procurement systems and subsequently strategize on necessary reforms. This study applied MAPS to assess the efficacy of the bid disputes management mechanism in Kenya. Findings: It was established that overall, the public procurement bid dispute management mechanism in Kenya was effective and efficient. However, shortcomings were noted concerning resourcing of Public Procurement Administrative Review Board (PPARB), unaffordable filing fees and accessibility of comprehensive, simple, and clear information on filing procedures. Research, Practical and Social Implications: The findings of this study offer policy recommendations that will help promote effective and efficient management of public procurement bid disputes. Originality/Value: The results of this study contribute to theory, practice and literature in the field of public procurement which is less researched in the Eastern Africa Region. The findings of the study can be replicated in countries that have based their procurement reforms on the United Nations Commission on International Trade Law (UNCITRAL) Model Law on Public Procurement. It is to be appreciated that application of MAPS indicators allows for subjective judgments by the assessor.

Methodology for Assessing Procurement Systems: the Case of Public Procurement Bid Disputes Management in KenyaP.

Purpose: This study sought to assess the efficacy of bid disputes management mechanism in Kenya. Theoretical Framework: Transparency in public procurement proceedings is a concern for most stakeholders. Allowing aggrieved bidders to challenge decisions of procuring entities is considered a critical development in the space of public procurement. The effectiveness and efficiency of bid disputes management mechanism should therefore be a concern for procuring entities, bidders, and development partners especially. This aspect is yet to be explored extensively in Africa. Design/Methodology/Approach: The Methodology for Assessing Procurement Systems (MAPS) as propagated by the Organisation for Economic Co-operation and Development (OECD) helps evaluate the quality of public procurement systems and subsequently strategize on necessary reforms. This study applied MAPS to assess the efficacy of the bid disputes management mechanism in Kenya. Findings: It was established that overall, the public procurement bid dispute management mechanism in Kenya was effective and efficient. However, shortcomings were noted concerning resourcing of Public Procurement Administrative Review Board (PPARB), unaffordable filing fees and accessibility of comprehensive, simple, and clear information on filing procedures. Research, Practical and Social Implications: The findings of this study offer policy recommendations that will help promote effective and efficient management of public procurement bid disputes. Originality/Value: The results of this study contribute to theory, practice and literature in the field of public procurement which is less researched in the Eastern Africa Region. The findings of the study can be replicated in countries that have based their procurement reforms on the United Nations Commission on International Trade Law (UNCITRAL) Model Law on Public Procurement. It is to be appreciated that application of MAPS indicators allows for subjective judgments by the assessor.

Analyzing the Unsafe Behaviors of Frontline Construction Workers Based on Structural Equation Modeling

The unsafe behavior of frontline workers at construction sites is the most important cause of construction accidents. This study proposed a comprehensive model of frontline workers’ unsafe behaviors based on a systems perspective and used structural equation modeling (SEM) to explore the influence mechanisms between the objective conditions (e.g., work environment, work climate, and task complexity), safety management (e.g., safety education and training, safety reward and punishment regulations, safety inspection, safety technology disclosure, and safety warning signs), group influence (propagation of unsafe behaviors among workers), personal perception (subjective judgment of operators on their safety knowledge and skills), and unsafe behaviors. Data from 460 frontline workers were collected through questionnaires and the correlation hypotheses were tested using SPSS 26.0 and Amos 26.0 software. The following conclusions were obtained: (1) objective conditions directly positively influence safety management, group influence, and personal perception but indirectly negatively influence unsafe behavior; (2) safety management not only directly positively affects personal perception but also directly negatively affects unsafe behavior. However, the direct effect of safety management on group influence is not significant; (3) group influence has a direct positive effect on unsafe behavior, but the direct effect on personal perception is not significant; (4) the direct effect of personal perception on unsafe behavior is insignificant. These findings can be used as preliminary data to guide decision makers or managers in construction companies to develop reasonable management plans to curb the unsafe behaviors of frontline workers.

Ai-aided diagnosis of oral X-ray images of periapical films based on deep learning

Oral X-ray images provide a useful technical means by which dentists examine teeth for dental problems, but the diagnostic process is defective due to its over-reliance on dentists’ subjective judgments, lack of objective criteria, etc. In this context, this study examined the AI-aided diagnosis of periapical films based on deep learning..Based on YOLOv7-X, a YOLO-DENTAL network architecture was used to detect dental caries, dental defects, periapical lesions, and coronal restorations in periapical films. Firstly, the coordinate attention (CA) mechanism was introduced into the backbone feature extraction network, and a backbone-CA structure was presented to enhance the feature extraction capability of the network. Secondly, a simplified Bi-FPN structure was put forward and applied to the feature fusion part of the network to effectively improve its multi-scale feature fusion effect. Thirdly, the existing anchor-based detection head was replaced by an anchor-free decoupled head to simplify operational parameters while improving the generalized detection capability of the model over lesion regions. In the loss function part, existing CIoU loss was replaced by SIoU loss, a border loss function containing direction information. The focal loss containing a weight factor was introduced in calculating confidence loss as a substitute for the existing binary cross entropy loss function to balance positive and negative samples. Meanwhile, a study of ablation experiment was completed. The results validated the positive gain effect of each optimization strategy on the model. The final YOLO-DENTAL network structure exhibited an mAP value of 86.81%, higher than that of YOLOv7-X (79.95%). The effect of aided diagnosis was well achieved.

Conceptual and Measurement Issues in Assessing Democratic Backsliding

During the past decade, analyses drawing on several democracy measures have shown a global trend of democratic retrenchment. While these democracy measures use radically different methodologies, most partially or fully rely on subjective judgments to produce estimates of the level of democracy within states. Such projects continuously grapple with balancing conceptual coverage with the potential for bias (Munck and Verkuilen 2002; Przeworski et al. 2000). Little and Meng (L&M) (2023) reintroduce this debate, arguing that “objective” measures of democracy show little evidence of recent global democratic backsliding.1 By extension, they posit that time-varying expert bias drives the appearance of democratic retrenchment in measures that incorporate expert judgments. In this article, we engage with (1) broader debates on democracy measurement and democratic backsliding, and (2) L&M’s specific data and conclusions.

The analysis of marking reliability through the approach of gauge repeatability and reproducibility (GR&R) study: a case of English-speaking test

Ensuring consistent and reliable scoring is paramount in education, especially in performance-based assessments. This study delves into the critical issue of marking consistency, focusing on speaking proficiency tests in English language learning, which often face greater reliability challenges. While existing literature has explored various methods for assessing marking reliability, this study is the first of its kind to introduce an alternative statistical tool, namely the gauge repeatability and reproducibility (GR&R) approach, to the educational context. The study encompasses both intra- and inter-rater reliabilities, with additional validation using the intraclass correlation coefficient (ICC). Using a case study approach involving three examiners evaluating 30 recordings of a speaking proficiency test, the GR&R method demonstrates its effectiveness in detecting reliability issues over the ICC approach. Furthermore, this research identifies key factors influencing scoring inconsistencies, including group performance estimation, work presentation order, rubric complexity and clarity, the student’s chosen topic, accent familiarity, and recording quality. Importantly, it not only pinpoints these root causes but also suggests practical solutions, thereby enhancing the precision of the measurement system. The GR&R method can offer significant contributions to stakeholders in language proficiency assessment, including educational institutions, test developers and policymakers. It is also applicable to other cases of performance-based assessments. By addressing reliability issues, this study provides insights to enhance the fairness and accuracy of subjective judgements, ultimately benefiting overall performance comparisons and decision making.

Measuring Democratic Backsliding

ABSTRACTDespite the general narrative that the world is in a period of democratic decline, there have been surprisingly few empirical studies that assess whether this is systematically true. Most existing studies of global backsliding are based largely if not entirely on subjective indicators that rely on expert coder judgment. Our study surveys objective indicators of democracy (e.g., incumbent performance in elections) and finds little evidence of global democratic decline during the past decade. To explain the discrepancy in trends between expert-coded and objective indicators, we consider the role of coder bias and leaders strategically using more subtle undemocratic action. Although we cannot rule out the possibility that the world is becoming less democratic exclusively in ways that require subjective judgment to detect, this claim is not justified by existing evidence.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

Individual differences in the evaluation of ambiguous visual and auditory threat-related expressions.

This study investigated the neural correlates of the judgement of auditory and visual ambiguous threat-related information, and the influence of state anxiety on this process. Healthy subjects were scanned using a fast, high-resolution functional magnetic resonance imaging (fMRI) multiband sequence while they performed a two-alternative forced-choice emotion judgement task on faces and vocal utterances conveying explicit anger or fear, as well as ambiguous ones. Critically, the latter was specific to each subject, obtained through a morphing procedure and selected prior to scanning following a perceptual decision-making task. Behavioural results confirmed a greater task-difficulty for subject-specific ambiguous stimuli and also revealed a judgement bias for visual fear, and, to a lesser extent, for auditory anger. Imaging results showed increased activity in regions of the salience and frontoparietal control networks (FPCNs) and deactivation in areas of the default mode network for ambiguous, relative to explicit, expressions. In contrast, the right amygdala (AMG) responded more strongly to explicit stimuli. Interestingly, its response to the same ambiguous stimulus depended on the subjective judgement of the expression. Finally, we found that behavioural and neural differences between ambiguous and explicit expressions decreased as a function of state anxiety scores. Taken together, our results show that behavioural and brain responses to emotional expressions are determined not only by emotional clarity but also modality and the subjects' subjective perception of the emotion expressed, and that some of these responses are modulated by state anxiety levels.