External Behavior Research Articles

Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.

Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior. It utilizes multiple variables to simulate the extent to which isolation policies influence the spread of the pandemic. Empirical evidence indicates that the progression of the epidemic is primarily driven by factors such as public willingness and regulatory intensity. The improved model, in comparison to traditional infectious disease models, offers greater flexibility and accuracy, addressing the need for frequent modifications in fundamental models within complex environments. Meanwhile, we introduce "swarm entropy" to evaluate infection intensity under various policies. By linking isolation policies with swarm entropy, considering population structure, we quantify the effectiveness of these isolation measures. It provides a novel approach for complex population simulations. These findings have facilitated the enhancement of control strategies and provided decision-makers with guidance in combating the transmission of infectious diseases.

Explanation of Organizational Stress in Hotel Businesses with Attribution (Causality Attribution) Theory

This research is aimed to determine the sources of organizational stress and to associate the stress dimensions that stand out as a result of the research with attribution behavior. For this purpose, the relevant data were analyzed with the SPSS Program. The dimensions of organizational stress were determined by Explanatory Factor Analysis. As a result of the research, stress originating from the manager and employee relations, which is one of the prominent dimensions of organizational stress, has been associated with external attribution behavior. Stress stemming from organizational structure, which is another prominent dimension, has been associated with internal attribution behavior. For managing the external attribution behavior, it is necessary to provide appropriate working conditions within the enterprise. For managing internal attribution behavior, an effective communication environment should be established.

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application of digital twin can increase efficiency and security of systems by providing reliable model and decision supports. In this paper, we propose a state-aware model learning method to simulate and analyze the lightweight protocol implementations in edge/cloud environments. We introduce the data flow of program execution and network interaction inputs/outputs (I/O) into the extended finite state machine (EFSM) to expand the modeling scope and insight. We aim to calibrate the states and construct an accurate state-machine model using a digital twin based layered approach to reasonably reflect the correlation of a device’s external behavior and internal data. This, in turn, improves our ability to verify the logic and evaluate the security for protocol implementations. This method firstly involves instrumenting the target device to monitor variable activity during its execution. We then employ learning algorithms to produce multiple rounds of message queries. Both the I/O data corresponding to these query sequences and the state calibration information derived from filtered memory variables are obtained through the mapper and execution monitor, respectively. These two aspects of information are combined to dynamically and incrementally construct the protocol’s state machine. We apply this method to develop SALearn and evaluate the effectiveness of SALearn on two lightweight protocol implementations. Our experimental results indicate that SALearn outperforms existing protocol model learning tools, achieving higher learning efficiency and uncovering more interesting states and security issues. In total, we identified two violation scenarios of rekey logic. These situations also reflect the differences in details between different implementations.

A Focus on the Psychological Well-Being of Muslim Religious Students from Low-Income Families in Malaysia

Purpose: The psychological well-being of students plays an important role that needs to be examined as it revolves around aspects of internal psychology in a less tangible manner. While a student’s external behavior may provide several insights into well-being, it does not reflect the internal psychological state of a student. This study will be carried out to give a detailed picture of the psychological well-being of primary school students. Methodology: An in-depth interview method was used to collect data. The individuals involved in this study consisted of 10 primary school Muslim Religious students in the residential area of Pahang, Malaysia. Findings: The analysis of these interviews revealed several key themes, including strength in oneself, social involvement, and family support system. The findings of this study can be used as required certificates to help practitioners understand the diverse perspectives on psychological well-being. These themes can be used to enhance family functionality to achieve and maintain psychological well-being and academic achievement in confronting life’s challenges. Implications for Research and Practice: Firstly, it is important to develop customized strategies that teachers, school counselors and professionals can use based on the findings of this study. These strategies should aim to improve the welfare of elementary school students from low-income backgrounds. Additionally, involving families in the process can be incredibly beneficial in creating a nurturing and supportive home environment for students. Lastly it is crucial to provide social skills training that incorporates activities promoting peer relationships, teamwork, and effective communication.

A System for Monitoring Animals Based on Behavioral Information and Internal State Information.

Managing the risk of injury or illness is an important consideration when keeping pets. This risk can be minimized if pets are monitored on a regular basis, but this can be difficult and time-consuming. However, because only the external behavior of the animal can be observed and the internal condition cannot be assessed, the animal's state can easily be misjudged. Additionally, although some systems use heartbeat measurement to determine a state of tension, or use rest to assess the internal state, because an increase in heart rate can also occur as a result of exercise, it is desirable to use this measurement in combination with behavioral information. In the current study, we proposed a monitoring system for animals using video image analysis. The proposed system first extracts features related to behavioral information and the animal's internal state via mask R-CNN using video images taken from the top of the cage. These features are used to detect typical daily activities and anomalous activities. This method produces an alert when the hamster behaves in an unusual way. In our experiment, the daily behavior of a hamster was measured and analyzed using the proposed system. The results showed that the features of the hamster's behavior were successfully detected. When loud sounds were presented from outside the cage, the system was able to discriminate between the behavioral and internal changes of the hamster. In future research, we plan to improve the accuracy of the measurement of small movements and develop a more accurate system.

Modulation of Carbon Nanotube Electronic Structure by Grain Boundary Defects in RbI on Au(111).

The electronic properties of single-walled carbon nanotubes (SWCNTs) are known to be highly sensitive to environmental effects. Here, we use scanning tunneling microscopy and spectroscopy to investigate the electronic properties of SWCNTs deposited on RbI monolayer films grown on Au(111). We find that grain boundary defects in RbI monolayers cause the appearance of spatially confined localized states in the SWCNTs. Our density functional theory calculations show that grain boundary defects in RbI/Au(111) produce a stabilizing electrostatic potential caused by reduced coordination of iodine atoms at the RbI grain boundary. The presented results may offer insights into the performance of devices involving transport through SWCNTs subjected to external electrostatic disorder.

Predicting child externalizing behavior ratings in Head Start: Investigating the impact of child and teacher influences

One of the primary goals of early education is to promote overall school readiness, especially for children most at risk for educational challenges. However, there is a persistent disparity in equitable access to high quality early learning environments. This is due in part to children being suspended or expelled for displaying challenging behaviors even though for many children, some of these behaviors may be developmentally appropriate. The effects of exclusionary discipline can have long-term implications for their overall emotional and social development. Racial and gender-based disparities in school discipline begin as early as preschool. Some of these disparities can stem from disproportionality in teachers’ perceptions of children's behaviors. The ways in which teachers perceive and subsequently respond to preschoolers’ behavior have significant implications for children's experience in the classroom and are an important link in understanding decision-making pathways related to preschool expulsion. The goal of the present study was to examine factors that might predict teacher perceptions (i.e., ratings) of child externalizing (hyperactive and aggressive) behaviors. This study analyzed secondary data from a nationally representative study of Head Start. Findings indicated there were differences in how teachers rated children's externalizing behaviors based on child's sex and race. Further, a teacher's field of study, beliefs about family engagement, and attitudes toward Developmentally Appropriate Practices significantly predicted their ratings of children's aggressive and hyperactive behaviors. Policy recommendations are discussed.

Verification of Compositional Frameworks in Coq

In this paper we aim to verify in Coq basic properties of compositional behavioural frameworks for dynamic systems. We analyse systems by their external behaviour rather than a state space model, and use the parallel composition operator to build complex systems from simpler subsystems. It is important that the composition is well-defined and avoids undesirable behaviour such as deadlocks. To ensure the our framework is correct, we formulate and prove the results in the proof assistant Coq. We consider discrete-time deterministic systems and timed-event systems, with a view to eventually proving results on a framework for hybrid systems.

Investigating the impact of the COVID-19 pandemic on the cognitive dissonance of adolescents

The COVID-19 pandemic has had a profound impact on adolescent mental health, leading to the formation of distinct perspectives on the pandemic. These perspectives often give rise to internal conflict within individuals. In this study, we aimed to examine the variability and complexity of these perspectives through the lens of cognitive dissonance, the psychological discomfort one feels when their external behaviors do not align with their internal beliefs. We collected the data through self-reported surveys, which assessed participant (teenagers aged 13-18) agreement with various opinions and statements related to the pandemic. The survey encompassed five main categories, or “batteries”: general COVID-19, vaccination, masking, government/authority, and school. We had three primary hypotheses: 1) adolescents would experience dissonance, 2) adolescents would consider different aspects of the pandemic independently from each other and hence cross-battery scores would be statistically different, and 3) both gender and vaccination status would have an effect on dissonance levels. Our results corroborated the conjecture that study participants experienced dissonance, as three out of the five batteries displayed mean scores indicative of dissonance. Furthermore, as evidenced by the absence of significant correlation (no r constant was greater than 0.341) and the presence of statistically significant differences between individual batteries (with seven out of nine comparisons having p-values <0.05), we proved our second hypothesis true. Lastly, through a linear regression test, we identified gender, albeit not vaccination status, to have a considerable effect on adolescents' dissonance scores.

Midfrontal conflict theta and parietal P300 are linked to a latent factor of DSM externalising disorders.

Psychiatric illnesses form spectra rather than categories, with symptoms varying continuously across individuals, i.e., there is no clear break between health and disorder. Dimensional measures of behaviour and brain activity are promising targets for studying biological mechanisms that are common across disorders. Here, we assessed the extent to which neural measures of the sensitivity of the three biological systems in the reinforcement sensitivity theory (RST) could account for individual differences in a latent general factor estimated from symptom counts across externalising disorders (EXTs). RST explanatory power was pitted against reduced P300, a reliable indicator of externalising per previous research. We assessed 206 participants for DSM-5 EXTs (antisocial personality disorder, conduct disorder, attention-deficit/hyperactivity disorder, intermittent explosive disorder symptoms, alcohol use disorder, and cannabis use disorder). Of the final sample, 49% met diagnostic criteria for at least one of the EXTs. Electroencephalographic measures of the sensitivities of the behavioural activation system (BAS), the fight/flight/freeze system, and the behavioural inhibition system (BIS), as well as P300 were extracted from the gold bar-lemon and stop-signal tasks. As predicted, we found that low neural BIS sensitivity and low P300 were uniquely and negatively associated with our latent factor of externalising. Contrary to prediction, neural BAS/"dopamine" sensitivity was not associated with externalising. Our results provide empirical support for low BIS sensitivity and P300 as neural mechanisms common to disorders within the externalising spectrum; but, given the low N involved, future studies should seek to assess the replicability of our findings and, in particular, the differential involvement of the three RST systems.

Research on intelligent identification algorithm for preventing external damage behavior of power cable based on machine learning

Abstract As the key carrier of power distribution line power transmission and the supporting artery of urban functioning, power cables are widely used in electric power engineering construction. The number of users is increasing, and the distribution of locations is becoming more complex. However, if a power cable failure occurs, if not eliminated in time, it will threaten the safety of the power grid, seriously affecting the production of enterprises and the lives of residents. The article first on the power cable vibration signal recognition algorithm design, extraction of power cable external damage fault signal characteristics, proposed based on PSO-SVM power cable external damage fault waveform recognition algorithm, and the design of power cable anti-external damage intelligent early warning system, to achieve a certain period according to the trend of signal changes in the vibration event to discriminate. Research results show that under the same computer operating conditions, the average running time of the algorithm proposed in this paper is only 3.43. Compared to the other two algorithms, the algorithm has the highest recognition accuracy, the fastest convergence speed, and the optimal comprehensive performance. In the external damage signal recognition and warning test, the recognition rate of the excavator passing through defense zone one and two at a distance of 3 meters is 100%, which is greater than 80%, in line with the expected effect.

External and Internal Attribution in Human-Agent Interaction: Insights from Neuroscience and Virtual Reality

Agents are designed in the image of humans, both internally and externally. The internal systems of agents imitate the human brain, both at the levels of hardware (i.e., neuromorphic computing) and software (i.e., neural networks). Furthermore, the external appearance and behaviors of agents are designed by people and based on human data. Sometimes, these humanlike qualities of agents are purposely selected to increase their social influence over human users, and sometimes the human factors that influence perceptions of agents are hidden. Inspired by Blascovich’s “threshold of social influence’, a model designed to explain the effects of different methods of anthropomorphizing embodied agents in virtual environments, we propose a novel framework for understanding how humans’ attributions of human qualities to agents affects their social influence in human-agent interaction. The External and Internal Attributions model of social influence (EIA) builds on previous work on agent-avatars in immersive virtual reality and provides a framework to link previous social science theories to neuroscience. EIA connects external and internal attributions of agents to two brain networks related to social influence. the external perception system, and the mentalizing system. Focusing human-agent interaction research along each of the attributional dimensions of the EIA model, or at the functional integration of the two, may lead to a better understanding of the thresholds of social influence necessary for optimal human-agent interaction.

Domestic turmoil and diversionary hypothesis: A linear relationship?

Why do individual case studies reveal evidence in favor of diversionary behavior by states, yet quantitative studies often produce many mixed results? In this paper, I help to explain this puzzle by arguing that there is a curvilinear relationship between internal conflict and external diversionary behavior as opposed to a linear one. By looking at contentious issues mainly in the Western Hemisphere from 1962 to 2001 using data from the Issue Correlates of War (ICOW) project, I find that countries with moderate levels of domestic unrest are most likely to initiate a militarized interstate conflict. Furthermore, I find that in times of elite unrest, states are more likely to have diversionary behaviors. In contrast, mass unrest is unlikely to incentivize states to use foreign policy adventures to rally domestic support. Such a finding helps deepen our understanding of the complex conditions leading to diversionary actions.

Maternal pre-pregnancy weight status and gestational weight gain in association with child behavior: The mediating role of prenatal systemic inflammation

Maternal pre-pregnancy obesity and excessive gestational weight gain (EGWG) may predispose children to behavioral problems through increased prenatal inflammation. We investigated the association between maternal body mass index (BMI) and gestational weight gain (GWG), and child behavioral problems (primary aim), and the mediating role of prenatal inflammation (secondary aim). We used self-reported pre-pregnancy BMI and estimated-GWG data (N=1137) from a longitudinal cohort study. Maternal serum C-reactive protein (CRP) was measured in the 3rd-trimester. Parent-reported Child Behavior Checklist (CBCL) was used to assess child internalizing and externalizing behaviors at 3-years-of-age. We used analysis of covariance (ANCOVA), multiple linear regression, and mediation analyses for data analysis. Maternal obesity (F=21.98, df 3836), EGWG (F=6.53, df 2764), and their combination (F=18.51, df 3764) were associated with the 3rd trimester CRP, but not child behavior in the whole sample. Maternal underweight was associated with withdrawal problems in all children (β=0.56, 95%CI, 0.11,1.00) and aggressive behaviors in female children (β=2.59, 95%CI, 0.28,4.91). Obesity had a significant association with externalizing behaviors in female children after controlling for maternal CRP (β=3.72, 95%CI, 0.12,7.32). Both inadequate and EGWG were associated with somatic complaints in male children (β=0.50, 95%CI, 0.05,0.95; β=0.36, 95%CI, 0.01,0.71, respectively). Combined obesity/EGWG was associated with externalizing (β=6.12, 95%CI, 0.53,11.70) and aggressive (β=4.23, 95%CI, 0.90,7.56) behaviors in female children. We found no significant effects through CRP. Maternal pre-pregnancy BMI and GWG showed sex-specific associations with child behavioral problems. Prenatal CRP, although increased in obesity and EGWG, did not mediate these associations.

Automated analysis of animal behavior and its relation to key aspects of the environment reveals new cognitive specializations of neurons

A thorough analysis of animal behavior is essential for examining the relationship between specific neuron activations with the elements of the external environment, behavior, or internal state. Machine learning techniques have made some advancements in automatic segmentation of animal behavior based on data concerning the location of animal body parts [1–3]. At present, these methods cannot achieve the level of segmentation accuracy desired or make correlations between an animal’s behavioral acts and key environmental factors. To address this issue, the authors have created a software package that can extract a variety of behavioral variables from video recordings of animals in experimental settings, enabling mathematical analysis of a behavioral act continuum. The identification of specific aspects of an animal’s anatomy is crucial for extracting a vast array of behavioral variables. In order to accomplish this task, our team employed DeepLabCut, an accessible toolkit for tracking experimental animal behavior that operates on the principle of transfer learning through deep neural networks. We have devised a technique to ascertain the positions of animal body parts in diverse behavioral situations, resulting in a body parts collection meeting two criteria: offering superior responsiveness to small motor movements of the animal and delivering a high percentage of correct body part locations. In scenarios employing camera shooting from above, such a collection encompasses the nose, ears, tail base, body center, forelimbs, hind limbs, and both flanks of the animal’s body. Next, we created software tools to extract and annotate behavioral variables from data on animal kinematics in various cognitive tasks. Our automated system comprises two main scripting modules: CreatePreset and BehaviorAnalyzer. The CreatePreset module interacts with users to select the type of arena geometry, object location, and necessary temporal and spatial parameters for analysis. The script’s result saves as a mat-file for analyzing the behavior of all experiment videos, assuming a constant relative position of the arena and the video camera alongside the experiment’s design. The BehaviorAnalyzer module conducts initial processing on time series data consisting of coordinates of an animal’s body parts. This results in the formation of a kinematogram, which details the kinematics of the body parts. The module then isolates individual behavioral acts of the animal and annotates its behavior based on motivational and environmental factors. Using mutual information-based methods, we analyzed the specialization of hippocampal CA1 neurons in animals as they explored arenas with varying degrees of novelty. Through the analysis, we have identified neurons that exhibit selectivity in relation to specific continuous kinematic parameters governing the posture and trajectory of the animal. These parameters include the animal’s location in the arena space (X and Y coordinates), as well as the speed and angle of rotation of the animal’s head (i.e. absolute orientation in the arena). Neurons specialized in discrete acts of behavior were identified, including rests, locomotions, freezing, rears, and acts of interaction with objects. Furthermore, a selective activation of neurons was found with regard to an additional set of distinct parameters, which combine the animal’s location in the arena and its speed.

Effectiveness of a video-feedback intervention to promote positive parenting for foster children

Foster children are at risk for developing disorganized attachment relationships (Van den Dries et al., 2009; Vasileva & Petermann, 2018) and behavioural problems (Goemans et al., 2015; Vanschoonlandt et al., 2012). Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline – Foster Care/Adoption (VIPP-FC/A) is an intervention aimed at improving sensitive parenting in order to promote secure attachment relations and reduce behavioural problems. In this study, a Randomized Controlled Trial (RCT) was used in order to examine the effectiveness of VIPP-FC/A in a Flemish foster care sample ( n = 100), using questionnaires at three measurement moments. The hypothesis was that VIPP-FC/A would increase foster mothers’ sensitive parenting and decrease the externalizing behavioural problems and insecure attachment behaviour of the foster children and parenting stress of the foster mothers. The intervention effects on these four outcome variables were analysed by constructing four multilevel models. After controlling for the baseline measurement, there was no significant main effect of condition for sensitive parenting, externalizing problems, insecure attachment behaviour and parenting stress. There was however a significant effect of condition and age on insecure attachment behaviour: within the group of youngest foster children (under 30 months), children in the intervention group scored lower insecure attachment scores than children in the control group. Additionally, there was a significant effect of condition and type of foster care on externalizing behaviour problems. In the group of children placed in kinship foster care, children in the intervention group had lower external behavior scores than children in the control group.

Structural behavior of skewed prestress concrete superstructure

The construction of span-skewed bridges has become widespread worldwide and in Turkey. This article focuses on the structural behavior of simply supported prestressed concrete bridges with varying skewness angles. Specifically, supported I girder pretensioned prestressed concrete bridges are investigated using different models (five models) with skewness angles 0° - 21°- 37.6°- 57°–66.6°. These models are analyzed using the Midas Civil software to analyze and design various bridge components and types through the finite element method. This method employs the most commonly accepted codes worldwide. This research showed that shear forces and bending moments were significantly affected by increasing the degree of skewness angle when it exceeds 37.5°. External I-girders behavior was similar yet inversely proportional behavior. Internal I-girder behavior was also identical but inversely, too.

IoT and ML approach for ornamental fish behaviour analysis

Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish (Carassius auratus). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.

Longitudinal Relationships Among Maternal Depression, Social Parenting, and Young Children’s Externalizing Behavior Problems: Moderated Mediation Effect of Young Children’s Negative Emotionality

Objectives: This study investigated whether young children’s negative emotionality moderates the longitudinal mediating effect of maternal social parenting on the relationship between maternal depression and young children’s externalizing behavioral problems.Methods: The 2008 (T1)~2012 (T5) data of the Panel Study on Korean Children (PSKC) were used. Participants were 1703 children aged 0 to 4 years old, their mothers, and their fathers. Descriptive statistics were performed for the variables and Pearson’s correlation analyses were carried out to identify the association with the variables. SPSS PROCESS macro version 4.0 software was used for analyzing the mediating effect (Model 4) and moderated mediation effect (Model 14). The significance of the indirect effect was verified via a bootstrapping test.Results: According to the results, first, maternal social parenting (T4) had a partial mediating effect on the relationship between maternal depression (T1-T3) and young children’s externalizing behavioral problems (T5). Second, young children’s negative emotionality (T4) had a moderating effect on the relationship between maternal social parenting(T4) and young children’s externalizing behavioral problems (T5). Third, the mediating effect of maternal social parenting (T4) on the relationship between maternal depression (T1-T3) and young children’s externalizing behavioral problems (T4) was moderated by young children’s negative emotionality (T4). Specifically, the indirect effect of maternal depression on young children’s externalizing behavior problems through maternal social parenting was more impactful among young children with average or high on negative emotionality. The findings supported a diathesis-stress model rather than a differential susceptibility model regarding the interaction effect between young children’s temperament and maternal parenting on young children’s externalizing behavior problems.Conclusion:In the context of the diathesis-stress model, adjusting interventions to young children’s negative emotionality, which serves as a vulnerability for negative parenting environments, may be a useful way to prevent their externalizing behavior problems.

PSYCHOLOGICAL FACTORS TOWARDS SPEAKING ABILITY AT THE FIRST SEMESTER OF ENGLISH DEPARTMENT STUDENTS IN UNDIKMA

This study was meant to analyze the psychological factors that influence the speaking ability of first semester students of English Department students in UNDIKMA, Mataram, West Nusa Tenggara Province. Psychology is the study of the nature of the human soul as well as its process. Psychology deals with the study of external behavior using objective observation methods for stimulation. In addition, there are some psychological factors that hinder students from speaking in English classes such as fear of making mistakes, shyness, anxiety, lack of confidence and lack of motivation. Those factors were commonly caused by their fear of being laughed at by their friends. Some psychological factors such as shyness and anxiety are considered as the main causes of students' reluctance to speak. This study was conducted in a qualitative approach. The population of this research was taken from A and B Classes from the first semester students of English Department students in UNDIKMA, Mataram, West Nusa Tenggara Province. The sample of the study consisted of 30 students. The researchers determined some techniques as the data sources, namely observation, documentation, field notes and tests or interviews. The researchers found that the psychological factors that contribute toward students' speaking abilities are as follows: students who have a fear of making mistakes, shame, anxiety, have low self-confidence, and those have low motivation will make them reluctant to speak in English because they found difficulties in speaking English.