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This research was conducted to design a model to influence the strategic orientation and management of export market orientation on export performance in business environment transformations. The method of this research is Grounded theory. For this purpose, the topic was investigated by interviewing experts who were selected by a purposeful method of judgment from the managers and marketing experts of 52 sample exporting companies in Isfahan province in 2020. The validity of the interview questions was assessed with the Binominal test and the reliability was assessed with the Cronbach's alpha test. The research results indicate that; Strategic export orientation, export market orientation, and export performance are the three main pillars of exports in the trans formatives. To direct an effective export strategy in the current situation, six key factors of information systems strategy, customer-oriented engineering strategy, increasing innovation strategy, Competitive orientation strategy, learning orientation strategy, and cost control strategy must be included. Moreover, the results of the analysis aimed at extracting four components for export market orientation, including; Virtual marketing, distribution through capillary networks, video branding, and health brand led. Furthermore, the results of content analysis for export performance include 2 components financial and operational. Examining the export performance in transformations should be considered as the contribution of the article in the management of knowledge in the field.

An analytical framework linking network architecture, neuronal nonlinearity and population activity offers a guide map to infer the hidden input architecture to neural trios from observed interactions in empirical data.

In this study, we improve the existing model for force distribution over the muscles by considering reflex excitation as a nonvoluntary mechanism of our neuromuscular system. The improved model can explain the large difference between biological torque and experimentally optimized assistive torque profiles. Accordingly, we hypothesize that the “nonvoluntary nature of reflexive excitation highly restricts biological torque compensation”. The proposed model can also potentially characterize co-activation behavior in antagonistic muscles. Using our improved model, we introduce a well-posed framework to optimize the exoskeleton torque profile by metabolic rate minimization. Methods: To support our hypothesis and the proposed method, we utilize two experimental datasets for exoskeleton torque optimization; passive and active ankle exoskeletons. First, we use the passive exoskeleton dataset to identify the parameters of our model; i.e., reflex gains. Then, to validate the proposed model, the identified parameters are used to optimize the exoskeleton torque profile for the second experimental study. Limitations: It is assumed that joint kinematic and reflex gains are fixed with and without exoskeleton. Results: 74% of biological torque at the ankle joint cannot be experimentally compensated and the existing models can only explain that 17% of the biological torque is uncompensable. Our improved model can explain that 58% of biological torque is uncompensable (but still 16% remains unexplained). This achievement provides support for our hypothesis and shows undeniable contribution of reflex excitation for exoskeleton torque profile optimization.

Purpose: This research aims to enhance understanding of online retailing through electronic channels (such as mobile devices) and touch points of electronic channels (such as mobile shopping software) from the customer's perspective. Methods: This research is applied for the purpose and descriptive in terms of survey and library data collection methods. The statistical population for this research consists of students at Tehran Azad University. The community consists of more than 4000 people, so according to Morgan's table, the number of samples should be 384. Sampling has been conducted using the available method, and data analysis has been performed using the LSD test. The dependent variable in online shopping is purchase intention, while the independent variables include usefulness, ease of use, pleasure, privacy, and satisfaction. Findings: The research findings indicate that customers currently use eight different devices, including laptops/notebooks, personal computers (PCs), smartphones, tablets, internet-equipped TVs, and in-store kiosks. Additionally, the research findings revealed that purchasing devices can be categorized into four electronic channel categories from the perspective of online retail customers. The floors are categorized as A, B, C, and D, and the coordinates of each are listed in the article. The research findings indicate that both the technological quality and the situational benefitsof the context influence consumers' use of electronic channels. Also, customers engage inonline shopping through various electronic channels (sets of Internet-enabled devices, such as mobile devices) and multi-channel touchpoints (digital shopping formats, such as mobile shopping apps). Conclusion: The results indicate that there is no significant difference between the first cluster (A), which involves shopping using personal computers (PCs), laptops, and netbooks, and the second cluster (B), which involves shopping using smartphones and tablets, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is no significant difference between the first cluster (A) and the third cluster (C), i.e., Internet TV (IE TV), in terms of usefulness, ease of use, shopping pleasure, satisfaction, and purchase intention. There is a significant difference between the first cluster (A) and the fourth cluster (D), specifically in-store kiosks, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is a significant difference between the second cluster (B) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention. Also, there is no significant difference between the third cluster (C) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention.

Abstract Due to the complexity and high degrees of freedom, the detailed assessment of human biomechanics is necessary for the design and optimization of an effective exoskeleton. In this paper, we present full kinematics, dynamics, and biomechanics assessment of unpowered exoskeleton augmentation for human running gait. To do so, the considered case study is the assistive torque profile of I-RUN. Our approach is using some extensive data-driven OpenSim simulation results employing a generic lower limb model with 92-muscles and 29-DOF. In the simulation, it is observed that exoskeleton augmentation leads to $$4.62\%$$ 4.62 % metabolic rate reduction for the stiffness coefficient of $$\alpha ^*=0.6$$ α ∗ = 0.6 . Moreover, this optimum stiffness coefficient minimizes the biological hip moment by $$26\%$$ 26 % . The optimum stiffness coefficient ( $$\alpha ^*=0.6$$ α ∗ = 0.6 ) also reduces the average force of four major hip muscles, i.e., Psoas, Gluteus Maximus, Rectus Femoris, and Semimembranosus. The effect of assistive torque profile on the muscles’ fatigue is also studied. Interestingly, it is observed that at $$\alpha ^{\#}=0.8$$ α # = 0.8 , both all 92 lower limb muscles’ fatigue and two hip major mono-articular muscles’ fatigue have the maximum reduction. This result re-confirm our hypothesis that ”reducing the forces of two antagonistic mono-articular muscles is sufficient for involved muscles’ total fatigue reduction.” Finally, the relation between the amount of metabolic rate reduction and kinematics of hip joint is examined carefully where for the first time, we present a reliable kinematic index for prediction of the metabolic rate reduction by I-RUN augmentation. This index not only explains individual differences in metabolic rate reduction but also provides a quantitative measure for training the subjects to maximize their benefits from I-RUN.

Context remarkably affects learning behavior by adjusting option values according to the distribution of available options. Displaying counterfactual outcomes, the outcomes of the unchosen option alongside the chosen one (i.e., providing complete feedback), would increase the contextual effect by inducing participants to compare the two outcomes during learning. However, when the context only consists of the juxtaposition of several options and there is no such explicit counterfactual factor (i.e., only partial feedback is provided), it is not clear whether and how the contextual effect emerges. In this research, we employ Partial and Complete feedback paradigms in which options are associated with different reward distributions. Our modeling analysis shows that the model that uses the outcome of the chosen option for updating the values of both chosen and unchosen options in opposing directions can better account for the behavioral data. This is also in line with the diffusive effect of dopamine on the striatum. Furthermore, our data show that the contextual effect is not limited to probabilistic rewards, but also extends to magnitude rewards. These results suggest that by extending the counterfactual concept to include the effect of the chosen outcome on the unchosen option, we can better explain why there is a contextual effect in situations in which there is no extra information about the unchosen outcome.

Background and aims: Observance of Health, Safety and Environment (HSE) guidelines is becoming more pervasive in all organizations, and governments and regulators are becoming more strict on it. The onset and prevalence of Coronavirus disease (COVID-19) is an example of a lack of attention or disregard for such standards and guidelines. Adherence to such standards is much more important in all organizations, especially in health-oriented organizations such as hospitals, and they increases safety and improves the efficiency of hospitals and the community (environment). Because health and safety factors are closely related to ergonomic factors (E), attention to this factor has led to the evolution of HSE and the emergence of HSEE. Much research has been done on HSE management, related evaluations, and other related issues, but due to the novelty of HSEE, such models have rarely been researched in HSEE. Also, in previous models, the weight of all factors was considered the same, and the evaluations were practically turned into checklists. While in any organization or in any situation, the importance of observing some factors may be more or less than others, and at the same time the ergonomic factor should be considered in this model. The purpose of this study is to provide a quantitative model for measuring the performance of organizational units based on the observance of the usual HSE criteria and consider the ergonomic factor (HSEE). Numerous studies have shown that ergonomics play a major role in increasing human performance in all areas. Developing such a model can lead to more accurate information to managers and their better decision-making in order to reduce harm in the organization and in the organization environment. It will also provide more accurate feedback to the organizationchr('39')s units for self-assessment and calculation of any points for them, and ultimately ranking the units. Methods: Since the result of this research is a model that can be used in practice by organizations (especially hospitals), this research is an applied research. It is also a descriptive study because it explores the current situation and tries to describe and clarify it and present the result in the form of a quantitative model. Finally, because the data in this study is collected through a questionnaire, it is a survey study. In the first phase, studies related to the HSE and HSEE models were reviewed. In each model, the main variables (health, safety, environment and ergonomics) were collected along with the following variables for each. Then, by combining them into one large model, all variables entered the model and overlaps were eliminated. Because this model is made for a hospital, the variables were redefined based on the characteristics of the hospital and the model was modified. Finally, a model including the main variables of safety, health, environment and ergonomics and 12 sub-variables were designed and based on it, a research questionnaire was developed. To calculate the reliability of this questionnaire, Cronbachchr('39')s alpha method with 11 samples were used. This sample was selected by emphasizing on peoplechr('39')s familiarity with HSE and HSEE models. The alpha value was calculated to be greater than 0.8 and the reliability of the questionnaire was confirmed. In order to discover the relationship between the 4 main factors and 12 sub-factors, the DEMATEL method with 8 samples was used. DEMATELchr('39')s method is a well-known method used to models whose variables are not independent of each other. Using the initial classification of factors as well as HSE(E), the decision tree was designed based on ANP method and also the weight of the indicators was obtained. The ANP method is a method based on pairwise comparisons and a subset of multi-criteria decision-making (MCDM) techniques used to discover the weight of criteria simultaneously with ranking alternatives. Because each of the ANP and DEMATEL questionnaires requires a lot of patience and concentration, the YungLan method was used to combine DEMATEL and ANP. In this method, the data collection is required only once by the questionnaire and this data is reused in both models. At this stage, the weight of 12 factors was calculated and the research model was completely architectural. The designed model was implemented in ArabNia Hospital in Qom. The goal is to determine how well each of the eight units in the hospital complies with HSEE. Qom ArabNia Hospital is the first hospital in Iran in which patients with COVID-19 were quarantined in February 2020. Because there was no information on how well each of the HSEE components were observed in the hospital, a simple questionnaire using the Likertchr('39')s spectrum was designed to collect this information. This questionnaire was published in a total of 8 units and 11 questionnaires were completed. This data was entered into the model as input, and after analysis, hospital units were ranked based on compliance with safety, health, environmental and ergonomic criteria. Results: The most important achievement of this research is a quantitative model including 4 areas of safety, health, environment and ergonomics with 12 sub-variables. The results showed a correlation between the main domains as well as the research variables. Identifying these connections revealed that the decision hierarchy is network type and the model governing the problem is ANP type. However, in some research the AHP model was used for this purpose. The premise of this model is that the variables are sufficiently independent of each other, which was rejected in this study. The results of using this model in ArabNia Hospital in Qom also showed that ergonomic factor has the greatest impact on other macro factors, and employee factor has the greatest impact on other 11 sub-variables of research. This model was used to evaluate all 8 units of the hospital. The goal is to determine how well each of these units complies with HSEE. If there is data related to the observance of each sub-variable by each unit in the hospital documents, the relevant data can be entered into the model and after analysis, the amount of HSEE observance by each unit can be determined. Because many cases are not recorded in organizations, access to the necessary data is not possible through the hospitalchr('39')s databases. Since the organizationchr('39')s staff are the only ones who have a lot of data and metadata in their mental history (albeit to some extent, vague and erroneous), a questionnaire was used to solve this problem. For ease of response, the Likert range was used in qualitative measurements. This questionnaire was distributed in 8 units and 11 questionnaires were completed and received. The required data were analyzed by the research model and 8 hospital units were ranked based on HSEE compliance. The results showed that, firstly, the weight of each of the 12 factors of the HSEE model is not the same, and secondly, there is a relationship between them, and finally, the ranking of the units is not the same. Conclusion: The findings of using the research model in ArabNia Hospital in Qom proved the power of model differentiation. Using the results obtained in this study, the extent to which each of the components of the HSEE model is affected and different from each other. The use of the model in ArabNia Hospital in Qom showed that the medical, financial and administrative equipment units are ranked first to third in the field of HSEE, respectively. With the development of the HSEE measurement model, including the determination of indicators, sub-indicators, their relationship and the weight of each of them, it was found that the weight of the employee invoice is higher than other sub-factors. This study provides a framework for measuring hospital performance in terms of HSEE compliance. This model can be used in other hospitals as well. The point to keep in mind is that it is necessary to recalculate the weight of the variables and sub-variables, which is very simple. This model can also be used in other organizations. Provided that all sub-variables are redefined in accordance with the same organization and replaced in the model. It should be noted here that the main foundation and architecture of the model is fixed, but due to the difference in the nature of organizations, such a change in the details of the model is necessary. Also, the relationship between the factors and their weight must be recalculated. The results of this study are consistent with many studies conducted in the field of HSE. Including the fact that the human factor is very important in establishing HSE in organizations and complying with the relevant rules and regulations. The difference between the model presented in this study and other research is that the relationship between the factors in this model is considered. Although this is not explicitly stated, the use of AHP in some studies is due to the fact that independent HSE variables have been considered and the relationship between them have not been considered. Some recent research has shown that the combination of DEMATEL and ANP has been used in the HSE model, which indicates an increase in accuracy in such research. However, ergonomic factors are absent in these studies. In the present study, an attempt has been made to pay attention to all these aspects.

Information is personally useless if its beholder cannot individually benefit from it further unless she shares it with those who can exploit that information to increase their mutual outcome. We study sharing such information anonymously in a non-strategic and non-competitive setting, where selfish and cooperative motives align. Although sharing information was cost-free and resulted in expected mutual payoff, almost all subjects showed some levels of hesitancy toward sharing information, and it was more severe in the introverts. According to our mechanistic model, this irrationality could arise because of the excessive subjective value of personally useless information and low other-regarding motives, that necessitated over-attainable personal benefit to drive sharing. Interestingly, other-regarding element correlated with the subjects' belief about how others are cooperative in general. In addition, sensitivity to the value of information correlated with their extraversion level. The results open a new window towards understanding inefficient motives that deprive people of collective benefit.

we present and implement a validated validation model of the technical and engineering curriculum. In this research the statistical population included all the specialized courses in computer. The descriptive-survey and applied method used in this research. The statistical sample of this research is selected by random sampling from the specialized courses of computer engineering. So the "Advanced Programming" course, which is specialized, was selected as a statistical sample. In the next step, the information and statistics needed to answer the research questions were completed by a researcher made questionnaire and received from the professor of the course in the form of a list of students' scores. Following the implementation of the eleventh step-by-step validation, the following results were achieved: By choosing the methods of evaluation and performance of students, the goal of learning "the student can use the necessary methods to test and debug the program in order to use the program's function correctly" With the lowest percentage of realization and learning goal, "Student can solve issues by up to down design method." Had the highest percentage of realization. Considering the choice of methods of evaluation of performance of students and the extent of realization of learning goals in the aforementioned course one of the main objectives of computer engineering field was the acquisition and preservation of strategic superiority and independence of the country in important areas of processing with the contextualization for continuous innovation by the graduates, gained the lowest percentage of achievement and the goal. Design and construction of national communication networks by graduates had the highest percentage of realization

When the statistical distribution of products under study is not normal or symmetrical, in order to use the X control chart it cannot be expected that the process variability is timely detected. In this study, to improve the performance of X control chart, Generalized Lambda Distribution (GLD) was applied. To illustrate how to implement the proposed method, the data pertaining to tensile straights of eighteen aluminum plates were utilized and upper and lower limits of X control chart were calculated. Chi-Square test and Average Run Length (ARL) method were employed to ensure the obtained results and to accredit the proposed method, respectively. Due to the flexibility of this distribution, applying the proposed method could ensure that the process variability is discovered ahead of time.

Abstract Drug addiction has been associated with lack of insight into one’s own abilities. However, the scope of metacognition impairment among drug users in general and opiate dependent individuals in particular is not fully understood. Investigating the impairments of metacognitive ability in Substance Dependent Individuals (SDIs) in different cognitive tasks could contribute to the ongoing debate over whether metacognition has domain-general or domain-specific neural substrates. We compared metacognitive self-monitoring ability of a group of SDIs during methadone maintenance treatment (n = 23) with a control group (n = 24) in a memory and a visual perceptual task. Post decision self judgements of probability of correct choice were obtained through trial by trial confidence ratings and were used to compute metacognitive ability. Results showed that despite comparable first order performance in the perceptual task, SDIs had lower perceptual metacognition than the control group. However, although SDIs had poorer memory performance, their metacognitive judgements in the memory task were as accurate as the control group. While it is commonly believed that addiction causes pervasive impairment in cognitive functions, including metacognitive ability, we observed that the impairment was only significant in one specific task, the perceptual task, but not in the memory task.

Abstract Two psychophysical experiments examined multisensory integration of visual-auditory (Experiment 1) and visual-tactile-auditory (Experiment 2) signals. Participants judged the location of these multimodal signals relative to a standard presented at the median plane of the body. A cue conflict was induced by presenting the visual signals with a constant spatial discrepancy to the other modalities. Extending previous studies, the reliability of certain modalities (visual in Experiment 1, visual and tactile in Experiment 2) was varied from trial to trial by presenting signals with either strong or weak location information (e.g., a relatively dense or dispersed dot cloud as visual stimulus). We investigated how participants would adapt to the cue conflict from the contradictory information under these varying reliability conditions and whether participants had insight to their performance. During the course of both experiments, participants switched from an integration strategy to a selection strategy in Experiment 1 and to a calibration strategy in Experiment 2. Simulations of various multisensory perception strategies proposed that optimal causal inference in a varying reliability environment not only depends on the amount of multimodal discrepancy, but also on the relative reliability of stimuli across the reliability conditions.

Abstract Neuronal networks of the brain adapt their information processing according to the history of stimuli. Whereas most studies have linked adaptation to repetition suppression, recurrent connections within a network and disinhibition due to adaptation predict more complex response patterns. The main questions of this study are as follows: what is the effect of the selectivity of neurons on suppression/enhancement of neural responses? What are the consequences of adaptation on information representation in neural population and the temporal structure of response patterns? We studied rapid face adaptation using spiking activities of neurons in the inferior-temporal (IT) cortex. Investigating the responses of neurons, within a wide range from negative to positive face selectivity, showed that despite the peak amplitude suppression in highly positive selective neurons, responses were enhanced in most other neurons. This enhancement can be attributed to disinhibition due to adaptation. Delayed and distributed responses were observed for positive selective neurons. Principal component analysis of the IT population responses over time revealed that repetition of face stimuli resulted in temporal decorrelation of the network activity. The contributions of the main and higher neuronal dimensions were changed under an adaptation condition, where more neuronal dimensions were used to encode repeated face stimuli.

This paper examines the impact of marketing strategies alignment on financial performance with the mediator role of supply chain performance. This is an applied research and the research methodology is descriptive-casual. Statistical population of the research includes active manufacturing companies in the Industrial Estates of Ahvaz city and its sample size was equal to 216 companies using Cochran sampling formula. Moreover, data were collected using questionnaire. 216 questionnaires were filled by the corporate executives. Furthermore, data were analyzed using structural equation modeling and correlation analysis. The obtained results showed the positive and significant impact of marketing strategies alignment on supply chain performance and financial performance variables. Also, the mediator role of supply chain performance was not confirmed.

Programming by demonstrations is one of the most efficient methods for knowledge transfer to develop advanced learning systems, provided that teachers deliver abundant and correct demonstrations, and learners correctly perceive them. Nevertheless, demonstrations are sparse and inaccurate in almost all real-world problems. Complementary information is needed to compensate these shortcomings of demonstrations. In this paper, we target programming by a combination of nonoptimal and sparse demonstrations and a limited number of binary evaluative feedbacks, where the learner uses its own evaluated experiences as new demonstrations in an extended inverse reinforcement learning method. This provides the learner with a broader generalization and less regret as well as robustness in face of sparsity and nonoptimality in demonstrations and feedbacks. Our method alleviates the unrealistic burden on teachers to provide optimal and abundant demonstrations. Employing an evaluative feedback, which is easy for teachers to deliver, provides the opportunity to correct the learner’s behavior in an interactive social setting without requiring teachers to know and use their own accurate reward function. Here, we enhance the inverse reinforcement learning (IRL) to estimate the reward function using a mixture of nonoptimal and sparse demonstrations and evaluative feedbacks. Our method, called IRL from demonstration and human’s critique (IRLDC), has two phases. The teacher first provides some demonstrations for the learner to initialize its policy. Next, the learner interacts with the environment and the teacher provides binary evaluative feedbacks. Taking into account possible inconsistencies and mistakes in issuing and receiving feedbacks, the learner revises the estimated reward function by solving a single optimization problem. The IRLDC is devised to handle errors and sparsities in demonstrations and feedbacks and can generalize different combinations of these two sources expertise. We apply our method to three domains: a simulated navigation task, a simulated car driving problem with human interactions, and a navigation experiment of a mobile robot. The results indicate that the IRLDC significantly enhances the learning process where the standard IRL methods fail and learning from feedbacks (LfF) methods has a high regret. Also, the IRLDC works well at different levels of sparsity and optimality of the teacher’s demonstrations and feedbacks, where other state-of-the-art methods fail.

In conflict tasks, like the Simon task, it is usually investigated how task-irrelevant information affects the processing of task-relevant information. In the present experiments, we extended the Simon task to a multimodal setup, in which task-irrelevant information emerged from two sensory modalities. Specifically, in Experiment 1, participants responded to the identity of letters presented at a left, right, or central position with a left- or right-hand response. Additional tactile stimulation occurred on a left, right, or central position on the horizontal body plane. Response congruency of the visual and tactile stimulation was orthogonally varied. In Experiment 2, the tactile stimulation was replaced by auditory stimulation. In both experiments, the visual task-irrelevant information produced congruency effects such that responses were slower and less accurate in incongruent than incongruent conditions. Furthermore, in Experiment 1, such congruency effects, albeit smaller, were also observed for the tactile task-irrelevant stimulation. In Experiment 2, the auditory task-irrelevant stimulation produced the smallest effects. Specifically, the longest reaction times emerged in the neutral condition, while incongruent and congruent conditions differed only numerically. This suggests that in the co-presence of multiple task-irrelevant information sources, location processing is more strongly determined by visual and tactile spatial information than by auditory spatial information. An extended version of the Diffusion Model for Conflict Tasks (DMC) was fitted to the results of both experiments. This Multimodal Diffusion Model for Conflict Tasks (MDMC), and a model variant involving faster processing in the neutral visual condition (FN-MDMC), provided reasonable fits for the observed data. These model fits support the notion that multimodal task-irrelevant information superimposes across sensory modalities and automatically affects the controlled processing of task-relevant information.

In our daily life, we continually exploit already learned multisensory associations and form new ones when facing novel situations. Improving our associative learning results in higher cognitive capabilities. We experimentally and computationally studied the learning performance of healthy subjects in a visual-auditory sensory associative learning task across active learning, attention cueing learning, and passive learning modes. According to our results, the learning mode had no significant effect on learning association of congruent pairs. In addition, subjects' performance in learning congruent samples was not correlated with their vigilance score. Nevertheless, vigilance score was significantly correlated with the learning performance of the non-congruent pairs. Moreover, in the last block of the passive learning mode, subjects significantly made more mistakes in taking non-congruent pairs as associated and consciously reported lower confidence. These results indicate that attention and activity equally enhanced visual-auditory associative learning for non-congruent pairs, while false alarm rate in the passive learning mode did not decrease after the second block. We investigated the cause of higher false alarm rate in the passive learning mode by using a computational model, composed of a reinforcement learning module and a memory-decay module. The results suggest that the higher rate of memory decay is the source of making more mistakes and reporting lower confidence in non-congruent pairs in the passive learning mode.

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.

Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions.