Psychological stress occurs when an individual perceives that environmental demands tax or exceed his or her adaptive capacity. Its association with severe health and emotional diseases, points out the necessity to find new efficient strategies to treat it. Moreover, psychological stress is a very personal problem and requires training focused on the specific needs of individuals. To overcome the above limitations, the INTERSTRESS project suggests the adoption of a new paradigm for e-health - Interreality - that integrates contextualized assessment and treatment within a hybrid environment, bridging the physical and the virtual worlds. According to this premise, the aim of this study is to investigate the advantages of using advanced technologies, in combination with cognitive behavioral therapy (CBT), based on a protocol for reducing psychological stress.The study is designed as a randomized controlled trial. It includes three groups of approximately 50 subjects each who suffer from psychological stress: (1) the experimental group, (2) the control group, (3) the waiting list group. Participants included in the experimental group will receive a treatment based on cognitive behavioral techniques combined with virtual reality, biofeedback and mobile phone, while the control group will receive traditional stress management CBT-based training, without the use of new technologies. The wait-list group will be reassessed and compared with the two other groups five weeks after the initial evaluation. After the reassessment, the wait-list patients will randomly receive one of the two other treatments. Psychometric and physiological outcomes will serve as quantitative dependent variables, while subjective reports of participants will be used as the qualitative dependent variable.What we would like to show with the present trial is that bridging virtual experiences, used to learn coping skills and emotional regulation, with real experiences using advanced technologies  is a feasible way to address actual limitations of existing protocols for psychological stress.http://clinicaltrials.gov/ct2/show/NCT01683617 According to Cohen and colleagues , psycholExtreme levels of stress can have a negative influence on one’s professional life and can disrupt both the social and personal life of an individual. Stress can also cause different physiological and psychological disorders, such as anxiety, chronic headaches, depression, withdrawal symptoms, nausea, phobias, blood pressure problems, heart impairments and others -5.Repeated and early exposure to stress, above all in persons with a particular genetic disposition, as well as high level of trait anxiety, may result in a decreased threshold for developing anxiety -8. OvereCochrane Database of Systematic Reviews[Stress management therapy can help to overcome the counter effects of stress. Usually various techniques are used, including relaxation, interaction, biofeedback and cognitive behavioral therapy (CBT) methods. According to the c Reviews-15, the The intervention also encourages emotional expression, replacing doubt appraisals with a sense of confidence by means of cognitive restructuring, and honing skills in anxiety reduction (by progressive muscle relaxation or diaphragmatic breathing and relaxing imagery). The CBT package thus includes both problem-focused  and emotion-focused  coping strategies.Nevertheless, the Cochrane database underlines that “the poor quality of trials, considerable heterogeneity observed between trials and evidence of significant publication bias make the pooled findings insecure.” In other words, even if CBT is the best-validated approach for the treatment of stress -19, furtThe trouble with stress is that it is so very personal. Thus, stress-related disorders cannot be explained simply on the basis of the terrible things that happen to people. These disorders depend a great deal on how the person experiencing a stressor is put together, psychologically and physically. So the focus for assessment, prediction and treatment has to be the situational experience of the person. For this aim, the INTERSTRESS project suggests the adoption of a new paradigm for e-health - Interreality, which integrates contextualized assessment and treatment within a hybrid environment, bridging the physical and the virtual worlds -24.The Interreality claim is that bridging virtual experiences  with real experiences , using advanced technologies  is a feasible way to address the complexity of many mental disorders, including psychological stress. On one hand, patients are continuously assessed in the virtual and real worlds by tracking their behavioral and emotional status in the context of challenging tasks (customization of the therapy according to the characteristics of the patient). On the other hand, feedback is continuously provided to improve patients’ skills through a conditioned association between performance and execution of assigned tasks (improvement of self efficacy). Within the Interreality paradigm, INTERSTRESS aims to create a feasible way to address the current in vivo  limitations of psychological stress for existing protocols.In the standard CBT protocol for stress management, imagination and/or exposure evoke emotions, and the meaning of the associated feelings can be changed through reflection and relaxation. The INTERSTRESS project, based on the Interreality paradigm, would suggest an alternative, which is able to control experiences that evoke emotions resulting in meaningful new feelings, which can be reflected upon and eventually changed through reflection and relaxation.Although CBT focuses directly on modifying dysfunctional thoughts through a rational and deliberate process, INTERSTRESS will focus on modifying them through a more contextualized experiential process. In the INTERSTRESS training, in fact, individuals will be actively involved in the learning process, experiencing stressful situations reproduced in virtual environments and reflecting on the stress level in their daily life with the help of advanced technology, such as smartphones and unobtrusive biosenors.From the clinical point of view, the INTERSTRESS solution may offer the following innovations to current traditional protocols for stress management: (1) integrated and quantitative assessment of the user’s stress level using biosensors and behavioral analysis: the level of stress will be continuously assessed in the virtual and in the real world by recording the participant's behavioral and emotional status; (2) provision of warnings and motivating feedback to improve self awareness, compliance and long-term outcomes: participants will constantly receive feedback to improve their appraisal and coping skills in an entertaining and motivating fashion.The main goal of the INTERSTRESS project is the development of a highly innovative information and computer technology (ICT)-based system for the assessment and management of psychological stress from a strictly context-bound and interactive perspective, which means offering specific tools for addressing the complexity of the phenomenon.The general aim of the trial is to test the efficacy of the INTERSTRESS service, an advanced ICT-based solution for the diagnosis and treatment of psychological stress. In particular, the purpose is to test the efficacy of the INTERSTRESS service for the objective and quantitative assessment of psychological stress symptoms and to integrate the decision support system (DSS) for treatment planning through data fusion and detection algorithms.The assessment will be conducted continuously in the virtual and real worlds through the tracking of individuals’ behavioral and emotional status over time in the context of realistic task challenges. On the other hand, the information will be constantly used to improve both the appraisal and the coping skills of the participant through conditioned association between effective performance state and task execution behaviors. The devices are integrated around two subsystems: the clinical platform  and the mobile platform . Furthermore, from a scientific point of view the INTERSTRESS trial aims to: (1) define a multidimensional approach to the assessment of psychological stress, able to integrate traditional measures with real-time assessment of the quality of the individual experiences, through the integration of biosensors and behavioral analysis; (2) provide support for efficient functioning in everyday life; and (3) verify the efficacy of the proposed methodological approach and of the system for psychological stress prevention, through an evidence-based approach in real-world organizational settings.The present trial is based on a controlled trial, including three groups of approximately 50 individuals who suffer from psychological stress: (1) the experimental group (EG), (2) the control group (CG), and (3) the waiting list (WL) group. Persons who feel stressed at their place of employment and who lack coping skills will be randomly assigned to one of these three groups.Istituto Auxologico Italiano (AUXO) in public school institutes in Milan, and up to 75 nurses will be recruited by the National Council of Research (CNR) in a public hospital in Messina, Italy. The number of participants (sample size) was calculated in order to have an effect size sufficient to prove the effect of the training, reducing type II  statistical errors. Regarding type I errors , a test threshold of 0.05 will be used.We will recruit up to 150 participants: up to 75 teachers will be collected by Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR); (2) age between 25 and 60 years; (3) no psychotherapy received for their psychological stress; (4) no pharmacotherapy; (5) no history of neurological diseases, psychosis, alcohol or drug dependence; and (6) no migraine, headache, or vestibular abnormalities. Both male and female patients will be included. Before participating in the study, each participant will be provided with written information about the study and will be required to give written consent for inclusion in the study.In order to be included in the study, individuals have to meet the following criteria: (1) no Axis-I disorders as defined in the For the assessment of the stress management training outcome, we will administer questionnaires at different time points and for different categories of interest.In order to exclude participants suffering from DSM-IV-TR Axis-I disorders, individuals will be assessed before the start of the training by a Masters-level charted psychologist or a PhD-level psychotherapist using a semi-structured interview, the mini-international neuropsychiatric interview (MINI) ,26. The At the beginning of the training a self-assessment scale will be used to evaluate patients’ technological abilities. The four-item questionnaire was created to assess individuals’ perceived technological skills in the use of personal computers and smartphones.The following questionnaires will be administered to each participant at pre-treatment, upon completion of the trial, and after an 18-month follow-up period. The psychological stress measure (PSM) ,28 consiThe perceived stress scale (PSS)  is a 10-The questionnaire, coping orientation to problems experienced inventory (COPE) ,31, was The Pittsburgh sleep quality index (PSQI) ,33 asses, also known as STAI-Y2). For the initial and the final assessment in the trial we will use the STAI Y-2, that is, the trait version of the STAI, which measures characteristic tendencies towards anxiety.The state-trait anxiety inventory (STAI) ,35 consiThe satisfaction with life scale (SWLS) ,37 is a Individuals will also be assessed at the beginning and at the end of each of the eight training sessions using the following questionnaires. The STAI Y-1 ,35 addreClinical and mobile settings, planned in the INTERSTRESS training, require different participants’ psychophysiological assessment as shown specifically below.Psychophysiological data (heart and respiratory rates) will be obtained from a cardiovascular belt developed for this project by Pisa National Centre of research. These belts will be connected to a multi-modality device for real-time computerized data acquisition through a Bluetooth connection. Heart and respiratory channels will be acquired at a 100-Hz sampling rate. Cardiorespiratory activity will be monitored to evaluate voluntary and autonomic effects of respiration on heart rate (HR) in physical and virtual environmental interactions, analyzing the R-R interval (from an electrocardiogram (ECG)) and the breath respiration (BR) (from a chest-strip sensor) and their interaction. Furthermore, spectral methods for assessment of standard indexes of heart rate variability (HRV) will be used to evaluate the response of the autonomic nervous system. Spectral analysis will be performed by means of autoregressive (AR) spectral methods with custom software. The Levinson-Durbin recursion will be used to identify the coefficients of an autoregressive model. The AR spectral decomposition procedure, for example, will be applied to calculate the power of the oscillations embedded in the series. Moreover, the quantification of respiratory sinus arrhythmia (RSA) will provide information about the mechanisms involved in respiratory coupling.During the training, participants in the EG will also be monitored outside of the trainer’s office through a cardiovascular belt developed by University of Pisa that will send their psychophysiological data to a web-based Central Database Repository though a smartphone. Electrocardiography will be used to monitor heart rate and cardiac cycle through the inter-beat-interval (IBI). These cardiovascular data will be used to monitor individual psychophysiological changes related to psychological stress in real-life settings. Moreover, HR signals will be integrated into an application on the participants’ smartphones, to give them the possibility to perform biofeedback exercises, base on HRV, every time they are required .Epidemiological, clinical and psychophysiological data from each individual will be recorded by trainers across the study using advanced technologies. Data collected in the clinical and in the mobile setting will be stored in the web-based Central Database Repository. Security and privacy issues will be taken care of according to the respective situation and the consent of the participant. At the end of the training, the trainers will arrange a review appointment with the participant, both in the EG and the CG. Clinical follow up data  will be collected at those time points.In order to study the efficacy of the INTERSTRESS training, a between-subject design will be used with two experimental conditions and repeated measurements (pre-training and post-training). Specifically, we will compare the following conditions: (1) individuals in the EG will receive training based on CBT and new technologies , VR combined with biofeedback, and mobile phones). Relaxation will be induced through the immersion in different virtual environments , which will be customized with different pre-recorded audio narratives that describe the specific setting and that guide the execution of a series of relaxation exercises. During biofeedback exercises a wearable biosensor system will provide suggestions to the trainer based on the reactions of the participants, and the biosensor data will directly modify the VR experience in real time; (2) individuals in the CG will receive training based on traditional CBT for stress management, without the use of new technologies. Relaxation will be induced by guided imagery, through auditory narratives; (3) individuals in the WL group will be reassessed and compared with the two other groups five weeks after the initial evaluation. After the reassessment, the WL patients will randomly receive one of the two other treatments.Psychometric and psychophysiological outcomes will serve as quantitative dependent variables, and subjective reports of participants will be used as qualitative dependent variables Figure .The present trial is a randomized controlled trial. Participants will be randomly assigned to one of the three groups of the trial.Different hardware elements will be used in each setting of the INTESTRESS training, as shown specifically below.® Core™i5, graphic processor Nvidia GeForce GT 540M and Bluetooth support; a head-mounted display: Vuzix VR Bundle with twin high-resolution 640 × 480  LCD displays, iWear® 3D-compliant; and a joypad (Xbox controller)  and behavioral signals . We will use a VR control unit, the ACER ASPIRE portable computer with CPU IntelA shimmer, which is a small wireless sensor platform developed by University of Pisa, will be used to record and transmit psychophysiological (ECG) and kinematic data in real time. We will use the iPhone 4S smartphone Figure .We will use a portable computer (ACER ASPIRE 5742G-484G64MNKK) with Internet connection. Only participants in the EG will receive technological devices for use in the mobile and home settings, whereas those in the CG will instead receive a paper-and-pencil diary and a compact disc (CD) audio with relaxing narratives. Trainers and participants in the EG and the CG will have the possibility to access the Patient Management System (PMS). This web platform (content management system) will play the role of the front-end component used by the trainer and the participant to interact with the whole INTERTRESS system. Through PMS, both the trainer and the participant can access the relevant data stored in the central database, helping to monitor the evolution of the stress levels in patients under treatment, and to organize the therapist-patient interaction .VR can be considered one of the preferred environments for the empowerment processes, since it is a special, sheltered setting where users can start to explore and act without feeling threatened. In this sense, the virtual experience is an empowering environment that psychologists provide for their clients. In addition, VR allows the situation to be graded so the user can start at the easiest level and progress to the most difficult. Gradually, because of the knowledge and control afforded by interaction in the virtual world, the participants will be able to face the real world.Starting from a literature review focused on exploring the daily life situations that produce stress -41, and Thus, the storyboards for each stressful scenario were written and the virtual environments were created according to these storyboards. The stressful scenarios were then played by real actors, who were inserted into the virtual environments after a video post-production Figure . For theStressful virtual scenarios for the other sample of the trial (nurses) are currently being created following the same procedure. The virtual environments created will include stressful situations that could be experienced by nurses at work, such as to be reproached by colleagues, to manage an emergency and to cope with patients’ criticism.As already explained, the CG will not be exposed to VR. They will receive the same script of the seven stressful scenarios through an audio version (guided imagery). Participants will be asked to close their eyes and to imagine themselves, as vividly as possible, during the situations described, following an audio narrative matching the VR experiences.In the last years, VR has been used in different clinical protocols to facilitate relaxation processes in stressed or anxious subjects -46 by viIn the INTERSTRESS trial, for the relaxation training, relaxing environments will be presented together with relaxing audio narratives based on guided imagery procedures and developed according to emotive engineering principles ,51. The Through the smartphones participants will have the opportunity to receive warning and feedback about their stress level and to perform relaxing or biofeedback exercises. This technology offers the chance to close the gap between in-office and out-of-office (or homework) sessions, improving compliance and long-term outcomes. Recent work ,55-57 veBiofeedback has demonstrated value for stress-reduction training ,63. UnfoThe protocol will be based on 5 weeks of stress management training (two sessions per week). During each week participants in the EG and the CG will meet a psychologist in two sessions. Our protocol is based on CBT in general, and specifically on the stress management program of Kaluza  and on tDuring the session, the participant will be in a quiet room, sitting in a comfortable chair. Every session will last about 50 minutes and will consist of a first part with an introduction and a discussion about homework, a central part with exposition to a stressful scenario, followed by cognitive restructuring aimed at teaching participants about useful coping strategies related to the specific stressful situation. After this exposition and discussion, there will be a final part for relaxation training, and finally a debriefing about the session.Participants will have the possibility to access the contents useful for stress management logging in from their personal computer (PC) via Second Life and visiting the INTERSTRESS Learning Island . Simply Participants will be able to rehearse/relax outside the psychologist’s office using a smartphone application developed for this specific purpose. To improve the efficacy of the experience with the clinician, during the homework the mobile system will present a non-navigable version of the same VR environment experienced during the training in the clinic setting. Through the smartphone, participants will also be taught how to become aware of their physiological levels of stress. On the mobile phone they will have the possibility to observe a simple graphic of their stress level on that day and during their week. Moreover, the mobile application provides warning signals when critical thresholds are overcome, giving the participant the opportunity to perform a relaxing exercise to reduce his or her stress level.A detailed description of the protocol for the EG and the CG follows: In the EG  and give these to the clinician at the beginning of the session. During the intake session, the participant will meet the trainer, who will explain in detail the training purpose and procedures, and will finalize the assessment. After discussion and explanation of the aims and methods of the training, each participant will be provided with written information about the study and will be asked to provide written consent for inclusion. Then, the participant will be assessed through clinical interview (MINI) in order to verify if he or she could be included in the training.After this preliminary phase, if the participant satisfies the inclusion criteria, the psychologist will introduce the specific processes of the training, which will be structured into ten sessions  where technologies will be employed as training tools. Each session will be divided into four parts: homework checking and dialogue with the participant on difficulties experienced in the week between the previous session and the current session; exploration of virtual environments to practice coping skills during stressful situations; comments about the experience ; and relaxation training and new homework assignments. Then, for familiarization with VR, the clinician will expose the participant to a neutral virtual environment. After this exposition, the trainer will explain that biosensors will be worn during the training to monitor physiological parameters, to track the emotional/heath status, and to directly influence experience in the virtual world. The session will be concluded with a detailed explanation of how to use the portable biosensors and the smartphone during the following assessment week. The clinician will also explain how to use the patient management system through the computer at home and the three-dimensional virtual spaces (Learning Island) in Second Life during all the training.Assessment session (session 1): this will start with a discussion with the clinician about the assessment week. Then, after a brief introduction to the specific content of this session, the physiological baseline of the participant will be recorded for 3 minutes. In order to measure the psychological variations occurring during the different stressful environmental exposures, subjects will complete the VAS-A and STAI-Y1 at baseline and after each scenario. During the stressful exposition, the participant’s physiological measurements will also be recorded. In addition to the seven stressful scenarios, which replicate daily life stressors, each participant will be assessed in a neutral virtual environment and in one in which he or she will complete a cognitive task in front of a virtual audience. These situations will be used to assess the maximum and minimum stressor. After the virtual assessment, the participant will be exposed to a relaxing virtual environment. At the end of this session, the clinician will explain to the participant the homework to be completed using the smartphone and the PC .Training session (sessions 2 to 9): these sessions will be dedicated to teaching participants to cope with stress through cognitive restructuring techniques and the teaching of relaxation exercises. Each session will last about one hour and be divided into four parts: homework checking; exposition to a stressful scenario and cognitive restructuring with the clinician about the experience; relaxation; and homework assignment. The clinician will decide with the participant, which specific stress scenarios he or she will focus on during the training. The participant will be virtually exposed to the selected stress, working on it with the clinicians during two consecutive sessions. Relaxation will be induced through immersion in different virtual environments , which will be customized with different pre-recorded audio narratives that describe the specific setting and guide the execution of a series of relaxation exercises. Relaxation will be induced by navigation in a virtual environment in which the user may move and interact, following auditory narratives that support images. Participants will be immersed in different environments , each one associated with different pre-recorded audio narratives.During sessions, the EG will be alternately exposed to relaxing VR environments and to VR biofeedback. The psychologist will provide explanations about biofeedback, the role of biofeedback methodology and the participant's role and involvement. After an introductory explanation, the trainer will start the biofeedback session, while explaining how the physiological signals change with variations in physiologic activity, and how these may be associated with slight differences in subjective feeling states. The participant will be instructed about relaxation and various strategies used to achieve these states during exercises in virtual reality environments that will last about 10 minutes. At the end of the session, the psychologist will discuss the participant's response to the biofeedback experience and will encourage his participation and interaction in the experience. Traditional relaxation exercises will be alternated during sessions with biofeedback.Follow up session (session 10): in order to verify the efficacy of the training, during the final session, participants will be reassessed through the administration of questionnaires to measure their stress and anxiety level  and their perceived quality of life (PSQI and SWLS). Moreover, participants will again be exposed to the stressful scenarios, following the same procedure as in the assessment session (session 1). At the end of this assessment, the clinician will discuss with the participant the training and its efficacy, and eventually the difficulties he or she has experienced.In the CG  and everything is described in detail, so that a realistic and vivid exposure may be possible.Participants included in the CG will not use the smartphone for stress assessment, but will use a traditional diary, in which they will mark every stressful event during their assessment week. Moreover, instead of Second Life, participants will receive a book about stress, with the same contents included in those virtual environments. In order to obtain comparable measurements with the EG, participants in the CG will be assessed during the training with the same questionnaires and their physiological measurements will be recorded during each session.An 18-month follow up is planned in order to verify the efficacy of the training over a long-term period.t-test for matched groups.Data analyses will be carried out using SPSS software. Descriptive methods will be used to demonstrate the consistency of the two groups, to describe participants' characteristics and to report levels of participation and drop out. Analysis of variance will be used to evaluate baseline characteristics of the two groups involved in the study, overall significance of improvement across outcome measures, and drop outs versus maintainers. For each participant, change in psychometric and physiological measures will be calculated and analyzed using the Istituto Auxologico Italiano of Milan.This study was approved by Ethics Committee in the Despite CBT being the best validated approach for the treatment of stress -19, furtWhat we would like to show with the present trial is that to bridge the use of virtual experiences, which are fully controlled by the trainer, with real experiences, could be effective for teaching coping skills and emotional regulation. This will allow for both the identification of any critical stressors and assessment of what has been learned by using advanced technologies . The aim of this proposal is to create a feasible way to address the actual limitations of the existing protocols for the treatment of psychological stress.We believe that the use of this innovative paradigm represents a promising approach, because it enhances the quality of assessment and training of psychological stress within a hybrid, closed-loop empowering experience bridging physical and virtual worlds. Our outcome measures are well-suited to measuring both the subjective and objective impact related to the intervention and the presence of the CG, which will adopt a traditional CBT protocol for psychological stress, to guarantee that the final results are truly attributable to the intervention. This controlled trial will be able to evaluate the effects of the use of the INTERSTRESS solution in the assessment and training of psychological stress, while preserving the benefits of randomization to reduce bias. Its design takes into account the need for internal and external validity and that the results are attributable to the intervention.Patient recruitment was ongoing at the time of manuscript submission. Data collection will continue at least until November 2013.Istituto Auxologico Italiano; BR: Breathing rate; CBT: Cognitive behavioral therapy; CD: Compact disc; CG: Control group; CNR: National Council of Research; COPE: Coping orientation to problems experienced inventory; CVD: Cardiovascular disease; DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders, fourth edition; DSS: Decision support system; DVD: Digital video disc; ECG: Electrocardiogram; EG: Experimental group; HPA: Hypothalamic-pituitary-adrenocortical axis (HPA); HR: Heart rate; HRV: Heart rate variability; IBI: Inter-beat interval; ICD: International Classification of Diseases; ICT: Information and computer technology; MINI: Mini-international neuropsychiatric interview; PBS: Personal biomonitoring system; PC: Personal computer; PDA: Personal digital assistants; PMS: Patient management system; PSM: Psychological stress measure; PSQI: Pittsburgh sleep quality index; PSS: Perceived stress scale; RSA: Respiratory sinus arrhythmia; SAM: Sympathetic adrenal medullary system; STAI: State-trait anxiety inventory; SWLS: Satisfaction with life scale; VAS-A: Visual analog scale for anxiety; VR: Virtual reality; WL: Waiting list.AR: Autoregressive; AUXO: The authors declare that they have no competing interests.FP prepared the first draft of the manuscript. SR, BM and FP supervised the study in its clinical aspect. FP, SR and SS collected literature and supervised the background of the study. PC supervised the psychophysiological aspect of the study. CV created the web material. SS, PC and LM developed the first draft of the manuscript into the final version suitable for publication. AG, BW and GR conceived the idea of the study and supervised its scientific design. All authors read and approved the final manuscript.


Arabidopsis thaliana.In this project we want to determine pairs of single nucleotide polymorphisms (SNPs) which have a statistically significant effect on the phenotypic variation of the flowering time of For a large-scale dataset of over 200,000 SNPs from about 200 individuals together with several phenotypes, published by Atwell et al. , we deveIn the first approach we employ a branch-and-bound strategy to reduce the search space by pruning insignificant pairs of SNPs. Based on this branch-and-bound strategy we develop the two methods fastHSIC and COAT, which use as association measures the Hilbert-Schmidt Independence Criterion (HSIC)  and PearArabidopsis thaliana. These candidate genes and interactions between them make up a network of 1,452 nodes or genes and 938 edges or gene-gene interactions, and allow us to select a subset of SNPs that lie within or in close proximity to the genes of the network.In our second approach we use prior biological knowledge to select a much smaller subset of candidate genes which, according to other findings, affect the flowering time of Empirical evaluation of our own as well as traditional methods on the original and the reduced dataset shows that both our approaches can greatly reduce the runtime.


What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness. in silico evolution experiment to study the relationship between complexity, as measured using different information-theoretic measures, and fitness, by evolving animats with brains of twelve binary variables over 60,000 generations. We compute the complexity of these evolved networks using three measures based on mutual information and one measure based on the extent to which their brain contain states that are both differentiated and integrated. All measures show the same trend - the minimal complexity at any one fitness level increases as the organisms become more adapted to their environment, that is, as they become fitter. Above this minimum, there exists a large degree of degeneracy in evidence.It has often been asserted that as organisms adapt to natural environments with many independent forces and actors acting over a variety of different time scales, they become more complex. We investigate this question from the point of view of information theory as applied to the nervous systems of simple creatures evolving in a stereotyped environment. We performed a controlled  What is the relationship between complexity and the fitness of evolved organisms, whether natural or artificial? It is often assumed Recently, a computer model of simple animats evolving in an environment with fixed statistics, randomly generated mazes that they had to traverse as quickly as possible , reporteet. al.In all experiments - and also in our setup - the evolutionary change takes place via two mutually disjoint processes, namely a purely stochastic mutation of the genome followed by a selection process. The stochastic nature of the genetic mutation allows us to equate ensemble-averages over many evolutionary histories to the time-averages over a single history, provided sufficient time has passed for an equilibrium to be established locally. By exploiting this ergodicity, we could greatly scale up the statistic from our evolutionary runs. This enabled us to reproduce the simulations of Edlund  of 0.24 . Even thintegrated information  of the fittest agent resulted after 60,000 generations. To reduce the error in fitness as well as complexity estimation, we generated 20 random mazes each time over which performance of an agent is tested to calculate fitness. SMMI and other complexity measures are calculated using the sensory-motor data collected while the agent was navigating through these mazes.After every 1000th generation, we estimate the SMMI and complexity in terms of the mutual information between two variables The predict a future state of a system, based on its current state, hence the name predictive information. Considering the system as a channel connecting two consecutive states, the predictive information has been proposed as a possible measure of functional complexity of the system. The predictive information of a system The predictive information of a time series, as defined in its original form We here consider the predictive information between one discrete time step, integrated information or We use the state-averaged version of minimal information partition or MIP. The effective information, defined over the MIP, is thus an intrinsic property of the connectivity of the system and signifies the degree of integration or irreducibility of the information generated within the system. This quantity is called One proceeds by defining a quantity called the effective informationBy definition, Bell number, Evaluating atomic partition stochastic interactionWe denote the effective information calculated over the The To confirm that selection by fitness is actually necessary to selectively evolve high In a first control experiment, agents never experienced any selection-pressure, as each new generation was populated by randomly selecting agents from the previous one. Animats unsurprisingly failed to evolve any significant fitness - maximal fitness was In a second control experiment, organisms evolved as usual for 45,000 generations. This selected for agents able to rapidly traverse through the maze. The resulting s 91.27% . We then.12 bits .Analyzing various information-theoretical measures that capture the complexity of the processing of the animats as they evolve over 60,000 generations demonstrate that in order to achieve any fixed level of fitness, a minimum level of complexity has to be exceeded. It also demonstrates that this minimal level of complexity increases as the fitness of these organisms increase.main complex, Einstein, a near-optimal hand-designed agent within the constraints of our stochastic Markov network, is plotted as a magenta asterisk in Not only SMMI, but also predictive information It should be noted, that our terminologies differ slightly from those in necessary to attain a particular level of fitness. The existence of such a boundary had been previously surmised in empirical studies Even a cursory inspection of the plots of SMMI, Conversely, no upper value for complexity is apparent in any of the plots  to achieve the same level of fitness, which is exactly what we observe. This provides enough diversity for future selection to occur when the environment changes in unpredictable ways. Curiously, the hand-designed agent, Einstein, has little degeneracy, lying just above the minimal complexity level appropriate for its In two control experiments, we showed that selection by fitness is necessary to attain fitness and high circuit complexity. Yet complexity and fitness were neither explicitly connected by construction nor measured in terms of each other. Hence, any network complexity evolved in this manner must be a consequence of the underlying relationship between fitness and complexity. While this complexity is completely determined by the transition table associated with the brain's nodes, its fitness can only be evaluating by monitoring the performance of the agent in a particular environment. This and the fact that all complexity measures studied in this work show similar behaviors support the notion of a general trend between fitness and minimal required complexity.Thus, complexity can be understood as arising out of chance and necessity next door . This door bit can only be read by the agent inside the doorway. Thus, the organism must evolve a simple one-bit memory that would enable it to efficiently move through the maze and it must evolve circuitry to store this information in a 1-bit memory.Our maze is a two-dimensional labyrinth that needs to be traversed from left to right  and thatThe maze has circular-periodic boundary conditions. Thus, if the agent passes exit door before its life ends after 300 time steps, it reappears on the left side of the same maze.While the wall sensors receive information about the current local environment faced by the agent at each time-step, the information received from the door bit only has relevance for its future behavior. During evolution of the brain of these animats, they have to assimilate the importance of this one bit, store it internally and use it to seek passage through the next wall as quickly as possible.The connectome of the agent, encoded in a set of stochastic transition tables or hidden Markov modeling units elite, from every generation is copied exactly to the next generation without mutation, while those of other agents selected with probabilities proportional to their fitness are operated over by mutation, deletion and insertion. The probabilities that a site on the genome is affected by these evolutionary operators are respectively 2.5%, 5% and 2.5%.Each evolutionary history was initiated with a population of 300 randomly generated genomes and subsequently evolved through 60,000 generations. At the end of each generation, the agents ranked according to their fitness populate the next generation of 300 agents. The genome of the fittest agent, or the time averaging - to that sampled only after 50,000th generation over 64 evolutionary histories - or ensemble averaged - as in Evolutionary operators are applied purely stochastically and the selection acts only after the random mutations have taken place. This allows us to relate the fitness-complexity data sampled along each evolutionary line after every 1000th generation - similar to The fitness of the agent is a decreasing function of how much it deviates from the shortest possible path between the entrance and exit of the maze, calculated using the Dijkstra search algorithm Movie S1Typical behavior of an agent from early generations. The movie shows behavior of an agent from one of the evolutionary trials at (FLV)Click here for additional data file.Movie S2An evolved agent traversing through a maze. The movie shows behavior of an agent from the same evolutionary trial as in Movie S1, but after (FLV)Click here for additional data file.Movie S3An optimally designed agent - Einstein, traversing through a maze. This movie shows the maze-solving capabilities of an agent with optimally engineered connectome. It exhibited a fitness of (FLV)Click here for additional data file.


In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt. The reasons are that this subject is not only necessary for better understanding the various features of different tree species in precision plant management , but alsTerrestrial hyperspectral imaging systems and terrestrial laser scanning systems have both been enthusiastically developed and attempted for fine-scale tree properties investigation. For hyperspectral imaging, the related ground-based systems have been used for such as characterization of vegetation spectral features . For lasetc. As regards mobile hyperspectral imaging, its implementation tends to be concisely mentioned in the specs of mobile mapping systems [i.e., the average of the spectra for a whole tree was used to characterize that tree. This is due to the fact the resulting spectra set for each tree has large variations. The effect of directional light scattering in different plant land surfaces has also been studied [Mobile mapping ,21, whic systems , and lit systems , the fea studied . The resAimed at bridging this technical gap, this study was dedicated to investigating the spectral reflectance characteristics of single trees with a mobile mapping system. The system comprising a hyperspectral imaging module and a laser scanning module was preferred. The latter facilitates recording the structural characteristics of the target trees synchronously, and this can help reveal the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. In addition, since it is difficult to manipulate the whole spectra, the traditional concept of vegetation indices (VI) –33 based2.2.1.i.e., an inertial measurement unit/global positioning system (IMU/GPS) attitude/location module, two laser scanners, an optical camera, a line spectrometer and a thermal camera. The Sensei system is constructed under a modular structural frame, which means that only the modules required for the specific measurement campaign need to be installed. Thus, new sensors can be easily added to Sensei. In the present study, a Specim V10H hyperspectral camera , an Ibeo Lux laser scanner  and a NovAtel SPAN-CPT inertial navigation system  were integrated. The configuration of the resulting measurement system can refer to [The Sensei mobile mapping system developed in the Finnish Geodetic Institute  was assurefer to .i.e., once sampling results into a “line-set” of spectral data) to a monochromatic charge-coupled device (CCD) sensor with 659 × 493 pixels. 659 denotes the spatial grids and 493 indicates the spectral channels. Note that the spectral channels here have been averaged during data acquisition by binning the pixels corresponding to the same samplings into 123 channels to reduce the measurement noise and the amount of data to be stored and post-processed. The reflectance spectra were normalized using a Spectralon™ reference panel . The panel was fixed with a tilt angle of 45° onto the frame of Sensei the way that the outermost 10 pixels of the line spectrometer measured the reference spectra during every spectrum collection operation. The specific setup and the related description can refer to [R.Specifically, the Specim V10H is a line spectrometer. It has an instantaneous field of view of 0.067° and a spectral resolution of 8.5 nm. Its spectral range covers from 397 nm to 1,086 nm and its opening angle of the optics is 44.4° for all of the pixels arrayed vertically in the configuration of the Sensei system. The spectrometer measures the incident light by passing it through a line-pattern diffraction grating , its ranging accuracy is 10 cm, and its angular resolution is 0.25°. The divergence of its laser beam is 0.8° horizontally and 0.08° vertically with respect to the scanner body in the assumed instrument configuration.2.2.The test site is located in Vanttila, Southern Finland , which covers a garden and the two sides of the street leading to it for experiment. The garden consists of over 200 tree and shrub specimens representing over 20 different species. The chosen specimens are briefly planted with small distances between each other and with clear understory. In this scenario, the obscuration impacts often occurring between the target trees can be neglected. The tree species and the related numbers of trees per species are listed in The campaign of data collection was conducted at the earlier September 2010. The date was in the late summer in Finland and the leaves of the trees were still green. The time was after 9 o'clock in the morning. This means that the zenith angle of sun was ∼71° from nadir. This large zenith angle alludes that the sunlight-impacting effects were serious in the hyperspectral data. The data were collected by driving along the street and the paths around and within the test garden. Thus, the scanned sides of the trees range from fully illuminated sides to shaded sides. The data were measured over a time span of 7 min and it consisted of five million laser points and 10,000 line-sets of spectral data.2.3.Investigation of the spectral reflectance characteristics of an object based on hyperspectral imaging tends to be an ill-posed inverse problem  due to ni.e., once measurement relates to an echo-comprising profile for laser scanner (termed as scan-profile) and a pixel-arrayed strip for line spectrometer (termed as spec-profile). Then, the observed space can be represented by concatenation of the parallel scan-profiles or spec-profiles individually. Within these profiles, the geometric model of laser scanner can be characterized by a “central-radiation” model, and the geometric model of line spectrometer can be featured by a central perspective projection model. The parameters of the two instruments can be learnt from their manuals and their spatial offsets can be precisely measured in prior [The basic projection geometric models of mobile terrestrial line spectrometer and laser scanner are displayed in in prior .The detailed data integration process was as follow: The IMU-derived attitudes/locations were first interpolated by using the time stamps of the laser points. Then, the point cloud data were transformed into the Sensei inertial frame coordinates. The integration of the hyperspectral imaging data and laser scanning data was fulfilled by direct georeferencing based on the post-processed attitude/position data of the IMU/GPS system. The post-processing was done through Kalman filtering using the Waypoint Inertial Explorer software and GPS base station data from Geotrim's Virtual Reference Station network. The exact information about the accuracy of the data was not available, since no control points were measured. But based on the error metrics such as the standard deviation and gap between the forward and reverse solutions, the accuracy turned out to be better than 10 cm. After the integration, possible overlapping between the spectrometer line images and the laser points was first tested in Sensei's moving direction . Next, the laser footprint here was reckoned as a pure point with three coordinates, and the overlap test was repeated in the vertical direction for the laser points found within the pixels of each spectrometer profile. The reflectance values of every pixel located in a single laser point were normalized by division against the reference spectra measured from the Spectralon™ reference plate. Then, the processed data was saved into a new data structure of line array, in which each element serially contained the three coordinate values of the laser point and the spectrum values of the related pixel. Finally, the targeted individual trees were extracted manually from the resulting laser point clouds and hyperspectral imaging data.2.4.As shown in , the speRi means the reflectance of the ith wavelength band. Note that the chosen VIs were initially proposed in the previous endeavors aimed at different measurement scales, e.g., at leaf or canopy scales. The determination of scales is generally up to the spatial resolutions of the used surveying devices. The VIs at canopy scales were proposed to deal with the cases of measurement devices with coarse resolutions. In this study, the mobile terrestrial mapping system was adjacent to the target trees, the VIs at leaf scales can also be explored. Thus, to learn tree spectral reflectance characteristics more comprehensively, the VIs at both leaf and canopy scales were calculated and compared.The six VIs were chosen for illustratively assessing the spectral reflectance of the target trees. Their formulas are also listed in the Equations 2.5.The short distances between the mobile terrestrial measurement system and the target trees make it possible to calculate an amount of VI values for each individual tree. However, the values of the same VI type may differ largely for a same tree. The possible reasons lie in that the different parts of a tree have different structures, which may put different influence on sunlight reflectance. In the traditional works on hyperspectral imaging, it is hard to analyze such impacts. But in this study, the synchronous data collected by laser scanning can represent the structural features of the trees, which are helpful for quantification of the VI spatial distribution for each individual tree. This is also one of the advantages of the assumed mobile line spectrometer and laser scanner system.The strategy for the assessment of VI spatial distributions for single trees is to partition each tree in space for consideration. This idea stemmed from the previous findings that the VIs at the edges of trees are more easily influenced . Moreove2.6.In addition to the assessment of VI spatial distributions for individual trees, the spectral reflectance characteristics for different tree species were also taken into account. The reasons are that different tree species generally present different structural patterns, which may result into different modes of spectral reflectance. Specifically, the assessments focused on two aspects: (1) How can the parts with spectra variations at different levels be determined for each of the tree species listed in 3.3.1.After the integration of the two heterogeneous data-sets, the data for investigation of tree spectral reflectance characteristics were acquired. Based on the mobile laser scanning data, the represented trees of the CWB specie are illustrated in an oblique view in The spectra of the four target tree species are illustrated in Note that in practical measurements, the mobile mapping data collections may cover hundreds of, even thousands of, road-side trees within ten minutes, and it is difficult to model the radiative-transfer models of the trees each by each. As illustrated by the target trees in this study, the scanned sides of the trees range from fully illuminated sides to shaded sides. Thus, this work was aimed at this outdoor scenario and attempted to find some ways in data preprocessing to handle the problem of tree spectral reflectance with large variations. As regards precise modeling of tree spectral reflectance, the works involving such as indoor measurement and radiative transfer modeling will be deployed by the authors in the next-step work.3.2.As a whole spectrum was complicated when analyzing its spatial distribution, the vegetation indices capable of featuring the typical spectral features of trees were calculated instead. The calculations were individually conducted in terms of the six vegetation indices as listed in In fact, although VIs cannot remove all of the influences on the hyperspectral data, they still can be valid in the process of locating the points with their VI values out of the normal range in a spatial sense. Specifically, the laser points lying within the trees but corresponding to the pixels with such as NDVIs  less tha3.3.i.e., NDVI being larger than zeros, SR3 being larger than ones, and the values of relative reflectance at the band of 700 nm being less than ones. The latter one was derived based on the pre-examination of the targeted trees. The resulting spatial distributions of VIs are illustrated in The investigation of VI spatial distributions started from the artificial specifications of VI ranges, which can show the rough distributions of various tree parts. Here, three presumptions were made, The feasible explanations are that the top parts are influenced mainly by the direct incidence of sunlight. That is why the top parts have many echoes with relative reflectance values larger than ones. As regards the crown-edge parts, they are located at the edges of trees with low density of leaves, and thus, the collected hyperspectral pixels each comprise a large ratio of the spectra of background. This impact also exists in the top parts. With the tree parts that are easily impacted in mobile hyperspectral imaging determined, the RP parts can be found. The spectra of the RP parts are illustrated in i.e., the height of the laser point, its distance to the central laser scanning profile and its distance to the central vertical plane perpendicular to the laser scanning profile. With the three parameters introduced, the locations of the points with the spectra of interest in the tree spaces can be explicitly determined, and then, the spatial variations of the VI values can be represented in a relatively continuous mode. As illustrated in In order to better understand the characteristics of tree spectral reflectance, a rough partition is not enough and the spatial distributions of vegetation indices need to be quantitatively analyzed. With the frame of the rough tree parts as the reference, the quantification of VI spatial distributions was conducted in terms of three variables, 2w + 0.8 in w means the distances between the points and the central scan profile), or a Gaussian function. The fitting functions need to be chosen by users in accordance to the specific technical demands in various practical applications.From From Overall, a more comprehensive grasp of the spatial characteristics of tree spectral reflectance needs to assume mobile terrestrial line spectrometers with higher spatial resolutions, mobile laser scanners with higher sampling densities and positioning accuracies, or even hyperspectral laser scanners  in the n3.4.After the parts of the trees with relatively pure spectra have been located, the spectral reflectance characteristics for different tree species can be compared. 4.The evaluations have validated the feasibility that mobile terrestrial hyperpsectral imaging and laser scanning can be applied for investigation of tree spectral reflectance characteristics. Although the line spectrometer and laser scanner assumed in this study present relatively low sampling resolutions, the parts of the trees with large spectral reflectance variations can be located. Based on vegetation indices, the spatial distributions of the spectra with variations at different levels can further be quantitatively depicted for the point cloud of each tree. With the crown parts that can demonstrate the real spectral reflectance characteristics of trees extracted, the properties of trees can be retrieved more accurately. The spectral reflectance characteristics for different tree species can also be shown in the VI-composed feature space. Overall, with the inferences of this study incorporated, mobile terrestrial hyperspectral imaging and laser scanning is potential for more comprehensive and accurate forestry applications.


In order to control behavior in an adaptive manner the brain has to learn how some situations and actions predict positive or negative outcomes. During the last decade cognitive neuroscientists have shown that the brain is able to evaluate and learn from outcomes within a few hundred milliseconds of their occurrence. This research has been primarily focused on the feedback-related negativity (FRN) and the P3, two event-related potential (ERP) components that are elicited by outcomes. The FRN is a frontally distributed negative-polarity ERP component that typically reaches its maximal amplitude 250 ms after outcome presentation and tends to be larger for negative than for positive outcomes. The FRN has been associated with activity in the anterior cingulate cortex (ACC). The P3 (~300–600 ms) is a parietally distributed positive-polarity ERP component that tends to be larger for large magnitude than for small magnitude outcomes. The neural sources of the P3 are probably distributed over different regions of the cortex. This paper examines the theories that have been proposed to explain the functional role of these two ERP components during outcome processing. Special attention is paid to extant literature addressing how these ERP components are modulated by outcome valence (negative vs. positive), outcome magnitude , outcome probability (unlikely vs. likely), and behavioral adjustment. The literature offers few generalizable conclusions, but is beset with a number of inconsistencies across studies. This paper discusses the potential reasons for these inconsistencies and points out some challenges that probably will shape the field over the next decade. The global function of the nervous system can be characterized as the adaptive control of behavior, a process that involves learning which action is relevant in a given context and switching to a different behavioral policy or scenario when outcomes are less optimal than expected. Hence “outcome” refers to the consequences that an organism faces as direct result of its own actions  or resulting from its situation . In order to successfully adapt behavior the brain has to determine, as quickly and as accurately as possible, whether the current scenario and behavioral policy results in positive or negative outcomes. This paper reviews the extant literature on how two event-related potential (ERP) components, the feedback-related negativity (FRN), and the P3, shed light on the neural substrate of outcome processing.The relevance of outcome processing is highlighted by the association between individual differences in its functioning and personality constructs  to those of the response-locked error-related negativity (ERN), an ERP component that reaches maximum amplitude about 100 ms following error commission in speeded response time tasks  the human brain solves reinforcement learning problems by implementing an “actor-critic architecture” Barto, . This thThe role of the critic in the actor-critic architecture is to evaluate ongoing events and predict whether future events will be favorable or unfavorable. When the critic revises its predictions for the better or for worse, it computes a temporal-difference reward prediction error (RPE). A positive or a negative RPE indicates that ongoing events are better than expected or worse than expected, respectively. The RPE is used to update both the value attached to the previous state and the strength of the state-action associations that determined the last response selection. The RL-theory attributes the role of the critic to the basal ganglia and assumes that the RPE signal corresponds to the phasic increase  or decrease  in the activity of midbrain DA neurons. Indeed, dopaminergic neurons in the monkey midbrain have been shown to code positive and negative errors in reward prediction  and to the difference between correct trials and error trials . This factor makes the FRN especially relevant both for researchers interested in economic decision-making, and for researchers interested in the neural mechanisms of cognitive control. Gehring and Willoughby  questionFinally, another contribution of RL-theory to the growing interest in the FRN is that this theory affirms that the events are treated as good or bad outcomes not in absolute terms but relative to their relationship with expectations. Since the FRN would reflect the difference between the actual and the expected outcomes, the FRN would potentially provide both a measure of the impact of events  and a measure of anteceding expectations . Most of the research about outcome processing in the brain has explored these factors; several research groups have studied the modulation of the FRN by both properties of the outcome, such as valence (loss vs. win) and magnitude , or by properties of the context in which the outcome is presented, such as reward probability and the potential magnitude of reward. Few studies have also explored another critical component of the RL-theory, which implies that the amplitude of the FRN in a particular trial should predict the degree of behavioral adjustment in subsequent trials , outcome magnitude , outcome probability (unlikely vs. likely), and behavioral adjustment.The most consistent finding in the FRN literature is that such ERP component is larger for negative feedback than for positive feedback. This valence dependency has been confirmed using monetary rewards . Confirming this observation, several studies have noted the absence of a main effect of outcome magnitude . Aligned with this notion, Goyer et al.  found thOther studies have found effects that are less consistent with the RL-theory. Hajcak et al.  reportedThe probability of the experienced outcome is yet another factor that is crucial to the RPE and the RL-theory predicts that the probability of reward would affect the FRN responses to the upcoming outcome. Unexpected losses therefore should be associated with greater negativities than expected losses, and unexpected gains should elicit greater positivities than expected gains. Several studies have supported this prediction  are embedded in a train of non-target stimuli (standards). Typically the subject is required to actively respond to each target stimulus. Using this task, Duncan-Johnson and Donchin  structures, including hippocampus and amygdala in cats  norepinephrine (NE) system in the neocortex  will be associated with a large P3. The specific conditions associated with these factors, however, may change depending on the goal and the context of the task at hand. For example, losing when losses could be avoided should elicit a larger P3 than losing when losses are unavoidable. This section evaluates whether the LC-P3 hypothesis provides a plausible account of the P3 in brain studies of outcome evaluation and feedback-guided learning.Early ERP studies of outcome processing suggested that feedback indicating a bad performance  elicited larger P3s than positive feedback . Determining the impact of task demands on these ERP components will be crucial to be able to generalize conclusions about their role. Finally, the studies reviewed here portray outcome processing as two relatively disconnected processes. Our modern view of the brain, however, suggests that outcome processing probably involves several subprocesses concurring and interacting in time. ERP studies should take advantage of their high temporal resolution to study the temporal cascade of outcome processing in the brain. These challenges are further discussed in the remainder of the article.During the last decade, ERP studies of outcome processing have produced a wealth of evidence employing a rich variety of experimental paradigms. While this heterogeneity is needed to generalize conclusions about the properties of a neural correlate beyond a particular experimental task, there is a risk associated with designing tasks that do not adequately isolate the variables of interest. For example, as already commented, early P3 studies concluded that negative feedback elicited larger P3s than positive feedback, but subsequently Campbell et al.  showed tMore contemporaneous studies, especially some of those using feedback-guided learning, could be associated with a similar confound. These groups found that losing was associated with larger P3s than winning, but it is equally possible that this result reflects a probability effect: as learning progresses, losing becomes less likely. In order to dissociate a valence effect from a learning/probability/expectancy effect, ERP studies of outcome processing might benefit from measuring ERP components on different stages of the experimental session, or by comparing the ERP response from participants that demonstrate learning with those who do not.Another confound that can limit the validity of the results is the potential gap between the goal that the participants really pursue during an experimental session and the goal that the experimenter is trying to elicit. For example, paradigms designed for studying the modulatory effect of participants' prediction on brain activity elicited by gains and losses might unintentionally emphasize the goal of predicting the upcoming outcome. If participants are asked about their belief on the incoming outcome, they might even perceive a predicted loss as a positive feedback . To limit the effect of this confound while still being able study the effect of participants' predictions, paradigms should emphasize the goal of ensuring gains and avoiding losses. For example, experimenters could make outcomes contingent on participants' behavior and not purely probabilistic. Also, researchers could benefit from verbal reports about the task and its goals during pilot studies.The study of outcome processing is associated with a large number of variables  that, depending on the task at hand, might covary in a way that undermines empirical results. Paradigms should be designed acknowledging this complexity in a way that minimizes the chances of introducing confounds.An inherent difficulty of the ERP technique is that, because of potential component overlap, the comparison of ERPs elicited by different experimental conditions is often difficult to interpret Luck, . AlthougTwo alternative methods to measure ERP components have been systematically employed in ERP studies of outcome processing. The first approach is to compute the mean amplitude in a time window defined for each ERP component  post-onset of the outcome and to enter that mean amplitudes into statistical analyses  . The extraction of outcome-locked ERPs from the EEG signal is a good way to identify and study regularities in the neural processing of outcomes. However, ERP research presents two important limitations: it is relatively insensitive to the involvement of large-scale brain networks and it has a limited ability to identify the neural generators of the scalp-recorded signals . Complementary techniques can be used to overcome such limitations. Specifically, time-frequency-based approaches could complement ERP studies by shedding light on the interactions among large-scale networks and fMRI can be used in a complementary fashion to help resolve the so-called inverse problem: a given distribution of scalp-recorded electrical activity could have been generated by any one of a large number of different sets of neural generators.In order to provide a better account of the neural dynamics of outcome processing, EEG/ERP studies can benefit from the time-frequency information that is present in the same EEG signal from which ERPs are extracted. Event-related oscillations can be extracted using time-frequency decomposition analyses such as complex wavelet convolutions, from which one can obtain estimates of phase synchronization, spectral coherence, power-power correlations, spectral Granger causality, and cross-frequency coupling among recording sites . Behavioral adjustment might depend on the system underlying the FRN in tasks de-incentivizing the adoption of an explicit rule (cf. Cohen and Ranganath, Future studies might elucidate whether different ERP components reflect the recruitment of different learning systems depending upon different tasks demands. In general, it is foreseeable that future ERP studies will, in greater proportion, be concerned with how different task demands and task contexts modulate the effect that outcomes have on ERP components.ERP studies of outcome processing have been focused primarily on the FRN and secondarily on the P3. There have been few attempts to present an account of outcome processing in the brain that integrates FRN and P3 effects. In this regard, the independent coding model (Yeung and Sanfey, ERP effects are not always the real-time reflections of the underlying processes. For example, according to the LC-P3 hypothesis the P3 is an indirect index of LC phasic responses occurring 300–400 ms before the peak of the P3. However, the ubiquitous temporospatial succession between the frontally distributed FRN and the parietally distributed P3 probably reveals something meaningful about the manner in which the brain processes and learns from outcomes. Much of the contribution of ERP research to cognitive neuroscience has to do with describing the temporal cascade of neurocognitive processes involved in solving a particular task. ERP studies of outcome processing, in this regard, have traditionally sub-exploited the temporal resolution of the ERP technique.There is also evidence suggesting that the FRN and the parietal P3 are not the only ERP deflections reflecting outcome processing in the brain. Studies could benefit from measuring the frontally distributed P3a that peaks 60–80 ms earlier than the P3b (Courchesne et al., By considering the whole sequence of ERP deflections that are modulated by outcomes, ERP studies might contribute to build models of outcome processing that incorporate the inter-relationship between different cognitive processes that probably take part in outcome processing and learning, such as attention, valuation, and memory.In order to control the behavior in an adaptive manner the brain has to learn how certain situations predict positive or negative outcomes and what actions are appropriate in a given situation. ERP research has shown that the brain is able to evaluate and learn from outcomes within a few hundred milliseconds of their occurrence. However, the accumulated literature presents a high degree of scientific uncertainty regarding the factors that modulate different ERP components during outcome processing. The FRN, in most cases, is larger for negative than for positive outcomes, but the effect of outcome magnitude and outcome probability over the FRN is less clear and contradicting evidence has been found regarding the relationship between the FRN and behavioral adjustment. The P3 is consistently more positive for large magnitude and unexpected outcomes than for small magnitude and expected outcomes, respectively, but the modulatory effect of feedback valence and the relationship between P3 and behavioral adjustment is much less clear.During the last decade, ERP research has accumulated rich evidence of how outcomes are processed in the human brain. The next decade of research will probably be characterized by growing efforts to reach conclusions that generalize across task scenarios demands. In doing so, this research will advance our understanding of how the brain is able produce adaptive behavior in a large variety of situations.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. A suspension system of an automobile improves the ride quality by absorbing shock and other external disturbances under various operating conditions. While doing so, it has to support both static and dynamic loading of the vehicle as well. The external disturbances may arise from a variety of sources such as road surface irregularities, aerodynamics forces, nonuniformity of the tire/wheel assembly , and eveAn active suspension system requires external power to function, which results in added complexity, cost, and weight, as well as reliability problems, though it improves the controllability , 4. The Fuzzy logic control has been used in many applications including cruise control, automatic transmissions, Sendai subway operation, cold-rolling mills, fish processing machine, robots, self-parking car, image stabilizer for video camera, and a fully automated washing machine . Cherry Rao et al.  considerSlaski and Maciejewski  presenteGiven the proven diversity of fuzzy logic control, this technique is incorporated for control of a semi-active suspension system in the present paper. It is difficult to control the parameters of a fuzzy logic control system because it may not be possible to design the rules without the help of an expert or the process may be rigorous involving a number of iterations. Therefore, a biologically inspired optimization method is utilized in the tuning of the important scaling parameters of the controller. Specifically, particle swarm optimization (PSO) method is used, which gives better results both in terms of convergence and computation time in comparison with genetic algorithms (GA). PSO uses a mathematical model that somewhat mimics a flock of birds in search of food. Every bird is considered a particle and it keeps record of its best position and velocity. The best position and velocity of the complete flock are also recorded. In this manner a bird continuously orients itself in the best path based on its own experience and that of the entire flock. This technique has been widely used in engineering problems .The present paper uses quarter car passive and semi-active suspension systems, which are modeled in Simulink. Fuzzy logic control is used in the semi-active suspension system. The inputs and the output of fuzzy logic controller are normalized and gain factors are incorporated into the system. These gain factors are evaluated by performing off-line tuning using PSO in an optimal manner. Based on the optimized tuning parameters, the maximum output of the damper is determined. Various models are designed based on a variety of control algorithms, input parameters, damping coefficient limits, and membership functions. The models are compared for road handling and ride comfort. Two different types of road disturbance profile are considered in the present paper. The rest of the paper is organized as follows. This section describes the modeling aspects of the considered systems. The block diagram fuzzy logic control system, which is through PSO, is presented in The quarter car model of a passive suspension system is shown in The mathematical model of the passive suspension system is described by  and 2)  2) whilems denotes the sprung mass, mu denotes the unsprung mass, ca is the adaptable damping coefficient of suspension, cs is the fixed damping coefficient that is used in both passive and semi-active suspensions, ks and kt are the spring coefficients of the suspension and the tire, respectively, zs is the displacement of the sprung mass, zu is the displacement of the unsprung mass, and zr is the road input disturbance.Here, In order to carry out a comparison between the passive and the fuzzy-logic-based semi-active suspension systems, two types of road profiles are considered. The sinusoidal profile is given by  and showThe road disturbance is a combination of two different sinusoidal functions and hence the performance of the suspension systems can be clearly predicted for a variety of road profiles.A step input of 0.5 m step height is considered as shown in For a semi-active suspension model, the damping coefficient needs to be varied. In order to incorporate variability of the damping coefficient, a fuzzy logic controller is included in the design scheme.A fuzzy logic controller involves fuzzification interface, fuzzy rule base, decision making logic, and defuzzification interface , 15. In Based on the three membership functions for each input, a maximum of nine rules may be formulated. These rules are given in PSO is inspired by the behavior exhibited by flocks of birds and schools of fish. The main approach of this algorithm is to search through an n-dimensional problem to optimize an objective function. The PSO technique was selected because its implementation is relatively simple and the convergence behavior is good. Furthermore, it has the ability to reach the global optimum while avoiding local optima .The swarm comprises a fixed number of particles. These particles collaboratively search for an optimal position. A cognitive component encourages the particles to improve upon their individual best position thus far, while a social component always pulls the particles toward the global best position thus far. A flow chart of the algorithm is given in Libest, while the corresponding best known position for the complete swarm is denoted by Gbest. In the k + 1th iteration, the position of each particle (pi) is updated by velocity (vi) as given byThe objective function used in the present application is the suspension displacement with respect to the step road disturbance, which needs to be minimized. The best known position for each particle is tracked and memorized as c1 and c2 denote cognitive and social accelerations for the local best position and the global best position, respectively, w is the inertial weight constant, and r1(k) and r2(k) represent random numbers generated in the uniform distribution interval of . Each particle optimizes a set of three variables A, B, and C. Famax indicates the maximum damping coefficient limit of the available damper. The algorithm keeps on evaluating the optimized values of the desired parameters until the final value of C reaches Famax or the number of iterations reaches its limit.Here, The proposed approach is implemented on the suspension system of a quarter-vehicle model, and computer simulations are carried out. This section describes this simulation exercise and analyzes the obtained results. Modeling and simulation are performed in Matlab 7.8.0 (R2009a). The fuzzy logic controller is designed using the Matlab Fuzzy Tool Box while simulations are performed in Simulink. The PSO algorithm is programmed in Matlab and executed to determine the three optimized gain parameters of the fuzzy logic controller. The reference algorithm is employed on an active suspension system while the present work implements the technique on a semi-active suspension system. The parameters used in the PSO algorithm are given in A = 19.0129, B = 12.3983, and C = 3802.3. In order to evaluate the performance of a fuzzy-logic-based semi-active suspension system, several different systems have been developed and simulated, as presented next.Optimized values obtained through the PSO algorithm for the fuzzy semi-active suspension system are Fuzzy skyhook system is designed using the theoretical background of skyhook system in which the relative velocity across the two masses and the absolute velocity of the sprung mass are monitored in order to modulate the damping value. In case of the fuzzy groundhook system, the absolute velocity of the sprung mass is replaced by that of the unsprung mass. The fuzzy hybrid system combines the strategy of fuzzy groundhook and skyhook systems. Figures Values of important performance parameters in different control algorithms are tabulated in The bold values indicate percentage overshoot while the italic values indicate the stabilizing time expressed in seconds. The same format is maintained throughout the tabular presentation of the performance parameters. The top two values in each grid correspond to the initial disturbance and the bottom ones correspond to the second part of the disturbance. Suspension displacement is a good indicator of the ride comfort while tire load is a measure of vehicle handling.For a step road disturbance, the optimized fuzzy system outperforms all other systems, with regard to suspension displacement. There is no overshoot and the system gets stabilized at the earliest in relation to other systems in the comparison. It follows that the optimized fuzzy system gives the best ride comfort among all the control algorithms. The passive system does not stabilize at all and it gives the highest values of overshoot for the suspension displacement. Fuzzy hybrid approach combines the strategies of skyhook and groundhook. Therefore, it is better than each of the other control algorithms.With regard to the tire load, the shortest stabilizing time periods are obtained by the optimized fuzzy logic system while groundhook offers the minimum percentage overshoot. However, the optimized fuzzy system is still comparable with the groundhook system, with regard to vehicle handling.Figures The values of important performance parameters for various control algorithms are given in For a sinusoidal road disturbance, the optimized fuzzy system still provides the best performance by giving a zero overshoot for suspension displacement. However, the fuzzy hybrid gives the shortest stabilizing time periods. It is worth mentioning that the optimized fuzzy system performs better even for sinusoidal input, thereby validating its performance for different inputs.Various systems are modeled on the basis of different combinations of input parameters of the fuzzy logic controller. Two different systems are modeled on the basis of relative velocity and relative displacement across the sprung mass and the unsprung mass. While the fuzzy input variables having three membership functions each give rise to a total of nine rules, the selection of two membership functions each leads to a total of four rules, as given in Figures Various systems are designed based on the input parameters of the fuzzy logic controllers. The fuzzy systems that incorporate fuzzy inputs of relative displacement and relative velocity provide the best results for suspension displacement in response to a step road disturbance. This system is governed by nine rules . The fuzThe optimized fuzzy system gives better performance for the vehicle handling parameter of tire load while maintaining the combined factors of minimum overshoot and shortest stabilizing time. Figures Values of important performance parameters of the systems based on various input parameters are given in The same fuzzy system outperforms all other systems even for sinusoidal road disturbance.Various systems are modeled and simulated for analyzing the impact of payload variation on the performance parameters. The weight of each passenger is taken as 100 kg, which transforms to 25 kg in the quarter car parameter. Figures The optimized fuzzy system is analyzed for its performance under conditions of variable payload. For a step road disturbance, there is a slight variation in the ride comfort in relation to the number of passengers, while vehicle handling improves with the increase in the number of passengers. Figures Values of important performance parameters of the systems for various payloads are given in For a sinusoidal road disturbance, both the ride comfort and the vehicle handling characteristics show improvement with the increase in the number of passengers. Therefore, the designed controller provides optimum results even with a variable payload.Using the optimized value of damping coefficient, four systems are designed with variable damping coefficient limits of 1000, 2000, 3000, and 4000 N s/m. Simulation of these systems presents an insight into the parametric analysis of the variable damping coefficient limits. Figures Values of some important performance parameters of the systems based on maximum damping coefficient limit are given in Selection of an appropriate damping coefficient limit is very significant performance consideration. Systems based on different damping coefficient limits exhibit different results as is evident from the tabulated values. A damping coefficient limit of 4000 N s/m  gives the best results for ride comfort and road handling under a step road disturbance. The performance deteriorates as the damping value decreases. Figures Values of important performance parameters of systems based on maximum damping coefficient limit are given in For a sinusoidal road disturbance, a damping coefficient of 1000 N s/m yields a nonstabilizing behavior of the system with regard to the ride comfort parameters. A damping coefficient of 4000 N s/m yields the best results for suspension displacement, and with regard to the tire load it gives the shortest stabilizing time and comparable overshoot values.This paper presented a successful application of a hybrid fuzzy-logic technique in the design of a semi-active suspension system for an automobile. The relative displacement and the relative velocity of the suspension were the inputs to the fuzzy logic controller. The input and output membership functions of the fuzzy system were normalized using scaling factors. The incorporation of particle swarm optimization (PSO) to tune the controller parameters is a novel approach in the context of semi-active suspensions. Specifically, PSO performed off-line tuning of the gain factors of the fuzzy logic controller of a quarter car suspension model. The PSO objective function was selected as the offset of the suspension displacement and the road disturbance, which was minimized. The developed algorithm generated a damping coefficient limit for the damper of the suspension system. The performance of the fuzzy logic controlled system was found to be much better than that of the passive system in terms of both road handling and ride comfort. Furthermore, by comparing the performance under different control algorithms, it was found that the optimized fuzzy logic system was superior. The designed semi-active system performed better within the entire range of payload variation. Furthermore, the fuzzy heuristic rules coped well with the changing disturbances.


In this work, we explore the feasibility of regulating the collective behavior of zebrafish with a free-swimming robotic fish. The visual cues elicited by the robot are inspired by salient features of attraction in zebrafish and include enhanced coloration, aspect ratio of a fertile female, and carangiform/subcarangiform locomotion. The robot is autonomously controlled with an online multi-target tracking system and swims in circular trajectories in the presence of groups of zebrafish. We investigate the collective response of zebrafish to changes in robot speed, achieved by varying its tail-beat frequency. Our results show that the speed of the robot is a determinant of group cohesion, quantified through zebrafish nearest-neighbor distance, which increases with the speed of the robot until it reaches  The use of engineered stimuli to study animal behavior is both inspired by and aids in the understanding of animal perception. In this context, animated images This interdisciplinary research area at the interface of ethology and robotics is often referred to as ''ethorobotics'' In this work, we integrate these two forms of movements in a robotic fish that propels itself through a carangiform/subcarangiform body undulation to freely swim in a test tank. The free-swimming capability allows the robot to actively interact with live subjects in different areas of the experimental tank, while the body undulations are expected to enhance its degree of biomimicry. Moreover, body undulations can elicit salient flow cues Danio rerio) are rapidly emerging as an experimental species for the investigation of functional and dysfunctional biological processes, due to the sequencing of their genome, their high reproduction rate, short intergeneration time, prominent shoaling tendency, and elevated stocking density compared to laboratory mammals Zebrafish  a free-swimming robot whose design and movement are inspired by a zebrafish will not elicit fear response in zebrafish; (ii) the subjects will change their social interaction in the presence of the robotic fish; and (iii) the speed of the robotic fish will differentially modulate fish collective behavior.The experiments were conducted following the protocols AWOC-2012-101 and AWOC-2013-103 approved by the Animal Welfare Oversight Committee of the Polytechnic Institute of New York University.We used wild-type zebrafish in our experiments. The subjects, approximately Following the design in The robot was controlled with a remote control unit consisting of an Arduino Duemilanove microcontroller  and a Nordic nRF2401A transceiver chip . The microcontroller unit was programmed to receive control parameters namely tail-beat frequency, tail-beat amplitude, and tail-section offset via a universal serial bus (USB). These parameters were then transmitted wirelessly to the robot every 3/5th of a second. In our experiments, we kept the tail-beat amplitude constant at 20 degrees. The heading of the robot was controlled by varying the tail-section offset from a trim value of zero degrees when the robot body was in line with the tail section.The subjects were filmed in a 120 n region . A  PentThe experimental conditions consisted of the robot swimming in the experimental tank with three zebrafish  To elucidate the behavior of the subjects, we scored the time spent freezing during each trial. This value was computed automatically from the trajectory data. In particular a fish was considered freezing during a frame if it spent two continuous seconds within a ball of radius of 2 cm To evaluate the effect of robot locomotion, a one-way analysis of variance (ANOVA) with the ANND, Pol, average speed, average distance to the robot, minimum distance to the robot, and time spent freezing, each as a dependent variable, and the robot speed as the independent variable was used. Tukey HSD post-hoc tests were performed to compare these quantities between pairs of robot speeds if a significant effect was found. Statistical power for the given sample size of 32 Click here for additional data file.