Imitative Behavior Research Articles

Self-Imitation Learning-Based Inter-Cell Interference Coordination in Autonomous HetNets

Recently, mobile operators have been shifting to an intelligent autonomous network paradigm, where the mobile networks are automated in a plug-and-play manner to reduce the manual intervention. Under this circumstance, serious inter-cell interference becomes inevitable which may severely deteriorate system throughput performance and users’ quality of service (QoS), especially for dense residential small base station (SBS) deployment. This paper proposes an intelligent inter-cell interference coordination (ICIC) scheme for autonomous heterogeneous networks (HetNets), where the SBSs agilely schedule sub-channels to individual users at each Transmit Time Interval (TTI) with aim of mitigating interferences and maximizing long-term throughput by sensing the environment. Since the reward function is inexplicit and only few samples can be used for prior-training, we formulate the ICIC problem as a distributed inverse reinforcement learning (IRL) problem following the POMDP games. We propose a non-prior knowledge based self-imitating learning (SIL) algorithm which incorporates Wasserstein Generative Adversarial Networks (WGANs) and Double Deep Q Network (Double DQN) algorithms for performing behavior imitation and few-shot learning in solving the IRL problem from both the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">value</i> . Numerical results reveal that SIL is able to implement TTI level’s decision-making to solve the ICIC problem, and the overall network throughput of SIL can be improved by up to 19.8% when compared with other known benchmark algorithms.

Higher empathy is associated with stronger social bonding when moving together with music.

Empathy—understanding and sharing the feelings and experiences of others—is one of our most important social capacities. Music is a social stimulus in that it involves communication of mental states, imitation of behavior, and synchronization of movements. As empathy and music are so closely linked, we investigated whether higher empathy is associated with stronger social bonding in interpersonal interactions that feature music. In two studies, participants watched videos in which we manipulated interpersonal synchrony between the movements of a virtual self and a virtual other person during walking with instrumental music or a metronome. In both studies, temporally aligned movements increased social bonding with the virtual other and higher empathy was associated with increased social bonding in movement interactions that featured music. Additionally, in Study 1, participants with lower empathy felt more connected when interacting with a metronome compared to music. In Study 2, higher trait empathy was associated with strong increases of social bonding when interacting with a temporally aligned virtual other, but only weak increases of social bonding with a temporally misaligned virtual other. These findings suggest that empathy plays a multifaceted role in how we enjoy, interpret, and use music in social situations.

The 2021 Bitcoin Bubbles and Crashes—Detection and Classification

In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.

Imitation and mirror systems in robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.

The Impact of Rural Population Aging on Farmers’ Cleaner Production Behavior: Evidence from Five Provinces of the North China Plain

The purpose of this article is to examine the impact of rural population aging on farmers’ cleaner production behavior through the intervening effects of three aspects (learning capacity, factor substitution and behavior imitation). The data used in this article were collected from a questionnaire survey of 916 farmers in the main grain producing provinces of the North China Plain. First, confirmatory factor analysis was carried out, and then data were analyzed through structural equation modeling using the bootstrap methodology in AMOS 24. The findings indicate that rural population aging inhibits farmers’ cleaner production behavior through learning capacity. In contrast, rural population aging promotes farmers’ cleaner production behavior through factor substitution and behavior imitation. From the perspective of both challenges and opportunities, this article investigates the impact mechanism and comprehensive effect of rural population aging on farmers’ cleaner production behavior, so as to explore new ways to promote cleaner agricultural production under the background of an accelerated aging the of rural labor force and provide reference for the formulation of relevant policies.

Knowledge Network Evolution of Smokers, Adaptive Behaviors and Enhancement of Knowledge Capability

Objectives: With the evolution of knowledge networks of smokers, we analyzed how adaptive behaviors affect the enhancement of the knowledge capability of their organization. Methods: Through the empirical analysis of 228 questionnaire data from smoker-dense zone, this paper conducts a regression analysis between the implementation of different behaviors and the capability improvement, in the context of knowledge flow evolution. Results: Conclusions show that different behavioral decisions of knowledge subjects have different influences on their knowledge capability, and the routine behaviors have a significant negative impact on their knowledge capability, while the imitative behavior and innovative behavior have a significant positive impact on the improvement of knowledge capability. These effects are also affected by the regulatory effects of the three stages of knowledge flow. Conclusion: In different stages of the functional evolution of the knowledge network of smokers, different behavioral decisions of knowledge subjects will promote or restrict their own knowledge capabilities. Based on the knowledge growth expectation, knowledge subjects can adopt different behavior strategies to achieve dynamic environment adaptation and gain competitive advantage.

Intertemporal imitation behavior of interbank offered rate submissions

This paper addresses a problem that may damage the reliability of an interbank offered rate (IBOR) system. Using evidence from the Shanghai Interbank Offered Rate (SHIBOR), we show that some SHIBOR panel banks imitate peers’ quotes after observing them on the next business day. The strength of the intertemporal behavior can be measured by a “Signed Active-minus-Stationary (SAmS)” index, which significantly predicts SHIBOR changes. Moreover, we find that the consequences of the imitation behavior are not fully perceived and understood by the market, and, as a result, SHIBOR-linked derivatives are mispriced. Our findings suggest that regulators of a poll-based interest rate benchmark should pay attention to the intertemporal imitation of submissions, in addition to bad faith collusion. The SAmS index can be utilized in the quality control of panel bank submissions.

Prosper Lucas (1808–1885), l’aliéniste inspirateur de Charles Darwin

Prosper Lucas (1808–1885) was an alienist who in 1847 proposed an in-depth theory of heredity. This theory became, for more than half a century, the reference used by physicians and biologists in their work and research, but also by writers in their novels. Following a brief account of Lucas's life, this article will not only explore the ideas he developed on heredity but also the content of his interesting thesis on the imitation of behavior and the role of imitation in the origin of mental pathologies.

A Pilot Study Evaluating the Effects of Early Intervention for Italian Siblings of Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a high-cost/high-burden problem. Early intervention may prevent development of the disorder, improving child outcomes and reducing long-term consequences. However, few studies have investigated the role of early intervention in children younger than two years. This study aims to examine the effect of early intervention in 18-month-old high-risk siblings of children with ASD (HR-ASD) with clinical signs of autism. The intervention is based on the principles of Applied Behavior Analysis and focuses on the development of early precursors to social and communicative competence (joint attention and imitation behaviors). After controlling for baseline differences, two comparison HR-ASD groups were included: 15 HR-ASD toddlers receiving behavioral intervention for 3 h per week for 5 months (INT+) and 15 HR-ASD toddlers who were only clinically monitored from age 18 months (INT−). Changes in social communication, restricted/repetitive behaviors, and language were assessed using standardized measures at pre- (T0) and post-intervention (T1). From T0 to T1, the INT+ group showed significant improvements in communication, social interaction, and language compared to INT− group. There was no effect on restricted/repetitive behaviors. Our findings highlighted the importance of early detection/intervention in autism and supported a positive impact of targeted interventions to improve outcomes in at-risk children.

Stanford Initiative Engages Media About Mental Health, Suicide

Stanford Initiative Engages Media About Mental Health, Suicide

Investigating the Effects of Robot Engagement Communication on Learning from Demonstration

Robot learning from demonstration (RLfD) is a technique for robots to derive policies from instructors’ examples. Although the reciprocal effects of student engagement on teacher behavior are widely recognized in the educational community, it is unclear whether the same phenomenon holds for RLfD. To fill this gap, we first design three types of robot engagement behavior (gaze, imitation, and a hybrid of the two) based on the learning literature. We then conduct, in a simulation environment, a within-subject user study to investigate the impact of different robot engagement cues on humans compared to a “without-engagement” condition. Results suggest that engagement communication has significantly negative influences on the human’s estimation of the simulated robots’ capability and significantly raises their expectation towards the learning outcomes, even though we do not run actual imitation learning algorithms in the experiments. Moreover, imitation behavior affects humans more than gaze does in all metrics, while their combination has the most profound influences on humans. We also find that communicating engagement via imitation or the combined behavior significantly improves humans’ perception towards the quality of simulated demonstrations, even if all demonstrations are of the same quality.

Facial mimicry in the congenitally blind

Imitation is one of the core building blocks of human social cognition, supporting capacities as diverse as empathy, social learning, and knowledge acquisition1. Newborns' ability to match others' motor acts, while quite limited initially, drastically improves during the first months of development2. Of notable importance to human sociality is our tendency to rapidly mimic facial expressions of emotion. Facial mimicry develops around six months of age3, but because of its late emergence, the factors supporting its development are relatively unknown. One possibility is that the development of facial mimicry depends on seeing emotional imitative behavior in others4. Alternatively, the drive to imitate facial expressions of emotion may be independent of visual learning and be supported by modality-general processes. Here we report evidence for the latter, by showing that congenitally blind participants facially imitate smiles heard in speech, despite having never seen a facial expression.

Evidence for a dual-process account of over-imitation: Children imitate anti- and prosocial models equally, but prefer prosocial models once they become aware of multiple solutions to a task.

Children imitate actions that are perceivably unnecessary to achieve the instrumental goal of an action sequence, a behavior termed over-imitation. It is debated whether this behavior is based on the motivation to follow behavioral norms and affiliate with the model or whether it can be interpreted in terms of a behavioral heuristic to copy observed intentional actions without questioning the purpose of each action step. To resolve this question, we tested whether preschool-aged children (N = 89) over-imitate a prosocial model, a helper in a prior third-party moral transgression, but refuse to over-imitate an antisocial model, the perpetrator of the moral transgression. After first observing an inefficient way to extract a reward from a puzzle box from either a perpetrator or a helper, children over-imitated the perpetrator to the same degree as they over-imitated the helper. In a second phase, children were then presented the efficient solution by the respective other model, i.e. the helper or the perpetrator. Over-imitation rates then dropped in both conditions, but remained significantly higher than in a baseline condition only when children had observed the prosocial model demonstrate the inefficient action sequence and the perpetrator performed the efficient solution. In contrast, over-imitation dropped to baseline level when the perpetrator had modelled the inefficient actions and the prosocial model subsequently showed children the efficient solution. In line with a dual-process account of over-imitation, results speak to a strong initial tendency to imitate perceivably irrelevant actions regardless of the model. Imitation behavior is then adjusted according to social motivations after deliberate consideration of different options to attain the goal.

Peer effects in government R&D subsidies: evidence from the awareness-motivation-capability perspective

ABSTRACT Previous studies have investigated the factors that influence firms’ decision engaging in government innovation programme, but some important horizontal factors have been largely ignored, such as the effect from industry peers. Based on signal theory and expectancy theory, this paper studies the role of industry peers who have obtained government R&D subsidies in raising awareness and motivation of other non-funded firms to engage in the same project. Using Chinese listed firms between 2008 and 2016 as the empirical sample, this paper introduces the awareness-motivation-capability (AMC) framework and uses the Logit regression to examine the associations between peer effects and strategy choice. The empirical results indicate that the number of funded firms in an industry and their superior performance have positive impact on non-funded firms to obtain government R&D subsidies, and these relationships are further strengthened by non-funded firms’ innovation ability and profitability. This study makes contributions to literature on government R&D subsidies and peer effect by providing a view of imitation behaviour in innovation activities.

Herding in the bad times: The 2008 and COVID-19 crises

The objective of this paper is to analyze the imitation behavior of investors in especially convulsed periods, such as the 2008 financial crisis and the recent global pandemic, both of which could affect investors' emotions and behavior, although both have different characteristics and might have different implications. The cross-sectional dispersion of returns is used to measure the level of herding in the markets of Spain and Portugal, using a survivorship-bias-free dataset of daily stock returns during the period January 2000–May 2021, in turn divided into several sub-periods classified as pre-2008 crisis, 2008 crisis, post-2008 crisis, Covid-19 and post Covid-19. Additionally, the existence is studied of differences between days of positive and negative returns, or between days of high volatility compared to the rest, and whether the cross-sectional dispersion of returns in one market is affected by the cross-sectional dispersion of returns in the other market. The results indicate that herding appears with greater intensity in periods prior to the crisis, disappearing during the financial crisis and reappearing, although with less intensity, after it, while it is not generally detected in Covid-19 times. However, herding behavior can be observed in the market during the pandemic on high volatility days.

Selective eye-gaze augmentation to enhance imitation learning in Atari games

This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Extensive evidence suggests that our eye movements convey a wealth of information about the direction of our attention and mental states and encode the information necessary to complete a task. Based on this evidence, we hypothesize that selective use of eye-gaze, as a clue for attention direction, will enhance the learning from demonstration. For this purpose, we propose a selective eye-gaze augmentation (SEA) network that learns when to use the eye-gaze information. The proposed network architecture consists of three sub-networks: gaze prediction, gating, and action prediction network. Using the prior 4 game frames, a gaze map is predicted by the gaze prediction network, which is used for augmenting the input frame. The gating network will determine whether the predicted gaze map should be used in learning and is fed to the final network to predict the action at the current frame. To validate this approach, we use publicly available Atari Human Eye-Tracking And Demonstration (Atari-HEAD) dataset consists of 20 Atari games with 28 million human demonstrations and 328 million eye-gazes (over game frames) collected from four subjects. We demonstrate the efficacy of selective eye-gaze augmentation compared to the state-of-the-art Attention Guided Imitation Learning (AGIL) and Behavior Cloning (BC). The results indicate that the selective augmentation approach (the SEA network) performs significantly better than the AGIL and BC. Moreover, to demonstrate the significance of selective use of gaze through the gating network, we compare our approach with the random selection of the gaze. Even in this case, the SEA network performs significantly better, validating the advantage of selectively using the gaze in demonstration learning.

Imitation and interindividual differences: Belief in free will is not related to automatic imitation

It is well known that individuals have the tendency to automatically imitate each other and that such imitative behavior is fostered by perceiving intentions in others' actions. That is, past research has shown that perceiving an action as internally driven enhances the shared representation of observed and executed actions increasing automatic imitation. An interpersonal factor that increases the perception that a behavior is internally driven is belief in free will. Consequently, we hypothesized that the more individuals believe in free will, the more they automatically imitate others. To test this prediction, we conducted two high-powered (total N = 642) and preregistered studies in which we assessed automatic imitation with the imitation-inhibition task. Contrary to our predictions, belief in free will did not correlate with automatic imitation. This finding contributes to current findings challenging the assumption that automatic imitation is modulated by interindividual differences. Further theoretical implications are discussed.

Bass Models for EHR, RIS and PACS Diffusion in Finland and Germany.

Building on Rogers' Diffusion of Innovation Theory, Bass models describe the diffusion processes distinguishing between innovation (p) and imitation (q). This study aimed at modelling the uptake of RIS, PACS and EHR systems in Germany and Finland. The Bass models revealed a quick and almost identical uptake process across all three systems for Finland. In contrast, the Bass models mirrored a slower uptake in Germany. Consequently, the Finnish "imitation" coefficients were larger than the German ones. While in Germany almost free market forces were driving the adoption through imitation but without tail wind from policy, the adoption process in Finland was centrally governed. This suggests that the diffusion process in Finland reflected a well-managed roll-out of the systems rather than imitation behaviour. Thus, in order for Bass model coefficients to be understood properly, additional contextual information is required.

Evaluating imitation and rule-based behaviors of eye contact and blinking using an android for conversation

In this paper, we investigate which approach to generate eye behaviors using an android robot makes what impressions on humans and clarify which are the important factors for attractive eye behaviors. Thus, we evaluate the human impression of eye behaviors displayed by an android robot while talking to a human by comparing the motions generated by the two approaches. In the first approach, we develop a method to imitate human eye behavior obtained from eye trackers. In the second approach, we control the eye direction, eye-contact duration, and eyeblinks based on the findings of human eye behavior in psychology and cognitive research. In the experiments, we asked male and female subjects to evaluate their impression by comparing the eye behaviors with an android that controls the eye-contact duration and eyeblinks by editing the imitation parameters or the rule-based behavior. In the experiments, we asked subjects to evaluate their impression of different eye behaviors displayed by an android. The eye behaviors were generated by modifying the imitation parameters or the rule-based behavior, which resulted in different eye-contact duration and eyeblink duration and timing. From the results, we found that (1) the imitation and rule-based behaviors showed no difference in terms of human-likeness, (2) the 3-second eye contact obtained better scores regardless of the imitation or rule-based eye behavior, (3) the subjects might regard the long eyeblinks as voluntary eyeblinks, with the intention to break eye contact, and (4) female subjects preferred short eyeblinks rather than long ones and considered that short eyeblinks might be one of the keys to making eye contact more suitable, in contrast to male subjects who preferred long eyeblinks.

New evidence of a fish–bird interspecific feeding association between the European seabass and the European shag in the Mediterranean Sea

Although they have received little attention, interspecific feeding associations are a particular predation behavior in which two or more different species temporary feed simultaneously on the same food sources to obtain greater success in predation. These collaborations can be either mutualistic, commensalistic, or parasitic. One of the best-known examples is the seabird–cetacean associations. Prey herding is the foraging tactic most used in these associations and consists of swimming around and under prey balls to trap them in the water column. Here we reported some new evidence of a fish–bird interspecific feeding association between the European seabass, Dicentrarchus labrax (Linnaeus, 1758) and the European shag, Gulosus aristotelis (Linnaeus, 1761). We analyzed this association through the photograph and video documentation provided by “citizen scientists” in the Mediterranean Sea. We collected reliable proofs of this behavior, at least, in three instances from observations made by recreational fishermen in the Kvarner bay (Croatia, North Adriatic Sea). From the photographs and videos made available, we attempted to reconstruct a predation strategy adopted by these associated species to better understand the advantage this provides during hunting. Although it is not yet clear whether this association is an opportunistic behavior of the European seabass or a mutualistic strategy used by both species to improve their catching success, this feeding association seems to increase the chances to defeat prey defense strategies. Notably, some behavioral imitation of hunting strategies may be put in place by seabasses, which often feed in association with dolphins and other birds using similar prey herding strategies.