Imitation of the successful choices of others is a simple and superficially attractive learning rule. It has been shown to be an important driving force for the strategic behavior of (young) adults. ...In this study we examine whether imitation is prevalent in the behavior of children aged between 8 and 10. Surprisingly, we find that imitation seems to be cognitively demanding. Most children in this age group ignore information about others, foregoing substantial learning opportunities. While this seems to contradict much of the literature in the field of psychology, we argue that success-based imitation of peers may be harder for children to perform than non-success-based imitation of adults.
Vehicle path planning is one of the effective ways to relieve the huge traffic flow pressure of modern urban transportation system, and it is also an important way to realize carbon emission ...reduction and to build green transportation system as well as smart city. At present, the artificial intelligence (AI) algorithms with reinforcement learning (RL) as the mainstream have achieved great success in the field of vehicle path planning. However, RL only conducts policy learning based on the evaluation feedback of the environment, whereas imitation learning (IL) can obtain more direct feedback from expert decision data, and then obtain a decision model close to the expert level by comparing with RL. At present, there are very few vehicle path planning algorithms based on IL, and they are often hindered by the compounding error and sample complexity dilemma, resulting in poor path planning effectiveness. In order to overcome these problems, in this paper, a mixed generative adversarial IL (MixGAIL) algorithm has been proposed, which effectively integrates the transition aware adversarial IL (TAIL) and generative adversarial IL (GAIL) based on minimum-distance functions (MIMIC-MD) methods under the framework of GAIL. In order to overcome the optimization dilemma of non-convex and non-smooth objective function after the integration, the proposed MixGAIL uses mixed policy gradient actor-critic model with random escape term and filter optimization (MPGACEF), and pioneers the noise projected subgradient descent method with momentum (MNPSGD) for global optimization. Experiments have shown that by learning expert decision data, MixGAIL has better vehicle path planning performance and faster iteration speed than classic IL algorithms such as behavioral cloning (BC), dataset aggregation (DAgger), feature expectation matching (FEM), game theoretical appraisal learning (GTAL), TAIL, and MIMIC-MD, and is closer to expert level.
The present study sought to use a paradigm that allows the study of mental representations of observed actions. We investigated whether retrieval of observationally acquired stimulus-response ...bindings are impaired in participants with high (compared with low) autistic traits. In an extreme group comparison, participants with high versus low autistic traits worked through an observational SR binding and a standard SR binding task (to control for general deficits in cognitive performance). As expected, groups did not differ with regard to retrieval of transient bindings between stimuli and self-performed responses (standard SR binding & retrieval effects). Against our expectations, the same was true for the retrieval of observationally acquired SR bindings, which was of comparable magnitude in both high and low autistic trait groups. Bayes Factor analysis indicates that our evidence for this null finding has to be regarded as weak evidence. Our findings provide tentative evidence against the view that imitative effects are reduced (hypo-imitation) or increased (hyper-imitation) when autistic trait expression is high.
Public Significance Statement
This study shows that individuals with high autistic trait expression do not differ from individuals with low autistic traits in their capability to mentally represent actions that were observed in another person. This argues against any autism-related impairment of mentally representing observed actions. Mental representation of observed actions is comparable with mental representation of self-initiated actions in individuals with high autistic trait expression. Individuals with high autistic traits therefore show effects of imitative action regulation, reflected in automatic retrieval of transient episodic associations between stimuli and observed responses, to a similar degree as individuals with low autistic trait expression.
This study investigated whether congenital amusia, a neuro-developmental disorder of musical perception, also has implications for speech intonation processing. In total, 16 British amusics and 16 ...matched controls completed five intonation perception tasks and two pitch threshold tasks. Compared with controls, amusics showed impaired performance on discrimination, identification and imitation of statements and questions that were characterized primarily by pitch direction differences in the final word. This intonation-processing deficit in amusia was largely associated with a psychophysical pitch direction discrimination deficit. These findings suggest that amusia impacts upon one’s language abilities in subtle ways, and support previous evidence that pitch processing in language and music involves shared mechanisms.
Children selectively imitate in‐group over outgroup individuals under certain experimental conditions. We investigated whether this bias applies to gender in‐groups in China. Three‐ and ...five‐year‐olds were shown how to operate novel objects by same‐gender and opposite‐gender models. Results indicate that the combination of verbally highlighting the gender identity of the model (e.g., ‘I am a girl’) and making gender norms explicit (e.g., ‘girls play this way’) significantly enhances high‐fidelity imitation. This ‘double social effect’ was more robust in 5‐year‐olds than 3‐year‐olds. Our results underscore how language about gender and the norms for gender‐based groups influence behavioural imitation. The pattern of findings enhances our knowledge about pre‐schoolers’ social learning and imitation as well as the powerful influence of language and group norms on children’s voluntary actions and learning.
Launched in 2005 as a video-sharing website, YouTube has become an emblem of participatory culture. A central feature of this website is the dazzling number of derivative videos, uploaded daily by ...many thousands. Using the ‘meme’ concept as an analytic tool, this article aims at uncovering the attributes common to ‘memetic videos’ – popular clips that generate extensive user engagement by way of creative derivatives. Drawing on YouTube popularity-measurements and on user-generated playlists, a corpus of 30 prominent memetic videos was assembled. A combined qualitative and quantitative analysis of these videos yielded six common features: focus on ordinary people, flawed masculinity, humor, simplicity, repetitiveness and whimsical content. Each of these attributes marks the video as incomplete or flawed, thereby invoking further creative dialogue. In its concluding section, the article addresses the skyrocketing popularity of mimicking in contemporary digital culture, linking it to economic, social and cultural logics of participation.
Research has shown that the observation of another's movement activates the corresponding motor representation in the observer. However, it is largely unknown how activation of these shared ...representations is influenced by the number of observed agents. In recent work, we have studied automatic imitation while participants saw 2 hands of which either one hand or both hands made a movement. These studies found that 2 hands produced a stronger imitative response than a single hand when the hands made an identical movement but not when they made different movements. It was argued that identical movements were mapped onto the same motor representation and therefore produced a stronger motor response. Nevertheless, an alternative explanation is that participants randomly represented 1 hand on each trial. The goal of the current study was to disentangle these 2 hypotheses. In Experiments 1 and 2, we replicate our results using a stimulus setup that made random sampling unlikely. In Experiment 3, we show that an additive effect was still present when attention was directed to 1 hand that always made a movement. Finally, in Experiment 4, we show that intentional imitation of 1 hand did not preclude automatic imitation of a second hand. Together, these findings support the hypothesis that the actions of multiple persons can be represented together in the motor system.
Public Significance Statement
The current study shows that people can represent the movements of multiple persons at the same time in their motor system. In particular, it shows that automatic imitation is modulated by the number of agents that perform a simultaneous movement and rules out that this is due to a random sampling mechanism. These findings provide insight into the neurocognitive mechanisms that support social interaction at the group level. In particular, our work has implications for research on the social psychology of synchronized group behavior and could lead to a sensorimotor interpretation of the relation between group size and social contagion.
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional ...environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.
Imitation learning has been recognized as a method to accelerate the training process of deep reinforcement learning agents in search of optimal strategies. Nevertheless, existing imitation learning ...algorithms have limitations in effectively leveraging expert demonstrations when confronted with dynamic environments, as the behavior cloning loss weight cannot be adaptively updated. To overcome this challenge, we propose a novel approach called Soft Imitation Reinforcement Learning (SIRL), which combines imitation learning and reinforcement learning to guide the training of reinforcement learning agents in an adaptive manner. Additionally, we addressed the challenge of high-dimensional action spaces for reinforcement learning in portfolio management with value decomposition, and provide theoretical proof of convergence for this method. To validate the effectiveness of the SIRL algorithm, we conduct extensive experiments using stock market data from emerging (China) and developed (the US) countries. Our experiments indicate the versatility of the proposed SIRL across different types of trading data, encompassing both high-frequency (5-minute interval) and low-frequency (daily and weekly) data.
•We develop a novel soft imitation reinforcement learning (SIRL) to make good use of expert demonstrations.•The weight of the behavior cloning loss in the SIRL framework can be updated adaptively when environments change.•Instead of using the L2 norm distance, KL-divergence function is adopted to measure the behavior cloning loss.•Robustness and profitability of SIRL suggest that the SIRL could contribute to portfolio management.
Stability vs. flexibility Young, Susan L; Welter, Christopher; Conger, Michael
Journal of international business studies,
05/2018, Volume:
49, Issue:
4
Journal Article
Peer reviewed
How entrepreneurial opportunities are formed and exploited depends upon the institutional environment in which they are embedded. The varying amounts of risk and uncertainty across and within ...heterogeneous institutional environments have important implications for the types of opportunity developed. While the international business and entrepreneurship literatures consider the effect of environmental risk and uncertainty on firms, risk and uncertainty are often treated as interchangeable or synonymous, and rarely are both considered to be present together. To address this, we develop a new theoretical model based on institutional economics, describing how institutional arrangements promoting stability–thus supporting an entrepreneur’s ability to assess risk – will lead to more imitative opportunities, while institutions promoting flexibility – thus supporting an entrepreneur’s ability to respond to uncertainty by iterating – will foster more innovative opportunities. We test this framework using crossnational data across 40 countries from the GEM survey, finding that institutional arrangements that promote stability do lead to more imitation, while institutions that promote flexibility foster more innovation. By treating risk and uncertainty as distinct constructs, our study makes theoretical contributions to research on institutional environments and opportunity types, with implications for future research on subsidiary initiatives, the evolution of MNEs, and born-global firms.
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