Neural systems that facilitate the representation of social rank

Across species, animals organize into social dominance hierarchies that serve to decrease aggression and facilitate survival of the group. Neuroscientists have adopted several model organisms to study dominance hierarchies in the laboratory setting, including fish, reptiles, rodents and primates. We review recent literature across species that sheds light onto how the brain represents social rank to guide socially appropriate behaviour within a dominance hierarchy. First, we discuss how the brain responds to social status signals. Then, we discuss social approach and avoidance learning mechanisms that we propose could drive rank-appropriate behaviour. Lastly, we discuss how the brain represents memories of individuals (social memory) and how this may support the maintenance of unique individual relationships within a social group. This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Mechanisms of Human Motor Learning Do Not Function Independently

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.

Unlocking adults’ implicit statistical learning by cognitive depletion

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

Probabilistic Graphical Model for Continuous Variables

Most of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

Effects of familiarity with musical style on music-evoked emotions: An EEG study

Learning mechanisms have been postulated to be one of the primary reasons why different individuals have similar or different emotional responses to music. While existing studies have largely examined mechanisms related to learning in terms of cultural familiarity or recognition, few studies have conceptualized it in terms of an individual’s level of familiarity with musical style, which could be a better reflection of an individual’s composite musical experiences. Therefore, the current study aimed to bridge this research gap by investigating the electrophysiological correlates of the effects of familiarity with musical style on music-evoked emotions. 49 non-musicians listened to 12 musical excerpts of a familiar musical style (Japanese animation soundtracks) and eight musical excerpts of an unfamiliar musical style (Greek Laïkó music) with their eyes closed as electroencephalography is being recorded. Participants rated their felt emotions after each musical excerpt is played. Behavioral ratings showed that music of the familiar musical style was felt as significantly more pleasant as compared to the unfamiliar musical style while no significant differences in arousal were observed. In terms of brain activity, music of the unfamiliar musical style elicited higher (1) theta power in all brain regions (including frontal midline), (2) alpha power in frontal region, and (3) beta power in fronto-temporo-occipital regions as compared to the familiar musical style. This is interpreted to reflect the need for greater attentional resources when listening to music of an unfamiliar style, where listeners are less familiar with the syntax and structure of the music as compared to music of a familiar style. In addition, classification analysis showed that unfamiliar and familiar musical styles can be distinguished with 67.86% accuracy, Thus, clinicians should consider the musical profile of the client when choosing an appropriate selection of music in the treatment plan, so as to achieve better efficacy.

Revisiting Subject–Object Asymmetry in the Production of Cantonese Relative Clauses: Evidence From Elicited Production in 3-Year-Olds

Emergentist approaches to language acquisition identify a core role for language-specific experience and give primacy to other factors like function and domain-general learning mechanisms in syntactic development. This directly contrasts with a nativist structurally oriented approach, which predicts that grammatical development is guided by Universal Grammar and that structural factors constrain acquisition. Cantonese relative clauses (RCs) offer a good opportunity to test these perspectives because its typologically rare properties decouple the roles of frequency and complexity in subject- and object-RCs in a way not possible in European languages. Specifically, Cantonese object RCs of the classifier type are frequently attested in children’s linguistic experience and are isomorphic to frequent and early-acquired simple SVO transitive clauses, but according to formal grammatical analyses Cantonese subject RCs are computationally less demanding to process. Thus, the two opposing theories make different predictions: the emergentist approach predicts a specific preference for object RCs of the classifier type, whereas the structurally oriented approach predicts a subject advantage. In the current study we revisited this issue. Eighty-seven monolingual Cantonese children aged between 3;2 and 3;11 (Mage: 3;6) participated in an elicited production task designed to elicit production of subject- and object- RCs. The children were very young and most of them produced only noun phrases when RCs were elicited. Those (nine children) who did produce RCs produced overwhelmingly more object RCs than subject RCs, even when animacy cues were controlled. The majority of object RCs produced were the frequent classifier-type RCs. The findings concur with our hypothesis from the emergentist perspectives that input frequency and formal and functional similarity to known structures guide acquisition.

Out-of-vocabulary but not meaningless: Evidence for semantic-priming effects in pseudoword processing

Non-arbitrary phenomena in language, such as systematic association in the form-meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we re-analyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants’ reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a general-purpose capacity of extracting knowledge from complex statistical patterns.

Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolve the body and brain of a single robot to optimize performance on a specified task. The review is grounded in a general framework for evolution which permits the interaction of artificial evolution acting on a population with individual and cultural learning mechanisms. We discuss examples of variations of the general scheme of ‘evolution plus learning’ from a broad range of robotic systems, and reflect on how the interaction of the two paradigms influences diversity, performance and rate of improvement. Finally, we suggest a number of avenues for future work as a result of the insights that arise from the review. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

USAGE OF MACHINE LEARNING IN THE PROCESS OF RISK-MANAGEMENT

Background. Currently, artificial intelligence (AI) and machine learning are frequently implemented into the corporate structure and are aimed to transform the risk management system. Not only AI is useful for detection the interconnections between business processes, but also allows to accurately predict financial indicators and the reasons for possible deviations from standard values. Thus, the implementations of artificial intelligence and machine learning mechanisms makes it possible to increase the efficiency of operational activities and detect hidden risks. Method. The article discusses the main types of risks, identidication and minimization of which can be carried out using machine learning and also reveals key difficulties that arise while introducing innovative mechanisms into the structure of risk-management. The scientific novelty of the work lies in the relevance of using artificial intelligence mechanisms while minimizing the risks of an economic entity, as well as in identifying the main incentives for the efficient usage of machine learning in risk management. Result. As a result, the potential of introducing innovative methods into the structure of risk management to improve the efficiency of operating activities was revealed. Conclusion. In the process of the methodological study, the features of the application of machine learning methods in the risk management process were identified, moreover the article main limitations and possibilities of using artificial intelligence in order to minimize risks were revealed.