The effects of different learning environments on students’ motivation for learning and their achievement

Background. Research in higher education on the effects of student‐centred versus lecture‐based learning environments generally does not take into account the psychological need support provided in these learning environments. From a self‐determination theory perspective, need support is important to study because it has been associated with benefits such as autonomous motivation and achievement. Aim. The purpose of the study is to investigate the effects of different learning environments on students’ motivation for learning and achievement, while taking into account the perceived need support. Sample. First‐year student teachers (N= 1,098) studying a child development course completed questionnaires assessing motivation and perceived need support. In addition, a prior knowledge test and case‐based assessment were administered. Method. A quasi‐experimental pre‐test/post‐test design was set up consisting of four learning environments: (1) lectures, (2) case‐based learning (CBL), (3) alternation of lectures and CBL, and (4) gradual implementation with lectures making way for CBL. Results. Autonomous motivation and achievement were higher in the gradually implemented CBL environment, compared to the CBL environment. Concerning achievement, two additional effects were found; students in the lecture‐based learning environment scored higher than students in the CBL environment, and students in the gradually implemented CBL environment scored higher than students in the alternated learning environment. Additionally, perceived need support was positively related to autonomous motivation, and negatively to controlled motivation. Conclusions. The study shows the importance of gradually introducing students to CBL, in terms of their autonomous motivation and achievement. Moreover, the study emphasizes the importance of perceived need support for students’ motivation.     

Multi-Agent neurocognitive models of semantics of spatial localization of events

The purpose of the study is to develop a learning system for internal representation of the events localization space to realize orientation and navigation of autonomous mobile systems. The task of the research is the development of simulation models of the semantics of the event localization space based on multi-agent neurocognitive architectures. The paper proves that the multi-agent neurocognitive architecture is an effective formalism for describing the semantics of the spatial localization of events. Main theoretical foundations have been developed for the simulation of spatial relations using the so-called multi-agent facts, consisting of software agents-concepts, reflecting semantic categories corresponding to parts of speech. It is shown that locative software agents that describe the spatial location of objects and events, forming homogeneous connections, compose the so-called field locations. The latter describes a holistic view of the intellectual agent about the environment. The paper defines conceptual foundations of multi-agent modeling of the semantics of subjective reflexive mapping of the interaction between real objects, space and time.     

The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.     

The Robot in the Crib: A Developmental Analysis of Imitation Skills in Infants and Robots

Interesting systems, whether biological or artificial, develop. Starting from some initial conditions, they respond to environmental changes, and continuously improve their capabilities. Developmental psychologists have dedicated significant effort to studying the developmental progression of infant imitation skills, because imitation underlies the infant's ability to understand and learn from his or her social environment. In a converging intellectual endeavour, roboticists have been equipping robots with the ability to observe and imitate human actions because such abilities can lead to rapid teaching of robots to perform tasks. We provide here a comparative analysis between studies of infants imitating and learning from human demonstrators, and computational experiments aimed at equipping a robot with such abilities. We will compare the research across the following two dimensions: (a) initial conditions-what is innate in infants, and what functionality is initially given to robots, and (b) developmental mechanisms-how does the performance of infants improve over time, and what mechanisms are given to robots to achieve equivalent behaviour. Both developmental science and robotics are critically concerned with: (a) how their systems can and do go 'beyond the stimulus' given during the demonstration, and (b) how the internal models used in this process are acquired during the lifetime of the system.     

Value systems for developmental cognitive robotics: A survey

This paper surveys value systems for developmental cognitive robotics. A value system permits a biological brain to increase the likelihood of neural responses to selected external phenomena. Many machine learning algorithms capture the essence of this learning process. However, computational value systems aim not only to support learning, but also autonomous attention focus to direct learning. This combination of unsupervised attention focus and learning aims to address the grand challenge of autonomous mental development for machines. This survey examines existing value systems for developmental cognitive robotics in this context. We examine the definitions of value used—including recent pioneering work in intrinsic motivation as value—as well as initialisation strategies for innate values, update strategies for acquired value and the data structures used for storing value. We examine the extent to which existing value systems support attention focus, learning and prediction in an unsupervised setting. The types of robots and applications in which these value systems are used are also examined, as well as the ways that these applications are evaluated. Finally, we study the strengths and limitations of current value systems for developmental cognitive robots and conclude with a set of research challenges for this field.     

Model learning for robot control: a survey

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.     

The early ontogeny of infants’ imitation of on screen humans and robots

Traditionally, infants have learned how to interact with objects in their environment through direct observations of adults and peers. In recent decades these models have been available over different media, and this has introduced non-human agents to infants’ learning environments. Humanoid robots are increasingly portrayed as social agents in on screen, but the degree to which infants are capable of observational learning from screen-based robots is unknown. The current study thus investigated how well 1- to 3-year-olds (N = 230) could imitate on-screen robots relative to on-screen and live humans. Participants exhibited an imitation deficit for robots that varied with age. Furthermore, the well-known video deficit did not replicate as expected, and was weak and transient relative to past research. Together, the findings documented here suggest that infants are learning from media in ways that differ from past generations, but that this new learning is nuanced when novel technologies are involved.     

From embodied to socially embedded agents – Implications for interaction-aware robots

First, this article proposes a minimal definition of embodiment that can be applied across animals and artefacts. We discuss the potential contributions of this operational definition with respect to assessing and measuring the degree of embodiment in different biological and artificial systems. Second, we outline how this definition can be extended to lead to the particular notion of social embeddedness. Socially embedded agents are structurally coupled with their social environment, in that their sensorimotor activity is grounded in the social environment that the agent is surrounded by. Lastly, based on research in the social sciences on human–human interaction, we discuss perceptual requirements for interaction-aware robotic agents—agents whose identification and interpretation of the (social) environment is facilitated by awareness of the structure of agent–agent interactions (including humans ‘in the loop’). We suggest relevant concepts and heuristics that can contribute to studies of degrees of embodiment of robots that interact with social environments. Manipulating and systematically investigating these heuristics permits variation of the degree of embodiment of such interaction-aware robots.     

Emotional facial expressions affect visual rule learning in 7- to 8-month-old infants

Rule learning (RL) is an implicit learning mechanism that allows infants to detect and generalize rule-like repetition-based patterns (such as ABB and ABA) from a sequence of elements. Increasing evidence shows that RL operates both in the auditory and the visual domain and is modulated by the perceptual expertise with the to-be-learned stimuli. Yet, whether infants’ ability to detect a high-order rule from a sequence of stimuli is affected by affective information remains a largely unexplored issue. Using a visual habituation paradigm, we investigated whether the presence of emotional expressions with a positive and a negative value (i.e., happiness and anger) modulates 7- to 8-month-old infants’ ability to learn a rule-like pattern from a sequence of faces of different identities. Results demonstrate that emotional facial expressions (either positive and negative) modulate infants’ visual RL mechanism, even though positive and negative facial expressions affect infants’ RL in a different manner: while anger disrupts infants’ ability to learn the rule-like pattern from a face sequence, in the presence of a happy face infants show a familiarity preference, thus maintaining their learning ability. These findings show that emotional expressions exert an influence on infants’ RL abilities, contributing to the investigation on how emotion and cognition interact in face processing during infancy.     

Animats and what they can tell us

Animats-autonomous robots or simulations of animals-and the animat approach represent the most recent attempt to comprehend the capacity of animals for autonomous generation of adaptive, intelligent behavior in complex, changing environments. Motivated by perceived limitations in classical artificial intelligence (AI), the animat approach promulgates an alternative, bottom-up route to understanding intelligent behavior. Important tenets include: (1) that adaptive behavior is best understood by focusing on the interaction between a behaving individual and its environment, hence the interest in `embodied' physical robots `situated' in natural environments; (2) that specific abilities, `behaviors', are more natural units of analysis and design than general, information-processing functions and world models; and (3) that high-level behaviors will emerge as systems composed of simple behavioral competences become more complex. Thus, animat research often begins with low-level sensorimotor abilities and then moves up towards higher, cognitive functions. Both in analysis and in design, the animat approach borrows heavily from ethology, psychology, neurobiology and evolutionary biology, as well as from connectionism. For AI and robotics researchers, understanding the mechanisms behind adaptive behavior is secondary to creating them, but natural scientists can hope for tools and concepts to aid understanding of biological systems.     

Confirmatory reinforcement learning changes with age during adolescence

Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11–32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages.

Research Highlights

• Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments.
• People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence.
• Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices.
• Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias.

     

An embodied model for sensorimotor grounding and grounding transfer: experiments with epigenetic robots

The grounding of symbols in computational models of linguistic abilities is one of the fundamental properties of psychologically plausible cognitive models. In this article, we present an embodied model for the grounding of language in action based on epigenetic robots. Epigenetic robotics is one of the new cognitive modeling approaches to modeling autonomous mental development. The robot model is based on an integrative vision of language in which linguistic abilities are strictly dependent on and grounded in other behaviors and skills. It uses simulated robots that learn through imitation the names of basic actions. Robots also learn higher order action concepts through the process of grounding transfer. The simulation demonstrates how new, higher order behavioral abilities can be autonomously built on previously grounded basic action categories following linguistic interaction with human users.     

Feedback Delays: How Can Decision Makers Learn Not to Buy a New Car Every Time the Garage Is Empty?

Decision makers in dynamic environments (e.g., stock trading, inventory control, and firefighting) learn poorly in experiments where feedback about the outcomes of their actions is delayed. In searching for ways to mitigate these effects, this paper presents two computational models of learning with feedback delays and contrasts them against human decision-makers' performance. The no-memory model hypothesizes that decision makers always perceive feedback as immediate. The with-memory model hypothesizes that, over time, decision makers are able to develop internal representations of the task that help them to perform with delayed feedback. As borne out by human subjects, both models predict that a display of past history improves learning with delay and that increasing delay increasingly degrades performance. Even though the length of training in this task exceeds that used in many laboratory-based dynamic tasks, neither the two models nor the subjects are able to effectively learn without decision aids when faced with feedback delays. When given an amount of training that more closely approximates that provided in functioning dynamic environments, the with-memory model predicts that human decision makers may learn without decision aids over the long term if feedback delays are simple. These results raise several issues for continued theoretical investigation as well as potential suggestions for training and supporting decision makers in dynamic environments with feedback delays. Copyright 2000 Academic Press.     

What Voters Do: Information Search During Election Campaigns

Learning about political candidates before voting can be a cognitively taxing task, given that the information environment of a campaign may be chaotic and complicated. In response, voters may adopt decision strategies that guide their processing of campaign information. This paper reports results from a series of process-tracing experiments designed to learn how voters in a presidential primary election adopt and use such strategies. Different voters adopt different strategies, with the choice of strategy dependent on the campaign environment and individual voter characteristics. The adoption of particular strategies can have implications for how voters evaluate candidates.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Evolution of learning strategies in changing environments

Learning is an important aspect of cognition that is crucial for the success of many species, and has been a factor involved in the evolution of distinct patterns of life history that depend on the environments in question. The extent to which different degrees of social and individual learning emerge follows from various species-dependent factors, such as the fidelity of information transmission between individuals, and that has previously been modelled in agent-based simulations with meme-based representations of learned knowledge and behaviours. A limitation of that previous work is that it was based on fixed environments, and it is known that different learning strategies will emerge depending on the variability of the environment. This paper will address that limitation by extending the existing modelling framework to allow the simulation of life history evolution and the emergence of appropriate learning strategies in changing environments.     

网络学习中师生交互与大学生学习投入的关系：自主动机与学业情绪的序列中介作用

为了探索大学生在网络学习中师生交互与学习投入的关系,以及自主动机和学业情绪在其中的中介作用,本研究采取问卷调查法,使用师生交互问卷、自主动机问卷、大学生学业情绪量表、学习投入量表,对563名大学生开展调研。结果发现:网络学习中的师生交互既可以直接显著正向预测学习投入,也可通过积极情绪间接影响学习投入,还可依次通过自主动机和积极情绪的链式中介作用正向预测学习投入,而消极情绪在其中的中介作用不显著。研究结果表明,高质量的师生交互能激发大学生网络学习的自主动机,让大学生体验到更多积极情绪,从而提高其学习投入水平。     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.     

Research on intelligent cognitive function enhancement of intelligent robot based on ant colony algorithm

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved.     

The Misguided Marriage of Responsibility and Autonomy

Much of the literature devoted to the topics of agent autonomy and agent responsibility suggests strong conceptual overlaps between the two, although few explore these overlaps explicitly. Beliefs of this sort are commonplace, but they mistakenly conflate the global state of being autonomous with the local condition of acting autonomously or exhibiting autonomy in respect to some act or decision. Because the latter, local phenomenon of autonomy seems closely tied to the condition of being responsible for an act, we tend to think of the former, global phenomenon as a condition of responsibility as well. But one can act autonomously, or manifest autonomy with respect to some occurrent state, without satisfying the conditions for autonomous agency. Autonomous agency and responsible agency are logically distinct in part due to the varient conceptions of rationality each calls for. Both agent responsibility and holding a person responsible imply a fairly ``thick' form of rationality, where rationality embodies a normative component and is a matter of satisfying criteria that are objective in the sense that they are independent of what a person happens to want or to value. But autonomous agency calls for a quite different, ``thin' conception of instrumental rationality.