Thirty years of research on online learning

This paper presents a personal account of developments in research on online learning over the past 30 years. Research on how to design online instruction represents an example of applying the science of learning to education. It contributes to the science of learning (as exemplified by developments in cognitive load theory, the cognitive theory of multimedia learning, and incorporating metacognitive, motivational, and affective aspects of learning), the science of instruction (as exemplified by the continuing development of research‐based principles of instructional design), and the science of assessment (as exemplified by supplementing self‐report surveys and retention tests with multilevel transfer tests, log file data during learning, and cognitive neuroscience measures of cognitive processing during learning). Some recurring themes are that learning is caused by instructional methods rather than instructional media, so research should focus on features that are uniquely afforded by digital learning environments; instructional practice should be grounded in rigorous and systematic research, including value‐added experiments aimed at pinpointing the active ingredients in online instruction; research in online learning should identify boundary conditions under which instructional techniques are most effective; and research in online learning should test and contribute to learning theory.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

Artificial life: organization, adaptation and complexity from the bottom up

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular self-organization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science.     

Consciousness,Trauma, and Health: A Cognitive Systems Response to Payne,Walsh, and Oman

Religion and cognitive science contribute complementary understandings of the human person, and an integrated perspective can bridge clinical, spiritual, and philosophical resources to facilitate healing and growth. Drawing upon cognitive science and systems theory, I respond to Richard Payne, Mary Walsh, and Doug Oman’s comments on my Mind, Brain, and the Elusive Soul (Graves 2008; Oman 2011; Payne 2011; Walsh 2011).     

Kognitive Lernermodellierung

Different types of learner models and their usefulness for tutoring have been discussed widely since the beginning of intelligent tutoring systems. In this paper we compare pragmatic and cognitive approaches of learner modeling. Pragmatic approaches consider relevant learner features for adaptive methods in learning environments and adapt different aspects of instruction to a restricted model representing these features. Cognitive approaches aim for a psychologically adequate modeling of human problem solving. We introduce the case-based learner model ELM as an example of a cognitive approach to learner modeling. The learning environments ELM-PE and ELM-ART use ELM for adaptional methods on conceptual, plan, and episodic levels and provide individual help and learning support. Especially in the case of integrated learning environments like ELM-ART which support a variety of learning activities, a combination of pragmatic and cognitive learner models is proposed to be a necessary and useful solution.     

Phenomenological coping skills and the striatal memory system

Most cognitive scientists are committed to some version of representationalism, the view that intelligent behavior is caused by internal processes that involve computations over representations. Phenomenologists, however, argue that certain types of intelligent behavior, engaged coping skills, are nonrepresentational. Recent neuroscientific work on multiple memory systems indicates that while many types of intelligent behavior are representational, the types of intelligent behavior cited by phenomenologists are indeed nonrepresentational. This neuroscientific research thus vindicates a key phenomenological claim about the nature of intelligent behaviour. It also provides a framework for the ongoing reconciliation of cognitive science and phenomenology.     

Intelligent technologies to optimize performance: Augmenting cognitive capacity and supporting self-regulation of critical thinking skills in decision-making

The purpose of the present paper is to (a) discuss how intelligent systems can augment cognitive capacity, and through self-regulation, assist learners to engage in critical thinking and (b) provide an example that highlights the information-rich context of the work environment and the role of self-regulation in working in partnership with technology to achieve peak performance. Self-regulation from a social cognitive perspective is conceptualized as a fluid, cyclical process whereby learners use externally provided or self-generated feedback to evaluate and adjust their learning strategies. With advances in technology, this paper attempts to illustrate how workers in jobs of the future will be supported in new ways to analyze data, make interpretations and draw inferences, evaluate situations, and make decisions within a work context before, during, and post-work. Educational implications will be discussed.     

Does it really matter whether students' contributions are spoken versus typed in an intelligent tutoring system with natural language?

There is the question of whether learning differs when students speak versus type their responses when interacting with intelligent tutoring systems with natural language dialogues. Theoretical bases exist for three contrasting hypotheses. The speech facilitation hypothesis predicts that spoken input will increase learning, whereas the text facilitation hypothesis predicts typed input will be superior. The modality equivalence hypothesis claims that learning gains will be equivalent. Previous experiments that tested these hypotheses were confounded by automated speech recognition systems with substantial error rates that were detected by learners. We addressed this concern in two experiments via a Wizard of Oz procedure, where a human intercepted the learner's speech and transcribed the utterances before submitting them to the tutor. The overall pattern of the results supported the following conclusions: (1) learning gains associated with spoken and typed input were on par and quantitatively higher than a no-intervention control, (2) participants' evaluations of the session were not influenced by modality, and (3) there were no modality effects associated with differences in prior knowledge and typing proficiency. Although the results generally support the modality equivalence hypothesis, highly motivated learners reported lower cognitive load and demonstrated increased learning when typing compared with speaking. We discuss the implications of our findings for intelligent tutoring systems that can support typed and spoken input.     

RELIGION IS EASY,BUT SCIENCE IS HARD … UNDERSTANDING McCAULEY'S THESIS

Robert N. McCauley's new book Why Religion Is Natural and Science Is Not (2011) presents a new paradigm for investigating the relationship between science and religion by exploring the cognitive foundations of religious belief and scientific knowledge. McCauley's contention is that many of the differences and disagreements regarding religion and science are the product of distinct features of human cognition that process these two domains of knowledge very differently. McCauley's thesis provides valuable insights into this relationship while not necessarily leading to a dismissive view of theology or religious belief. His paradigm allows the research lens to focus on cognitive differences in processing scientific versus religious information and the important role of automatic, unconscious, and intuitive cognitive processes in understanding both the natural and supernatural worlds.     

Learning biases predict a word order universal

How recurrent typological patterns, or universals, emerge from the extensive diversity found across the world's languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners' biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences.     

Cognitive solutions for security and cryptography

This paper describes the new security solutions based on cognitive approaches and new computing paradigm called cognitive cryptography. This new security area establish a new generation of computational methods and security systems, focused on creation intelligent cryptographic algorithms and security protocols using cognitive information processing approaches. Such systems are designed for semantic evaluation of encrypted data, and allow to select the most appropriate techniques of its encryption. This paper presents a possible application of such techniques for different security tasks like authentication, secret sharing, secure data management etc. Additionally, some cryptographic solutions inspired by biological models will be presented.     

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.     

One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning

The debate over unitary/multiple category-learning utilities is reminiscent of debates about multiple memory systems and unitary/dual codes in knowledge representation. In categorization, researchers continue to seek paradigms to dissociate explicit learning processes (yielding verbalizable rules) from implicit learning processes (yielding stimulus–response associations that remain outside awareness). We introduce a new dissociation here. Participants learned matched category tasks with a multidimensional, information-integration solution or a one-dimensional, rule-based solution. They received reinforcement immediately (0-Back reinforcement) or after one intervening trial (1-Back reinforcement). Lagged reinforcement eliminated implicit, information-integration category learning but preserved explicit, rule-based learning. Moreover, information-integration learners facing lagged reinforcement spontaneously adopted explicit rule strategies that poorly suited their task. The results represent a strong process dissociation in categorization, broadening the range of empirical techniques for testing the multiple-process theoretical perspective. This and related methods that disable associative learning—fostering a transition to explicit-declarative cognition—could have broad utility in comparative, cognitive, and developmental science.     

Assessment of cognitive load in multimedia learning using dual-task methodology

In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.     

The knowledge-learning-instruction framework: bridging the science-practice chasm to enhance robust student learning

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices.     

Distributed learning: Educating and assessing extended cognitive systems

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualizing how we learn to assemble extended cognitive systems by internalizing cultural norms and practices. Having a better grip on how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organize (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the internet during exams.     

Is vision continuous with cognition? The case for cognitive impenetrability of visual perception

Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to cognition. This target article sets out some of the arguments for both sides (arguments from computer vision, neuroscience, psychophysics, perceptual learning, and other areas of vision science) and defends the position that an important part of visual perception, corresponding to what some people have called early vision, is prohibited from accessing relevant expectations, knowledge, and utilities in determining the function it computes--in other words, it is cognitively impenetrable. That part of vision is complex and involves top-down interactions that are internal to the early vision system. Its function is to provide a structured representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues raised by this claim, such as whether signal detection theory and event-related potentials can be used to assess cognitive penetration of vision. A distinction is made among several stages in visual processing, including, in addition to the inflexible early-vision stage, a pre-perceptual attention-allocation stage and a post-perceptual evaluation, selection, and inference stage, which accesses long-term memory. These two stages provide the primary ways in which cognition can affect the outcome of visual perception. The paper discusses arguments from computer vision and psychology showing that vision is "intelligent" and involves elements of "problem solving." The cases of apparently intelligent interpretation sometimes cited in support of this claim do not show cognitive penetration; rather, they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and "hints" are alleged to affect what is seen. In each case it is concluded that the evidence is more readily assimilated to the view that when cognitive effects are found, they have a locus outside early vision, in such processes as the allocation of focal attention and the identification of the stimulus.     

Is statistical learning a mechanism?

Philosophers of science have offered several definitions of mechanism, most of which have biological or neuroscientific roots. In this paper, I consider whether these definitions apply equally well to cognitive science. I examine this question by looking at the case of statistical learning, which has been called a domain-general learning mechanism in the cognitive scientific literature. I argue that statistical learning does not constitute a mechanism in the philosophical sense of the term. This conclusion points to significant limitations in the scope of the mechanist philosophy when it comes to accounting for explanation in cognitive science.     

Primate Culture and Social Learning

The human primate is a deeply cultural species, our cognition being shaped by culture, and cultural transmission amounting to an “epidemic of mental representations” (Sperber, 1996). The architecture of this aspect of human cognition has been shaped by our evolutionary past in ways that we can now begin to discern through comparative studies of other primates. Processes of social learning (learning from others) are important for cognitive science to understand because they are cognitively complex and take many interrelated forms; they shape traditions, cultures and nonsocial aspects of cognition; and in turn they may be shaped by their cultural context. The study of primate social learning and culture has in recent years enjoyed a renaissance, providing a wealth of new findings, key aspects of which are reviewed. The focus is on cognitive issues, including learning about the consequences, sequential structure and hierarchical organization of actions; relating stored knowledge to the assimilation of new social knowledge; feedback guiding the construction of imitations; conceptual grasp of imitation; and the reciprocal relationship between social learning and culture.