Understanding the learning brain in the world
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Understanding the learning brain in the world How does the brain learn? Some answers to this question are briefly summarised in this presentation that was made in the symposium “The Mind and the Machine: Brain, mind and digital learning environments” at ascilite 2015. (They are elaborated in Epistemic fluency book.) Many of our insights draw on what we call “slow” or “long” neurosciences, to include evolutionary neuroscience, neuroanthropology, neuroarchaeology and neurolinguistics, that study cultural and social evolution of human brain and mind, rather than just traditional “fast” cognitive neuroscience that primarily look at micro processes in human brain. The main our claim is that neurosciences, broadly taken, already now offer a lot of useful knowledge for teaching, learning and educational design and contributions of these fields to education will be more radical than the emerging educational neuroscience field in its current shape tends to embrace and envision. Neuro-scientific evidence challenges us to rethinking cognitive theories of learning and link cognition with situated action within materially textured (digital and physical) world. To put it short “Brains, bodies and things play equal role in the drama of human cultural becoming” (Malafouris, 2013, 2) and this cannot be ignored. In educational technology field, “slow” neurosciences force us to move away from thinking about the Mind as the Machine to thinking about how Humans learn and think with the Machines.
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Understanding the learning brain in the world
- 1. The University of Sydney Page 1 Understanding the learning brain in the world Lina Markauskaite ascilite 29 November- 2 December 2015 Symposium: The Mind and the Machine: Brain, mind and digital learning environments
- 2. The University of Sydney Page 2 Acknowledgement Lina Markauskaite & Peter Goodyear “Epistemic Fluency and Professional Education: Innovation, knowledgeable action and actionable knowledge,” Springer, early 2016 Epistemic fluency: http://epistemicfluency.com
- 3. The University of Sydney Page 3 From the “slow” neurosciences Enculturated brain: much of “higher-order” features of the mind originate in culture and depend upon the developed functional brain’s architecture (Donald, 2001) Embodied brain: humans understand the world through the frame of their bodies (Damasio, 2010) Extended brain: “cognition leaks into body and world” (Clark, 2008) Enactive brain: knowledge and what is known are brought forth through action (Maturana &
- 4. The University of Sydney Page 4 Main claims Computer technologies have shaped much of our thinking about the mind’s architecture reinforcing an unproductive division between the mind/thinking and the body/action Brain research could help us to... 1. Link cognition with situated action 2. Move from learning from/through technology to learning with technology 3. Intelligently design environments for learning Moving from the Mind as the Machine to the huMans with the Machines
- 5. The University of Sydney Page 5 Descartes’ division: Mind vs. Body Brain/Mind Body/Action Symbols/Thinking Tools/Practice Habits (Physical & digital spaces) Ideas (Conceptual structures) Based on Jelle van Dijk, Embodied technology, 2013
- 6. The University of Sydney Page 6 Information Processing view of mind: Adaptive Control of Thought—Rational (ACT-R) architecture From Ohlsson, Deep learning, 2011
- 7. The University of Sydney Page 7 Cognitive theory of multimedia learning From Mayer, Cambridge handbook of multimedia learning, 2005
- 8. The University of Sydney Page 8 Challenges for IP from brain research 1. Thinking is all over the brain 2. No neural support for “slower-moving” working memory processes 3. No mechanisms to support historically evolved architecture of the enculturated human brain Damasio, Human decisions, 2012 Donald, The slow process, 2007
- 9. The University of Sydney Page 9 Cognition as a symbiosis between biology & culture “Human cognitive evolution is characterized by two special features that are truly novel in the primate line. – The first is the emergence of "mindsharing" cultures that perform cooperative cognitive work, and serve as distributed cognitive networks. – The second is the emergence of a brain that is specifically adapted for functioning within those distributed networks, and cannot realize its design potential without them.” Donald, The slow process, 2007
- 10. The University of Sydney Page 10 The origins of the modern mind Three stages in the development of “mind-sharing” culture 0. Episodic memory: perception 1. Mimetic memory: skill, gesture 2. Mythic memory: oral language, imagination 3. Theoretic + technological memory: formalisms, distributed storage technologies Donald, Origins of the modern mind, 2001 Brain is specifically adapted for functioning within distributed cognitive networks and cannot realize its design potential without them (Donald, 2001)
- 11. The University of Sydney Page 11 Embodied, Enacted, Extended, Enculturated cognition Brain/Mind Body/Action Symbols/Thinking Tools/Practice Habits (Physical & digital spaces) Ideas (Conceptual strictures) Neuroeducation & Design for eLearning space Embodied, Enacted (Active perception) Extended, Enculturated (Social situations) Embodied (inter)actions within co-constructed environments Based on Jelle van Dijk, Embodied technology, 2013
- 12. The University of Sydney Page 12 Grounded cognition: Re-enacting concepts – Add parts of the concepts Somatosensory cortex # (legs) $ (tails) ** (barks) @ (soft) Barsalou, Language and simulation in representation of abstract concepts, 2010
- 13. The University of Sydney Page 13 Learning as co-constructing epistemic environments “We do not just self-engineer better worlds to think in. We self-engineer ourselves to think and perform better in the worlds we find ourselves in. We self-engineer worlds in which to build better worlds to think in. We build better tools to think with and to use these very tools to discover still better tools to think with. We tune the way we use these tools by building educational practices to train ourselves to use our best cognitive tools better...” Clark, Supersizing the mind, 2008, p59
- 14. The University of Sydney Page 14 Main claims Computer technologies have shaped much of our thinking about the mind’s architecture reinforcing an unproductive division between mind/thinking and body/action Brain research could help us to... 1. Link cognition with situated action 2. Move from learning from/through technology to learning with technology 3. Intelligently design environments for learning Main dangers... 1. Not letting go Information Processing theories of the mind 2. Separating brain research from learning design 3. Doing brain research mainly in laboratories than in the world 4. Not engaging with “slow” neurosciences Epistemic fluency: http://epistemicfluency.com
Notas do Editor
- The Mind and the Machine: Brain, mind and digital learning environments Dr Jason M. Lodge (Chair) Prof Gregor Kennedy Prof Barney Dalgarno Dr Lina Markauskaite This panel session is aimed at examining the emerging role that the science of the mind is playing in the development of a deeper understanding of learning in digital learning environments. Specifically, the panel will focus on concepts and approaches based in cognitive neuroscience and cognitive science to better understand learning in digital environments. The aim of this session is to introduce these approaches to those unfamiliar with them in the ascilite community. From there, the panel will discuss ways in which the learning sciences broadly can have a greater impact on learning design in digital learning environments. Keywords: cognitive science, neuroscience, digital learning environments, learning design Panel background and aims Research from cognitive neuroscience and cognitive science is increasingly impacting on our understanding of learning (Della Sala & Anderson, 2012). Much of the emphasis of this work to date has been on developmental learning difficulties and on addressing “neuromyths” in education (e.g. the notion that we only use 10% of our brain; Howard-Jones, 2014). Over the last several years, however, a growing number of researchers have been attempting to use a range of techniques within these traditions to more generally target our understanding of students’ learning processes and outcomes within digital learning environments (e.g. Dalgarno, Kennedy & Bennett, 2010; 2014). Experimental research approaches combined psychophysiological, medical imaging and digital data collection methods – often eschewed by educational researchers – are increasingly being used to inform our understanding of learning in order to facilitate the effective use, design, and support of educational technology. But the applications of cognitive neuroscience and cognitive science theoretical models and frameworks and the use of associated methodologies are neither straightforward nor without controversy (Smeyers & Depaepe, 2012). This panel aims to explore with the ascilite delegates how areas of the learning sciences can help to understand learning, and in doing so contribute to effective learning design in digital learning environments. Within this broader aim, three overall themes will be covered as outlined below: The first theme of the panel will address how intra- and inter-individual characteristics or variables impact on the development of student understanding and skills in digital learning environments. Topics for discussion here will include metacognition and learner self-regulation, misunderstanding and conceptual change, and error and feedback. The second theme centers on the methodologies used in cognitive neuroscience and cognitive science that can be applied in educational technology research and development. Methodologies that will broadly be discussed include eye tracking, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The emphasis in this theme will be on the ease with which – and the appropriateness – of applying these methods in educational technology research and development. The final theme will focus on how research findings can be effectively translated into educational practice. Issues associated with the effective translation theories and methods discussed in the previous two themes will be discussed. An emphasis will be placed on the process of inference; that is, the principles and practices that can be inferred from the lab and translated into learning design for digital learning environments and virtual classrooms. The session will draw on the expertise of the panel members to show how collaborations between educators, educational technologists, neuroscientists, cognitive scientists, psychologists, and computer scientists are fundamental to furthering our understanding of learning in digital environments.
- My main insight that neurosciences already now offer a lot for education. And its contribution is (will be) more radical than neuro-education field tends to embrace and envision. What we already know about the brain and cognition, that worth embracing in education and we haven’t done (well) so far. My starting assumptions come from the “slow/long/soft” brain sciences (various interdisciplinary domains that embrace neuroscience): evolutionary neurosciences, neuroanthropology, neuroarchaeology, palaeontology, neurolinguistics Enculturated brain: Human cognition is the symbiosis between brain and culture. Much of “higher-order” features of the mind originate in culture and depend upon the developed functional brain’s architecture (e.g. reading, math, music skills) (Donald, 2002) Embodied brain: “Human brain and the rest of the body constitute an indissociable organism, integrated by means of mutually interactive biochemical and neural regulatory circuits (including endocrine, immune, and autonomous neural components)” (Damasio, 1994); “humans understand the world through the frame of body” (Damasio, 2010); “body is a foundation of the conscious mind” (Damasio, 2010); “It is certainly true that the mind learns of the outside world via the brain, but it is equally true that the brain can be informed only via the body.” (Damasio, 2010) Extended brain: Capability is not an outcome of a better “engineered” isolated brain, but of the entanglement and coordination between the environment (including tools) and the brain. “What is outside the head may be not necessary outside the mind” (Malafouris, 2013) Enactive brain: knowledge and what is known are brought forth through action (Maturana & Varela, 1992): organisms create their own experience through action; humans know world by enacting it, not receiving it; they actively participate in the generation of meanings. “Brains, bodies and things play equal role in the drama of human cultural becoming” (Malafouris 2013 p2) -------------------------- Mention also symbolic species, the role of language
- Technologies shaped how we think about the mind and learning reinforcing unproductive mind/body division Brain research forces us to rethinking cognitive theories of learning. Help us to link situated with cognitive theories Move from learning from/through technology to learning with technology and intelligently design better environments for learning Moving from the Mind as the Machine to the huMans with the Machines
- Cognitive view vs. situlative view Cognitive view adopts Brainbound view intelligence: intelligence is what happening in the head Information processing logic: It is implemented on symbolic information processing architecture (It is represented symbolically in an anodal form. Logic and rationality are the key. Therefore it is easy to model on machines) The outcome of learning is logically structured knowledge and the focus of learning design is how to represent the conceptual content effectivelly Environment, body, action, feeling may impact learning and cognition, but they are not a part of cognition/knowledge and could be dealt separately. Situative cognition Intelligence is what humans do Mind is without representations, without content. Skills are acquired trough participation in practices, repetition The outcome of learning is thus certain habits and the focus of design is physical/digital space that prompts certain ways of behaviour This division was very handy for those who wanted to do research... And should admit EDtech domain tended to act more often either with no theory or IP theory Eg of IP approach.... Conceptual change/transfer (yes or no measurement) Cognitive load Multimedia Learning theory Ideal for laboratory studies They are not wrong but not sufficient, simply it is not how human brain is designed. When we put the brain in such constraint environment we simply do not allow brain to realise its potential. -------------------- Descartes divided the world into two kinds of substances -- (1) res cogitans, or thinking substance, or mind, or soul, and (2) res extensa, or extended substance, or body. He was a substance dualist. http://home.wlu.edu/~mahonj/Spinoza.Descartes.htm -------------------------- Image based on Lecture Embodied Cognition 30oct Kolding Denmark (Jelle van Dijk, Embodied technology, 2013) https://www.youtube.com/watch?v=LXyRQRsrMRE
- ACT-R (Adaptive Control of Thought—Rational) is a cognitive architecture mainly developed by John Robert Anderson at Carnegie Mellon University.
- This is how this approach has been (widely) applied in Learning technology design. Body and the rest of human do not enter this view
- My main claims 1. Decision-making is all over the brain, and intelligent decision making draws on intuition and “gut” feeling 2. Scale of WM and LTM. No neural mechanisms to support “slower-moving” working memory processes that are necessary for mastering complexities of human cognitive cultural networks. No slow brain... 1-2 hrs learning, complex narrative.... Short term working memory is from 0.1 to 18 seconds without rehearsal (600 seconds with) 3. Most importantly, no mechanisms to support very historically evolved architecture of the human brain to reason and work with memory tools of this culture (ecology of thinking and learning within the digital environments) Antonio Damasio: INET Keynote Address entitled Human Decisions https://www.youtube.com/watch?v=elwLz8ypckI Around 23 min The slow process: a hypothetical cognitive adaptation for distributed cognitive networks. Merlin Donald http://www.queensu.ca/psychology/sites/webpublish.queensu.ca.psycwww/files/files/Faculty/Merlin%20Donald/03_slow_process2.pdf
- Origins of the modern brain J Physiol Paris. 2007 Jul-Nov;101(4-6):214-22. doi: 10.1016/j.jphysparis.2007.11.006. Epub 2008 Jan 8. Image https://it.finance.yahoo.com/notizie/wall-street-tassi-invariati-scattano-233300974.html Biologically modern human brain is entirely dependent on human culture, the brain cannot realise it design potential outside this culture
- Origins of the modern brain The key message, that we shouldn’t reduce human memory only to theory and symbolic representations. In fact cognitive neurosciences exactly show that there are at least four types of memory that historically evolved through human evolution and they build on each other Biologically modern human brain is entirely dependent on human culture, the brain cannot realise it design potential outside this culture By creating learning environments that do not allow this we in essence do not allow to utilise this potential J Physiol Paris. 2007 Jul-Nov;101(4-6):214-22. doi: 10.1016/j.jphysparis.2007.11.006. Epub 2008 Jan 8. Image https://it.finance.yahoo.com/notizie/wall-street-tassi-invariati-scattano-233300974.html
- The main claim is that current educational neuroscience has been too focussed on the cognitive view rather than tried to work within the space between cognitive and situated Personal: Active perception, Embodied action, Embodied cognition, Enactive cognition Builds on episodic and mimetic memory Social: social situations, social interactions, Extended/distributed, enculturated cognition Builds on Mythic and theoretic memory -------------------------- Based on Lecture Embodied Cognition 30oct Kolding Denmark (Jelle van Dijk, Embodied technology, 2013) https://www.youtube.com/watch?v=LXyRQRsrMRE http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-078X2000000200005
- Low hanging fruits: My best example of the potential could be “threshold concepts” research 6410 entries “Grounded cognition” “Situated conceptualisation” 3440 (Barsalou cited 29121) When I google for both I found 14 entries, only one from learning with iCT An example from Barsalou: understanding abstract concepts Language and Simulation in Representation of Abstract Concepts (around min 7-8 Part 2) https://www.youtube.com/watch?v=9gAkFdS6_MQ Human conceptual system: a higher cognitive system organises knowledge categorically. It supports higher level conceptual processes including higher order perception, inferential thinking, remembering, etc. Learning place essential role in establishing categorical knowledge. Eg. Lawyer will be incapable to do the work unless she/he masters a complex conceptual system to understand court case: crime, evidence, appeal, case law, prosecutor, defence, judgement They organise tremendous amount of cognition Dominant approach: Semantic memory approach. Modular and amodal. It is underpinned by transduction principles First once we encounter dog it activates visual system that represents dog visually Second Dog barks an this activates auditory cortex We pet the dog and we use motor system for this Dog feels soft and we activate somatosensory areas If brain is amodal – we activate transduced symbols and perform thinking on the symbols, original modality specific systems are irrelevant and not used in cognition An alternative view System is not modular it utilizes sensory motor and other modality specific systems Modality specific simulations represent concepts, Representations lean on modality specific systems Experiences are captured in near by association area Key idea: for shallow straightforward tasks people rely on language, but for complex tasks that require deep thinking, they re enact... It is not reflective thinking and not purely linguistic thinking, but thinking through active engagement In HE, one theory that evolved over recent years is learning/teaching threshold concepts. But much of pedagogies build on mastering community’s language and reflection, putting action/experience on the back seat Four types of situated information that is stored together with conceptual categories: selected properties of the conceptual category relevant to the current situation; information about the background settings; possible actions that could be taken; perceptions of internal states that one might have experienced during previous encounters with the conceptual phenomena, such as affects, motivations, cognitive states and cognitive operations. As Barsalou et al. (2007) note, in real-world, real-time cognition, it is impossible to understand cognitive processes in isolation from other processes, such as perception, action, and emotion. “Indeed, understanding how a process coordinates with other processes may be as important, if not more important, than understanding the internal structure of the process itself <….> the coordinated relationships between perception, action and cognition must be identified to characterise cognition adequately.” (Barsalou, et al., 2007, 80-81).
- My key message is that indeed students often learn in self engineered technologically rich environments. And creativity, innovation, etc relies on students capacities to co construct such environments In my view just by studying and engineering environments in which students learn is not sufficient to understand higher order cognition, in fact we need to understand how people co-construct those environments. We do need to know more how they assemble such environments. From this perspective, we actually do not have other chance, but to start building on the insights and propositions that come from the theories of “slow” brain science (enactive cognition) on how humans think, how they construct epistemic environments. But this is the next challenge for Brain sciences. ------ Learning as cognitive niche construction “Niche construction is the process in which an organism alters its own (or other species') environment, often but not always in a manner that increases its chances of survival. Changes that organisms bring about in their worlds that are of no evolutionary or ecological consequence are not examples of niche construction.[1] Several biologists have argued that niche construction is as important to evolution as natural selection (i.e., not only does an environment cause changes in species through selection, but species also cause changes in their environment through niche construction).[2] This back-and-forth creates a feedback relationship between natural selection and niche-construction: when organisms affect their environment, that change can then cause a shift in what traits are being naturally selected for.[3] The effect of niche construction is especially pronounced in situations where environmental alterations persist for several generations, introducing the evolutionary role of ecological inheritance. Less drastic niche-constructing behaviors are also quite possible for an organism. This theory, in conjunction with natural selection, shows that organisms inherit two legacies from their ancestors: genes and a modified environment. Together, these two evolutionary mechanisms determine a population's fitness and what adaptations those organisms develop in the continuation for their survival.” From wikipedia https://en.wikipedia.org/wiki/Niche_construction
- Brain research could help us to... Link cognition with situated action Move from learning from/through technology to learning with technology Intelligently design environments for learning Key challenges... Not letting go Information Processing theories of the mind Separating brain research from design Doing brain research in laboratories than in the world Not engaging with “slow” neurosciences