Student evaluations of teaching (SETs) have been used to evaluate higher education teaching performance for decades. Reporting SET results often involves the extraction of an average for some set of course metrics, which facilitates the comparison of teaching teams across different organisational units. Here, we draw attention to ongoing problems with the naive application of this approach. Firstly, a specific average value may arise from data that demonstrates very different pat- terns of student satisfaction. Furthermore, the use of distance measures (e.g. an average) for ordinal data can be contested, and finally, issues of multiplicity increasingly plague approaches using hypothesis testing. It is time to advance the methodology of the field. We demonstrate how multinomial distributions and hierarchical Bayesian methods can be used to contextualise the SET scores of a course to different organisa- tional units and student cohorts, and then show how this approach can be used to extract sensible information about how a distribution is changing.
Despite being proposed as a method for implementing networked learning approaches to learning over a lifetime, at present connectedness learning is usually implemented by close knit teams, and inside one institution. In this chapter we take a step back, considering what might be required to implement it at scale and over a lifetime. The importance of this agenda is highlighted with reference to the changing nature of work; as modern technologies disrupt a wide range of job roles traditionally considered safe it is essential that universities provide portable data that will help our students to demonstrate competencies, claim prior learning and navigate to new opportunities. We use the ongoing work at one Australian institution to guide our perspective, drawing upon the lessons learned in two ongoing projects to make a series of recommendations that we believe will help scale up connectedness learning across an individual’s entire lifetime of learning.
Constructive and formative feedback on writing is crucial to help Higher Degree Research (HDR) students develop effective writing skills and succeed, both in their degree and beyond. However, at the start students have a poor grasp of good academic writing, and HDR supervisors do not always have the time or the writing expertise to provide quality, constructive, formative feedback to students. One approach to address this problem is provided by Writing Analytics (WA), using text analytics to provide timely, formative feedback to students on their writing, in the process introducing a clear set of terms to describe important features of academic writing. This paper describes how Swales’ (1990) Create A Research Space (CARS) model was used to extend a writing analytics tool such that it could be applied to HDR students’ writing, and how good feedback practices were employed to design constructive automated feedback. This work summarises a process that can be used to develop theory driven writing analytics tools that should facilitate thesis writing.
Quantum Cognition has delivered a number of models for semantic memory, but to date these have tended to assume pure states and projective measurement. Here we relax these assumptions. A quantum inspired model of human word association experiments will be extended using a density matrix representation of human memory and a POVM based upon non-ideal measurements. Our formulation allows for a consideration of key terms like measurement and contextuality within a rigorous modern approach. This approach both provides new conceptual advances and suggests new experimental protocols.
Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational "imperfection" can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at "learning how to learn" require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons.
Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.
Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.
This paper proposes that cognitive humor can be modeled using the mathematical framework of quantum theory. We begin with brief overviews of both research on humor, and the generalized quantum framework. We show how the bisociation of incongruous frames or word meanings in jokes can be modeled as a linear superposition of a set of basis states, or possible interpretations, in a complex Hilbert space. The choice of possible interpretations depends on the context provided by the set-up vs. the punchline of a joke. We apply the approach to a verbal pun, and consider how it might be extended to frame blending. An initial study of that made use of the Law of Total Probability, involving 85 participant responses to 35 jokes (as well as variants), suggests that the Quantum Theory of Humor (QTH) proposed here provides a viable new approach to modeling humor.
While help seeking has been extensively studied using self report survey data and models, there is a lack of content analysis techniques that can be directly applied to classify help seeking behaviour. In this preliminary work we propose a coding scheme which is then applied to an open dataset that we have created by carefully selecting sub groups from two popular discussion sites (Reddit and StackExchange). We then explore the possibility for automatically classifying help seeking behaviour using machine learning models. A preliminary model provides good initial results, suggesting that it may indeed be possible to construct student support systems that build off of an accurate classifier.
Despite a narrative that sees Learning Analytics (LA) as a field that enhances student learning, few student-facing solutions have been developed. A lack of tools enable a sophisticated student focus, and it is difficult for educators to imagine how data can be used in authentic practice. This is unfortunate, as LA has the potential to be a powerful tool for encouraging metacognition and reflection. We propose a series of learning design patterns that will help people to incorporate LA into their teaching protocols: do-analyse-change-reflect, active learning squared, and group contribution. We discuss these learning design patterns with reference to a case study provided by the Connected Learning Analytics (CLA) toolkit, demonstrating that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.
An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.
In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.
It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.
Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods - such as the Community of Inquiry framework - rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the auto- mated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" - the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.
It is well known that different arguments appeal to different people. An underlying reason for this is that people process information in ways that are adapted to be consistent with their identity, which is represented by their underlying ideologies. Ideologies can sometimes be framed in terms of particular axes or dimensions, which makes it possible to represent some aspects of an ideology as a region or point of view in the kind of vector space that is typical of many generalized quantum models. Such models can then be used to explain and predict, in broad strokes, whether a particular argument or proposal is likely to appeal to an individual with a particular ideology. This is important when trying to persuade someone to cooperate with or take some course of action. The choice of suitable arguments to bring about desired actions is traditionally part of the art or science of rhetoric. This paper presents a basic model for understanding how different goals will appeal to people with different ideologies, and thus how different rhetorical positions can be adopted to promote different outcomes. As an example, we consider different actions with respect to the environment and climate change, an important but currently highly controversial topic.
Much of the work currently occurring in the field of Quantum Interaction (QI) relies upon Projective Measurement. This is perhaps not optimal, cognitive states are not nearly as well behaved as standard quantum mechanical systems, with violations of repeatability, and operators that do not appear to be naturally orthogonal, a frequently occurring phenomenon in cognitive systems. Here we attempt to map the formalism of Positive Operator Valued Measure (POVM) theory into the domain of semantic memory, showing how it might be used to construct Bell-type inequalities.
Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilised in everyday language. While the systematicity and productivity of language provide a strong argument in favour of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and philosophy. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. Rather than adjudicating between different grades of compositionality, the framework presented here contributes formal methods for determining a clear dividing line between compositional and non-compositional semantics. Compositionality is equated with a joint probability distribution modelling how the constituent concepts in the combination are interpreted. Marginal selectivity is emphasised as a pivotal probabilistic constraint for the application of the Bell/CH and CHSH systems of inequalities (referred to collectively as Bell-type). Non-compositionality is then equated with either a failure of marginal selectivity, or, in the presence of marginal selectivity, with a violation of Bell-type inequalities. In both non-compositional scenarios, the conceptual combination cannot be modelled using a joint probability distribution with variables corresponding to the interpretation of the individual concepts. The framework is demonstrated by applying it to an empirical scenario of twenty-four non-lexicalised conceptual combinations.
We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables.
Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.
Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n=533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario.
A system is something that can be separated from its surrounds, but this definition leaves much scope for refinement. Starting with the notion of measurement, we explore increasingly contextual system behaviour, and identify three major forms of contextuality that might be exhibited by a system: (a) between components; (b) between system and experimental method; and (c) between a system and its environment. Quantum Theory is shown to provide a highly useful formalism from which all three forms of contextuality can be analysed, offering numerous tests for contextual behaviour, as well as modelling possibilities for systems that do indeed display it. I conclude with the introduction of a Contextualised General Systems Theory based upon an extension of this formalism.
Incorporating a learner's level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom's taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.
Sophisticated models of human social behaviour are fast becoming highly desirable in an increasingly complex and interrelated world. Here, we propose that rather than taking established theories from the physical sciences and naively mapping them into the social world, the advanced concepts and theories of social psychology should be taken as a starting point, and used to develop a new modelling methodology. In order to illustrate how such an approach might be carried out, we attempt to model the low elaboration attitude changes of a society of agents in an evolving social context. We propose a geometric model of an agent in context, where individual agent attitudes are seen to self-organise to form ideologies, which then serve to guide further agent-based attitude changes. A computational implementation of the model is shown to exhibit a number of interesting phenomena, including a tendency for a measure of the entropy in the system to decrease, and a potential for externally guiding a population of agents towards a new desired ideology.
Biological systems exhibit a wide range of contextual effects, and this often makes it difficult to construct valid mathematical models of their behaviour. In particular, mathematical paradigms built upon the successes of Newtonian physics make assumptions about the nature of biological systems that are unlikely to hold true. After discussing two of the key assumptions underlying the Newtonian paradigm, we discuss two key aspects of the formalism that extended it, Quantum Theory (QT). We draw attention to the similarities between biological and quantum systems, motivating the development of a similar formalism that can be applied to the modelling of biological processes.
The contextuality of changing attitudes makes them extremely difficult to model. This paper scales up Quantum Decision Theory (QDT) to a social setting, using it to model the manner in which social contexts can interact with the process of low elaboration attitude change. The elements of this extended theory are presented, along with a proof of concept computational implementation in a low dimensional subspace. This model suggests that a society's understanding of social issues will settle down into a static or frozen configuration unless that society consists of a range of individuals with varying personality types and norms.
Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to-cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.
At the core of our uniquely human cognitive abilities is the capacity to see things from different perspectives, or to place them in a new context. We propose that this was made possible by two cognitive transitions. First, the large brain of Homo erectus facilitated the onset of recursive recall: the ability to string thoughts together into a stream of potentially abstract or imaginative thought. This hypothesis is supported by a set of computational models where an artificial society of agents evolved to generate more diverse and valuable cultural outputs under conditions of recursive recall. We propose that the capacity to see things in context arose much later, following the appearance of anatomically modern humans. This second transition was brought on by the onset of contextual focus: the capacity to shift between a minimally contextual analytic mode of thought, and a highly contextual associative mode of thought, conducive to combining concepts in new ways and "breaking out of a rut". When contextual focus is implemented in an art-generating computer program, the resulting artworks are seen as more creative and appealing. We summarize how both transitions can be modeled using a theory of concepts which highlights the manner in which different contexts can lead to modern humans attributing very different meanings to the interpretation of one concept.
We utilise the well-developed quantum decision models known to the QI community to create a higher order social decision making model. A simple Agent Based Model (ABM) of a society of agents with changing attitudes towards a social issue is presented, where the private attitudes of individuals in the system are represented using a geometric structure inspired by quantum theory. We track the changing attitudes of the members of that society, and their resulting propensities to act, or not, in a given social context. A number of new issues surrounding this "scaling up" of quantum decision theories are discussed, as well as new directions and opportunities.
Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being overestimated. This paper explores three variations of this model which are distinguished by the scaling factor designed to compensate the overestimation.
This paper proposes a well developed mathematical apparatus to determine whether a conceptual combination is compositional, or not. Within cognitive science, systematicity and productivity appear to require that conceptual representation should be compositional, but the need to represent prototypical information implies that concepts must be represented non-compositionally. As a consequence, the question of under what conditions conceptual representation is compositional (or not) remains debatable. By drawing on general probabilistic methods developed in quantum theory to test whether a system is decomposable, or not, a formal test is proposed that can determine whether a specific conceptual combination is compositional. This test examines a joint probability distribution modelling the combination, asking whether or not it is factorizable. Empirical studies indicate that some combinations should be considered as non-compositional.
As computers approach the physical limits of information storable in memory, new methods are needed to advance computing power. We propose proposed is a quantum vector-based approach to memory which may overcome the difficulty of mapping symbolic to subsymbolic representations. The approach is inspired by the structure of human memory and incorporates elements of Gardenfors' vector-based Conceptual Space approach and Humphries et. al's matrix model of memory. Though in its infancy, the quantum information retrieval approach can provide not just exceptionally high density memory storage but creative capacities as well.
Compositionality is a frequently made assumption in linguistics, and yet many human subjects reveal highly non-compositional word associations when confronted with novel concept combinations. This article will show how a non-compositional account of concept combinations can be supplied by modelling them as interacting quantum systems.
Consider the concept combination "pet human".
In word association experiments, human subjects produce the associate "slave" in relation this combination.
The striking aspect of this associate is that it is not produced as an associate of "pet", or "human" in isolation.
In other words, the associate "slave" seems to be emergent.
Such emergent associations sometimes have a creative character and cognitive science is largely silent about how we produce them.
Departing from a dimensional model of human conceptual space, this article will explore concept combinations, and will argue emergent associations are related to abductive reasoning within conceptual space, that is,
How do humans respond to their social context? This question is becoming increasingly urgent in a society where democracy requires that the citizens of a country help to decide upon its policy directions, and yet those citizens frequently have very little knowledge of the complex issues that these policies seek to address. Frequently, we find that humans make their decisions more with reference to their social setting, than to the arguments of scientists, academics, and policy makers. It is broadly anticipated that the agent based modelling (ABM) of human behaviour will make it possible to treat such social effects, but we take the position here that a more sophisticated treatment of context will be required in many such models. While notions such as historical context (where the past history of an agent might affect its later actions) and situational context (where the agent will choose a different action in a different situation) abound in ABM scenarios, we will discuss a case of a potentially changing context, where social effects can have a strong influence upon the perceptions of a group of subjects. In particular, we shall discuss a recently reported case where a biased worm in an election debate led to significant distortions in the reports given by participants as to who won the debate (Davis et al 2011). Thus, participants in a different social context drew different conclusions about the perceived winner of the same debate, with associated significant differences among the two groups as to who they would vote for in the coming election. We extend this example to the problem of modelling the likely electoral responses of agents in the context of the climate change debate, and discuss the notion of interference between related questions that might be asked of an agent in a social simulation that was intended to simulate their likely responses. A modelling technology which could account for such strong social contextual effects would benefit regulatory bodies which need to navigate between multiple interests and concerns, and we shall present one viable avenue for constructing such a technology. A geometric approach will be presented, where the internal state of an agent is represented in a vector space, and their social context is naturally modelled as a set of basis states that are chosen with reference to the problem space.
Modeling how a word is activated in human memory is an important feature for determining the probability of recall of a word in an extra-list cueing experiment. The spreading activation, spooky-action-at-a-distance and entanglement models have been used to model the activation of a word. Recently a hypothesis was put forward where the mean activation levels of the respective models are as follows: Spreading-activation ≤ Entanglement ≤ Spooky-action-at-a-distance. This article investigates this hypothesis by means of a substantial empirical analysis of each model using the University of South Florida word association, rhyme and word fragment norms.
Vector space based approaches to natural language processing are contrasted with human similarity judgements to show the manner in which human subjects fail to produce data which satisfies all requirements for a metric space. This result would constrains the validity and applicability vector space based (and hence also quantum inspired) approaches to the modelling of cognitive processes. This paper proposes a resolution to this problem, by arguing that pairs of words imply a context which in turn induces a point of view, so allowing a subject to estimate semantic similarity. Context is here introduced as a point of view vector (POVV) and the expected similarity is derived as a measure over the POVV's. Different pairs of words will invoke different contexts and different POVV's. We illustrate the proposal on a few triples of words and outline further research.
Measures and theories of information abound, but there are few formalised methods for treating the contextuality that can manifest in different information systems. Quantum theory provides one possible formalism for treating information in context. This paper introduces a quantum-like model of the human mental lexicon, and shows one set of recent experimental data suggesting that concept combinations can indeed behave non-separably. There is some reason to believe that the human mental lexicon displays entanglement.
Separability is a concept that is very difficult to define, and yet much of our scientific method is implicitly based upon the assumption that systems can sensibly be reduced to a set of interacting components. This paper examines the notion of separability in the creation of bi-ambiguous compounds that is based upon the CHSH and CH inequalities. It reports results of an experiment showing that violations of the CHSH and CH inequality can occur in human conceptual combination.
This article introduces a "pseudo classical" notion of modelling non-separability of phenomena. This form of non-separability can viewed as lying between separability and quantum-like non-separability. Non-separability is formalized in terms of the non-factorizabilty of the underlying joint probability distribution. A decision criterium for determining the non-factorizability of the joint distribution is related to determining the rank of a matrix as well as another approach based on the chi-square-goodness-of-fit test. This pseudo-classical notion of non-separability is discussed in terms of quantum games and concept combinations in human cognition.
Language exhibits a number of contextuality and non-separability effects. This paper reviews a new set of models showing promise for capturing this complexity which are based upon a quantum-like approach.
Science has been under attack in the last thirty years, and recently a number of prominent scientists have been busy fighting back. Here, an argument is presented that the `science wars' stem from an unreasonably strict adherence to the reductive method on the part of science, but that weakening this stance need not imply a lapse into subjectivity. One possible method for formalising the description of non-separable, contextually dependent complex systems is presented. This is based upon a quantum-like approach.
Following an early claim by Nelson & McEvoy suggesting that word associations can display `spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. In this paper quantum theory is proposed as a framework suitable for modelling the mental lexicon, specifically the results obtained from both intralist and extralist word association experiments. Some initial models exploring this hypothesis are discussed, and they appear to be capable of substantial agreement with pre-existing experimental data. The paper concludes with a discussion of some experiments that will be performed in order to test these models.
In this third Quantum Interaction (QI) meeting it is time to examine our failures. One of the weakest elements of QI as a field, arises in its continuing lack of models displaying proper evolutionary dynamics. This paper presents an overview of the modern generalised approach to the derivation of time evolution equations in physics, showing how the notion of symmetry is essential to the extraction of operators in quantum theory. The form that symmetry might take in non-physical models is explored, with a number of viable avenues identified.
Following an early claim by Nelson & McEvoy suggesting that word associations can display 'spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. This paper presents a simple quantum model of a word association system. It is shown that a quantum model of word entanglement can recover aspects of both the Spreading Activation model and the Spooky model of word association experiments.
According to recent studies in developmental psychology and neuroscience, symbolic language is essentially intersubjective. Empathetically relating to others renders possible the acquisition of linguistic constructs. Intersubjectivity develops in early ontogentic life when interactions between mother and infant mutually shape their relatedness. Empirical findings suggest that the shared attention and intention involved in those interactions is sustained as it becomes internalized and embodied. Symbolic language is derivative and emerges from shared intentionality. In this paper, we present a formalization of shared intentionality based upon a quantum approach. From a phenomenological viewpoint, we investigate the nonseparable, dynamic and sustainable nature of social cognition and evaluate the appropriateness of quantum interaction for modelling intersubjectivity.
Information systems are socio-technical systems. Their design, analysis and implementation requires appropriate languages for representing social and technical concepts. However, many symbolic modelling approaches fall into the trap of underemphasizing social aspects of information systems. This often leads to an inability of ontological models to incorporate effects such as contextual dependence and emergence. Moreover, as designers take the perspective of people living with and alongside the information system to be modelled social interaction becomes a primary concern. Ontologies are too prescriptive and do not account properly for social concepts. Based on State-Context-Property (SCoP) systems we propose a quantum-inspired approach for modelling information systems.
Despite the general recognition of complexity as an important concept and decades of work, very little progress has been made in the attempt to define complexity. It is suggested that this is due to the fact that the definition of complex behaviour is itself complex, forming a scale from the simple to the more and more complex. Those systems at the high end of the scale are not at present well modelled, and reasons why this might be the case are presented. The possibility that quantum theories may be able to model such high end complexity is investigated.
This article investigates one of the fundamental issues confronting a field that investigates quantum interaction; namely why is it necessary? The need to investigate an interaction using a quantum formalism is argued to arise when the system under study is sufficiently complex. In particular, if the system is displaying contextual behaviour then a quantum approach often incorporates this behaviour very naturally. Thus, a way in which much of the disparte work in the field of quantum interaction can be both justified to the broader community and eventually unified is presented. The nature of contextual behaviour and its relationship to nonlocality is explored.
Human memory experiments appear to be generating "nonlocal" effects. In this paper the possibility that words might be entangled in human semantic space is seriously entertained. This approach leads to a very natural picture of the way in which context might affect word association via the standard interpretation of quantum measurement. Two possible scenarios for testing such a hypothesis are suggested, both based upon potential violations of the CHSH inequality.
Originally based upon a pregeometric model of the Universe, Process Physics has now been formulated as far more general modelling paradigm that is capable of generating complex emergent behaviour. This article discusses the original relational model of Process Physics and the emergent hierarchical structure that it generates, linking the reason for this emergence to the historical basis of the model in quantum field theory. This historical connection is used to motivate a new interpretation of the general class of quantum theories as providing models of certain aspects of complex behaviour. A summary of this new realistic interpretation of quantum theory is presented and some applications of this viewpoint to the description of complex emergent behaviour are sketched out.
Despite its early successes, ALife has not tended to live up to its original promise, with any emergent behaviour very rarely manifesting itself in such a way that new higher level emergence can occur. This problem has been recognised in two related concepts; the failure of ALife simulations to display Open Ended Evolution, and their inability to dynamically generate more than two hierarchical levels of behaviour. This paper will suggest that these problems of ALife stem from a missing sense of contextuality in the models of ALife. A number of theories which exhibit some form of contextual dependence will be discussed, in particular, the gauge theories of quantum field theory.
The Michelson-Morley interferometer experiments were designed to measure the speed of the Earth through the aether. The results were always believed to have been null - no effect. This outcome formed the basis for Einstein's Special and General Relativity formalism. The new process physics shows that absolute motion, now understood to be relative to the quantum foam that is space, is observable, but only if the interferometer operates in gas mode. A re-analysis here shows that the results from the gas-mode interferometers were not null, but in fact large when re-analysed to take account of the effect of the air, or helium, in which the apparatus operated. The speed of absolute motion is comparable to that determined from the Cosmic Background Radiation anisotropy, but the direction is not revealed. So absolute motion is meaningful and measureable, thus refuting Einstein's assumption. This discovery shows that a major re-assessment of the interpretation of the Special and General Relativity formalism is called for, a task already provided by Process Physics. This new information-theoretic physics makes it clear that Michelson-Morley type experiments are detecting motion through the quantum foam, which is space. Hence we see direct evidence of quantum gravity effects, as predicted by Process Physics
A new process orientated physics is being developed at Flinders University. These ideas were initially motivated by deep unsolved problems in fundamental physics, such as the difficulty of quantizing gravity, the missing arrow of time, the question of how to interpret quantum mechanics, and perhaps most importantly, a problem with the very methodology of our fundamental descriptions of the Universe. A proposed solution to these problems, Process Physics, has led to what can be viewed as a hierarchical model of reality featuring a Universe that exhibits behaviour very reminiscent of living systems.
The new Process Physics models reality as self-organising relational information and takes account of the limitations of logic, discovered by Godel and extended by
Chaitin, by using the concept of self-referential noise. Space and quantum physics are
emergent and unified, and described by a Quantum Homotopic Field Theory of fractal
topological defects embedded in a three-dimensional fractal process-space.
Despite a general recognition of the importance of complex systems, there is a dearth of general models
capable of describing their dynamics. This is attributed to a complexity scale; the models are attempting to
describe systems at different parts of the scale and are hence not compatible. We require new
models capable of describing complex behaviour at different points of the complexity scale. This work identifies, and proceeds to examine systems at the high end of the complexity scale, those which have not to date been well understood by our current modelling
methodology. It is shown that many such models exhibit what might be termed contextual dependency, and that it is
precisely this feature which is not well understood by our current modelling methodology. A particular problem is
discussed; our apparent inability to generate systems which display high end complexity, exhibited by for example
the general failure of strong ALife. A new model, Process Physics, that has been developed at Flinders University
is discussed, and arguments are presented that it exhibits high end complexity. The features of this model that
lead to its displaying such behaviour are discussed, and the generalisation of this model to a broader range of
complex systems is attempted.
Themes: contextuality and complexity; reductive failure; Process Physics; quantum theories as models of complexity