Studying the integrated functional cognitive basis of sustained attention with a Primed Subjective-Illusory-Contour Attention Task

Sustained attention plays an important role in everyday life, for work, learning, or when affected by attention disorders. Studies of the neural correlates of attention commonly treat sustained attention as an isolated construct, measured with computerized continuous performance tests. However, in any ecological context, sustained attention interacts with other executive functions and depends on lower level perceptual processing. Such interactions occur, for example, in inhibition of interference, and processing of complex hierarchical stimuli; both of which are important for successful ecological attention. Motivated by the need for more studies on neural correlates of higher cognition, I present an experiment to investigate these interactions of attention in 17 healthy participants measured with high-resolution electroencephalography. Participants perform a novel 2-alternative forced-choice computerised performance test, the Primed Subjective Illusory Contour Attention Task (PSICAT), which presents gestalt-stimuli targets with distractor primes to induce interference inhibition during complex-percept processing. Using behavioural and brain-imaging analyses, I demonstrate the novel result that task-irrelevant incongruency can evoke stronger behavioural and neural responses than the task-relevant stimulus condition; a potentially important finding in attention disorder research. PSICAT is available as an open-source code repository at the following url, allowing researchers to reuse and adapt it to their requirements.Peer reviewe

How to advance general game playing artificial intelligence by player modelling

7 pagesGeneral game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.Non peer reviewe

Adaptive Artificial Intelligence in Games : Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

8 pages, 1 figureWe present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.Non peer reviewe

Reduced Power in Fronto-Parietal Theta EEG Linked to Impaired Attention-Sampling in Adult ADHD

Attention-deficit/hyperactivity disorder (ADHD) in adults is understudied, especially regarding neural mechanisms such as oscillatory control of attention sampling. We report an electroencephalography (EEG) study of such cortical mechanisms, in ADHD-diagnosed adults during administration of Test of Variables of Attention (TOVA), a gold-standard continuous performance test for ADHD that measures the ability to sustain attention and inhibit impulsivity. We recorded 53 adults (28 female, 25 male, aged 18-60), and 18 matched healthy controls, using 128-channel EEG. We analyzed sensor-space features established as neural correlates of attention: timing-sensitivity and phase-synchrony of response activations, and event-related (de)synchronization (ERS/D) of alpha and theta frequency band activity; in frontal and parietal scalp regions. TOVA test performance significantly distinguished ADHD adults from neurotypical controls, in commission errors, response time variability (RTV) and d' (response sensitivity). The ADHD group showed significantly weaker target-locked and responselocked amplitudes, that were strongly right-lateralized at the N2 wave, and weaker phase synchrony (longer reset poststimulus). They also manifested significantly less parietal prestimulus 8-Hz theta ERS, less frontal and parietal poststimulus 4-Hz theta ERS, and more frontal and parietal prestimulus alpha ERS during correct trials. These differences may reflect excessive modulation of endogenous activity by strong entrainment to stimulus (alpha), combined with deficient modulation by neural entrainment to task (theta), which in TOVA involves monitoring stimulus spatial location (not predicted occurrence onset which is regular and task-irrelevant). Building on the hypotheses of theta coding for relational structure and rhythmic attention sampling, our results suggest that ADHD adults have impaired attention sampling in relational categorization tasks.Peer reviewe

Utility of a Behavlets approach to a Decision theoretic predictive player model

6 pages, 3 figures, 1 tableWe present the second in a series of three academic essays which deal with the question of how to build a generalized player model. We begin with a proposition: a general model of players requires parameters for the subjective experience of play, including at least three areas: a) player psychology, b) game structure, and c) actions of play. Based on this proposition, we pose three linked research questions, which make incomplete progress toward a generalized player model: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence-based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments for each research question in each of the three essays, presented as three preprints. The second essay, in this preprint, illustrates how our 'Behavlets' method can improve the performance and accuracy of a predictive player model in the well-known Pac-Man game, by providing a simple foundation for areas a) to c) above. We then propose a plan for future work to address RQ2 by conclusively testing the Behavlets approach. This plan builds on the work proposed in the first preprint essay to address RQ1, and in turn provides support for work on RQ3. The Behavlets approach was described previously; therefore if citing this work please use the correct citation: Cowley B, Charles D. Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modelling and User-Adapted Interaction. 2016 Feb 8; online (Special Issue on Personality in Personalized Systems):150.Non peer reviewe

Neurofeedback Learning is Skill Acquisition but does not Guarantee Treatment Benefit : Continuous-Time Analysis of Learning-Curves from a Clinical Trial for ADHD

Neurofeedback for attention deficit/hyperactivity disorder (ADHD) has long been studied as an alternative to medication, promising non-invasive treatment with minimal side-effects and sustained outcome. However, debate continues over the efficacy of neurofeedback, partly because existing evidence for efficacy is mixed and often non-specific, with unclear relationships between prognostic variables, patient performance when learning to self-regulate, and treatment outcomes. We report an extensive analysis on the understudied area of neurofeedback learning. Our data comes from a randomised controlled clinical trial in adults with ADHD (registered trial ISRCTN13915109; N=23; 13:10 female:male; age 25-57). Patients were treated with either theta-beta ratio or sensorimotor-rhythm regimes for 40 one-hour sessions. We classify 11 learners vs 12 non-learners by the significance of random slopes in a linear mixed growth-curve model. We then analyse the predictors, outcomes, and processes of learners vs non-learners, using these groups as mutual controls. Significant predictive relationships were found in anxiety disorder (GAD), dissociative experience (DES), and behavioural inhibition (BIS) scores obtained during screening. Low DES, but high GAD and BIS, predicted positive learning. Patterns of behavioural outcomes from Test Of Variables of Attention, and symptoms from adult ADHD Self-Report Scale, suggested that learning itself is not required for positive outcomes. Finally, the learning process was analysed using structural-equations modelling with continuous-time data, estimating the short-term and sustained impact of each session on learning. A key finding is that our results support the conceptualisation of neurofeedback learning as skill acquisition, and not merely operant conditioning as originally proposed in the literature.Peer reviewe

Poker as a Domain of Expertise

Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong potential as a model system for studying high-stakes, high-risk expert performance. Poker has been increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In this review, we discuss how these studies have begun to inform us about the interaction between emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion regulation; poker thus offers a good environment for studying these skills in controlled experimental settings of high external validity.We conclude by suggesting ideas for future research on expertise, with new insights provided by poker.Peer reviewe

Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game : A Thought Experiment on AI Ethics

In this chapter, we reflect on the deployment of AI as a pedagogical and educational instrument. When AI enters into classrooms, it becomes as a project with diverse members who have differing stakes, and it produces various socio-cognitive-technological questions that must be discussed. Furthermore, AI is developing fast and renders obsolete old paradigms for, e.g. data access, privacy, and transparency. AI may bring many positive consequences in schools — not only for individuals, or teachers, but for the educational system as a whole. On the other hand, there are also serious risks. Thus, the analysis of the educational uses of AI in future schools pushes us to compare the possible benefits (for example, using AI-based tools for supporting different learners) with the possible risks (for example, the danger of algorithmic manipulation, or a danger of hidden algorithmic discrimination). Practical solutions are many, for example the Solid protocol by Tim Berners-Lee, but are often conceived as solutions to single problems, with limited application. We describe a thought experiment: "education as a massively multiplayer social online game". Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player: which is a role designed to maximise protection and benefit for human players. AI models that understand the game space provide an API for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with the game space. Our thought experiment clarifies the steep challenges, and also the opportunity, of AI in education.Peer reviewe

Temporal Pattern Models for Physiological Arousal During a Steering Task

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