Entraînement à la prise de décision avec un serious gaming : délibération et conflits de valeurs

This research analyzes the use of serious gaming (simulator of helicopter) in a management training program for executives. We have observed 16 game sessions and interviewed 44 volunteer participants from the same company through this training program. We have identified few sparring matches between executives, mostly concerning the tasks commanded. The discussion is based on the “how to do” and not on the pursued purposes even when the instructions are ethically questionable. From the theoretical and methodological frame of the psychosociology of the work perspective (Lhuilier, 2013), our analysis highlighted the lack even the absence of collective deliberation before the decision-making process and their modalities in game experience and in real work, after the training program.     

The Incomplete Tyranny of Dynamic Stimuli: Gaze Similarity Predicts Response Similarity in Screen-Captured Instructional Videos

Although eye tracking has been used extensively to assess cognitions for static stimuli, recent research suggests that the link between gaze and cognition may be more tenuous for dynamic stimuli such as videos. Part of the difficulty in convincingly linking gaze with cognition is that in dynamic stimuli, gaze position is strongly influenced by exogenous cues such as object motion. However, tests of the gaze-cognition link in dynamic stimuli have been done on only a limited range of stimuli often characterized by highly organized motion. Also, analyses of cognitive contrasts between participants have been mostly been limited to categorical contrasts among small numbers of participants that may have limited the power to observe more subtle influences. We, therefore, tested for cognitive influences on gaze for screen-captured instructional videos, the contents of which participants were tested on. Between-participant scanpath similarity predicted between-participant similarity in responses on test questions, but with imperfect consistency across videos. We also observed that basic gaze parameters and measures of attention to centers of interest only inconsistently predicted learning, and that correlations between gaze and centers of interest defined by other-participant gaze and cursor movement did not predict learning. It, therefore, appears that the search for eye movement indices of cognition during dynamic naturalistic stimuli may be fruitful, but we also agree that the tyranny of dynamic stimuli is real, and that links between eye movements and cognition are highly dependent on task and stimulus properties.     

A Slippery Myth: How Learning Style Beliefs Shape Reasoning about Multimodal Instruction and Related Scientific Evidence

The learning style myth is a commonly held myth that matching instruction to a student's “learning style” will result in improved learning, while providing mismatched instruction will result in suboptimal learning. The present study used a short online reasoning exercise about the efficacy of multimodal instruction to investigate the nature of learning styles beliefs. We aimed to: understand how learning style beliefs interact with beliefs about multimodal learning; characterize the potential complexity of learning style beliefs and understand how this short exercise might influence endorsements of learning styles. Many participants who believed in the learning style myth supported the efficacy of multimodal learning, and many were willing to revise their belief in the myth after the exercise. Personal experiences and worldviews were commonly cited as reasons for maintaining beliefs in learning styles. Findings reveal the complexity of learning style beliefs, and how they interact with evidence in previously undocumented ways.     

Taking a naïve other's perspective to debias the hindsight bias: Did it backfire?

Hindsight bias is a phenomenon that occurs when outcome knowledge interferes with the ability to accurately recall judgments made in a previous, naïve state. Also known as the “knew it all along” bias, we aimed to diminish the bias by having individuals take the perspective of a naïve other, as a way of encouraging acceptance that they had, in fact, not known it all along. Adult participants were given blurry-to-clear images incrementally until they were able to identify the object and were then re-presented with the same sequence of images and asked to make a judgment about when they had identified the item correctly the first time. They were also asked to judge when they thought a naïve peer (Experiments 1 and 2), or a naïve child (Experiment 2) could identify the objects. Results showed a robust hindsight bias in all perspectives, and sporadic success at eliminating the bias. When taking the perspective of a naïve peer, there were failures and successes; when taking the perspective of a naïve child, there was an ultra-debiasing, or a reverse hindsight bias. However, did the manipulation backfire? We conclude that while the manipulation of thinking like a naïve child may have eliminated the bias, participants seemed to use an “adults know best” rule rather than accepting past naivete for themselves.     

Future avenues for education and neuroenhancement

Education, in epistemological terms, presents an arena in which the development of knowledge and its transmission frame fundamentally distinct assignments: in social contexts knowledge exchange is prioritised to equip individuals as knowers; in theoretical contexts, an understanding of knowing as a state of mind is the primary objective. Distinguishing between education and its institutionalised product –the school–, this paper reviews the notion that today's modern classroom settings limit the applicability of neuroscience research by focusing on equipping individuals as knowers. In contrast, educational research has become increasingly grounded in brain sciences, with future premises examining artificial intelligence technologies (AI) targeted at enhancing learning and memory based on an understanding of the nature of knowing. Taking into account this bifurcation between education and school, this paper examines how the paths of educational, neuroscience, and AI research cross beyond school-enabled learning. This is contemplated across three sections. The first section examines the historical basis of the bifurcation between education and schooling. The second reviews the potential impacts of current neuroscience knowledge on educational implementations. The third discusses how and why neuroscience and AI technologies could be pertinent to educational discourse in the future.     

Educational gains of in-person vs. distance learning in primary and secondary schools: A natural experiment during the COVID-19 pandemic school closures in Switzerland

Using data from a computer-based formative feedback system, we compare learning gains in the 8 weeks of school closures related to the COVID-19 pandemic in Switzerland with learning gains in the 8 weeks before these school closures. The school performance in mathematics and language of N = 28,685 pupils is modelled in second-order piecewise latent growth models with strict measurement invariance for the two periods under investigation. While secondary school pupils remain largely unaffected by the school closures in terms of learning gains, for primary school pupils learning slows down and at the same time interindividual variance in learning gains increases. Distance learning arrangements seem an effective means to substitute for in-person learning, at least in an emergency situation, but not all pupils benefit to the same degree.     

Naïve and Robust: Class‐Conditional Independence in Human Classification Learning

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class‐conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence‐independence structure and category learning model, DISC‐LM) that incorporates varying degrees of prior belief in class‐conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class‐conditional independence, before adapting to the true environmental structure. Class‐conditional independence may be a strong and useful default assumption in category learning tasks.     

Neural Policy Style Transfer

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST)¹ algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.     

Mechanisms of developmental change in infant categorization

Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.