Symbiosis, Parasitism and Bilingual Cognitive Control: A Neuroemergentist Perspective

Interest in the intersection between bilingualism and cognitive control and accessibility to neuroimaging methods has resulted in numerous studies with a variety of interpretations of the bilingual cognitive advantage. Neurocomputational Emergentism (or Neuroemergentism for short) is a new framework for understanding this relationship between bilingualism and cognitive control. This framework considers Emergence, in which two small elements are recombined in an interactive manner, yielding a non-linear effect. Added to this is the notion that Emergence can be captured in neural systems using computationally inspired models. This review poses that bilingualism and cognitive control, as examined through the Neuroemergentist framework, are interwoven through development and involve the non-linear growth of cognitive processing encompassing brain areas that combine and recombine, in symbiotic and parasitic ways, in order to handle more complex types of processing. The models that have sought to explain the neural substrates of bilingual cognitive differences will be discussed with a reinterpretation of the entire bilingual cognitive advantage within a Neuroemergentist framework incorporating its neural bases. It will conclude by discussing how this new Neuroemergentist approach alters our view of the effects of language experience on cognitive control. Avenues to move beyond the simple notion of a bilingual advantage or lack thereof will be proposed

Complying with norms. a neurocomputational exploration

The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in essence, the goal of my project. The triplet goes like this: Q: How can we make progress in our understanding of social norms and norm compliance? A: Adopting a neurocomputational framework is one effective way to make progress in our understanding of social norms and norm compliance. Q: What could the neurocomputational mechanism of social norm compliance be? A: The mechanism of norm compliance probably consists of Bayesian - Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What could information about this mechanism tell us about social norms and social norm compliance? A: Information about this mechanism tells us that: a1: Social norms are uncertainty-minimizing devices. a2: Social norm compliance is one trick that agents employ to interact coadaptively and smoothly in their social environment. Most of the existing treatments of norms and norm compliance (e.g. Bicchieri 2006; Binmore 1993; Elster 1989; Gintis 2010; Lewis 1969; Pettit 1990; Sugden 1986; Ullmann‐Margalit 1977) consist in what Cristina Bicchieri (2006) refers to as “rational reconstructions.” A rational reconstruction of the concept of social norm “specifies in which sense one may say that norms are rational, or compliance with a norm is rational” (Ibid., pp. 10-11). What sets my project apart from these types of treatments is that it aims, first and foremost, at providing a description of some core aspects of the mechanism of norm compliance. The single most original idea put forth in my project is to bring an alternative explanatory framework to bear on social norm compliance. This is the framework of computational cognitive neuroscience. The chapters of this thesis describe some ways in which central issues concerning social norms can be fruitfully addressed within a neurocomputational framework. In order to qualify and articulate the triplet above, my strategy consists firstly in laying down the beginnings of a model of the mechanism of norm compliance behaviour, and then zooming in on specific aspects of the model. Such a model, the chapters of this thesis argue, explains apparently important features of the psychology and neuroscience of norm compliance, and helps us to understand the nature of the social norms we live by

Dynamics of Mood and Cognition in Depression and Anxiety: a Computational Approach

Psychiatric conditions such as depression and anxiety have far-reaching and devastating effects worldwide. Recent decades have seen a rapid advance in mental health science, in our understanding of the underlying neuroscience and cognitive science, and in the development of novel treatment approaches. Nevertheless, the situation remains challenging. While many treatments have proven effective, their effectiveness remains limited. To enhance the effectiveness of these interventions and develop new therapies, a deeper understanding of the mechanisms of action - why and how they work - is necessary. This thesis describes work aiming directly at this, with the hope that investigating the changes that arise from efficacious interventions can aid in improving treatment outcomes. The thesis focuses on emotional and cognitive change processes in the treatment of de- pression and anxiety. It introduces a novel approach, which models the interplay between emotion self-reports, to understand the dynamics underlying mood maintenance and how they relate to a person’s psychological state and changes therein. We found stability and controllability features that distinguish patients with depression from healthy controls and are associated with self-reported depression scores. We applied control theory to under- stand how the environment can influence depressed and healthy states, by investigating the characteristics of externally elicited transitions between states. In a second study using the same modelling approach, we employed a new experimental design featuring a series of intense visual emotional stimuli paired with a randomized distancing intervention. This showed that a core psychotherapeutic intervention (distancing) influences both the intrinsic emotional processes and the responsiveness to external emotional inputs. Finally, along a third strand of work, we investigated the link between the effects of the SSRI ser- traline on reinforcement learning mechanisms and symptom improvement in a multi-site placebo-controlled randomized clinical trial acquiring the well-established Go/Nogo task. Our results suggest that both sertraline and anxiety are related to processing aversive re- inforcement. However, limitations in behavioral data quality highlighted the urgent need to develop robust tasks for clinical settings. In summary, this thesis uses dynamical systems to gain an understanding of the impact of depression on mood dynamics, and how they are altered through treatments such as psychotherapy. Furthermore, it suggests that antidepressants affect aversive reinforcement learning in realistic clinical settings, but also emphasizes the importance of a renewed focus on measurement properties for translation of basic science insights to clinical applications

Progression paths in children’s problem solving: The influence of dynamic testing, initial variability, and working memory

The current study investigated developmental trajectories of analogical reasoning performance of 104 7- and 8-year-old children. We employed a microgenetic research method and multilevel analysis to examine the influence of several background variables and experimental treatment on the children’s developmental trajectories. Our participants were divided into two treatment groups: repeated practice alone and repeated practice with training. Each child received an initial working memory assessment and was subsequently asked to solve figural analogies on each of several sessions. We examined children’s analogical problem-solving behavior and their subsequent verbal accounts of their employed solving processes. We also investigated the influence of verbal and visual–spatial working memory capacity and initial variability in strategy use on analogical reasoning development. Results indicated that children in both treatment groups improved but that gains were greater for those who had received training. Training also reduced the influence of children’s initial variability in the use of analogical strategies with the degree of improvement in reasoning largely unrelated to working memory capacity. Findings from this study demonstrate the value of a microgenetic research method and the use of multilevel analysis to examine inter- and intra-individual change in problem-solving processes

THERAPEUTIC VIDEO GAMES AND THE SIMULATION OF EXECUTIVE FUNCTION DEFICITS IN ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulty paying attention, impulsivity, and hyperactivity. Diagnosis of ADHD rose 42% from 2003–2004 to 2011–2012. In 2011, 3.5 million children were treated with drugs. Optimizing therapy can take a year, and may not be completely effective. A clinical trial is currently being conducted of a device/drug combination using the computer game Minecraft, to determine how certain activities affect executive function, working memory, and restraint in patients diagnosed with ADHD. The human subjects’ responses are being modeled using artificial neural networks (ANNs), an artificial intelligence method that can be utilized to interpret highly complex data. We propose using ANNs to optimize drug and Minecraft therapy for individual patients based on the initial NICHQ Vanderbilt assessment scores. We are applying ANNs in the development of computational models for executive function deficiencies in ADHD. These models will then be used to develop a therapeutic video game as a drug/device combination with stimulants for the treatment of ADHD symptoms in Fragile X Syndrome. As a first step towards the design of virtual subjects with executive function deficits, computational models of the core executive functions working memory and fluid intelligence were constructed. These models were combined to create healthy control and executive function-deficient virtual subjects, who performed a Time Management task simulation that required the use of their executive functions to complete. The preliminary working memory model utilized a convolutional neural network to identify handwritten digits from the MNIST dataset, and the fluid intelligence model utilized a basic recurrent neural network to produce sequences of integers in the range 1-9 that can be multiplied together to produce the number 12. A simplified Impulsivity function was also included in the virtual subject as a first step towards the future inclusion of the core executive function inhibition

Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features

A foveated image can be entirely reconstructed from a sparse set of samples distributed according to the retinal sensitivity of the human visual system, which rapidly decreases with increasing eccentricity. The use of Generative Adversarial Networks has recently been shown to be a promising solution for such a task, as they can successfully hallucinate missing image information. As in the case of other supervised learning approaches, the definition of the loss function and the training strategy heavily influence the quality of the output. In this work,we consider the problem of efficiently guiding thetraining of foveated reconstruction techniques such that they are more aware of the capabilities and limitations of the human visual system, and thus can reconstruct visually important image features. Our primary goal is to make the training procedure less sensitive to distortions that humans cannot detect and focus on penalizing perceptually important artifacts. Given the nature of GAN-based solutions, we focus on the sensitivity of human vision to hallucination in case of input samples with different densities. We propose psychophysical experiments, a dataset, and a procedure for training foveated image reconstruction. The proposed strategy renders the generator network flexible by penalizing only perceptually important deviations in the output. As a result, the method emphasized the recovery of perceptually important image features. We evaluated our strategy and compared it with alternative solutions by using a newly trained objective metric, a recent foveated video quality metric, and user experiments. Our evaluations revealed significant improvements in the perceived image reconstruction quality compared with the standard GAN-based training approach