A Thesis Submitted for the Degree of Doctor of Philosophy at the University of Queensland in 2019 School of Psychology

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A Thesis Submitted for the Degree of Doctor of Philosophy at the University of Queensland in 2019 School of Psychology The Causal Neural Substrates of Statistical Learning Abbey S. Nydam Bachelor of Psychological Science (Hons 1) A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2019 School of Psychology Abstract Although humans often struggle to make rational decisions based on probability, there are types of information processing that appear highly sensitive to statistics. In fact, the human brain can extract information about complex statistical probabilities present in the environment through an automatic statistical learning process. Statistical learning is the primary way that human infants, with no developed frontal cortex or reasoing skills, can learn the complexities of language and grammar. As adults, statistical learning enables us to benefit from the many repetitive and predictable elements of our sensory environment, such as visual scenes or event sequences. Statistical knowledge is often implicit, but it can be used by the explicit system to facilitate other cognitive processes; such as decision-making, skill learning, and social capabilities. The reach of statistical learning across the cognitive landscape is broad, and this brings into question the nature of the neural substrates that govern this process. Neuroscientific research supports there being a critical role of the hippocampus, as it relates to long-term memory. But neuroimaging work suggests a role of higher-level cortical regions, within the frontal and parietal cortices. These areas are more commonly associated with explicit executive functions, and less so for implicit learning abilities. Recent advances in non-invasive brain stimulation allow is to directly control cortical brain acitvity in order to unearth causal relationships between the brain and behaviour. To date, there is little causal evidence regarding the neural substrates of statistical learning outside the hippocampus. In this thesis, I investigate how the direct manipulation of local activity in cortical brain regions is related to the incidental statistical learning process. Chapter 2 examines task parametres that produce the most robust statistical learning behaviour using a well-known contextual cuing paradigm (Chun & Jiang, 1998). In this task, observers search through visual scenes containing statistical regularities. They typically have around 2000 ms to scan the displays. To investigate the speed of statistical processing, I briefly flashed displays for only 300ms and controlled further processing with backward masking. In a series of experiments, I found that learning could occur with briefly presented displays, but only for a reduced amount of information. These findings illustrate an efficient learning mechanism that can operate rapidly, but may be capacity limited at these speeds. In Chapter 3 I report a large-scale tDCS study (n = 120) investigating the causal relationship between cortical activity and statistical learning. I compare bidirectional currents (i.e., anodal and cathodal) and two active brain regions (i.e., left prefrontal cortex and left ii posterior parietal cortex) to a sham stimulation condition - a placebo control. The contextual cuing task was used to tap the statistical learning process, and the stimulation was delivered online, during exposure to regularities. Cathodal currents selectively affected learning, compared to sham and anodal currentsm and this was for both the frontal and pareital target regions. Specifically, cathodal stimulation disrupted the early cuing benefit, suggesting that stimulation delayed learning or the effects of learning on behaviour, but did not abolish it altogether. The effect was not explained by a general change in response times, a change in awareness or a speed/accuracy tradeoff. Instead, the impact of tDCS on performance was specific to the learning effect over time. This revealed a causal link between activity in frontoparietal areas and the evolution of statistical learning in this task. Chapter 4 investigates the causal relationship using a different task believed to tap the same underlying process. Known as Visual Statistical Learning (Fiser & Aslin, 2001), this paradigm involves passive visual exposure to shapes with embeded probabilities, and learning is measured offline, in a recognition test after exposure. I focus on the cathodal effect, and compare stimulation of two brain regions (i.e., left posterior parietal cortex and left orbitofrontal cortex) to sham. Using the offline measure, I found no effect of stimulation (Experiment 1). This was despite having a large sample (n = 150) and observing robust learning at test. In Experiment 2, I develop an online measure of learning to investigate effects of stimulation that may be dynamic across time. Using a double-blind, pre-registerred design, I compare active (cathodal) stimulation to sham (n = 80) and observe that tDCS did influence statistical learning. As with chapter 3, the effect was selective to the early time- window. These findings provide converging evidence on the intervening role that cortical brain areas play in a visual statistical learning process. The empiricle studies provide novel insights into the causal relationship that cortical areas have in producing statistical learning behaviours. The observation of direct cortical involvement has implications for theoretical accounts of statistical learning; and is consistent with the idea that statistical learning should be operationalised as a principal of processing similar to the Bayesian brain hypothesis. The work presented in this thesis broadens our understanding of the neural substrates that support implicit learning abilities, particularly those that involve extracting regularities from the visual scene and using them for implicit prediction. iii Declaration by author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis. I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award. I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School. I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis. iv Publications included in this thesis Nydam, A, S., Sewell., D. S., & Dux, P. E. (2018). Cathodal Electrical Stimulation of Frontoparietal Cortex Disrupts Statistical Learning of Visual Configural Information. Cortex. 10.1016/j.cortex.2017.11.008. Submitted manuscripts included in this thesis Nydam, A, S., Sewell., D. S., & Dux, P. E. (Submitted). The Speed of Processing Contextual Regularities for Learning. Nydam, A, S., Sewell., D. S., & Dux, P. E. (Submitted). Cortical Involvement in Visual Statistical Learning with Identity Structure. Other publications during candidature Peer-Reviewed Papers Sale, M. V., Nydam, A. S., & Mattingley, J. B. (2017). Stimulus Uncertainty Enhances Long- term Potentiation-like Plasticity in Human Motor Cortex. Cortex, 88, 32–41. Kamke, M. R., Nydam, A. S., Sale, M. V., & Mattingley, J. B. (2016). Associative Plasticity in the Human Motor Cortex is Enhanced by Concurrently Targeting Separate Muscle Representations with Excitatory and Inhibitory Protocols. Journal of Neurophysiology, 115(4), 2191–2198. v Conference Abstracts Nydam, A. S., Sewell, D. S., & Dux, P. E. (Nov 2018). Cortical Involvement in Visual Statistical Learning of Shape Identities. Australian Cognitive Neuroscience Society Conference, Melbourne, Australia. Nydam, A. S., Sewell, D.S., & Dux, P. E. (Aug 2017). Cathodal electrical stimulation disrupts incidental learning of visual configural information. International Conference on Cognitive Neuroscience (ICON), Amsterdam, Netherlands. Nydam, A. S., Sewell, D. S., & Dux, P. E. (Sep 2017). Cathodal electrical stimulation disrupts incidental learning of visual configural information, Science of Learning Research Centre Conference, Brisbane, Australia. Nydam, A. S., Sewell, D. S., & Dux, P. E. (Jul 2017). Cathodal electrical stimulation disrupts incidental learning of visual configural information. Model-based Neuroscience Summer School, Amsterdam, Netherlands. Nydam, A. S., Sewell, D. S., & Dux, P. E. (Nov 2016). Statistical Learning of Irrelevant Visual Information is Disrupted by Electrical Stimulation of Frontoparietal
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