<p> Attention deficit hyperactivity disorder (ADHD) is one of the most challenging children&rsquo;s public health concerns today. Children diagnosed with ADHD struggle more academically and are at a significant risk of lower academic achievement, increased grade-level retention, and additional diagnoses of learning disabilities. Symptoms of ADHD primarily arise from deficits in specific executive function (EF) domains, one of which is working memory (WM). Children diagnosed are impaired on tasks that specifically measure WM capacity and short-term visuo-spatial memory. In this study, four fifth-grade students diagnosed with ADHD were administered a variety of assessments. WM was measured through a math vocabulary recall, visuo-spatial WM via a computerized Corsi Block Tapping Test, and WM capacity was assessed through an Operation Span Task. In addition, on-task behavior was determined using the partial interval recording process with overall mathematical skill based knowledge being evaluated through a pre and post assessment. Using the ABAB Withdrawal Single-Case Research Design, a 10-min intervention of short burst high intensity exercise was introduced. Participants were assessed each session (daily) and exhibited improvement on all measurements during the intervention conditions of the study. </p><p> The results suggest that a vigorous 10-min daily regime of short-burst-high-intensity exercise improves the working memory and on-task behavior of preadolescent children diagnosed with ADHD.</p>

<p> Vectors, defined by distance and direction information, can represent the spatial relationships between reference objects and target objects. Reference boundaries help to define the space and are mathematically definable by lines, while reference landmarks define specific locations and are definable by points. How do vectors, containing two sources of information relate references and targets? Congruent with neuroscientific evidence we argued that humans rely differentially on distance and direction information when recalling the spatial location of objects. We showed that direction information was better encoded or remembered than distance information relative to landmarks, and that distance information was better encoded or remembered than direction information relative to boundaries. We proposed that the type of reference influences the fidelity of distance and direction information in the spatial representation. </p><p>

<p> Covert attention allows us to prioritize relevant objects from the visual environment without directing our gaze towards them. Attention affects the quality of perceptual representations, quality which can be quantified with precision (or its inverse, variability) parameters in simple psychophysical models that capture the relationship between stimulus strength and an observer&rsquo;s behavior. Two main types of attention, divided and selective, have been studied in the recent decades with two corresponding classic paradigms, visual search and visual spatial orienting. </p><p> In this thesis, we developed variants of these tasks to address questions related to visual attention, in neurotypicals and ADHD. In addition to precision-related parameters derived from behavior, we measured the observers&rsquo; fixational eye movements, developed a new algorithm to detect microsaccades and explored their possible role as an oculomotor correlate of precision. </p><p> In a first investigation, we built upon a paradigm designed to increase the chances of probing divided attention. Specifically, we extended a visual search task with heterogenous distractors and explored the effects on performance of set size, task&mdash;detection and localization, time (perception and memory) and space. An optimal observer model with a variable precision encoding stage and an optimal decision rule was able to capture behavior in a task more naturalistic than target detection, namely target localization. Performance decreased with the set size of the search array for both detection and localization; so did precision. As expected, precision was higher in the perception condition relative to the memory condition. We found the same pattern of results with visual search arrays with reduced stimulus spacing; observers achieved comparable precision parameters, albeit with increased reaction times. </p><p> The nature of the attentional impairment in ADHD has been elusive. By using a new task that combines visuo-spatial orienting with feature dimension switch between orientation and color, we found an increased perceptual variability parameter in the ADHD group, which was correlated with an executive control metric. A classifier based on perceptual variability yielded high diagnosis accuracy. These results suggest that using basic psychophysical paradigms to capture encoding precision of low-level features deserves further study in ADHD, especially in conjunction with attention and executive function. </p><p> Measures of covert attention have included aspects of fixational eye movements, especially microsaccades. Inferences about the roles of microsaccades in perception and cognition depend on accurate detection algorithms. By using a new hidden semi-Markov model to capture sequences of microsaccades amongst drift and an inference algorithm based on this model, we found that microsaccades were more robustly detected under high measurement noise from the eye tracker. Applying this algorithm to the eye movement traces of ADHD and Control participants, we found a correlation between post-stimulus microsaccade rate and the perceptual variability parameter, suggesting a potential oculomotor mechanism for the less precise perceptual encoding in ADHD. </p><p> We conclude that by using and developing variants of visual attention paradigms, psychophysical models and oculomotor measurements, we can enhance our understanding about the brain processes in health and disease.</p><p>

<p> Understanding the relationship between sets of objects is a fundamental requirement of cognitive skills, such as learning from example or generalization. For example, recognizing that planets revolve around stars, and not the other way around, is essential for understanding astronomical systems. However, the method by which we recognize and apply such relations is not clearly understood. In particular, how a set of neurons is able to represent which object fulfills which role (role binding), presented difficulty in past studies. Here, we propose a systems-level model, which utilizes selective attention and working memory, to address issues of role binding. In our model, selective attention is used to perceive visual stimuli such that all relations can be reframed as an operation from one object unto another, and so binding becomes an issue only in the initial recognition of the direction of the relation. We test and refine this model, utilizing EEG during a second-order relational reasoning task. Epoched EEG was projected to the cortical surface, providing sourcespace estimates of event related potentials. Permutation testing revealed 8 cortical clusters which responded differentially based on the specifics of a trial. Dynamic connectivity between these clusters was estimated with the directed transfer function, to reveal the dynamic causality between regions. Our results support the model, identifying a distinct bottom-up network that identifies relations between single pairs of objects, along with a top-down biasing network that may reorient attention to sequential pairs of objects. Taken together, our results show that relational reasoning can be performed by a distributed network, utilizing selective attention</p><p>

Temporally selective attention allows for the preferential processing of stimuli presented at particular times, and is reasoned to be important for processing rapidly presented information such as speech. Recent event-related potential (ERP) evidence demonstrates that listeners direct temporally selective attention to times that contain word onsets in speech. This may be an effective listening strategy since these moments provide critical information to the listener, but the mechanism that underlies this process remains unexplored. In three experiments, putative attention cues including word recognition and predictability were manipulated in both artificial and natural speech and ERP responses at various times were compared to determine how listeners selectively process word onsets in speech. The results demonstrate that listeners allocate attention to word-initial segments because they are less predictable than other times in the speech stream. Attending to unpredictable moments may improve spoken language comprehension by allowing listeners to glean the most relevant information from an otherwise overwhelming speech signal.

Dynamic Attending Theory predicts that attention is allocated hierarchically across time during processing of hierarchically structured rhythms. Event-related potential (ERP) research demonstrates that attention to a moment in time modulates early auditory processing as evidenced by the amplitude of the first negative peak (N1) approximately 100 ms after sound onset. Four experiments were designed to test the hypothesis that hierarchically structured rhythms result in a hierarchical allocation of attention across time by comparing behavioral responses and N1 amplitudes for sounds presented at times of varying hierarchical strength. Specifically, ERPs elicited by tones presented at times of high and low strength were compared in short melodies (Experiment 2) of salient metric structure (Experiment 1), and in subjective metric hierarchies (Experiments 3 and 4). Experiment 4 also added a level of medium strength in a subjective metric hierarchy. A more negative N1 was observed for metrically strong beats compared to metrically weak beats under nearly all conditions in Experiments 2, 3 and 4, providing strong evidence that attention is allocated preferentially to hierarchically strong times and supporting the central hypothesis. This effect was evident for both stimulus-inherent and listener-imposed metric structure, suggesting it represents ongoing direction of attention to metrically strong times rather than establishment of a metric percept. A patterned distribution of N1 amplitude was evident among metrically weaker times, demonstrating that attention is not allocated to the strongest times in an all-or-none manner. However, this pattern was not fully hierarchical, suggesting that hierarchical rhythmic structure does not modulate early auditory processing in a one-to-one manner. Additionally, a late negativity and late positivity were associated with metric strength under some conditions, indicating that multiple cognitive processes are associated with metric perception. Interestingly, the primary finding of a more negative N1 for sounds presented at hierarchically strong times in musical and pseudomusical stimuli was not modulated by musical expertise, suggesting that it indexes the use of a more general cognitive process that may also be employed to efficiently process other complex auditory streams including speech.

Two interesting properties of the distribution of spatially selective attention have been noted in the behavioral and electrophysiological literature. First, there is a graded field of attention that expands from the center of the attended area. Second, the size of the attended area can be adjusted to be either larger or smaller in order to match the demands of the current task. Five event-related potential (ERP) studies are presented that extend these findings in several important ways; 1) The time frame of these two distribution properties is different. Results are consistent with a two stage model of spatial attention in which visual processing is initially enhanced for all stimuli presented near the center of the attended area as indexed by the amplitude of the first negative peak in the waveform (N1). Subsequently, the effects of narrowing or expanding the attentional field to the relevant size affects visual processing as indexed by the amplitude of the second negative peak (N2). 2) Object boundaries had limited impact on either the spread of the initial gradient of spatial selection or the scale of attention. 3) When selecting visual stimuli for attentive processing based on features such as color and size there is also a gradient of facilitation, but the impact of this graded selection on visual processing is not observed until later in processing, and is indexed by the amplitude of the selection negativity (SN). Furthermore, similar to the lack of interaction between object boundaries and the range of cued locations, the gradients of feature-based selection are not affected by the range of cued features.

<p> Research on reward-driven learning has produced and substantiated theories of model-free and model-based reinforcement learning (RL), which respectively explain how humans and animals learn reflexive habits and build prospective plans. A highly developed line of work has unearthed the role of striatal dopamine in model-free learning, while the prefrontal cortex (PFC) appears to critically subserve model-based learning. The recent theory of meta-reinforcement learning (meta-RL) explained a wide array of findings by positing that the model-free dopaminergic reward prediction error trains the recurrent prefrontal network to execute arbitrary RL algorithms&mdash;including model-based RL&mdash;in its activations. </p><p> In parallel, a nascent understanding of a third reinforcement learning system is emerging: a non-parametric system that stores memory traces of individual experiences rather than aggregate statistics. Research on such <i>episodic learning</i> has revealed its unmistakeable traces in human behavior, developed theory to articulate algorithms underlying that behavior, and pursued the contention that the hippocampus is centrally involved. These developments lead to a set of open questions about (1) how the neural mechanisms of episodic learning relate to those underlying incremental model-free and model-based learning and (2) how the brain arbitrates among the contributions of this abundance of valuation strategies. </p><p> This thesis extends meta-RL to provide an account for episodic learning, incremental learning, and the coordination between them. In this theory of episodic meta-RL (EMRL), episodic memory reinstates activations in the prefrontal network based on contextual similarity, after passing them through a learned gating mechanism (Chapters 1 and 2). In simulation, EMRL can solve episodic contextual water maze navigation problems and episodic contextual bandit problems, including those with Omniglot class contexts and others with compositional structure (Chapter 3). Further, EMRL reproduces episodic model-based RL and its coordination with incremental model-based RL on the episodic two-step task (Vikbladh et al., 2017; Chapter 4). Chapter 5 discusses more biologically detailed extensions to EMRL, and Chapter 6 analyzes EMRL with respect to a set of recent empirical findings. Chapter 7 discusses EMRL in the context of various topics in neuroscience.</p><p>

<p> Bad decisions can have devastating consequences, and there is a vast body of literature suggesting that human judgment and decision-making are riddled with numerous systematic violations of the rules of logic, probability theory, and expected utility theory. The discovery of these <i>cognitive biases</i> in the 1970s challenged the concept of Homo sapiens as the rational animal and has profoundly shaken the foundations of economics and rational models in the cognitive, neural, and social sciences. Four decades later, these disciplines still lack a rigorous theoretical foundation that can account for people&rsquo;s cognitive biases. Furthermore, designing effective interventions to remedy cognitive biases and improve human judgment and decision-making is still an art rather than a science. I address these two fundamental problems in the first and the second part of my thesis respectively. </p><p> To develop a theoretical framework that can account for cognitive biases, I start from the assumption that human cognition is fundamentally constrained by limited time and the human brain&rsquo;s finite computational resources. Based on this assumption, I redefine human rationality as reasoning and deciding according to cognitive strategies that make the best possible use of the mind&rsquo;s limited resources. I draw on the bounded optimality framework developed in the artificial intelligence literature to translate this definition into a mathematically precise theory of bounded rationality called <i>resource-rationality </i> and a new paradigm for cognitive modeling called <i>resource-rational analysis</i>. Applying this methodology allowed me to derive resource-rational models of judgment and decisionmaking that accurately capture a wide range of cognitive biases, including the anchoring bias and the numerous availability biases in memory recall, judgment, and decision-making. By showing that these phenomena and the heuristics that generate them are consistent with the rational use of limited resources, my analysis provides a rational reinterpretation of cognitive biases that were once interpreted as hallmarks of human irrationality. This suggests that it is time to revisit the debate about human rationality with the more realistic normative standard of resource-rationality. To enable a systematic assessment of the extent to which human cognition is resource- rational, I present an automatic method for deriving resource-rational heuristics from a mathematical specification of their function and the mind&rsquo;s computational constraints. Applying this method to multi-alternative risky-choice led to the discovery of a previously unknown heuristic that people appear to use very frequently. Evaluating human decision-making against resource-rational heuristics suggested that, on average, human decision-making is at most 88% as resource-rational as it could be. </p><p> Since people are equipped with multiple heuristics, a complete normative theory of bounded rationality also has to answer the question of when each of these heuristics should be used. I address this question with a rational theory of strategy selection. According to this theory, people gradually learn to select the heuristic with the best possible speed-accuracy trade-off by building a predictive model of its performance. Experiments testing this model confirmed that people gradually learn to make increasingly more rational use of their finite time and bounded cognitive resources through a metacognitive reinforcement learning mechanism. </p><p> Overall, these findings suggest that&mdash;contrary to the bleak picture painted by previous research on heuristics and biases&mdash;human cognition is not fundamentally irrational, and can be understood as making rational use of bounded cognitive resources. By reconciling rationality with cognitive biases and bounded resources, this line of research addresses fundamental problems of previous rational modeling frameworks, such as expected utility theory, logic, and probability theory. Resource-rationality might thus come to replace classical notions of rationality as a theoretical foundation for modeling human judgment and decision-making in economics, psychology, neuroscience, and other cognitive and social sciences. </p><p> In the second part of my dissertation, I apply the principle of resource-rationality to develop tools and interventions for improving the human mind. Early interventions educated people about cognitive biases and taught them the normative principles of logic, probability theory, and expected utility theory. The practical benefits of such interventions are limited because the computational demands of applying them to the complex problems people face in everyday life far exceed individuals&rsquo; cognitive capacities. Instead, the principle of resource-rationality suggests that people should rely on simple, computationally efficient heuristics that are well adapted to the structure of their environments. Building on this idea, I leverage the automatic strategy discovery method and insights into metacognitive learning from the first part of my dissertation to develop intelligent systems that teach people resource-rational cognitive strategies. I illustrate this approach by developing and evaluating a cognitive tutor that trains people to plan resource-rationally. My results show that practicing with the cognitive tutor improves people&rsquo;s planning strategies significantly more than does practicing without feedback. (Abstract shortened by ProQuest.)</p><p>

<p> When someone hears regular, periodic sounds, such as drum beats, footsteps, or stressed syllables in speech, these individual stimuli tend to be grouped into a perceived rhythm. One of the hallmarks of rhythm perception is that the listener generates expectations for the timing of upcoming stimuli, which theorists have described as endogenous periodic modulations of attention around the time of anticipated sounds. By constructing an internal representation of a rhythm, perceptual processes can be augmented by proactively deploying attention at the expected moment of an upcoming stressed syllable, the next step in an observed stride, or during the stroke of a co-speech hand gesture. A hypothetical benefit of this anticipatory allocation of attention is that it might facilitate temporal integration across the senses, binding multisensory aspects of our experiences into a unified &ldquo;now,&rdquo; anchored by temporally-precise auditory expectations. The current dissertation examines this hypothesis, exploring the effects of auditory singletons, and auditory rhythms, on electrophysiological indices of perception and attention to a visual stimulus, using the flash-lag paradigm. An electroencephalography study was conducted, where sounds, either isolated or presented rhythmically, occurred in alignment with a task-relevant visual flash. Results suggest a novel dissociation between the multisensory effects of discrete and rhythmic sounds on visual event perception, as assessed by the N1 component of the event-related potential, and by oscillatory power in the beta (15&ndash;20 Hz) frequency range. This dissociation is discussed in the context of classic and contemporary research on rhythm perception, temporal orienting, and temporal binding across the senses, and contributes to a more refined understanding of rhythmically-deployed attention. </p><p>