Estimating Latent Attentional States Based on Simultaneous Binary and Continuous Behavioral Measures

Cognition is a complex and dynamic process. It is an essential goal to estimate latent attentional states based on behavioral measures in many sequences of behavioral tasks. Here, we propose a probabilistic modeling and inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed model extends the standard hidden Markov model (HMM) by explicitly modeling the state duration distribution, which yields a special example of the hidden semi-Markov model (HSMM). We validate our methods using computer simulations and experimental data. In computer simulations, we systematically investigate the impacts of model mismatch and the latency distribution. For the experimental data collected from a rodent visual detection task, we validate the results with predictive log-likelihood. Our work is useful for many behavioral neuroscience experiments, where the common goal is to infer the discrete (binary or multinomial) state sequences from multiple behavioral measures

Process-oriented intelligence research: A review from the cognitive perspective

Despite over a century of research on intelligence, the cognitive processes underlying intelligent behavior are still unclear. In this review, we summarize empirical results investigating the contribution of cognitive processes associated with working memory capacity, processing speed, and executive processes to intelligence differences. Specifically, we (a) evaluate how cognitive processes associated with the three different cognitive domains have been measured, and (b) how these processes are related to individual differences in intelligence. Consistently, this review illustrates that isolating single cognitive processes using average performance in cognitive tasks is hardly possible. Instead, formal models that implement theories of cognitive processes underlying performance in different cognitive tasks may provide more adequate indicators of single cognitive processes. Therefore, we outlined which models for working memory capacity, processing speed, and executive processes may provide more specific insights into cognitive processes associated with individual differences in intelligence. Finally, we discuss implications of a process-oriented intelligence research using cognitive measurement models for psy- chometric theories of intelligence and argue that a model-based approach might overcome validity problems of traditional intelligence theories

The Effects of Chronic Sleep Deprivation on Sustained Attention: A Study of Brain Dynamic Functional Connectivity

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks\u27 adaptability, resulting in the disrupted dominance of small-world brain networks

Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR

A Preliminary Investigation of the Validity of Time-Based Measures of Sustained Attention for Children

This study is a preliminary investigation of the validity of using time-based measures to quantify sustained attention in children ages 6-12. Problems with sustained attention negatively affect childhood learning and development. The prevalence of disorders known to impact sustained attention performance continue to rise in the United States. Currently, commercially available, objective measures of sustained attention use normative comparisons that provide limited information about the effect such problems have on child performance in natural settings. We reviewed test data from 290 charts of children ages 6-12 referred for neuropsychological evaluation. The Test of Everyday Attention for Children (TEA-Ch) is an ecologically oriented measure of attention; however, the test provides only normative data about child sustained attention. We examined the validity of two time-based scores derived from the Code Transmission subtest of the TEA-Ch. The Code Transmission Time on Task (CT-TOT) estimates the total time a child spends processing the subtest stimulus and the Code Transmission Longest Duration (CT-LD) estimates the maximum duration of a child\u27s sustained attention before an attentional lapse. We correlated CT-TOT and CT-LD scores with age, criterion sustained attention measures from the TEA-Ch, and a measure of intelligence. Analysis of the data revealed significant differences in performance on the time-based measures by age-band. Correlations reached significance for both measures with the four criterion measures, with the CT-TOT achieving higher correlations with all criterion measures. Correlations were non-significant between both measures and intelligence. Overall, the findings of the present study suggest that the CT-TOT may provide additional, valid performance-based information about childrens\u27 sustained attention that, to date, is missing from any commercially available measure of sustained attention for children. The electronic version of this dissertation is available in the open-access Ohiolink ETD Center www.ohiolink.edu/et

Contributions to statistical analysis methods for neural spiking activity

With the technical advances in neuroscience experiments in the past few decades, we have seen a massive expansion in our ability to record neural activity. These advances enable neuroscientists to analyze more complex neural coding and communication properties, and at the same time, raise new challenges for analyzing neural spiking data, which keeps growing in scale, dimension, and complexity. This thesis proposes several new statistical methods that advance statistical analysis approaches for neural spiking data, including sequential Monte Carlo (SMC) methods for efficient estimation of neural dynamics from membrane potential threshold crossings, state-space models using multimodal observation processes, and goodness-of-fit analysis methods for neural marked point process models. In a first project, we derive a set of iterative formulas that enable us to simulate trajectories from stochastic, dynamic neural spiking models that are consistent with a set of spike time observations. We develop a SMC method to simultaneously estimate the parameters of the model and the unobserved dynamic variables from spike train data. We investigate the performance of this approach on a leaky integrate-and-fire model. In another project, we define a semi-latent state-space model to estimate information related to the phenomenon of hippocampal replay. Replay is a recently discovered phenomenon where patterns of hippocampal spiking activity that typically occur during exploration of an environment are reactivated when an animal is at rest. This reactivation is accompanied by high frequency oscillations in hippocampal local field potentials. However, methods to define replay mathematically remain undeveloped. In this project, we construct a novel state-space model that enables us to identify whether replay is occurring, and if so to estimate the movement trajectories consistent with the observed neural activity, and to categorize the content of each event. The state-space model integrates information from the spiking activity from the hippocampal population, the rhythms in the local field potential, and the rat's movement behavior. Finally, we develop a new, general time-rescaling theorem for marked point processes, and use this to develop a general goodness-of-fit framework for neural population spiking models. We investigate this approach through simulation and a real data application

Reinforcement Learning Describes the Computational and Neural Processes Underlying Flexible Learning of Values and Attentional Selection

Attention and learning are cognitive control processes that are closely related. This thesis investigates this inter-relatedness by using computational models to describe the mechanisms that are shared between these processes. Computational models describe the transformation of stimuli to observable variables (behaviour) and contain the latent mechanisms that affect this transformation. Here, I captured these mechanisms with the reinforcement learning (RL) framework applied in two different task contexts and three different projects to show 1) how attentional selection of stimuli involves the learning of values for stimuli, 2) how the learning of stimulus values is influenced by previously learned rules, and 3) how explorations of value-related mechanisms in the brain benefit from using intracranial EEG to investigate the strength of oscillatory activity in ventromedial prefrontal cortex. In the first project, the RL framework is applied to a feature-based attention task that required macaques to learn the value of stimulus features while ignoring non-relevant information. By comparing different RL schemes I found that trial-by-trial covert attentional selections were best predicted by a model that only represents expected values for the task relevant feature dimension. In the second project, I explore mechanisms of stimulus-feature value learning in humans in order to understand the influence of learned rules for the flexible, on-going learning of expected values. I test the hypothesis that naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type. I found that two-thirds of subjects (n=22/32) exhibited behaviour that was best fit by a ‘flexible-rule-selection’ model. Low-frequency oscillatory activity in frontal cortex has been associated with cognitive control and integrative brain functions, however, the relationship between expected values for stimuli and band-limited, rhythmic neural activity in the human brain is largely unknown. In the third project, I used intracranial electrocorticography (ECoG) in a proof-of-principle study to reveal spectral power signatures in vmPFC related to the expected values of stimuli predicted by a RL model for a single human subject

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal