In this thesis, I will explore the idea that choices between present, smaller value options and future, larger value options depend on how much individuals trust the future to deliver the reward. Due to this aspect of trust, the individual must build their estimate of trust based on information for their present environment and their future expectations. This estimate of future trust can change across different time points in the same environment (i.e., before and after a national election) and between environments in the same time point (i.e., between two countries experiencing different economic rates of change). In this set of presented experiments, I will explore the link between decision environment and delay discounting, as well as the relationship between the contents of future perception and delay discounting. These two experiments will test differences in delay discounting (a) across two economic systems (China and the U.S.), as well as (b) before and after a national election (2018 U.S. Midterms). The results of the different decision environments study show that the delay discounting rates are significantly different across the two countries, specifically within the framing of present and future. These differences are not explained by differences in culture effects or individual differences in personality traits, suggesting that difference in environment is driving the effect. The results from the Midterm election experiments show evidence for differences in delay discounting between political identities and income groups. There are also differences in how these two groups perceive the contents of their future before and after the election. Specifically with evidence that negative future projection corresponds with increased delay discounting. Overall, these experiments show that delay discounting can be affected by the way information is framed within an environment and how we expect our environments to change over time.

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<p dir="ltr">Metacontrol describes outsourcing cognitive control to environmental cues, allowing for efficient instantiation of appropriate cognitive control. While recent years have seen many studies characterizing metacontrol in behavioral terms, relatively little effort has been made to characterize the neural mechanisms underlying metacontrol. Thus, the current dissertation project aimed to investigate the neural systems and interactive features of metacontrol, more specifically, proactive and reactive metacontrol that exert context-appropriate control states in preparatory and just-in-time manners, respectively. Specifically, Study 1 employed a functional magnetic resonance imaging technique and identified brain regions activated under proactive and reactive metacontrol operations. Importantly, the study revealed that the two metacontrol modes were supported by distinct neural systems. Building on the premise that distinct neural systems supporting the two metacontrol modes would enable the concurrent operation of both modes, Study 2 tested whether a preparatory metacontrol mode, proactive metacontrol, can coexist with a just-in-time metacontrol mode, reactive metacontrol. Using electroencephalography, Study 2 revealed that the two metacontrol modes can operate in parallel. Lastly, extending Study 2, Study 3 investigated how proactive metacontrol interacts with reactive metacontrol. Study 3 observed that high preparatory control states during proactive metacontrol benefit reactive metacontrol. Specifically, reactive metacontrol was observed only when penalties were at stake, which promoted heightened preparatory control states. In summary, Study 1 demonstrated that the two metacontrol modes are not only operationally distinctive but also characterized by separate neural systems underlying them. This aligns with the observation from Study 2 showing that the parallel operation of two metacontrol modes is feasible. Lastly, Study 3 suggests that despite the two metacontrol modes being supported by distinct neural systems (as shown in Study 1), they are not entirely independent and can interact with each other. Collectively, these findings reveal the relationship between the two metacontrol modes and elucidate their intricate interactions, thereby deepening our understanding of the neurocognitive mechanisms underlying metacontrol.</p>

The representation of incoming information, goals and the flexible processing of these are required for cognitive control. Efficient mechanisms are needed to decide when it is important that novel information enters working memory (WM) and when these WM ‘gates’ have to be closed. Compared to neural foundations of maintaining information in WM, considerably less is known about what neural mechanisms underlie the representational dynamics during WM gating. Using different EEG analysis methods, we trace the path of mental representations along the human cortex during WM gate opening and closing. We show temporally nested representational dynamics during WM gate opening and closing depending on multiple independent neural activity profiles. These activity profiles are attributable to a ventral stream-prefrontal cortex processing cascade. The representational dynamics start in the ventral stream during WM gate opening and WM gate closing before prefrontal cortical regions are modulated. A regional specific activity profile is shown within the prefrontal cortex depending on whether WM gates are opened or closed, matching overarching concepts of prefrontal cortex functions. The study closes an essential conceptual gap detailing the neural dynamics underlying how mental representations drive the WM gate to open or close to enable WM functions such as updating and maintenance.

Some of the most important discoveries in cognitive neuroscience have come from recent innovations in experimental tools. Computational models that simulate human perception of environmental inputs have revealed the internal processes and features by which those inputs are learned and represented by the brain. We advance this line of work across two separate research studies in which we leveraged these models to both generate experimental task stimuli and make predictions about behavioral and neural responses to those stimuli. Chapter 1 details how nine language models were used to generate controversial sentence pairs for which two of the models disagreed about which sentence is more likely to occur. Human judgments about these sentence pairs were collected and compared to model preferences in order to identify model-specific pitfalls and provide a behavioral performance benchmark for future research. We found that transformer models GPT-2, RoBERTa and ELECTRA were most aligned with human judgments. Chapter 2 utilizes the GloVe model of semantic word vectors to generate a set of schematically structured poems comprising ten different topics whose specific temporal order was learned by a group of participants. The GloVe model was then used to investigate learning-induced changes in the spatial geometry of the representations of the topics across the cortex. A Hidden Markov Model was also used to measure neural event segmentation during poem listening. In both analyses we identified a consistent topography of learning-induced changes in the default mode network, which could be partially explained by the models.

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When searching for an object, we store a mental representation of the target, which guides our search through the use of attention. The effectiveness of this search guidance varies depending on the task and the relationship between target and distractors. With a better understanding of how search guidance changes over time within a trial, we can better compare the differences between experimental conditions. Eye tracking data from a variety of search tasks were analyzed to determine how color guidance varied over the course of the trial. Color guidance for a given fixation was evaluated based on the distance in color space between the nearest object and the target color. These color differences were averaged over all of the trials and plotted based on when the fixation occurred in the trial. The results indicate that color guidance does not begin working at maximum effectiveness immediately. As the trial progresses, the average color difference decreases. After this initial decrease, if the target is not present, guidance becomes less selective and target dissimilar distractors are increasingly fixated. The color distance graphs were compared between experiments to reveal significant differences arising from the experimental conditions.

This is a work in the epistemology of functional neuroimaging (fNI) and it applies the error-statistical (ES) philosophy to inferential problems in fNI to formulate and address these problems. This gives us a clear, accurate, and more complete understanding of what we can learn from fNI and how we can learn it. I review the works in the epistemology of fNI which I group into two categories; the first category consists of discussions of the theoretical significance of fNI findings and the second category discusses methodological difficulties of fNI. Both types of works have shortcomings; the first category has been too theory-centered in its approach and the second category has implicitly or explicitly adopted the assumption that methodological difficulties of fNI cannot be satisfactorily addressed. In this dissertation, I address these shortcomings and show how and what kind of experimental knowledge fNI can reliably produce which would be theoretically significant. I take fMRI as a representative fNI procedure and discuss the history of its development. Two independent trajectories of research in physics and physiology eventually converge to give rise to fMRI. Thus, fMRI findings are laden in the theories of physics and physiology and I propose how this creates a kind of useful theory-ladenness which allows for the representation of and intervention in the constructs of cognitive neuroscience. Duhemian challenges and problems of underdetermination are often raised to argue that fNI is of little, if any, epistemic value for psychology. I show how the ES notions of severe tests and error probabilities can be applied in epistemological analyses of fMRI. The result is that hemodynamic hypotheses can be severely tested in fMRI experiments and I demonstrate how these hypotheses are theoretically significant and fuel the growth of experimental knowledge in cognitive neuroscience. Throughout this dissertation, I put the emphasis on the experimental knowledge we obtain from fNI and argue that this is the fruitful approach that enables us to see how fNI can contribute to psychology. In doing so, I offer an error-statistical epistemology of fNI, which hopefully will be a significant contribution to the philosophy of psychology. / Ph. D.

There is evidence that language ability is related to a number of cognitive processes, including memory. This study used EEG to investigate the extent to which verbal encoding strategies account for individual differences in short-term recognition memory performance in 44 3-year-olds. As hypothesized, children with better language ability (as measured by the PPVT-III) performed better on the memory task. Analyses of EEG power at the hypothesized electrode sites were not significant, but the hypothesis that children who perform better on the recognition memory task will use more verbal encoding strategies than children who perform less well was partially supported by EEG coherence analyses. Children in the high memory group had significantly greater frontal-temporal coherence in the left hemisphere (F7-T3) than the low memory group. However, this was true both at baseline and during encoding, implying that children in the high memory group have greater overall connectivity between these brain areas and that they tend to use more verbal strategies than the low memory group, as they interact with their environments in general, not just during a memory task. / Master of Science