The Malleability of Cognitive Control and its Effects on Language Skills

Cognitive control, or executive function (EF), refers to the mental ability to regulate and adjust behavior across domains in the face of interference, conflict, or new rules. Evidence from psycholinguistics suggests a role for cognitive control in a range of language processing tasks including syntactic ambiguity resolution and verbal fluency. Separate work demonstrates that EF abilities are malleable with extensive practice, such that training improvements transfer across domains to novel tasks that rely on the same underlying EF mechanisms (an effect dubbed 'process-specificity'). In uniting these two growing literatures, this dissertation investigated the (causal) role of cognitive control for language processing through two longitudinal training interventions. In one study, I demonstrated that practicing a battery of cognitive tasks conferred selective benefits on untrained reading tasks requiring syntactic ambiguity resolution. Compared to controls, individuals who responded most to an EF training task exhibited (1) higher accuracy to comprehension questions indexing offline reinterpretation, and (2) faster real-time recovery efforts to resolve among conflicting interpretations. A second experiment extended these findings by addressing the degree to which training on a single EF task was necessary and sufficient to confer transfer to untrained, related language measures. Participants were assigned to practice a single training task that was minimally different from other training groups' tasks in terms of EF demands. By and large, participants who practiced a high-EF training task were exclusive in demonstrating a cross-assessment improvement profile consistent with a process-specific account: Pre/post benefits across a range of ostensibly different linguistic (verbal fluency, syntactic ambiguity resolution) and non-linguistic (Stroop, recognition memory) tasks were observed selectively for conditions with high-EF demands; no benefits were seen for cases when the need for cognitive control was minimized. Together, these findings provide support for the malleability of EF skills and suggest a critical (and perhaps causal) role for domain-general cognitive control in language processing. Further, the present studies indicate that within the right framework, and having appropriate linking hypotheses, cognitive training may be a viable way to improve language use

Life is an Adventure! An agent-based reconciliation of narrative and scientific worldviews\ud

The scientific worldview is based on laws, which are supposed to be certain, objective, and independent of time and context. The narrative worldview found in literature, myth and religion, is based on stories, which relate the events experienced by a subject in a particular context with an uncertain outcome. This paper argues that the concept of “agent”, supported by the theories of evolution, cybernetics and complex adaptive systems, allows us to reconcile scientific and narrative perspectives. An agent follows a course of action through its environment with the aim of maximizing its fitness. Navigation along that course combines the strategies of regulation, exploitation and exploration, but needs to cope with often-unforeseen diversions. These can be positive (affordances, opportunities), negative (disturbances, dangers) or neutral (surprises). The resulting sequence of encounters and actions can be conceptualized as an adventure. Thus, the agent appears to play the role of the hero in a tale of challenge and mystery that is very similar to the "monomyth", the basic storyline that underlies all myths and fairy tales according to Campbell [1949]. This narrative dynamics is driven forward in particular by the alternation between prospect (the ability to foresee diversions) and mystery (the possibility of achieving an as yet absent prospect), two aspects of the environment that are particularly attractive to agents. This dynamics generalizes the scientific notion of a deterministic trajectory by introducing a variable “horizon of knowability”: the agent is never fully certain of its further course, but can anticipate depending on its degree of prospect

Biological Explanation

One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy of science based explanation on derivation from scientific laws, there are many biological explanations in which laws play little or no role. Instead, the field of biology is a natural place to turn for support for the idea that causal information is explanatory. Biology has also been used to motivate mechanistic accounts of explanation, as well as criticisms of that approach. Ultimately, the most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach, and the intense disagreements that result. In my view, such debates are better understood as arising not from different theoretical commitments, but commitments to different explanatory projects. Biology education would thus be enriched by attending to approaches to biological explanation, as well as the unexpected ways that these explanatory aims influence other features of biology. I suggest five lessons for teaching about explanation in biology that follow from the considerations of this chapter

Consciousness is learning: predictive processing systems that learn by binding may perceive themselves as conscious

Machine learning algorithms have achieved superhuman performance in specific complex domains. Yet learning online from few examples and efficiently generalizing across domains remains elusive. In humans such learning proceeds via declarative memory formation and is closely associated with consciousness. Predictive processing has been advanced as a principled Bayesian inference framework for understanding the cortex as implementing deep generative perceptual models for both sensory data and action control. However, predictive processing offers little direct insight into fast compositional learning or the mystery of consciousness. Here we propose that through implementing online learning by hierarchical binding of unpredicted inferences, a predictive processing system may flexibly generalize in novel situations by forming working memories for perceptions and actions from single examples, which can become short- and long-term declarative memories retrievable by associative recall. We argue that the contents of such working memories are unified yet differentiated, can be maintained by selective attention and are consistent with observations of masking, postdictive perceptual integration, and other paradigm cases of consciousness research. We describe how the brain could have evolved to use perceptual value prediction for reinforcement learning of complex action policies simultaneously implementing multiple survival and reproduction strategies. 'Conscious experience' is how such a learning system perceptually represents its own functioning, suggesting an answer to the meta problem of consciousness. Our proposal naturally unifies feature binding, recurrent processing, and predictive processing with global workspace, and, to a lesser extent, the higher order theories of consciousness.Comment: This version adds 5 figures (new) and only modifies the text to reference the figure

COPING WITH DISCREPANT INFORMATION TECHNOLOGY EVENTS: A LITERATURE REVIEW

Coping theory has been used to explain and predict the behaviors of users facing discrepant information technology (IT) events, i.e., unexpected, negative events that occur due to problems and difficulties when using such technology. However, researchers have examined coping by using a vast array of conceptualizations, discrepant IT events, coping strategies, and behaviors, which have led to considerable heterogeneity in the existing literature. Therefore, the present paper demonstrates the results of a comprehensive literature review, identifying and analyzing 27 relevant investigations. The present literature review contributes to the literature by identify-ing six theoretical implications: (1) coping literature can subdivided into research streams on technostress, IT adoption and usage, and IT security, (2) the literature disagrees about the ante-cedents of coping strategies, (3) coping strategies are heterogeneous, (4) coping strategies show interdependences, (5) coping strategies show paradoxical effects, and (6) development of a framework of coping with discrepant IT events. In addition, the paper proposes new directions for future coping research for all three identified research streams

Information Markets and Nonmarkets

As large amounts of data become available and can be communicated more easily and processed more e¤ectively, information has come to play a central role for economic activity and welfare in our age. This essay overviews contributions to the industrial organization of information markets and nonmarkets, while attempting to maintain a balance between foundational frameworks and more recent developments. We start by reviewing mechanism-design approaches to modeling the trade of information. We then cover ratings, predictions, and recommender systems. We turn to forecasting contests, prediction markets, and other institutions designed for collecting and aggregating information from decentralized participants. Finally, we discuss science as a prototypical information nonmarket with participants who interact in a non-anonymous way to produce and disseminate information. We aim to make the reader familiar with the central notions and insights in this burgeoning literature and also point to some open critical questions that future research will have to address

The Effectiveness of the Support to Development of the Islamic Higher Education Project in Improving the Quality of Education

This study aims to explore the Support to Development of the Islamic Higher Education Project (SDIHE) in an effort to improve the quality of Islamic Religious Higher Educations (PTKIs). This research is a policy research conducted at UIN Sumatera Utara, UIN Raden Fatah, UIN Walisongo and UIN Mataram. It employs exploratory descriptive analysis which explains the SDIHE phenomenon. The results show that SDIHE which is run by PTKIs (UIN Sumatera Utara, UIN Raden Fatah, UIN Walisongo and UIN Mataram) through curriculum development and expert training programs is effective in improving the quality of PTKIs. SDIHE has an impact on increasing the ranking of these universities globally and locally. This shows an increase in the performance of PTKIs. There has been no research on the effectiveness and impact of projects from foreign grants in Higher Education that comprehensively discusses quality from the perspective of curriculum development and expert training programs

Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane

The paper addresses the design of adaptive radar detectors with desired behavior, in Gaussian disturbance with unknown statistics. Specifically, based on detection probability specifications for chosen signal-to-noise ratios and steering vector mismatch levels, a methodology for the design of customized constant false alarm rate (CFAR) detectors is devised in a suitable feature plane obtained from two maximal invariant statistics. To overcome the analytical and numerical intractability of the resulting optimization problem, a novel general reduced-complexity algorithm is developed, which is shown to be effective in providing a feasible solution (i.e., fulfilling a constraint on the probability of false alarm) while controlling the behavior under both matched and mismatched conditions, so enabling the design of fully customized adaptive CFAR detectors