The Benefits of Executive Control Training and the Implications for Language Processing

Recent psycholinguistics research suggests that the executive function (EF) skill known as conflict resolution – the ability to adjust behavior in the service of resolving among incompatible representations – is important for several language processing tasks such as lexical and syntactic ambiguity resolution, verbal fluency, and common-ground assessment. Here, we discuss work showing that various EF skills can be enhanced through consistent practice with working-memory tasks that tap these EFs, and, moreover, that improvements on the training tasks transfer across domains to novel tasks that may rely on shared underlying EFs. These findings have implications for language processing and could launch new research exploring if EF training, within a “process-specific” framework, could be used as a remediation tool for improving general language use. Indeed, work in our lab demonstrates that EF training that increases conflict-resolution processes has selective benefits on an untrained sentence-processing task requiring syntactic ambiguity resolution, which relies on shared conflict-resolution functions. Given claims that conflict-resolution abilities contribute to a range of linguistic skills, EF training targeting this process could theoretically yield wider performance gains beyond garden-path recovery. We offer some hypotheses on the potential benefits of EF training as a component of interventions to mitigate general difficulties in language processing. However, there are caveats to consider as well, which we also address

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

New Directions in Compensation Research: Synergies, Risk, and Survival

We describe and use two theoretical frameworks, the resource-based view of the firm and institutional theory, as lenses for examining three promising areas of compensation research. First, we examine the nature of the relationship between pay and effectiveness. Does pay typically have a main effect or, instead, does the relationship depend on other human resource activities and organization characteristics? If the latter is true, then there are synergies between pay and these other factors and thus, conclusions drawn from main effects models may be misleading. Second, we discuss a relatively neglected issue in pay research, the concept of risk as it applies to investments in pay programs. Although firms and researchers tend to focus on expected returns from compensation interventions, analysis of the risk, or variability, associated with these returns may be essential for effective decision-making. Finally ,pay program survival, which has been virtually ignored in systematic pay research, is investigated. Survival appears to have important consequences for estimating pay plan risk and returns, and is also integral to the discussion of pay synergies. Based upon our two theoretical frameworks, we suggest specific research directions for pay program synergies, risk, and survival

Pupillometry as a test of infant word recognition

By 11 months of age, infants recognize commonly occurring word forms in their environment. The Head Turn Preference Paradigm (HTPP) is the one method of measuring infant word form recognition. The HTPP uses looking times as judged by a head turn of the infant towards or away from a speaker. This method is thus subject to infant attention, which can make it difficult to get accurate results when infants are not paying attention due to external factors (for example, teething). Pupillometry is a non-invasive, physiological measurement that uses pupil dilation to assess cognitive processes. Pupil dilations have been found to be an accurate measurement of cognitive load in infants because pupil dilations reflect involuntary activity in the nervous system. Therefore, pupillometry is a non-behavioral assessment of infant word form recognition that may provide richer data than the HTPP. This Honors project was a pilot study for a larger study. The larger study assesses infant word form recognition through pupil dilations and head turns using a common form of the HTPP called the One Screen Head Turn Preference Test. This Honors project compares the results of pupillometry and the One Screen Head Turn Preference Test in one infant from the larger study. It makes a direct comparison of the two methodologies to determine if they can be used side by side to assess infant behavior

The Systems Approach to Teaching Business Associations

The systems approach applies the methods of systems analysis to law. The principal method is to describe the system, situate a problem within the system, and take system mechanics into account in solving it. The system might be the “legal system”—essentially litigation. But more often, it is a “law-related system”—one not composed of law, but one in which law plays a role. That system might be crime, the Internet, the corporation, or any other activity substantially affected by law. The analyst situates the application of law in the context of the physical system as it actually operates. In business associations, that context may be law offices, boardrooms, the daily interactions of business co-owners, as well as courtrooms and settlement discussions. One situates the application of law in the context of the physical system by describing the system with emphasis on the causal connections through which it operates

Language-driven Scene Synthesis using Multi-conditional Diffusion Model

Scene synthesis is a challenging problem with several industrial applications. Recently, substantial efforts have been directed to synthesize the scene using human motions, room layouts, or spatial graphs as the input. However, few studies have addressed this problem from multiple modalities, especially combining text prompts. In this paper, we propose a language-driven scene synthesis task, which is a new task that integrates text prompts, human motion, and existing objects for scene synthesis. Unlike other single-condition synthesis tasks, our problem involves multiple conditions and requires a strategy for processing and encoding them into a unified space. To address the challenge, we present a multi-conditional diffusion model, which differs from the implicit unification approach of other diffusion literature by explicitly predicting the guiding points for the original data distribution. We demonstrate that our approach is theoretically supportive. The intensive experiment results illustrate that our method outperforms state-of-the-art benchmarks and enables natural scene editing applications. The source code and dataset can be accessed at https://lang-scene-synth.github.io/.Comment: Accepted to NeurIPS 202