Seeing the invisible: The scope and limits of unconscious processing in binocular rivalry

When an image is presented to one eye and a very different image is presented to the corresponding location of the other eye, they compete for conscious representation, such that only one image is visible at a time while the other is suppressed. Called binocular rivalry, this phenomenon and its deviants have been extensively exploited to study the mechanism and neural correlates of consciousness. In this paper, we propose a framework, the unconscious binding hypothesis, to distinguish unconscious processing from conscious processing. According to this framework, the unconscious mind not only encodes individual features but also temporally binds distributed features to give rise to cortical representation, but unlike conscious binding, such unconscious binding is fragile. Under this framework, we review evidence from psychophysical and neuroimaging studies, which suggests that: (1) for invisible low level features, prolonged exposure to visual pattern and simple translational motion can alter the appearance of subsequent visible features (i.e. adaptation); for invisible high level features, although complex spiral motion cannot produce adaptation, nor can objects/words enhance subsequent processing of related stimuli (i.e. priming), images of tools can nevertheless activate the dorsal pathway; and (2) although invisible central cues cannot orient attention, invisible erotic pictures in the periphery can nevertheless guide attention, likely through emotional arousal; reciprocally, the processing of invisible information can be modulated by attention at perceptual and neural levels

Supercharging academic writing with generative AI: framework, techniques, and caveats

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.Comment: 14 pages, 2 figures, 1 table, 1 bo

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

Existence of Periodic Solutions and Stability of Zero Solution of a Mathematical Model of Schistosomiasis

A mathematical model on schistosomiasis governed by periodic differential equations with a time delay was studied. By discussing boundedness of the solutions of this model and construction of a monotonic sequence, the existence of positive periodic solution was shown. The conditions under which the model admits a periodic solution and the conditions under which the zero solution is globally stable are given, respectively. Some numerical analyses show the conditional coexistence of locally stable zero solution and periodic solutions and that it is an effective treatment by simply reducing the population of snails and enlarging the death ratio of snails for the control of schistosomiasis

Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page

The Role of Perceptual Load in Object Recognition

Predictions from perceptual load theory (Lavie, 19952005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were unaffected by a change in the distracter object view under conditions of low perceptual load. These results were found both with repetition priming measures of distracter recognition and with performance on a surprise recognition memory test. The results support load theory proposals that distracter recognition critically depends on the level of perceptual load. The implications for the role of attention in object recognition theories are discussed