This article argues that our attention is pervasively biased by embodied affects and that we are normatively assessable in light of this. From a contemporary perspective, normative theorizing about attention is a relatively new trend (Siegel 2017: Ch. 9, Irving 2019, Bommarito 2018: Ch. 5). However, Buddhist philosophy has provided us with a well-spring of normatively rich theorizing about attention from its inception. This article will address how norms of attention are dealt with in Buddhaghosa’s (5th-6th CE) claims about how wholesome forms of empathy can go wrong. Through this analysis, I will show that Buddhist philosophers like Buddhaghosa think there is an existential norm of attention, one that commands us not just to pay attention to ourselves and the world properly, but one whereby we are exhorted to attend to ourselves in a way that gradually transforms our cognitive-emotional constitution so that we become liberated from suffering.

Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly disregard the dependence between consecutive time points. As a result, climate statistics with long-range temporal properties, such as the number of heatwaves and their frequency, cannot be corrected accurately. This makes it more difficult to produce reliable impact studies on such climate statistics. This article offers a novel BC methodology to correct temporal biases. This is made possible by rethinking the philosophy behind BC. We will introduce BC as a time-indexed regression task with stochastic outputs. Rethinking BC enables us to adapt state-of-the-art machine learning (ML) attention models and thereby learn different types of biases, including temporal asynchronicities. With a case study of number of heatwaves in Abuja, Nigeria and Tokyo, Japan, we show more accurate results than current climate model outputs and alternative BC methods.

A survey of instruction delivery and reinforcement methods in recent laboratory experiments reveals a wide and inconsistently reported variety of practices and limited research evaluating their effectiveness. Thus, we experimentally compare how methods of delivering and reinforcing experiment instructions impact subjects’ comprehension and retention of payoff-relevant information. We report a one-shot individual decision task in which non-money-maximizing behavior can be unambiguously identified and find that such behavior is prevalent in our baseline treatment which uses plain, but relatively standard experimental instructions. We find combinations of reinforcement methods that can eliminate half of non-money-maximizing behavior, and we find that we can induce a similar reduction via enhancements to the content of instructions. Residual non-money-maximizing behavior suggests that this may be an important source of noise in experimental studies.

When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the effects of attention, is sequential sampling of information. However, existing methods for estimating the attention parameters in these models are computationally costly and overly flexible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time. The RUM thus appears to be a useful tool for estimating the effects of attention on choice.

It is believed that the attentional engagement of language learners may reinforce deeper neuronal processing and promote later retrieval. To address language learners’ needs and facilitate language learning, we used audio-visual entertainment (AVE) and cranio-electro stimulation (CES), in addition to multisensory-based instruction, to modify attention and retention processes. Thus, we taught a set of words with the common procedure of audio-visual instruction to 32 English language learners in the control group, CES, and AVE sessions. However, they received five sensory involvements (i.e., auditory, visual, tactile, olfactory, and gustatory) for the target words in the multisensory session. Following each instruction, a pragmatic-Stroop task and a true/false test were conducted to examine the subjects’ attention and retention processes, respectively. Analyzing the response times acquired from the pragmatic-Stroop task, it was found that multisensory-based instruction led to quicker responses in comparison to the audio-visual method preceded by AVE and CES stimulations. The response accuracy results from the retention test also revealed that the subjects provided more accurate responses to the words taught during the multisensory session. The implication is that the enriched multisensory inputs can improve L2 learners’ mental agility and facilitate successful retention and retrieval of information after a short interval period.

Attention has been shown to modulate the visual evoked potential (VEP) recorded to reversing achromatic patterns. However, the chromatic onset VEP appears to be robust to attentional shifts. Functional magnetic resonance imaging (fMRI) responses to both chromatic and achromatic reversing patterns are also affected by attention. Resolution and comparison of these results is problematic due to differences in presentation mode, stimulus parameters, and the source of the response. Here, we report the results of experiments using comparable perceptual contrasts, pattern reversals, and a co-extensive and highly demanding multiple object tracking (MOT) task while exploring the effects of attentional modulation across both the chromatic (L − M) and (S − (L + M)) and the achromatic visual pathways. Our findings indicate that although achromatic VEPs are modulated by attention, chromatic VEPs are more robust to attentional modulation, even when using comparable stimulus presentation modes and in the presence of a highly demanding distractor task. In addition, we found that the majority of the modulation appears to be from a relative decrease in response due to the distractor task rather than a relative increase in response during heightened attention to the stimulus.

One kind of good listener aspires to be sensitive to the testimony of injustice. Under conditions of oppression, this testimony is silenced. One cause of the silencing is that a dominant rights-based model of distributive justice interferes with our appreciation of a needs-based model of radically egalitarian justice. Another cause is that ambient prejudices threaten to impair the listener. A good listener is not only an individual but also a social animal, one who needs to engage with others in a dialectic of attention in order to undo their own prejudices.

In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 $ {km}^2 $ and to the annual hourly $ {NO}_2 $ concentration in 2019 over the Oslo metropolitan region (1026 $ {km}^2 $). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

Applying transcranial alternating current stimulation (tACS) at 40 Hz to the frontal and parietal regions, either unilaterally (left or right) or bilaterally, can improve cognitive dysfunctions. This study aimed to explore the influence of tACS at gamma frequency over right fronto-parietal (FP) region on attention. The analysis is based on retrospective data from a clinical intervention. We administered test of variables of attention (TOVA; visual mode) to 44 participants with various neuropsychiatric diagnoses before and after 12 sessions of tACS treatment. Alternating currents at 2.0 mA were delivered to the electrode positions F4 and P4, following the 10–20 EEG convention, for 20 mins in each session. We observed significant improvement across 3 indices of the TOVA, including reduction of variability in reaction time (p = 0.0002), increase in d-Prime (separability of targets and non-targets; p = 0.0157), and decrease in commission error rate (p = 0.0116). The mean RT and omission error rate largely remained unchanged. Artificial injection of tACS at 40 Hz over right FP network may improve attention function, especially in the domains of consistency in performance, target/non-target discrimination, and inhibitory control.

Language identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior to utilisation. The LID task assumes a significant role in scenarios where ASR systems are unable to comprehend the spoken language in multilingual settings, leading to unsuccessful speech recognition outcomes. The present study introduces convolutional recurrent neural network (CRNN)-based LID, designed to operate on the mel-frequency cepstral coefficient (MFCC) characteristics of audio samples. Furthermore, we replicate certain state-of-the-art methodologies, specifically the convolutional neural network (CNN) and Attention-based convolutional recurrent neural network (CRNN with Attention), and conduct a comparative analysis with our CRNN-based approach. We conducted comprehensive evaluations on thirteen distinct Indian languages, and our model resulted in over 98 per cent classification accuracy. The LID model exhibits high-performance levels ranging from 97 per cent to 100 per cent for languages that are linguistically similar. The proposed LID model exhibits a high degree of extensibility to additional languages and demonstrates a strong resistance to noise, achieving 91.2 per cent accuracy in a noisy setting when applied to a European Language (EU) dataset.

Research on visual attention has uncovered significant anomalies, and some traditional methods may have inadvertently probed peripheral vision rather than attention. Vision science needs to rethink visual attention from the ground up. To facilitate this, for a year I banned the word “attention” in my lab. This constraint promoted a more precise discussion of attention-related phenomena, capacity limits, and mechanisms. The insights gained lead me to challenge attributing to “attention” those phenomena that can be better explained by perceptual processes, are predictable by an ideal observer model, or that otherwise may not require an additional mechanism. I enumerate a set of critical phenomena in need of explanation. Finally, I propose a unifying theory in which all perception results from performing a task, and tasks face a limit on complexity.