Iconicity facilitates learning signs, but it is unknown whether recognition of meaning from the sign form occurs automatically. We recorded ERPs to highly iconic (transparent) and non-iconic ASL signs presented to one group who knew they would be taught signs (learners) and another group with no such expectations (non-learners). Participants watched sign videos and detected an occasional grooming gesture (no semantic processing required). Before sign onset, learners showed a greater frontal negativity compared to non-learners for both sign types, possibly due to greater motivation to attend to signs. During the N400 window, learners showed greater negativity to iconic than non-iconic signs, indicating more semantic processing for iconic signs. The non-learners showed a later and much weaker iconicity effect. The groups did not differ in task performance or in P3 amplitude. We conclude that comprehending the form-meaning mapping of highly iconic signs is not automatic and requires motivation and attention.

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.

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.

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.