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Although both creativity and humor elicit experiences of surprise followed by appreciation, it remains unknown whether shared or distinct patterns of effective connectivity are involved in their processing. The present fMRI study used dynamic causal modeling and parametrical empirical Bayes analysis to examine the effective connectivity between the amygdala and frontoparietal network during two‐stage creativity and humor processing. We examined processing during the setup and punch line stages for creativity and humor, including typical forms (alternate uses for creativity and incongruity‐resolution humor), atypical forms (aesthetic uses for creativity and nonsense humor), and baseline forms. Our focus was on the mesolimbic pathway during the punch line stage. We found that the amygdala plays a key role in expectation violation and appreciation. Broadly, amygdala‐to‐IFG connectivity was important for evaluating typical and atypical forms of both creativity and humor, while amygdala‐to‐precuneus connectivity was involved in evaluating typical forms. Amygdala‐to‐IFG connectivity was involved in the expectation violation to resolution stage of processing for typical and atypical forms of creativity and humor. Amygdala‐to‐precuneus connectivity was involved in processing the novelty and usefulness of typical forms of creativity (alternate uses) and understanding others' intentions in typical forms of humor (incongruity‐resolution). Interestingly, VTA‐to‐amygdala connectivity was involved in processing the appreciation of both typical (incongruity‐resolution humor) and atypical (nonsense humor) forms of humor while amygdala‐to‐VTA connectivity was involved in processing the appreciation of atypical (aesthetic uses) forms of creativity. Altogether, these findings suggest that the amygdala and frontoparietal circuitry are critical for creativity and humor processing.

Schumacher et al. Psychological Science 12:101–108, ( 2001 ) demonstrated the elimination of most dual-task costs (“perfect time-sharing”) after extensive dual-task practice of a visual and an auditory task in combination. For the present research, we used a transfer methodology to examine this practice effect in more detail, asking what task-processing stages were sped up by this dual-task practice. Such research will be essential to specify mechanisms associated with the practice-related elimination of dual-task costs. In three experiments, we introduced postpractice transfer probes focusing on the perception, central response-selection, and final motor-response stages. The results indicated that the major change achieved by dual-task practice was a speed-up in the central response-selection stages of both tasks. Additionally, perceptual-stage shortening of the auditory task was found to contribute to the improvements in time-sharing. For a better understanding of such time-sharing, we discuss the contributions of the present findings in relation to models of practiced dual-task performance.

The brain tracks and encodes multi‐level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta‐ and theta‐band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta‐band tracking encodes prediction errors (enhanced processing of unexpected features) while theta‐band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal‐hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real‐words, (2) pseudo‐words and (3) time‐reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta‐band accuracies for pseudo‐words than real‐words and time‐reversed speech, especially during encoding of phonetic features. Notably, individual time‐lag analyses showed that significantly higher accuracies for pseudo‐words than real‐words started at early processing stages for phonetic encoding (<100 ms post‐feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post‐feature, respectively). Theta‐band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real‐words > pseudo‐words > time‐reversed speech). Such effects also started at early stages (<100 ms post‐feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta‐band tracking may play a role in predictive coding leading to greater tracking of pseudo‐words due to the presence of unexpected/unpredicted semantic information, while theta‐band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi‐level speech features during spoken language processing.

It is known that in developing countries, a large quantity of fruit and vegetable losses results at postharvest and processing stages due to poor or scarce storage technology and mishandling during harvest. The use of new and innovative technologies for reducing postharvest losses is a requirement that has not been fully covered. The use of edible coatings (mainly based on biopolymers) as a postharvest technique for agricultural commodities has offered biodegradable alternatives in order to solve problems (e.g., microbiological growth) during produce storage. However, biopolymer-based coatings can present some disadvantages such as: poor mechanical properties (e.g., lipids) or poor water vapor barrier properties (e.g., polysaccharides), thus requiring the development of new alternatives to solve these drawbacks. Recently, nanotechnology has emerged as a promising tool in the food processing industry, providing new insights about postharvest technologies on produce storage. Nanotechnological approaches can contribute through the design of functional packing materials with lower amounts of bioactive ingredients, better gas and mechanical properties and with reduced impact on the sensorial qualities of the fruits and vegetables. This work reviews some of the main factors involved in postharvest losses and new technologies for extension of postharvest storage of fruits and vegetables, focused on perspective uses of edible coatings and nano-laminate coatings.

Understanding visual narrative sequences, as found in comics, is known to recruit similar cognitive mechanisms to verbal language. As measured by event-related potentials (ERPs), these manifest as initial negativities (N400, LAN) and subsequent positivities (P600). While these components are thought to index discrete processing stages, they differentially arise across participants for any given stimulus. In language contexts, proficiency modulates brain responses, with smaller N400 effects and larger P600 effects appearing with increasing proficiency. In visual narratives, recent work has also emphasized the role of proficiency in neural response patterns. We thus explored whether individual differences in proficiency modulate neural responses to visual narrative sequencing in similar ways as in language. We combined ERP data from 12 studies examining semantic and/or grammatical processing of visual narrative sequences. Using linear mixed effects modeling, we demonstrate differential effects of visual language proficiency and “age of acquisition” on N400 and P600 responses. Our results align with those reported in language contexts, providing further evidence for the similarity of linguistic and visual narrative processing, and emphasize the role of both proficiency and age of acquisition in visual narrative comprehension.

In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.

Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF 2-YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the internal feature information of objects. DFConv preserves small object information by preventing the mixing of object information with the background. The optimized feature fusion structure produces high-quality feature maps for efficient real-time small object detection while maximizing the use of limited information. Additionally, modifying the input data processing stage and reducing the detection layer to suit small object detection also contributes to performance improvement. When compared to the performance of the latest YOLO-based models (such as DCN-YOLO and YOLOv7), DCEF 2-YOLO outperforms them, with a mAP of +6.1% on the DOTA-v1.0 test set, +0.3% on the NWPU VHR-10 test set, and +1.5% on the VEDAI512 test set. Furthermore, it has a fast processing speed of 120.48 FPS with an RTX3090 for 512 × 512 images, making it suitable for real-time small object detection tasks.

Language processing studies show that native speakers anticipate linguistic elements before their occurrence. However, it is debated to what extent second language (L2) learners do the same. To address this question, this study examines the processing of cataphora by Chinese-speaking L2 English learners. Additionally, we query whether L2 learners’ expectations of upcoming antecedents are modulated by first language (L1) influence and constrained by Principle B of the Binding Theory (Chomsky, 1981). Two self-paced reading studies show that L1 English speakers’ anticipation of upcoming referents is active and strictly constrained by Principle B. Crucially, L2 English learners also actively predict upcoming referents and are sensitive to Principle B. However, L2 processing patterns suggest that Principle B competes with semantics at later processing stages. Together with data from L1 Chinese and English control participants, these results support the view that anticipatory processing in English is not fundamentally different between monolinguals and bilinguals.

Disparaging rhetoric about Arab people was prevalent during Donald Trump’s political rise in the United States. Although this rhetoric was intended to energize conservative Americans, it also echoed throughout many liberal parts of the United States and around the world. In this research, we experimentally examined the effects of such rhetoric on American and Arab people’s attitudes and visual representations of each other before and after Trump was elected. Although people overwhelmingly reported not liking the negative rhetoric, the rhetoric alone did not influence explicit and implicit intergroup biases in either location, as measured by feeling thermometers and Implicit Association Tests. However, the election outcome moderated the way rhetoric influenced how American and Arab people visually represented each other. Our research sheds light on nuanced effects of global politics on various information processing stages within intergroup perception.

Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ​ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.