Alpha-to-beta- and gamma-band activity reflect predictive coding in affective visual processing

Processing of negative affective pictures typically leads to desynchronization of alpha-to-beta frequencies (ERD) and synchronization of gamma frequencies (ERS). Given that in predictive coding higher frequencies have been associated with prediction errors, while lower frequencies have been linked to expectations, we tested the hypothesis that alpha-to-beta ERD and gamma ERS induced by aversive pictures are associated with expectations and prediction errors, respectively. We recorded EEG while volunteers were involved in a probabilistically cued affective picture task using three different negative valences to produce expectations and prediction errors. Our data show that alpha-to-beta band activity after stimulus presentation was related to the expected valence of the stimulus as predicted by a cue. The absolute mismatch of the expected and actual valence, which denotes an absolute prediction error was related to increases in alpha, beta and gamma band activity. This demonstrates that top-down predictions and bottom-up prediction errors are represented in typical spectral patterns associated with affective picture processing. This study provides direct experimental evidence that negative affective picture processing can be described by neuronal predictive coding computations.

Python - Based Image Processing

The main goal of the Image Process project is to extract important information from photographs. The machine may produce a description, interpretation, and comprehension of the scene based on this extracted data. The main goal of image processing is to transform photos in the desired way. This technique allows users to obtain the text of picture processing printing processes and to save the data to disc in a variety of formats. In other terms, image processing is the process of neutering and analysing graphical information in photographs. In our lives, we frequently come across many types of image processing. The clearest example of image processing in our lives is our brain's perceiving of visuals. Once we perceive pictures with our eyes, the process takes relatively little time

Enhanced MAC controller architecture for 2D processing based on FPGA with configurable resource allocation

The bulks of image processing algorithms are either two-dimensional (2D) or confined by their very nature. As a result, the 2D convolution function has a large impact on picture processing requirements. The methodology of 2D convolution and media access control (MAC) design can also be used to perform a variety of image processing tasks, and even as picture blurring, softening, and feature extraction. The main goal of this research is to develop a more efficient MAC control block-based 2D convolution architecture. This 2D algorithm can be implemented in hardware using fewer modules, multipliers, adders, and control blocks, resulting in significant hardware savings and look up table (LUT) reductions. The simulations were run in Verilog, and the Xilinx Vertex family field programmable gate array (FPGA) was used to build and test them. The recommended 2D convolution architectural solution is significantly faster and consumes significantly less hardware resources than the traditional 2D convolution implementation. The proposed architecture will result in technology that saves a substantial amount of processing time when it comes to LUTs.

Multiple mental representations in picture processing

This article investigates whether goal-directed learning of pictures leads to multiple mental representations which are differently useful for different purposes. The paper further investigates the effects of prompts on picture processing. 136 undergraduate students were presented maps of a fictitious city. One half of the participants were instructed to learn their map as preparation to draw it from memory as precisely as possible (PrepDraw), which should stimulate the creation of an elaborated surface representation. The other half were instructed to learn the map as preparation for finding the shortest traffic connection from various locations to other locations (PrepConnect), which should stimulate the construction of a task-oriented deep-structure representation (mental model). Within both experimental groups, one-third of the participants received the map without prompts. Another third received the map with survey prompts (stimulating processing of what is where), and the final third received the map with connect prompts (stimulating processing of how train stations are connected). In the following test phase, participants received a recognition task, a recall task, and an inference task. For recognition and recall, two surface structure scores (extent, accuracy) and two deep structure scores (extent, accuracy) were calculated. The inference task served also to indicate deep structure accuracy. The PrepDraw group outperformed the PrepConnect group in terms of surface structure related variables, whereas the PrepConnect group outperformed the PrepDraw group in terms of deep structure-related variables. Map processing was not enhanced by prompts aligned with the instruction, but non-aligned prompts tended to interfere with learning.

Alpha-to-beta- and gamma-band activity reflect predictive coding in affective visual processing

Processing of negative affective pictures typically leads to desynchronization of alpha-to-beta frequencies (ERD) and synchronization of gamma frequencies (ERS). Given that in predictive coding higher frequencies have been associated with prediction errors, while lower frequencies have been linked to expectations, we tested the hypothesis that alpha-to-beta ERD and gamma ERS induced by aversive pictures are associated with expectations and prediction errors, respectively. We recorded EEG while volunteers were involved in a probabilistically cued affective picture task using three different negative valences to produce expectations and prediction errors. Our data show that alpha-to-beta band activity was related to the expected valence of the stimulus as predicted by a cue. The absolute mismatch of the expected and actual valence, which denotes an absolute prediction error was related to gamma band activity. This demonstrates that top-down predictions and bottom-up prediction errors are represented in specific spectral patterns associated with affective picture processing.

New Roles for Clinicians in the Age of Artificial Intelligence

With the rapid developments of digital picture processing, pattern recognition, and intelligent algorithms, artificial intelligence (AI) has been widely applied in the medical field. The applications of artificial intelligence in medicine (AIM) include diagnosis generation, therapy selection, healthcare management, disease stratification, etc. Among the applications, the focuses of AIM are assisting clinicians in implementing disease detection, quantitative measurement, and differential diagnosis to improve diagnostic accuracy and optimize treatment selection. Thus, researchers focus on creating and refining modeling processes, including the processes of data collection, data preprocessing, and data partitioning as well as how models are configured, evaluated, optimized, clinically applied, and used for training. However, there is little research on the consideration of clinicians in the age of AI. Meanwhile, AI is more accurate and spends less time in diagnosis between the competitions of AI and clinicians in some cases. Thus, AIM is gradually becoming a hot topic. Barely a day goes by without a claim that AI techniques are poised to replace most of today’s professionals. Despite huge promise surrounding this technology, AI alone cannot support all the requirements for precision medicine, rather AI should be used in cohesive collaboration with clinicians. However, the integration of AIM has created confusion among clinicians on their role in this era. Therefore, it is necessary to explore new roles for clinicians in the age of AI.Statement of significanceWith the advent of the era of AI, the integration of medical field and AI is on the rise. Medicine has undergone significant changes, and what was previously labor-intensive work is now being solved through intelligent means. This change has also raised concerns among scholars: Will doctors eventually be replaced by AI? From this perspective, this study elaborates on the reasons why AI cannot replace doctors, and points out how doctors should change their roles to accelerate the integration of these fields, so as to adapt to the developing times.