Reward priming differentially modulates enhancement and inhibition in auditory decision-making

In cognitive sciences, rewards, such as money and food, play a fundamental role in individuals' daily lives and well-being. Moreover, rewards that are irrelevant to the task alter individuals' behavior. However, it is unclear whether explicit knowledge of reward irrelevancy has an impact on reward priming enhancements and inhibition. In this study, an auditory change-detection task with task-irrelevant rewards was introduced. The participants were informed explicitly in advance that the rewards would be given randomly. The results revealed that while inhibition related to reward priming only occurred when the participants were explicitly informed about rewards, implicit instruction thereof resulted in enhancement and inhibition associated with reward priming. These findings highlight the contribution of explicit information about rewards associated with auditory decisions.

Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was massively disrupted after the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.

Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task

The neocortex can be viewed as a tapestry consisting of variations of rather stereotypical local cortical microcircuits. Hence understanding how these microcircuits compute holds the key to understanding brain function. Intense research efforts over several decades have culminated in a detailed model of a generic cortical microcircuit in the primary visual cortex from the Allen Institute. We are presenting here methods and first results for understanding computational properties of this large-scale data-based model. We show that it can solve a standard image-change-detection task almost as well as the living brain. Furthermore, we unravel the computational strategy of the model and elucidate the computational role of diverse subtypes of neurons. Altogether this work demonstrates the feasibility and scientific potential of a methodology based on close interaction of detailed data and large-scale computer modelling for understanding brain function.

SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network

Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.

Gaze cueing, mental States, and the effect of autistic traits

The ability to interpret and follow the gaze of our social partners is an integral skill in human communication. Recent research has demonstrated that gaze following behaviour is influenced by theory of mind (ToM) processes. However, it has yet to be determined whether the modulation of gaze cueing by ToM is affected by individual differences, such as autistic traits. The aim of this experiment was to establish whether autistic traits in neurotypical populations affect the mediation of gaze cueing by ToM processes. This study used a gaze cueing paradigm within a change detection task. Participants’ perception of a gaze cue was manipulated such that they only believed the cue to be able to ‘see’ in one condition. The results revealed that participants in the Low Autistic Traits group were significantly influenced by the mental state of the gaze cue and were more accurate on valid trials when they believed the cue could ‘see’. By contrast, participants in the High Autistic Traits group were also more accurate on valid trials, but this was not influenced by the mental state of the gaze cue. This study therefore provides evidence that autistic traits influence the extent to which mental state attributions modulate social attention in neurotypical adults.

Unsupervised Online Assessment of Visual Working Memory in 4- to 10-Year-Old Children: Array Size Influences Capacity Estimates and Task Performance

The events of the COVID-19 Pandemic forced many psychologists to abandon lab-based approaches and embrace online experimental techniques. Although lab-based testing will always be the gold standard of experimental precision, several protocols have evolved to enable supervised online testing for paradigms that require direct observation and/or interaction with participants. However, many tasks can be completed online in an unsupervised way, reducing reliance on lab-based resources (e.g., personnel and equipment), increasing flexibility for families, and reducing participant anxiety and/or demand characteristics. The current project demonstrates the feasibility and utility of unsupervised online testing by incorporating a classic change-detection task that has been well-validated in previous lab-based research. In addition to serving as proof-of-concept, our results demonstrate that large online samples are quick and easy to acquire, facilitating novel research questions and speeding the dissemination of results. To accomplish this, we assessed visual working memory (VWM) in 4- to 10-year-old children in an unsupervised online change-detection task using arrays of 1–4 colored circles. Maximum capacity (max K) was calculated across the four array sizes for each child, and estimates were found to be on-par with previously published lab-based findings. Importantly, capacity estimates varied markedly across array size, with estimates derived from larger arrays systematically underestimating VWM capacity for our youngest participants. A linear mixed effect analysis (LME) confirmed this observation, revealing significant quadratic trends for 4- through 7-year-old children, with capacity estimates that initially increased with increasing array size and subsequently decreased, often resulting in estimates that were lower than those obtained from smaller arrays. Follow-up analyses demonstrated that these regressions may have been based on explicit guessing strategies for array sizes perceived too difficult to attempt for our youngest children. This suggests important interactions between VWM performance, age, and array size, and further suggests estimates such as optimal array size might capture both quantitative aspects of VWM performance and qualitative effects of attentional engagement/disengagement. Overall, findings suggest that unsupervised online testing of VWM produces reasonably good estimates and may afford many benefits over traditional lab-based testing, though efforts must be made to ensure task comprehension and compliance.

Using change detection to objectively evaluate whether novel over-the-counter drug labels can increase attention to critical health information among older adults

Over-the-counter (OTC) drugs have many benefits but also carry risks, such as adverse drug reactions, which are more prevalent in older adults. Because these products do not require the oversight of a physician or pharmacist, labeling plays a key role in communicating information required for their safe and effective use. Research suggests that current labels are not terribly effective at communicating potential risk. One reason for their lack of effectiveness is that few consumers attend to critical information (active ingredients and warnings) when making purchases. In two experiments, we used a change detection task to objectively evaluate how novel label designs that employ highlighting and a warning label placed on the package’s front impact attention to critical information among older participants (65 and older). The change detection task is a unique form of visual search which allowed us to assess the attentional priority of critical information among participants who were not explicitly instructed to search for this critical information. This unique aspect of the task is important given research suggesting that consumers rarely have the explicit goal of seeking out warnings and active ingredients when making OTC selections. Our results provide empirical support that both highlighting critical information and positioning it on the package’s front increase its attentional prioritization relative to current, commercial practice. Given that attending to the critical information is prerequisite to utilizing that information, strategies that elicit attention in this way are likely to reduce medication errors.

Impaired and Intact Aspects of Attentional Competition and Prioritization during Visual Working Memory Encoding in Schizophrenia

Objective: People with schizophrenia (PSZ) are impaired in the attentional prioritization of non-salient but relevant stimuli over salient but irrelevant distractors during visual working memory (VWM) encoding. Conversely, the guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ overcome impaired attentional prioritization in the presence of salient distractors. Methods: We employed a visuospatial change-detection task using four Gabor Patches with differing orientations in 69 PSZ and 74 healthy controls (HCS). Two patches flickered to reflect saliency and either a predictive or a non-predictive cue was displayed resulting in four conditions. Results: Across all conditions, PSZ stored significantly less information in VWM than HCS (all p < 0.001). With a non-predictive cue, PSZ stored significantly more salient than non-salient information (t140 = 5.66, p < 0.001, dt = 0.5). With a predictive cue, PSZ stored significantly more non-salient information (t140 = 5.70, p < 0.001, dt = 0.5). Conclusion: Our findings support a bottom-up bias in schizophrenia with performance significantly better for visually salient information in the absence of a predictive cue. These results indicate that bottom-up attentional prioritization is disrupted in schizophrenia, but the top-down utilization of cues is intact. We conclude that additional top-down information significantly improves performance in PSZ when non-salient visual information needs to be encoded in working memory.

Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was abruptly disrupted following the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.