Temporal processes of information transmission from visual search to behavior

Each of four electrocutaneous codes, alike with respect to the number of code signals, but different with respect to the dimensions used in composing the signals, was learned by 10 Ss. When response time (RT) was used as the index of performance after practice, the codes were ranked in order of increasing difficulty (or RT) as follows: the location code, location-by-intensity, location-by-duration, the location-by-intensity-by-duration codes. When errors were taken as the index of performance and when Ss had received a moderate amount of practice, the codes were arranged in order of increasing difficulty (or errors) as follows: the location-by-intensity code, location-by-duration, the location code, and the location-by-intensity-by-duration code. When the rate of information transmission (which takes into account both time and errors) was employed as the index of performance, the codes were ranked in order of increasing difficulty (or decreasing efficiency) as follows: the location-by-intensity code, the location code, location-by-duration, and the location-by-intensity-by-duration code.

If attention is distributed among multiple moving objects, how does this divided attention affect the temporal process for detecting a specific target motion? Well-trained observers in three experiments monitored ongoing random motions of multiple objects, trying to rapidly detect non-random target motions. Response time hazard rates revealed a simple lawful structure of the detection processes. Target detection rates (hazard rates, in bits /s) were inversely proportional to the number of observed objects. Detection rates at any response time and in any condition equaled a product of two parallel (functionally independent and concurrent) visual processes: visual awareness and motion integration. The rate of visual awareness was inversely proportional to Set Size (n = 1-12), constant over time, and invariant with integrated motion information. Thus, a single rate parameter, indicating a constant channel capacity of visual awareness, described detection rates over a wide range of conditions and response times. During an initial interval of roughly 0.5s, detection rates increased proportionally with the duration and length of motion; but after this initial integration, detection rates were constant, independent of the time the target remained undetected. The relationship between the quantity of visual information and detection rates was simpler than anticipated by contemporary theories of attention, perception, and performance.

With the development of the information age, the transmission rate and transmission capacity of information continue to increase. As a wireless communication method to achieve high bit rate communication, space laser communication plays an increasingly obvious role in global communications. In the current space laser communication system, the optical communication terminal uses a detector to detect the position of the received optical signal, and then the servo mechanism on it performs facula tracking. However, when a four-quadrant detector (4QD) is used to detect optical signal, the received optical signal generally contains three main types of noise, which are background radiation noise, shot noise and thermal noise. These three kinds of noise can be equivalent to Gaussian white noise. In this paper, a single-frequency cosine signal is used to modulate the intensity of the optical signal received by the 4QD, and then the modulated optical signal is detected by a spectrum analysis method based on the cross-correlation algorithm. This method reduces the relative error of the spectral line amplitude when the SNR is -5dB from 2.23% to 0.88%, and reduces the relative error of the spectral line amplitude when the SNR is -20dB from 17.6% to 5.49%. Therefore, this method can well suppress Gaussian white noise and improve the detection accuracy of modulated optical signal under extremely low SNR conditions.

Based on a unified approach to all kinds of quantum capacities we show that the rate of quantum information transmission is bounded by the maximal attainable rate of coherent information. Moreover, we show that if for any bipartite state the one-way distillable entanglement is no less than coherent information, then one obtains Shannon-like formulas for all the capacities. The inequality also implies that the decrease of distillable entanglement due to the mixing process does not exceed that of the corresponding loss of information about a system.

As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A general trend in many recent works pushing the model adaptability or the application diversity of neural JSCC is based on the convolutional neural network (CNN) backbone, whose model capacity is yet limited, inherently leading to inferior system coding gain against traditional coded transmission systems. In this paper, we establish a new neural JSCC backbone that can also adapt flexibly to diverse channel conditions and transmission rates within a single model, our open-source project aims to promote the research in this field. Specifically, we show that with elaborate design, neural JSCC codec built on the emerging Swin Transformer backbone achieves superior performance than conventional neural JSCC codecs built upon CNN, while also requiring lower end-to-end processing latency. Paired with two spatial modulation modules that scale latent representations based on the channel state information and target transmission rate, our baseline SwinJSCC can further upgrade to a versatile version, which increases its capability to adapt to diverse channel conditions and rate configurations. Extensive experimental results show that our SwinJSCC achieves better or comparable performance versus the state-of-the-art engineered BPG + 5G LDPC coded transmission system with much faster end-to-end coding speed, especially for high-resolution images, in which case traditional CNN-based JSCC yet falls behind due to its limited model capacity.

Patients with schizophrenia exhibit impairments in working memory that often appear in attenuated form in persons at high risk for the illness. The authors hypothesized that deviations in task-related brain activation and deactivation would occur in persons with an at-risk mental state performing a working memory task that entailed the maintenance and manipulation of letters. Participants at ultra high risk for developing psychosis (N=60), identified using the Comprehensive Assessment of At-Risk Mental States, and healthy comparison subjects (N=38) 14 to 29 years of age underwent functional MRI while performing a verbal working memory task. Group differences in brain activation were identified using analysis of covariance. The two groups did not show significant differences in speed or accuracy of performance, even after accounting for differences in education. Irrespective of task condition, at-risk participants exhibited significantly less activation than healthy comparison subjects in the left anterior insula. During letter manipulation, at-risk persons exhibited greater task-related deactivation within the default-mode network than comparison subjects. Region-of-interest analysis in the at-risk group revealed significantly greater right dorsolateral prefrontal cortex activation during manipulation of letters. Despite comparable behavioral performance, at-risk participants performing a verbal working memory task exhibited altered brain activation compared with healthy subjects. These findings demonstrate an altered pattern of brain activation in at-risk persons that contains elements of reduced function as well as compensation.

Recently, methods of statistical estimation theory have been applied by Bialek and collaborators (1991) to reconstruct time-varying velocity signals and to investigate the processing of visual information by a directionally selective motion detector in the fly's visual system, the H1 cell. We summarize here our theoretical results obtained by studying these reconstructions starting from a simple model of H1 based on experimental data. Under additional technical assumptions, we derive a closed expression for the Fourier transform of the optimal reconstruction filter in terms of the statistics of the stimulus and the characteristics of the model neuron, such as its firing rate. It is shown that linear reconstruction filters will change in a nontrivial way if the statistics of the signal or the mean firing rate of the cell changes. Analytical expressions are then derived for the mean square error in the reconstructions and the lower bound on the rate of information transmission that was estimated experimentally by Bialek et al. (1991). For plausible values of the parameters, the model is in qualitative agreement with experimental data. We show that the rate of information transmission and mean square error represent different measures of the reconstructions: in particular, satisfactory reconstructions in terms of the mean square error can be achieved only using stimuli that are matched to the properties of the recorded cell. Finally, it is shown that at least for the class of models presented here, reconstruction methods can be understood as a generalization of the more familiar reverse-correlation technique.

Spread spectrum watermarking (SSW) is one of the most widespread watermarking methods. In this paper, the characterization of the SSW from the information theoretic point of view is performed. Previous works on channel capacity analysis of watermarking systems are reviewed. Since SSW method is designed so that the host signal is unknown to the embedding encoder, Costa's results for capacity analysis of watermarking systems are not directly applicable. In this paper, watermarking embedding and detection is modeled as a channel that transmits information in the presence of noise of host and channel capacity analysis of that model is derived and the most reliable transmission rate of hidden information obtained. In addition, channel capacity for the case of presence of additive noise in the watermark channel is derived and robustness of the SSW against additive noise examined

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7-14 days in young adults with type 1 diabetes. In addition, person-level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined. In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring over 6-14 days. At baseline, participants completed the Psychomotor Vigilance Test, the Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries. Forty-six participants (mean age, 22.3 ± 3.2 years) wore a wrist actigraph and underwent continuous glucose monitoring concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences; r = .33, P = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences; r = .50, P = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for type 1 diabetes duration (P > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, P = .010, pr2 = 0.40) after controlling for type 1 diabetes duration and accounting for higher daytime sleepiness. Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with type 1 diabetes. Sleep habits in this population may explain higher glucose variability, and optimizing sleep may improve overall diabetes management. Griggs S, Hickman RL Jr, Strohl KP, Redeker NS, Crawford SL, Grey M. Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes. J Clin Sleep Med. 2021;17(9):1865-1874.

Although response times (RTs) are the dependent measure of choice in the majority of studies of visual attention, changes in RTs can be hard to interpret. First, they are inherently ambiguous, since they may reflect a change in the central tendency or skew (or both) of a distribution. Second, RT measures may lack sensitivity, since meaningful changes in RT patterns may not be picked up if they reflect two or more processes having opposing influences on mean RTs. Here we describe RT distributions for repetition priming in visual search, fitting ex-Gaussian functions to RT distributions. We focus here on feature and conjunction search tasks, since priming effects in these tasks are often thought to reflect similar mechanisms. As expected, both tasks resulted in strong priming effects when target and distractor identities repeated, but a large difference between feature and conjunction search was also seen, in that the σ parameter (reflecting the standard deviation of the Gaussian component) was far more affected by search repetition in conjunction than in feature search. Although caution should clearly be used when particular parameter estimates are matched to specific functions or processes, our results suggest that analyses of RT distributions can inform theoretical accounts of priming in visual search tasks, in this case showing quite different repetition effects for the two differing search types, suggesting that priming in the two paradigms partly reflects different mechanisms.

Prism adaptation improves visual search in hemispatial neglect

Prior research has demonstrated that scene cues within real-world environments significantly expedite visual search tasks, particularly when co-occurrence objects—those that tend to appear concurrently and in close proximity—are present. The underlying mechanisms driving these enhancements remain a subject of ongoing debate. In this study, we recruited forty-five participants to perform a visual search task using real-world scene images, with conditions involving either the presence or absence of co-occurrence objects, while recording their right eye movements. Our findings revealed that the presence of co-occurrence objects exclusively enhanced search accuracy, without impacting response times or fixation durations. As participants engaged in repeated searches, their performance improved, characterized by increased accuracy, reduced response times, fewer fixations, and shorter fixation durations, applicable to both target objects and co-occurrence objects alike. Notably, the effect of co-occurrence objects remained consistent across search iterations, indicating no change with accumulated search experience. Collectively, these outcomes suggest a strategic role for co-occurrence objects in guiding visual search processes, potentially mediated through the spatial processing of target objects. This study contributes to our understanding of how contextual cues influence visual attention and search precision in complex environments.

Visual search strategy and perceptual organization covary with individual preference and structural complexity

Event Abstract Back to Event How do temporal stimulus correlations influence the performance of population codes? Kamiar Rahnama Rad1* and Liam Paninski1 1 Columbia University, United States It has long been argued that many key questions in neuroscience can best be posed in information-theoretic terms; the efficient coding hypothesis discussed by Attneave, Barlow, Atick, et al represents perhaps the best-known example. Answering these questions quantitatively requires us to compute the Shannon information rate of neural channels, whether numerically using experimental data or analytically in mathematical models. The non-linearity and non-Gaussianity of neural responses has complicated these calculations, particularly in the case of stimulus distributions with temporal dynamics and nontrivial correlation structure. In this work we extend a method proposed in [1] that allows us to compute the information rate analytically in some cases. In our approach the stimulus is modeled as a temporally correlated stationary process. Analytical results are available in both the high and low signal-to-noise (SNR) regimes: the former corresponds to the case in which a large population of neurons responds strongly to the stimulus, while the latter implies that the available neurons are only weakly tuned to the stimulus properties, or equivalently that the stimulus magnitude is relatively small. In intermediate SNR regimes, good numerical approximations to the information rate are available using efficient forward-backward decoding methods and Laplace approximations [2,3]. We find that when the number of neurons increases unboundedly, the mutual information for temporarily correlated stimuli has a simple form depending only on the Fisher information, paralleling the classical connection between Fisher and Shannon information for static stimuli [4]. For finite-size populations we are able to calculate the performance difference between a decoder based solely on temporally instantaneous observations and one which integrates temporal observations; interestingly, in both the high- and low-SNR regimes, a simple decoder which only includes temporally local neural responses is able to optimally extract information about the stimuli.

Down syndrome (DS) is the most common genetic cause of intellectual disability (ID). Abilities relating to executive function, memory and language are particularly affected in DS, although there is a large variability across individuals. People with DS also show an increased risk of developing dementia. While assessment batteries have been developed for adults with DS to assess cognitive abilities, these batteries may not be suitable for those with more severe IDs, dementia, or visual / hearing difficulties. Here we report the development of an informant rated questionnaire, the Cognitive Scale for Down Syndrome (CS-DS), which focuses on everyday abilities relating to executive function, memory and language, and is suitable for assessing these abilities in all adults with DS regardless of cognitive ability. Complete questionnaires were collected about 128 individuals with DS. After final question selection we found high internal consistency scores across the total questionnaire and within the executive function, memory and language domains. CS-DS scores showed a wide range, with minimal floor and ceiling effects. We found high interrater (n = 55) and test retest (n = 36) intraclass correlations. CS-DS scores were significantly lower in those aged 41+ with significant cognitive decline compared to those without decline. Across all adults without cognitive decline, CS-DS scores correlated significantly to measures of general abilities. Exploratory factor analysis suggested five factors within the scale, relating to memory, self-regulation / inhibition, self-direction / initiation, communication, and focussing attention. The CS-DS therefore shows good interrater and test retest reliability, and appears to be a valid and suitable informant rating tool for assessing everyday cognitive abilities in a wide range of individuals with DS. Such a questionnaire may be a useful outcome measure for intervention studies to assess improvements to cognition, in addition to detecting dementia-related cognitive decline. The CS-DS may also be a useful tool for other populations with ID.