EEG signatures of contextual influences on visual search with real scenes

The use of scene context is a powerful way by which biological organisms guide and facilitate visual search. Although many studies have shown enhancements of target-related electroencephalographic activity (EEG) with synthetic cues, there have been fewer studies demonstrating such enhancements during search with scene context and objects in real world scenes. Here, observers covertly searched for a target in images of real scenes while we used EEG to measure the steady state visual evoked response to objects flickering at different frequencies. The target appeared in its typical contextual location or out of context while we controlled for low-level properties of the image including target saliency against the background and retinal eccentricity. A pattern classifier using EEG activity at the relevant modulated frequencies showed target detection accuracy increased when the target was in a contextually appropriate location. A control condition for which observers searched the same images for a different target orthogonal to the contextual manipulation, resulted in no effects of scene context on classifier performance, confirming that image properties cannot explain the contextual modulations of neural activity. Pattern classifier decisions for individual images was also related to the aggregated observer behavioral decisions for individual images. Together, these findings demonstrate target-related neural responses are modulated by scene context during visual search with real world scenes and can be related to behavioral search decisions.Significance StatementContextual relationships among objects are fundamental for humans to find objects in real world scenes. Although there is a larger literature understanding the brain mechanisms when a target appears at a location indicated by a synthetic cue such as an arrow or box, less is known about how the scene context modulates target-related neural activity. Here we show how neural activity predictive of the presence of a searched object in cluttered real scenes increases when the target object appears at a contextual location and diminishes when it appears at a place that is out of context. The results increase our understanding of how the brain processes real scenes and how context modulates object processing.

The Language of Recollection in Support of Recognition Memory Decisions

Researchers often augment recognition memory decisions with confidence ratings or reports of “Remember” and “Know” experiences. While important, these ratings are subject to variation in interpretation and misspecification. Here we review recent findings from a “verbal reports as data” procedure in which subjects justify, in their own words, the basis of recognition. The application of a language pattern classifier to these justifications demonstrates that it: (a) is sensitive to the presence of recollection, (b) tracks individual differences in recognition accuracy, and (c) generalizes in a theoretically meaningful way to justifications from a separate experiment. More broadly, this approach should be useful for any cognitive decision task in which competing theories suggest different explicit bases underlying the judgments, or for which the explicit versus implicit basis of the decisions is in question.

Image Features Based Intelligent Apple Disease Prediction System

A pattern classifier (PC) is used to solve a variety of non-separable and complex computing problems. One of the key problems is to efficiently predict a type of disease in a typical fruit tree. The timely and accurately predicted disease in an apple tree may help a farmer to take appropriate preventive measures in advance. In this article, an apple disease diagnosis system is developed to predict the apple scab and leaf/spot blight diseases. In this article, low level and shape-based features are used for the development of an intelligent apple disease prediction system. First, the key image features like entropy, energy, inverse difference moment (IDM), mean, standard deviation (SD), perimeter, etc., are extracted from the apple leaf images. The model for the proposed system is trained by using multi-layer perceptron (MLP) pattern classifier and eleven apple leaves image features. The Gradient descent back-propagation algorithm is used for building the intelligent system to carry out the pattern classification. The proposed system is tested using some random samples and exhibits excellent diagnosis accuracy of 99.1%. The sensitivity of the proposed prediction model is 98.1% and specificity of ~99.9%.

Multivariate pattern classification on BOLD activation pattern induced by deep brain stimulation in motor, associative, and limbic brain networks

ABSTRACTFunctional magnetic resonance imaging (fMRI) concurrently conducted with the deep brain stimulation (DBS) has shown that diffuse BOLD activation occurred not only near stimulation locus, but in multiple brain networks, supporting that network-wide modulation would underlie its therapeutic effect. While the extent and pattern of activation varies depending on specific anatomical locus stimulated by DBS, some stimulation targets could induce similar activation pattern in cerebral cortex, albeit different therapeutic and adverse effects were yielded.In order to characterize the unique network-level activation effects of three DBS targets (subthalamic nucleus, the globus pallidus internus, and the nucleus accumbens), we trained the pattern classifier with DBS-fMRI data from three stimulation groups (21 healthy swine), wherein five six seconds of electrical stimulation was conducted while gradient-echo echo planar imaging was on going. Then whole brain regions were systematically grouped into different size of network-of-interest and the classification accuracy for individual target region was quantitatively assessed. We demonstrated that the pattern classifier could successfully differentiate BOLD activation pattern of cortical and subcortical brain regions originated from each individual stimulation target. Moreover, the success rate of classification indicated that some brain regions evoked indistinguishable BOLD pattern, suggesting the presence of commonly activated regions, which was influenced by stimulating different DBS targets.Our results provide an understanding of the biomarker of BOLD pattern that is associated with clinical effectiveness as well as an adverse effect associated to the stimulation. Further, we provide the proof-of-concept for multivariate pattern analysis that is capable of disentangling the complicated BOLD activation pattern, which cannot be readily achieved by a conventional univariate analysis.