On Feature Binding in Space and Time
نویسنده
چکیده
When presented with a yellow Volkswagen and a red Ferrari, how does the brain figure out which color goes with which car? The binding problem refers to how the visual system pre-consciously combines visual features of objects in the physical world to create coherent mental equivalents in our consciousness. I discuss why feature binding is a problem for our brains despite its seemingly effortless resolution in everyday life. Drawing from experimental cognitive psychology, I demonstrate how it manifests in space and time.
منابع مشابه
Online Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کاملSupervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کاملTime-Dependent Real-Space Renormalization Group Method
In this paper, using the tight-binding model, we extend the real-space renormalization group method to time-dependent Hamiltonians. We drive the time-dependent recursion relations for the renormalized tight-binding Hamiltonian by decimating selective sites of lattice iteratively. The formalism is then used for the calculation of the local density of electronic states for a one dimensional quant...
متن کاملNewborn EEG Seizure Detection Based on Interspike Space Distribution in the Time-Frequency Domain
This paper presents a new time-frequency based EEG seizure detection method. This method uses the distribution of interspike intervals as a criterion for discriminating between seizure and nonseizure activities. To detect spikes in the EEG, the signal is mapped into the time-frequency domain. The high instantaneous energy of spikes is reflected as a localized energy in time-frequency domain. Hi...
متن کاملتعیین ماشینهای بردار پشتیبان بهینه در طبقهبندی تصاویر فرا طیفی بر مبنای الگوریتم ژنتیک
Hyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional space, they are efficient tool for classification of hyper spectral imagery. However, there are two optimization issues which s...
متن کاملA New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, form...
متن کامل