PubMed Health⌕ Search

PubMed · 15744014

Contextual masking of oriented lines: interactions between surface segmentation cues.

Abstract

The ability of human observers to detect and discriminate a single feature of a visual image deteriorates markedly when the targeted feature is surrounded by others of a similar kind. This perceptual masking is mirrored by the suppressive effects of surround stimulation on the responses of neurons in primary visual cortex (area V1). Both perceptual and neuronal masking effects are partially relieved, however, if the targeted image feature is distinguished from surrounding features along some dimension, such as contour orientation. Masking relief is likely to play an important role in perceptual segmentation of complex images. Because dissimilar surfaces usually differ along multiple feature dimensions, we tested the possibility that those differences may influence segmentation in an invariant manner. As expected, we found that the presence of surrounding features resulted in perceptual masking and neuronal response suppression in area V1, but that either orientation or contrast polarity differences between the target and surrounding features was sufficient to partially relieve these effects. Simultaneous differences along both dimensions, however, yielded no greater relief from masking than did either difference alone. Although the averaged neuronal effects of orientation polarity cues were thus invariant, the time course over which these effects emerged after each stimulus appearance was different for the two cues. These findings refine our understanding of the functions of nonclassical receptive fields, and they support a key role for V1 neurons in surface segmentation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maarten J van der Smagt, Christian Wehrhahn, Thomas D Albright. 2005-03-02. Contextual masking of oriented lines: interactions between surface segmentation cues.. https://doi.org/10.1152/jn.00366.2004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Bootstrap-based methods for testing factor-by-curve interactions in generalized additive models: assessing prefrontal cortex neural activity related to decision-making.

In many situations the effect of a continuous covariate on response varies across groups defined by levels of a categorical variable. This paper addresses generalized additive models incorporating the so-called factor-by-curve interaction. A local scoring algorithm based on local linear kernel smoothers was used to estimate the model. Two different types of bootstrap-based procedures are proposed for testing interaction terms, namely, the likelihood ratio test, and a procedure based on an estimate of the interaction terms. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to studying prefrontal cortex neural activity associated with decision-making in monkeys. The proposed statistical procedure proved very useful in revealing the neural activity correlates of decision-making strategies adopted by monkeys in accordance with different behavioural tasks.

Action Potentials↗

Excitatory response of prefrontal cortical fast-spiking interneurons to ventral tegmental area stimulation in vivo.

Prefrontal cortical (PFC) pyramidal neurons (PN) and fast spiking interneurons (FSI) receive dopaminergic (DA) and non-DA inputs from the ventral tegmental area (VTA). Although the responses of PN to VTA stimulation and DA administration have been extensively studied, little is known about the response of FSI to mesocortical activation. We explored this issue using single and double in vivo juxtacellular recordings of medial PFC PN and FSI with chemical VTA stimulation. Electrophysiological characteristics combined with Neurobiotin staining and parvalbumin immunohistochemistry allowed identification of recorded cells as FSI or PN. NMDA injection into the VTA increased firing in all FSI tested (n = 7), whereas most PN (7/11) responded with an inhibition. Furthermore, FSI excitation matching the temporal course of PN inhibition was observed with FSI-PN paired recordings (n = 5). These divergent electrophysiological responses to mesocortical activation could reflect PFC GABAergic interneurons contributing to silencing PN. Thus, the mesocortical system could provide a critical control of PFC circuits by simultaneously affecting FSI and PN firing.

Action Potentials↗

Online analysis method for intrinsic signal optical imaging.

The intrinsic optical imaging technique has been widely applied for the visualization of functional maps in the sensory cortices of mammals. Many current studies refer this mapping in order to focus thereafter on particular features, at some particular locations: a fast and accurate mapping is therefore required. However, even during a successful experiment, the recorded raw data are usually contaminated by some kinds of noise that cannot necessarily be averaged out over the trials. An adequate image data analysis method has to be applied to extract signals closely related neural activities in response to presented stimuli. Thus far two different analysis methods could be adopted: the band-pass filtering and the GIF method [Yokoo T, Knight BW, Sirovich L. An optimization approach to signal extraction from noisy multivariate data. NeuroImage 2001:14;1309-26]. While the latter one is very efficient but requires the whole data in order to maximize the signal to noise ratio, the simple band-pass filtering technically reaches its limits very quickly. Here we propose another filtering method based on the polynomial subtraction of spatially smoothly modulated components. This simple method can visualize well-organized iso-orientation domains of the cat visual cortex with reliability similar to more sophisticated ones while allowing an online visualization of the clean data.

Action Potentials↗