We investigated the influences of different types of temporal correlations in the input signal on the functions and coding properties of neurons in the primary visual cortex (V1). We found that the temporal transfer functions of V1 neurons exhibit higher gain, and the spike responses exhibit higher coding efficiency and information transmission rates, for the 1/f (natural long-term correlation) signals than for 1/f(0) (no correlation) and 1/f(2) (stronger long-term correlation) signals. These results suggest that the intermediate long-term correlation ubiquitous to natural signals may play an important role in shaping and optimizing the machinery of neurons in their adaptation to the natural environment.

This study compared embedded and nonembedded (unilateral) television news coverage during the invasion and the occupation of Iraq. Content analysis was conducted of ABC, CBS, NBC, and CNN news during the invasion and during the occupation examining whether embedded and nonembedded news reports were different and, if so, how. The results revealed that compared to nonembedded reports, embedded network television news stories were more favorable in overall tone toward the military, more favorable in depictions of military personnel, and featured greater use of episodic frames which, as a result, elicited somewhat more positive relational cues. In addition, the results indicated that compared to network news coverage of the occupation, news stories of the invasion were more positive in tone and employed more episodic framing.

Sensory systems have been found to adapt their machinery to process natural signals efficiently. What are the statistical features in natural signals that could be driving neuronal adaptation? Our study demonstrates that neural coding of macaque primary visual cortex (area V1) neurons is adaptive to the scale-invariant long-term correlation in the signals. Power spectrum of such signals displays a 1/f power law behavior, which is universal in natural signals. We found that V1 neurons exhibit higher efficiency, transmit more information and carry less noise when responding to 1/f signals than to other kinds of signals. These findings suggest that the appropriate long-term correlation might be a key driving force in shaping and optimizing the machinery of neurons in their adaptation to the natural environment.

This study examines broadcast news coverage of Operation Iraqi Freedom (OIF) to assess differences between embedded television reporters and non-embedded reporters. A variety of communication theories are presented that posit that embedded journalists will produce more positive coverage of the military and its personnel, that these journalists will develop increased organizational commitment, that their coverage will be more episodic, and have increased levels of affect and positive relational messages. Thirty days of OIF television news coverage from four major news networks were evaluated using content analysis. The results indicate embedded television reporters produce stories that are more positive and use more episodic framing in their coverage compared to non-embedded reporters. Insufficient data is present to determine if stories produced by embedded reporters produced more positive relational messages. The results only partially support the hypotheses that interviews of military personnel conducted by embedded reporters elicit more positive affect. Finally, two additional research questions found that the tone of coverage differs between the invasion and occupation, and that there is a difference in several dependent variables across network newscasts. Embedded Reporting 3 Embedding Journalists in Military Combat Units: During the Invasion and Occupation of Iraq The relationship between the military and the news media has seen many seasons; some dry and some fruitful. Operation Iraqi Freedom (OIF) began a productive season in the long history of embedded journalists. The 2003 invasion of Iraq gave embedded journalists unprecedented access to relatively unrestricted front-line coverage. More than 600 U.S. and foreign journalists embedded with military units and have reported from aircraft carriers, Special Forces units and infantry and Marine divisions (McLane, 2004). Before Operation Iraqi Freedom (OIF), journalists had never “worked alongside U.S. military units...in such numbers [or] in such an organized fashion” (Knickmeyer, 2003). The Pentagon’s aggressive and ambitious embedding program was directed by Victoria Clark, a senior spokesperson for Secretary of Defense Donald Rumsfeld at the outset of OIF. She defined the process as, “living, eating, moving, in combat with the unit that [the journalist is] attached to” (DoD News Transcript, 2003). The Department of Defense’s motives for embedding journalists are not clear. There has been much speculation, however. Possible reasons range from using the media as a tool against propaganda to reducing the impact of casualties to ensure public support for the war (Brightman, 2003; Miskin, Rayner, & Lalic, 2003). Other speculation has been that the Department of Defense knew the effects or influence that embedded reporting has on news coverage. Britain’s experience with embedding during the Falkland’s War against Argentina indicated that journalists develop “feelings of camaraderie that may affect [their] ability to be independent and objective” (Miskin et al., 2003, p.2). The “Stockholm Syndrome,” is the influence on reporters work due to a close relationship with their units (McClane, 2004). Many journalists wrote about their fear of succumbing to this syndrome. Embedded Reporting 4 “While this closeness did not necessarily prevent them from objective or critical reporting, journalists worried about losing their impartiality” (p. 81). Although there is speculation of the influence of embedded journalism, the Department of Defense’s support of embedding was most likely motivated by a genuine desire to “facilitate maximum, in-depth coverage of U.S. forces in combat and related operations” as well as giving this access to national and international media (Secretary of Defense, 2003). In an interview with Dick Gordon, from NPR’s The Connection, Brian Whitman, Deputy Assistant Secretary of Defense for Public Affairs, stated that the Department of Defense was working to close the gap between reporters and the media. He said, “an embedded reporter is going to see the good, the bad, and the ugly” (DoD News Transcript, 2003). This study sought to determine whether embedding journalists with military units during combat produces different television news reports and, if so, the nature of such differences. In addition, this study investigates whether there are any differences in news stories during the initial invasion and reports more than a year later during the occupation. This study is a followup to a previous investigation conducted in early 2004.

Traditional view of macaque V1 neurons is that they are static spatiotemporal filters. This study shows that V1 neurons’ temporal receptive fields (kernels) are dynamic and adaptive. In order to study the adaptation of the kernels to a variety of stimuli, we developed a regression approach to recover kernels using naturalistic stimuli. Using this technique, we studied how the kernels of the V1 neurons in macaque monkeys adapt to the frequency bandwidth of naturalistic signals. We found that the temporal frequency tunings and the gains of the receptive fields of the neurons change according to the statistical context of the stimulus environment.

Traditional methods in neural data analysis are not appropriate for analyzing the spike train of a single experimental trial. We show that, by constructing a model of firing statistics, a more accurate estimate of the firing rate for a single spike train can be obtained. The model is based on the assumption that the neuron's spikes are generated by a non-homogeneous Poisson process which follows Markovian dynamics. We test the method by reconstructing the input stimulus based on the neurons’ responses either on the raw spike data or the firing rate estimate. The spike data were recorded from macaque V1 neurons in response to a sinewave grating undergoing pseudo-random walk. For a large percentage of the cells studied, the reconstruction is significantly improved by using the estimated firing rate over the raw spikes, suggesting that estimated rate reflects more accurately the underlying state of the neurons.

We report here that shape-from-shading stimuli evoked a long-latency contextual pop-out response in V1 and V2 neurons of macaque monkeys, particularly after the monkeys had used the stimuli in a behavioral task. The magnitudes of the pop-out responses were correlated to the monkeys' behavioral performance, suggesting that these signals are neural correlates of perceptual pop-out saliency. The signals changed with the animal's behavioral adaptation to stimulus contingencies, indicating that perceptual saliency is also a function of experience and behavioral relevance. The evidence that higher-order stimulus attributes and task experience can influence early visual processing supports the notion that perceptual computation is an interactive and plastic process involving multiple cortical areas.

We describe a new, publicly accessible Chinese character recognition system based on a nearest neighbor classifier that utilizes a number of sophisticated techniques to improve its performance. To increase throughput, a 400dimensional feature space is compressed through multiple discriminant analysis techniques to 100 dimensions. Recognition accuracy is improved by scaling these dimensions to achieve uniform variance. Two neural network classifiers are compared using the new feature space, Kohonen’s Learning Vector Quantization and Geva and Sitte’s Decision Surface Mapping. Experiments with a 37,000 character ground truthed dataset show performance comparable to other systems in the literature. We are now employing noise and distortion models to quantify the robustness of the recognizer on realistic page images.

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons' responses reflecting higher order perceptual computations related to Ullman's (Cognition 1984; 18:97-159) visual routines and Marr's (Vision NJ: Freeman 1982) full primal sketch, 2 1/2D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons' precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons' activities.

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons’ responses reflecting higher order perceptual computations related to Ullman’s (Cognition 1984;18:97–159) visual routines and Marr’s (Vision NJ: Freeman 1982) full primal sketch, 22D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons’ precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons’ activities. © 1998 Elsevier Science Ltd. All rights reserved.

Building on previous work in Chinese character recognition, we describe an advanced system of classification using probabilistic neural networks. Training of the classifier starts with the use of distortion modeled characters from four fonts. Statistical measures are taken on a set of features computed from the distorted character. Based on these measures, the space of feature vectors is transformed to the optimal discriminant space for a nearest neighbor classifier. In the discriminant space, a probabilistic neural network classifier is trained. For classification, we present some modifications to the standard approach implied by the probabilistic neural network structure which yields significant speed improvements. We then compare this approach to using discriminant analysis and Geva and Sitte's Decision Surface Mapping classifiers. All methods are tested using 39,644 characters in three different fonts.