What crowds a letter in the periphery?

In the periphery, nearby letters (flankers) can impair the identification of a target letter, but white-noise patches or gratings do not (Palomares et al., ARVO99), suggesting that letter crowding is unlike contrast masking (Chung, Levi, Legge, 2001). The aim of this study was to systematically test different types of flankers that may induce letter crowding in the periphery. Subjects identified a target letter presented at 5 deg inferior to fixation. Letters were in Times New Roman, of size 0.4 log units above the subject's acuity. Six conditions were ested: target letter alone, flanked by same-polarity letter flankers, opposite-polarity letter flankers, "letter"- noise patches, or white-noise patches at 1 x-height spacing, or flanked by same-polarity letter flankers at 2 x-height spacing. The letternoise patch was created by scrambling the phase spectrum of a letter but retaining its power spectral density (PSD). Moreover, both types of noise had the same contrast energy and bounding box as the letter they replaced. Flankers were at 20% contrast. Threshold contrast of the target letter was determined at 79% correct. Average threshold contrast (across 3 Ss) for the target-alone condition was 12.8% (+/-0.04 log units). No significant threshold elevation was observed for white-noise patches or letter flankers at 2x spacing. All other conditions led to significant crowding (avg. threshold elevation in log units - letter: 0.35, letter noise: 0.26, reverse contrast: 0.21). Threshold elevation between letter and letter noise flankers was not significant for 2 out of the 3 Ss. Letter-noise patches caused significant crowding, while white-noise patches did not. Spatial frequency distribution thus seems to play a major role in letter crowding. Phase, however, which defines the visual form of the flankers, had only a limited role. We speculate that a main component of letter crowding may be noise masking, with noise being induced by and having a similar PSD to the flankers

The Tsallis Entropy in the EEGs of Normal and Demented Individuals

The electroencephalogram (EEG) is a recording of the brain's total electrical activity. Since the brain processes information, the information in the brain's total electrical activity probably corresponds to the information processing in the brain. This assumption was used to study the entropy or 'self-information' in the EEGs of participants who were performing a short-term memory task. There were two groups of participants in this study; one group had a medical diagnosis of "normal aging,"(Normal) and the other group had a diagnosis of "very mild dementia," (Dementia). The dementia diagnosis means that they have short-term memory impairment. The EEG of each participant was recorded while they performed a short-term memory task; face recall. Their EEG was also recorded while they performed a second short-term memory task; object recall. These EEG data were used to test a basic hypothesis; the entropy in the EEGs of the Dementia group would be significantly different than the entropy in the EEGs of the Normal group. When choosing a method to test this hypothesis, it is important to account for how the brain processes a recall stimulus. The brain information processing that occurs during a recall task has informational, temporal and spatial properties. Therefore, to accurately analyze the entropy in the EEG data three things must be specified: 1) which entropy measure to use, 2) which time intervals of the EEG data to use, and 3) what locations on the participant's scalp (choice of EEG electrodes) to use. The specifications used are: 1) Entropy measure. The entropy measure used was the Tsallis entropy. Tsallis entropy is a generalization of the Shannon entropy to a non-additive entropy measure. An additive entropy measure assumes that the entropy of a whole system is equal to the sum of the entropies of each part of the system. EEG data may not conform to this assumption, so we used the Tsallis entropy instead of the Shannon entropy. 2) EEG time interval. The EEG data used were those data that occurred during the first 300 milliseconds (ms) after the appearance of the recall stimulus. A participant's response is purely perceptual/cognitive for about 300 ms after the appearance of the recall stimulus (Sternberg 1966, 1969). Muscle movement responses, responses which are more variable, begin later in the task. This suggests that the first 300 ms of the EEG data will be more specific to the task. 3) Spatial component - the choice of EEG electrodes. Electrodes chosen for this EEG data analysis should correspond with the way that information moves through the brain during the first 300 ms of the recall task. The task began with a visual stimulus. The information from this stimulus enters the posterior cortex at V1 (Broadman area 17). After about 150 ms, this (transformed and partially altered) information enters the anterior cortex. Therefore, the EEG data which correspond to the recall task are those data recorded by posterior electrodes during the first 150 ms and those data recorded by anterior electrodes for the next 150 ms. The entropy analysis of these data was accomplished by computing the Tsallis entropy in two posterior electrodes; P3 and P4 for the first 150 ms after the appearance of the stimulus. Then, the Tsallis entropy of the EEG data in the second, contiguous 150 ms time interval was computed. The EEG data for this second entropy measurement were recorded by two anterior electrodes: T7Fp3 and T8Fp4 (electrodes placed slightly behind the temples). Thus, the EEG data analyzed were data which corresponded to the flow of brain information during the first 300 ms of the recall task. It has been assumed that the entropy in an EEG corresponds to brain information processing during the recall task. However, these entropies, alone, do not show how brain information changes when moving from posterior cortex to anterior cortex. A commonsense solution to this difficulty would be to compute the mutual entropy (mutual information) measure. However, this measure is based on the assumption of a closed information channel. This assumption does not hold true for the brain. The neural information pathway from early visual cortex to anterior cortex is not a closed pathway. For this reason, a relative entropy measure, a measure of the amount of anterior EEG entropy relative to the amount of posterior EEG entropy is more appropriate. This relative entropy measure is the ratio of the anterior EEG entropy to the posterior EEG entropy. This ratio of entropies or "entropy ratio" was the measure used to compare Normal and Dementia participants. Normal participants were expected to have larger entropy ratios than those with dementia. Thus, the quantitative hypothesis is, the entropy ratios of the Normal participants will be higher than the entropy ratios of the Dementia participants. For the most part, this hypothesis was formulated before the testing of the 47 participants. It was ante hoc. To be more exact, the specifics of the method for testing the hypothesis were formulated during the EEG testing of the first few participants; about eight participants. A total of 33 normal aging participants and 14 very mildly demented participants were tested. The results are: 31 of the 33 Normals had higher entropy ratios than the 14 entropy ratios of the Dementia group. These 31 entropy ratios were all greater than one. 14 of the 14 entropy ratios of the Dementia group were less than or equal to one (at two decimal places of precision). Participant's entropy ratios can be used to discriminate between the Normal and Dementia groups. Assume that entropy ratios greater than one denote normal aging and entropy ratios less than or equal to one denote dementia (impaired short-term memory). Then these criteria distinguish between the Normal and Dementia groups with a specificity of 100% (14 of 14) and a sensitivity of 94% (31 of 33). This means that two Normal participants were incorrectly classified as having dementia. However, both of these participants have a family history of Alzheimer's Disease dementia. One participant had a parent, now deceased, who had severe dementia. This participants other parent has Alzheimer's Disease. The second participant also has a parent with Alzheimer's a genetic predisposition to Alzheimer's Disease dementia. For this reason, these two participants may have very early Alzheimer's Disease. This remains to be seen, as does further refinement and testing of this hypothesis

Disentangling topdown from bottom up influences on attentional allocation in dynamic scenes

Motivation: Attentional allocation is determined by the interplay between bottom-up and top-down influences. Here we try to quantify the relative contributions of different influences on attentional allocation in dynamic scenes, as well as examine how they change over time. Methods: In order to manipulate the availability of top-down influences on attentional allocation, heterogeneous video clips were cut into clippets (M=2s), which were scrambled and re-assembled into MTV-style clips. Two groups of 8 Subjects each were instructed to "follow the main actors and actions". One group viewd the original stimuli while the other group viewd the MTV-style clips. Eye positions were recorded using an ISCAN eye-tracker (240Hz, yielding a total of more than a million samples for each group), and segmented into saccades, blinks, and fixation/smooth pursuit periods. A saliency-based model of attention capture (Itti & Koch 2000) was used to probe the relative contribution of bottom-up influences on attentional allocation based on a novel performance metric - Chance-Adjusted Saliency Accumometric (CASA). CASA values were computed based on the weighted sum of differences between normalized saliency at human vs. random saccade targets. Results: Total CASA based on the full saliency model was 6% higher in the MTV group compared to the original group. In both original and MTV groups, CASA based on either motion or flicker features alone was ~95% of the CASA based on the full saliency model. CASA based on either color, intensity, or orientation features alone was ~66% of the full model CASA. Generally, CASA values for earlier saccades after stimulus onset (clip or clippet start) were higher than for later saccades, but tapered off and flactuated around a fairly high value after the first several saccades. Conclusions: The 6% CASA difference between the original and MTV groups shows that eliminating visual context beyond the first ~2s of viewing barely increased the overall relative weight of bottom-up influences on attentional allocation. Our results imply that the relative weight of top-down influences on attentional allocation in dynamic scenes does not increase with viewing time (beyond the first ~2s). We also found that either motion or flicker are ~150% stronger than either color, intensity, or orientation as bottom-up attractors of attention

Ruby: A Robotic Platform for Real-time Social Interaction

The majority of our waking hours are spent engaging in social interactions. Some of these interactions occur at the level of long-term strategic planning while others take place at faster time scales, such as in conversations or card games. The abilityto perceive subtle gestural, postural, and facial cues, in addition to verbal language, in real-time is a critical component. An understanding of the underlying perceptual primitives that support this kind of real-time social cognition is key to understanding social development. Robots present an ideal opportunity to study the development of social interaction in infants [Fasel,Deak,Triesch,Movellan 2002]. It is possible to create robots that exhibit precisely controlled contingency structures. By observing how infants interact with these robots we gain an opportunity to understand how infants identify the operating characteristics of the social agents with whom they interact. We have recently developed a social interaction robot, "Ruby", designed to communicate with children. Ruby is endowed with the following real-time perceptual primitives to facilitate social interaction: face tracking, motor control and speech detection. It communicates via head and eye movements and we have recently run pilot studies indicating that Ruby is fun and non-threatening to children. Ruby's face tracking system consist of 3 cues taken from 3 inputs. The first 2 inputs are high-resolution pan-tilt-zoom color cameras which are the "eyes". The third input is an omni-directional camera acting as Ruby's peripheral vision. Each eye uses the MPLab's contrast-feature based frontal face finder [Fasel et al CVIU2004] and adaptive color-based tracker [Ishiguro et al 2003] [Hershey et al CVPR2004]. Ruby combines both of these to find both frontal and rotated faces at more than 30 frames per second. Ruby's motor control system currently has 3 components; neck control, eye control, and control of external objects for experiments. Ruby also features speech detection [Pellom 2004] and response with variable delay parameters. We are now adding eye and eye-blink detection[Fasel et al CVIU2004], expression recognition[Littlewort-Ford, Bartlett et al 2004], recognition of common communicative words in English, arm movements, finger pointing, and touch sensors. We hope to use Ruby to collect and analyze data on social interaction and contingency and on the development of social interaction in infants

Spatial content of faces may be critical for individualizing faces

Many of the phenomena associated with face (vs. object) recognition can be understood in terms of a representation for individuating faces that retains aspects of the original spatial filter activations, as posited by Malsburg's Gabor Jet model that mimics the functions of the columns of V1 simple cells. Objects, in contrast, may be represented by a structural description specifying explicit relations among view invariant properties of edges of simple parts. Subjects judged whether a sequentially presented pair of images was the face of the same person, in one condition, or the same chair, in another (Biederman & Kalocsai, 1997). The images were filtered (in the Fourier domain) into 8 scales and 8 orientations. Complementary pairs of each person or chair image were created by assigning the content of every other combination of scale and orientation to a given image. (If the scales are ordered as rows and the orientations as columns to form a checkerboard, then one member of a complementary pair would have the content from the red squares and the other member the content from the black squares.) On half the matching trials (i.e., the same chair or the same person), the images were complements; on the other half they were identical. Consistent with the hypothesis that face representations retained the original spatial content, matching complements of faces resulted in markedly greater error rates and RTs than the identical images. No such costs were apparent when matching chairs. However, the chairs different in small parts that could be discerned from their edges. The present study examined the costs of complementizing smooth, non-face, blobby objects (variations in the amplitudes of the harmonics of a sphere) that differed from each other metrically, as did the faces. A cost of complementizing was observed for the blobs but this cost was smaller than that for the faces. The necessity to make fine metric judgments of smooth surfaces may underlie part of the sensitivity of faces to the spatial content of the imag

A mathematical framework for the design and analysis of feature biasing strategies

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Bayesian local directional accuracy of a directionally selective Ganglion-cell ensemble

On-Off directionally selective ganglion cells (DSGC) of the rabbit retina send information about the direction of motion to the rest of the brain. Each of these cells responds best for motions in a preferred direction. There are four types of DSGC, each type preferring motions along one Cartesian axis (up, down, left or right). Every point in visual space is viewed by one cell of each type. We have measured the distribution of responses of DSGC as a function of contrast and direction of motion. With this information, and knowing the distribution of contrasts and directions of motion in natural images, we can apply Bayesian Analysis to infer the possible local directional accuracy of a DSGC ensemble's output. We applied Bayes Theorem: P[c,f|r] = P[r|c,f]P[c,f] P[r] where r, c, and f are response, contrast, and direction of motion respectively. We performed both Maximum-Likelihood and Maximum-a-Posteriori analyses. The former took only the biology into account (i.e., our measurements), while the latter assumed an exponential distribution for contrasts (Balboa & Grzywacz, 2000; Tadmor & Tolhurst, 2000) and a homogeneous distribution of directions of motion (Balboa & Grzywacz, unpublished observations). We found that a local DSGC ensemble could provide highly accurate directional estimates, with RMS errors of less than 3° for stimuli with contrasts of 100%. Moreover, an ensemble could provide directional estimates with less than10° RMS error for stimuli with contrasts of only 15% (the mean contrast in natural images). Interestingly, including the contrast and direction-of-motion priors did not improve performance significantly. This is because the Maximum-Likelihood estimate only fails when the biological system is uncertain. DSGCs have relatively low noise and therefore, low uncertainty. It remains to be seen whether other prior information, such as the distribution of speeds or spatial frequencies, can improve the DSGC system's directional accuracy

Neural control of behavior in a neuromechanical salamander simulation

With arguably the simplest vertebrate nervous system, the salamander is a model organism used to study basic issues in vertebrate neuroscience. We are using a neuromechanical simulation program to investigate behavioral consequences of the amphibian's neural organization. The system's Central Pattern Generators (CPGs)produce biologically plausible spinal waveforms, thus producing characteristic walking and swimming movement. The system has been used to investigate prey approach behavior with the realistic mechanical simulation and a simplified visual system model. We are using this model to investigate the relative contributions of sensory feedback and direct CPG control on head stabilization during locomotion. We will use computer vision techniques to determine, from video obtained under controlled conditions, precise kinematic parameters of the locomotion gaits, particularly head movement parameters. These kinematic parameters will constrain the simulation. From a current study, it appears likely that the pattern generators play the predominant role in head stabilization. We will describe an approach to meeting the kinematic constraints using the CPG model. A somewhat related effort studies a model of the amphibian medial pallium (MP) -- a likely homology to the hippocampal formation. Recent work on the salamander has produced neuroanatomical results that will constrain an older model of the toad MP that explained intricate patterns of habituation and ishabituation to prey-like stimuli. Investigators have hypothesized that hippocampus homologies are involved in evolutionarily conserved patterns of vertebrate behavior – e.g., spatial memory involved in navigation and perhaps other forms of territorial behavior. This computational investigation analyzes such a hypothesis in the context of a model which has demonstrated biological plausibility at other levels of analysis. The fusion of these disparate investigations will be a unified, modular, biologically plausible model of an autonomous agent successfully interacting with a complex environment in order to satisfy biological drives – a computational neuroethological model. Questions about the interfaces of these systems will be discussed

Recognition of non face objects, designed to require the same stimulus processing as that for faces, show only minimal effects of differences in contrast polarity or orientation direction

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Geometric Analysis of the Axo-Dendritic Interface in Neocortical Pyramidal Neurons

Since the time of Hebb, the physical substrate for learning and memory in the brain has been most often discussed in relation to activity-dependent synaptic "weight" changes mediated by LTP or LTD. However, two recent theoretical studies suggest that long term information storage in neural tissue could also depend (heavily) on structural plasticity at the interface between axons and dendrites (Poirazi & Mel, 2001; Stepanyants et al. 2002). According to both theories, the capacity for structure-based information storage depends on the interaccessibility of afferent axons and their dendritic targets within the neuropil. For example, how many different axons are likely to be accessible to any given postsynaptic dendrite with only minor structural modification? How much overlap exists in the set of axons accessible to two different dendritic branches? Using axonal and dendritic arborizations of a reconstructed pyramidal neuron from cat visual cortex as a reference point (courtesy J. Hirsch), we quantified the tradeoffs among several morphological variables that parameterize the axo-dendritic interface in neocortex, including spine length, spine density, dendritic branch length, branches per neuron, etc. We then used an extended version of the formula derived in Poirazi & Mel (2001) to understand how each of these variables separately and together contribute to the tissue's capacity to learn