Descriptive analysis of the test of everyday attention for children in a Spanish normative sample

Introduction. Test of Everyday Attention for Children (TEA-Ch) has been validated in different countries demonstrating that it is an instrument with a correct balance between reliability and duration. Given the shortage of trustworthy instruments of evaluation in our language for infantile population we decide to explore the Spanish version of the TEA-Ch. Methods. We administered TEA-Ch (version A) to a sample control of 133 Spanish children from 6 to 11 years enrolled in school in the Community of Madrid. Four children were selected at random by course of Primary Education, distributing the sex of equivalent form. Descriptive analysis and comparison by ages and sex in each of the TEA-Ch's subtests were conducted to establish a profile of the sample. In order to analyze the effect of the age, subjects were grouped in six sub-samples: 6, 7, 8, 9, 10 and 11 years-old. Results. This first descriptive analysis demonstrates age exerted a significant effect on each measure, due to an important "jump" in children's performance between 6 and 7 years-old. The effect of sex was significant only in two visual attention measures (Sky Search & Map) and interaction age and sex exerted a significant effect only in the dual task (Score DT). Conclusions. The results suggest that the Spanish version of the TEA-Ch (A) might be a useful instrument to evaluate attentional processes in Spanish child population.

The left visual field attentional advantage: No evidence of different speeds of processing across visual hemifields.

Temporal-order judgment (TOJ) and simultaneity judgment (SJ) tasks are used to study differences in speed of processing across sensory modalities, stimulus types, or experimental conditions. Matthews and Welch (2015) reported that observed performance in SJ and TOJ tasks is superior when visual stimuli are presented in the left visual field (LVF) compared to the right visual field (RVF), revealing an LVF advantage presumably reflecting attentional influences. Because observed performance reflects the interplay of perceptual and decisional processes involved in carrying out the tasks, analyses that separate out these influences are needed to determine the origin of the LVF advantage. We re-analyzed the data of Matthews and Welch (2015) using a model of performance in SJ and TOJ tasks that separates out these influences. Parameter estimates capturing the operation of perceptual processes did not differ between hemifields by these analyses, whereas parameter estimates capturing the operation of decisional processes differed. In line with other evidence, perceptual processing also did not differ between SJ and TOJ tasks. Thus, the LVF advantage occurs with identical speeds of processing in both visual hemifields. If attention is responsible for the LVF advantage, it does not exert its influence via prior entry.

Impaired Mesopic Visual Acuity in Eyes with Early Age-Related Macular Degeneration

Purpose.: To determine photopic and mesopic distance high-contrast visual acuity (HC-VA) and low-contrast visual acuity (LC-VA) in eyes with early age-related macular degeneration (AMD). Methods.: Measurements were made in 22 subjects with early AMD and 28 healthy control subjects. Inclusion criteria included a photopic HC-VA of 20/25 or better. Distance VA was measured using HC (96%) and LC (10%) Bailey-Lovie logMAR letter charts under photopic (85 cd/m2) and mesopic (0.1–0.2 cd/m2) luminance conditions. Results.: Mean mesopic distance HC-VA and LC-VA were significantly worse (0.1 logMAR and 0.28 logMAR, respectively) in the early AMD group than in the control group. Under mesopic conditions, the mean difference between LC-VA and HC-VA was significantly greater in the early AMD (0.45 logMAR) than the control group (0.27 logMAR). Mean differences between mesopic versus photopic HC-VA and mesopic versus photopic LC-VA were significantly greater in the early AMD than the control group (0.13 and 0.32 logMAR of difference between the means, respectively). Sensitivity and specificity were significantly greater for mesopic LC-VA than for mesopic HC-VA (Receiver Operating Characteristics, area under the curve [AUC], 0.94 ± 0.030 and 0.76 ± 0.067, respectively). AUC values for photopic HC-VA and LC-VA were below 0.70. Conclusions.: Visual acuity testing under low luminance conditions emerged as an optimal quantitative measure of retinal function in early AMD.

Power spectrum model of visual masking: simulations and empirical data

In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.