An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m(2)). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes.

People can estimate the current position of an occluded moving target. This is called motion extrapolation, and it has been suggested that the performance in such tasks is mediated by the smooth-pursuit system. Experiment 1 contrasted a standard position extrapolation task with a novel number extrapolation task. In the position extrapolation task, participants saw a horizontally moving target become occluded, and then responded when they thought the target had reached the end of the occluder. Here the stimuli can be tracked with pursuit eye movements. In the number extrapolation task, participants saw a rapid countdown on the screen that disappeared before reaching zero. Participants responded when they thought the hidden counter would have reached zero. Although this stimulus cannot be tracked with the eyes, performance was comparable on both the tasks. The response times were also found to be correlated. Experiments 2 and 3 extended these findings, using extrapolation through color space as well as number space, while Experiment 4 found modest evidence for similarities between color and number extrapolation. Although more research is certainly needed, we propose that a common rate controller guides extrapolation through physical space and feature space. This functions like the velocity store module of the smooth-pursuit system, but with a broader function than previously envisaged.

Neural selectivity in the early visual cortex strongly reflects the statistics of our environment (Barlow, 2001; Geisler, 2008). Although this has been described extensively in literature through various encoding hypotheses (Barlow and Földiák, 1989; Atick and Redlich, 1992; Olshausen and Field, 1996), an explanation as to how the cortex might develop the computational architecture to support these encoding schemes remains elusive. Here, using the more realistic example of binocular vision as opposed to monocular luminance-field images, we show how a simple Hebbian coincidence-detector is capable of accounting for the emergence of binocular, disparity selective, receptive fields. We propose a model based on spike timing-dependent plasticity, which not only converges to realistic single-cell and population characteristics, but also demonstrates how known biases in natural statistics may influence population encoding and downstream correlates of behavior. Furthermore, we show that the receptive fields we obtain are closer in structure to electrophysiological data reported in macaques than those predicted by normative encoding schemes (Ringach, 2002). We also demonstrate the robustness of our model to the input dataset, noise at various processing stages, and internal parameter variation. Together, our modeling results suggest that Hebbian coincidence detection is an important computational principle and could provide a biologically plausible mechanism for the emergence of selectivity to natural statistics in the early sensory cortex.