Humans exploit a range of visual depth cues to estimate three-dimensional (3D) structure. For example, the slant of a nearby tabletop can be judged by combining information from binocular disparity, texture and perspective. Behavioral tests show humans combine cues near-optimally, a feat that could depend on: (i) discriminating the outputs from cue-specific mechanisms, or (ii) fusing signals into a common representation. While fusion is computationally attractive, it poses a significant challenge, requiring the integration of quantitatively different signals. We used functional magnetic resonance imaging (fMRI) to provide evidence that dorsal visual area V3B/KO meets this challenge. Specifically, we found that fMRI responses are more discriminable when two cues (binocular disparity and relative motion) concurrently signal depth, and that information provided by one cue is diagnostic of depth indicated by the other. This suggests a cortical node important when perceiving depth, and highlights computations based on fusion in the dorsal stream.

Learning is thought to facilitate the recognition of objects by optimizing the tuning of visual neurons to behaviorally relevant features. However, the learning mechanisms that shape neural selectivity for visual forms in the human brain remain essentially unknown. Here, we combine behavioral and functional magnetic resonance imaging (fMRI) measurements to test the mechanisms that mediate enhanced behavioral sensitivity in the discrimination of visual forms after training. In particular, we used high-resolution fMRI and multivoxel pattern classification methods to investigate fine learning-dependent changes in neural preference for global forms. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from fMRI activity. Comparing the performance of human observers and classifiers demonstrated that learning alters the observers' sensitivity to visual forms and the tuning of fMRI activation patterns in visual areas selective for task-relevant features. In particular, training on low-signal stimuli enhanced the amplitude but reduced the width of pattern-based tuning functions in higher dorsal and ventral visual areas. Thus, our findings suggest that learning of visual patterns is implemented by enhancing the response to the preferred stimulus category and reducing the response to nonpreferred stimuli in higher extrastriate visual cortex.

Eggshell maculation of most passerines is due to the deposition of the pigment protoporphyrin which is produced during biosynthesis of blood haem. Its functional signifi cance has only received empirical attention in recent years. Th is interest has generated a number of hypotheses of which some remain untested partly because the quantifi cation of protoporphyrin is analytically challenging and can be prohibitively expensive. Many studies have therefore used the extent of eggshell spotting as a proxy for total eggshell protoporphyrin concentration, although this has not been formally tested. Pigment scoring involves recording visible eggshell pigment attributes, such as spot intensity, distribution and size. Since even immaculate eggs can contain some protoporphyrin, there remains doubt over the degree to which visible pigment correlates with total pigment content of the shell. In this study, we test whether visible pigment scoring can be used as a proxy for protoporphyrin concentration of an eggshell. We use pigmented eggshells of two common British passerine species to compare eggshell spot intensity, distribution and spot size (as used by the visual pigment scoring method) with direct measures of eggshell protoporphyrin concentration. In addition, we compared an alternative method of pigment scoring, the pixel pigment scoring method, using a computer programme to quantify the number of pixels exceeding a specifi ed colour threshold. We demonstrate that although results from both scoring methods were positively correlated with eggshell protoporphyrin concentrations, the correlations were not suffi ciently strong to be used as surrogates in studies where actual pigment concentrations are required.

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.