Nonaccidental properties underlie shape recognition in Mammalian and nonmammalian vision

An infinite number of 2D patterns on the retina can correspond to a single 3D object. How do visual systems resolve this ill-posed problem and recognize objects from only a few 2D retinal projections in varied exposure conditions? Theories of object recognition rely on the nonaccidental statistics of edge properties, mainly symmetry, collinearity, curvilinearity, and cotermination. These statistics are determined by the image-formation process (i.e., the 2D retinal projection of a 3D object ); their existence under a range of viewpoints enables viewpoint-invariant recognition. An important question in behavioral biology is whether the visual systems of nonmammalian animals have also evolved biases to utilize nonaccidental statistics . Here, we trained humans and pigeons to recognize four shapes. With the Bubbles technique, we determined which stimulus properties both species used to recognize the shapes. Both humans and pigeons used cotermination, the most diagnostic nonaccidental property of real-world objects, despite evidence from a model computer observer that cotermination was not the most diagnostic pictorial information in this particular task. This result reveals that a nonmammalian visual system that is different anatomically from the human visual system is also biased to recognize objects from nonaccidental statistics.     

Measuring internal representations from behavioral and brain data

The study of internal knowledge representations is a cornerstone of the research agenda in the interdisciplinary study of cognition. An influential proposal assumes that the brain uses its internal knowledge of the external world to constrain, in a top-down manner, high-dimensional sensory data into a lower-dimensional representation that enables perceptual decisions and other higher-level cognitive functions [1-9]. This proposal relies on a precise formulation of the observer-specific internal knowledge (i.e., the internal representations, or models) that guides reduction of the high-dimensional retinal input onto a low-dimensional code. Here, we directly revealed the content of subjective internal representations by instructing five observers to detect a face in the presence of only white noise, to force a pure top-down, knowledge-based task. We used reverse correlation methods to visualize each observer's internal representation that supports detection of an illusory face. Using reverse correlation again, this time applied to observers' electroencephalogram activity, we established where and when in the brain specific internal knowledge conceptually interprets the input white noise as a face. We show that internal representations can be reconstructed experimentally from behavioral and brain data, and that their content drives neural activity first over frontal and then over occipitotemporal cortex.     

Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain

Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations.     

Identification of the two missing bacterial genes involved in thiamine salvage: thiamine pyrophosphokinase and thiamine kinase

The genes encoding thiamine kinase in Escherichia coli (ycfN) and thiamine pyrophosphokinase in Bacillus subtilis (yloS) have been identified. This study completes the identification of the thiamine salvage enzymes in bacteria.