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Perception of a stimulus can be characterized by two fundamental psychophysical measures: how well the stimulus can be discriminated from similar ones (discrimination threshold) and how strongly the perceived stimulus value deviates on average from the true stimulus value (perceptual bias). We demonstrate that perceptual bias and discriminability, as functions of the stimulus value, follow a surprisingly simple mathematical relation. The relation, which is derived from a theory combining optimal encoding and decoding, is well supported by a wide range of reported psychophysical data including perceptual changes induced by contextual modulation. The large empirical support indicates that the proposed relation may represent a psychophysical law in human perception. Our results imply that the computational processes of sensory encoding and perceptual decoding are matched and optimized based on identical assumptions about the statistical structure of the sensory environment.

Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle.We provide a theoretical result thatmakes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n-dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficiently encodes. Our result suggests that divisive normalization may have evolved to efficiently represent stimuli with Pareto distributions. We demonstrate that this efficiently encoded distribution is consistent with stylized features of naturalistic stimulus distributions such as their characteristic conditional variance dependence, and we provide empirical evidence suggesting that it may capture the statistics of filter responses to naturalistic images. Our theoretical finding also yields empirically testable predictions across sensory domains on howthe divisive normalization parameters should be tuned to features of the input distribution.

Topographic sensory representations often do not scale proportionally to the size of their input regions, with some expanded and others contracted. In vision, the foveal representation is magnified cortically, as are the fingertips in touch. What principles drive this allocation, and how should receptor density, for example, the high innervation of the fovea or the fingertips, and stimulus statistics, for example, the higher contact frequencies on the fingertips, contribute? Building on work in efficient coding, we address this problem using linear models that optimally decorrelate the sensory signals. We introduce a sensory bottleneck to impose constraints on resource allocation and derive the optimal neural allocation. We find that bottleneck width is a crucial factor in resource allocation, inducing either expansion or contraction. Both receptor density and stimulus statistics affect allocation and jointly determine convergence for wider bottlenecks. Furthermore, we show a close match between the predicted and empirical cortical allocations in a well-studied model system, the star-nosed mole. Overall, our results suggest that the strength of cortical magnification depends on resource limits.

Sensory systems preferentially strengthen responses to stimuli based on their reliability at conveying accurate information. While previous reports demonstrate that the brain reweighs cues based on dynamic changes in reliability, how the brain may learn and maintain neural responses to sensory statistics expected to be stable over time is unknown. The barn owl’s midbrain features a map of auditory space where neurons compute horizontal sound location from the interaural time difference (ITD). Frequency tuning of midbrain map neurons correlates with the most reliable frequencies for the neurons’ preferred ITD (Cazettes et al., 2014). Removal of the facial ruff led to a specific decrease in the reliability of high frequencies from frontal space. To directly test whether permanent changes in ITD reliability drive frequency tuning, midbrain map neurons were recorded from adult owls, with the facial ruff removed during development, and juvenile owls, before facial ruff development. In both groups, frontally tuned neurons were tuned to frequencies lower than in normal adult owls, consistent with the change in ITD reliability. In addition, juvenile owls exhibited more heterogeneous frequency tuning, suggesting normal developmental processes refine tuning to match ITD reliability. These results indicate causality of long-term statistics of spatial cues in the development of midbrain frequency tuning properties, implementing probabilistic coding for sound localization.

The main role of structures in ascending sensory systems is to extract raw features of sensory input and compartmentalize the information-bearing elements for use by the brain. Information-bearing elements can be apparent, as in the case of stimulus frequency or intensity (Ehret and Merzenich 1988; Tramo et al. 2002; Yu et al. 2010). The features of sound that drive neuronal ring at higher auditory centers, however, remain elusive.

The primate visual system analyzes statistical information in natural images and uses it for the immediate perception of scenes, objects, and surface materials. To investigate the dynamical encoding of image statistics in the human brain, we measured visual evoked potentials (VEPs) for 166 natural textures and their synthetic versions, and performed a reverse-correlation analysis of the VEPs and representative texture statistics of the image. The analysis revealed occipital VEP components strongly correlated with particular texture statistics. VEPs correlated with low-level statistics, such as subband SDs, emerged rapidly from 100 to 250 ms in a spatial frequency dependent manner. VEPs correlated with higher-order statistics, such as subband kurtosis and cross-band correlations, were observed at slightly later times. Moreover, these robust correlations enabled us to inversely estimate texture statistics from VEP signals via linear regression and to reconstruct texture images that appear similar to those synthesized with the original statistics. Additionally, we found significant differences in VEPs at 200–300 ms between some natural textures and their Portilla–Simoncelli (PS) synthesized versions, even though they shared almost identical texture statistics. This differential VEP was related to the perceptual “unnaturalness” of PS-synthesized textures. These results suggest that the visual cortex rapidly encodes image statistics hidden in natural textures specifically enough to predict the visual appearance of a texture, while it also represents high-level information beyond image statistics, and that electroencephalography can be used to decode these cortical signals.

Caesarean section (CS) rates continue to evoke worldwide concern because of their steady increase, lack of consensus on the appropriate CS rate and the associated additional short- and long-term risks and costs. We present the latest CS rates and trends over the last 24 years. We collected nationally-representative data on CS rates between 1990 to 2014 and calculated regional and subregional weighted averages. We conducted a longitudinal analysis calculating differences in CS rates as absolute change and as the average annual rate of increase (AARI). According to the latest data from 150 countries, currently 18.6% of all births occur by CS, ranging from 6% to 27.2% in the least and most developed regions, respectively. Latin America and the Caribbean region has the highest CS rates (40.5%), followed by Northern America (32.3%), Oceania (31.1%), Europe (25%), Asia (19.2%) and Africa (7.3%). Based on the data from 121 countries, the trend analysis showed that between 1990 and 2014, the global average CS rate increased 12.4% (from 6.7% to 19.1%) with an average annual rate of increase of 4.4%. The largest absolute increases occurred in Latin America and the Caribbean (19.4%, from 22.8% to 42.2%), followed by Asia (15.1%, from 4.4% to 19.5%), Oceania (14.1%, from 18.5% to 32.6%), Europe (13.8%, from 11.2% to 25%), Northern America (10%, from 22.3% to 32.3%) and Africa (4.5%, from 2.9% to 7.4%). Asia and Northern America were the regions with the highest and lowest average annual rate of increase (6.4% and 1.6%, respectively). The use of CS worldwide has increased to unprecedented levels although the gap between higher- and lower-resource settings remains. The information presented is essential to inform policy and global and regional strategies aimed at optimizing the use of CS.

A wide body of empirical evidence finds that approximately 25 percent of fiscal stimulus payments (e.g., tax rebates) are spent on nondurable household consumption in the quarter that they are received. To interpret this fact, we develop a structural economic model where households can hold two assets: a low-return liquid asset (e.g., cash, checking account) and a high-return illiquid asset that carries a transaction cost (e.g., housing, retirement account). The optimal life-cycle pattern of portfolio choice implies that many households in the model are \"wealthy hand-to-mouth\": they hold little or no liquid wealth despite owning sizable quantities of illiquid assets. Therefore, they display large propensities to consume out of additional transitory income, and small propensities to consume out of news about future income. We document the existence of such households in data from the Survey of Consumer Finances. A version of the model parameterized to the 2001 tax rebate episode yields consumption responses to fiscal stimulus payments that are in line with the evidence, and an order of magnitude larger than in the standard \"one-asset\" framework. The model's nonlinearities with respect to the rebate size and the prevailing aggregate economic conditions have implications for policy design.

Stroop-like interference effects are smaller in blocks of mostly incongruent (MI) trials than in blocks of mostly congruent (MC) trials. It is unclear, though, how control processes trigger this list-wide proportion congruency effect (LWPCE). The attentional shift account posits that a memory of experiencing conflict more frequently in MI blocks than in MC blocks leads control processes to shift attention toward the target in MI blocks. The response modulation account posits that a memory of block-wide congruency statistics (e.g., mostly incongruent) leads control processes to form expectations about upcoming trial congruency independent of conflict and modulate distractor-related response activation to prepare an expected congruent response (in MC blocks) or incongruent response (in MI blocks) to the target. This modulation occurs, however, only if the system translates the distractor into a response before the target. We conducted two experiments with the prime-probe task (М = 120) to investigate the response modulation account's prediction that giving the distractor a \"head start\" in stimulus-response translation increases the LWPCE independent of conflict. Confirming this prediction, the LWPCE was larger when the distractor appeared before - versus simultaneously with - the target, even though the overall congruency (i.e., conflict) effect was equivalent in these conditions (Experiment 1) or smaller when the distractor appeared before the target (Experiment 2). We also observed a negative congruency effect in the MI blocks of Experiment 2, which is inconsistent with a shift of attention toward the target. We conclude that a modulation of response activation contributes to the LWPCE.

Recently, Wang and Theeuwes ( Journal of Experimental Psychology: Human Perception and Performance , 44 (1), 13–17, 2018a ) demonstrated the role of lingering selection biases in an additional singleton search task in which the distractor singleton appeared much more often in one location than in all other locations. For this location, there was less capture and selection efficiency was reduced. It was argued that statistical learning induces plasticity within the spatial priority map such that particular locations that are high likely to contain a distractor are suppressed relative to all other locations. The current study replicated these findings regarding statistical learning (Experiment 1 ) and investigated whether similar effects can be obtained by cueing the distractor location in a top-down way on a trial-by-trial basis. The results show that top-down cueing of the distractor location with long (1,500 ms; Experiment 2 ) and short stimulus-onset symmetries (SOAs) (600 ms; Experiment 3 ) does not result in suppression: The amount of capture nor the efficiency of selection was affected by the cue. If anything, we found an attentional benefit (instead of the suppression) for the short SOA. We argue that through statistical learning, weights within the attentional priority map are changed such that one location containing a salient distractor is suppressed relative to all other locations. Our cueing experiments show that this effect cannot be accomplished by active, top-down suppression. Consequences for recent theories of distractor suppression are discussed.