Predictive context biases perceptual selection during binocular rivalry

Prediction may be a fundamental principle of sensory processing, such that the brain continuously generates predictions about forthcoming sensory information. However, little is known about how prediction contributes to the selection of a conscious percept from among competing alternatives. Here, we used binocular rivalry to investigate the effects of prediction on perceptual selection. In binocular rivalry, incompatible images presented to the two eyes result in a perceptual alternation between the images, even though the visual stimuli remain constant. If predictive signals influence the competition between neural representations of rivalrous images, this influence should generate a bias in perceptual selection that depends on predictive context. To manipulate predictive context, we developed a novel binocular rivalry paradigm in which orthogonal rivalrous test gratings were immediately preceded by rotating gratings presented identically to the two eyes. One of the rivalrous gratings had an orientation that was consistent with the preceding rotation direction (it was the expected next image in the series), and the other had an inconsistent orientation. We found that human observers were more likely to perceive the consistent grating, suggesting that predictive context biased selection in favor of the predicted percept. This prediction effect depended on only recent stimulus history, and it could be dissociated from another stimulus history effect related to orientation-specific adaptation. Since binocular rivalry between orthogonal gratings is thought to be resolved at an early stage of visual processing, these results suggest that predictive signals may exist at low levels of the visual processing hierarchy and that these signals can bias conscious perception. In the future, this paradigm could be used to test whether visual percepts are generated from the combination of prior information and incoming sensory information according to Bayesian principles

Predictive Context Influences Perceptual Selection during Binocular Rivalry

Prediction may be a fundamental principle of sensory processing: it has been proposed that the brain continuously generates predictions about forthcoming sensory information. However, little is known about how prediction contributes to the selection of a conscious percept from among competing alternatives. Here, we used binocular rivalry to investigate the effects of prediction on perceptual selection. In binocular rivalry, incompatible images presented to the two eyes result in a perceptual alternation between the images, even though the visual stimuli remain constant. If predictive signals influence the competition between neural representations of rivalrous images, this influence should generate a bias in perceptual selection that depends on predictive context. To manipulate predictive context, we developed a novel binocular rivalry paradigm in which rivalrous test images were immediately preceded by a sequence of context images presented identically to the two eyes. One of the test images was consistent with the preceding image sequence (it was the expected next image in the series), and the other was inconsistent (non-predicted). We found that human observers were more likely to perceive the consistent image at the onset of rivalry, suggesting that predictive context biased selection in favor of the predicted percept. This prediction effect was distinct from the effects of adaptation to stimuli presented before the binocular rivalry test. In addition, perceptual reports were speeded for predicted percepts relative to non-predicted percepts. These results suggest that predictive signals related to visual stimulus history exist at neural sites that can bias conscious perception during binocular rivalry. Our paradigm provides a new way to study how prior information and incoming sensory information combine to generate visual percepts

An auditory-visual tradeoff in susceptibility to clutter

Sensory cortical mechanisms combine auditory or visual features into perceived objects. This is difficult in noisy or cluttered environments. Knowing that individuals vary greatly in their susceptibility to clutter, we wondered whether there might be a relation between an individual's auditory and visual susceptibilities to clutter. In auditory masking, background sound makes spoken words unrecognizable. When masking arises due to interference at central auditory processing stages, beyond the cochlea, it is called informational masking. A strikingly similar phenomenon in vision, called visual crowding, occurs when nearby clutter makes a target object unrecognizable, despite being resolved at the retina. We here compare susceptibilities to auditory informational masking and visual crowding in the same participants. Surprisingly, across participants, we find a negative correlation (R = -0.7) between susceptibility to informational masking and crowding: Participants who have low susceptibility to auditory clutter tend to have high susceptibility to visual clutter, and vice versa. This reveals a tradeoff in the brain between auditory and visual processing.R01 DC019126 - NIDCD NIH HHS; R01 EY027964 - NEI NIH HHSAccepted manuscrip

The Atacama Cosmology Telescope: Modeling the Gas Thermodynamics in BOSS CMASS galaxies from Kinematic and Thermal Sunyaev-Zel'dovich Measurements

The thermal and kinematic Sunyaev-Zel'dovich effects (tSZ, kSZ) probe the thermodynamic properties of the circumgalactic and intracluster medium (CGM and ICM) of galaxies, groups, and clusters, since they are proportional, respectively, to the integrated electron pressure and momentum along the line-of-sight. We present constraints on the gas thermodynamics of CMASS galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) using new measurements of the kSZ and tSZ signals obtained in a companion paper. Combining kSZ and tSZ measurements, we measure within our model the amplitude of energy injection $\epsilon M_\star c^2$, where $M_\star$ is the stellar mass, to be $\epsilon=(40\pm9)\times10^{-6}$, and the amplitude of the non-thermal pressure profile to be $\alpha_{\rm Nth}<0.2$ (2$\sigma$), indicating that less than 20% of the total pressure within the virial radius is due to a non-thermal component. We estimate the effects of including baryons in the modeling of weak-lensing galaxy cross-correlation measurements using the best fit density profile from the kSZ measurement. Our estimate reduces the difference between the original theoretical model and the weak-lensing galaxy cross-correlation measurements in arXiv:1611.08606 by half, but does not fully reconcile it. Comparing the kSZ and tSZ measurements to cosmological simulations, we find that they under predict the CGM pressure and to a lesser extent the CGM density at larger radii. This suggests that the energy injected via feedback models in the simulations that we compared against does not sufficiently heat the gas at these radii. We do not find significant disagreement at smaller radii. These measurements provide novel tests of current and future simulations. This work demonstrates the power of joint, high signal-to-noise kSZ and tSZ observations, upon which future cross-correlation studies will improve.Comment: Accepted for publication in Physical Review D. Editors' Suggestion. New Fig. 1-2, Tab.

The Atacama Cosmology Telescope: Combined kinematic and thermal Sunyaev-Zel'dovich measurements from BOSS CMASS and LOWZ halos

The scattering of cosmic microwave background (CMB) photons off the free-electron gas in galaxies and clusters leaves detectable imprints on high resolution CMB maps: the thermal and kinematic Sunyaev-Zel'dovich effects (tSZ and kSZ respectively). We use combined microwave maps from the Atacama Cosmology Telescope (ACT) DR5 and Planck in combination with the CMASS and LOWZ galaxy catalogs from the Baryon Oscillation Spectroscopic Survey (BOSS DR10 and DR12), to study the gas associated with these galaxy groups. Using individual reconstructed velocities, we perform a stacking analysis and reject the no-kSZ hypothesis at 6.5$\sigma$, the highest significance to date. This directly translates into a measurement of the electron number density profile, and thus of the gas density profile. Despite the limited signal to noise, the measurement shows at high significance that the gas density profile is more extended than the dark matter density profile, for any reasonable baryon abundance (formally $>90\sigma$ for the cosmic baryon abundance). We simultaneously measure the tSZ signal, i.e. the electron thermal pressure profile of the same CMASS objects, and reject the no-tSZ hypothesis at 10$\sigma$. We combine tSZ and kSZ measurements to estimate the electron temperature to 20% precision in several aperture bins, and find it comparable to the virial temperature. In a companion paper, we analyze these measurements to constrain the gas thermodynamics and the properties of feedback inside galaxy groups. We present the corresponding LOWZ measurements in this paper, ruling out a null kSZ (tSZ) signal at 2.9 (13.9)$\sigma$, and leave their interpretation to future work. Our stacking software ThumbStack is publicly available at https://github.com/EmmanuelSchaan/ThumbStack and directly applicable to future Simons Observatory and CMB-S4 data.Comment: Accepted in Physical Review D, Editors' Suggestio

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects