Attention alters certainty but not appearance

Does paying attention to a stimulus change its appearance or merely influence the decision mechanisms involved in reporting it? Recently we proposed an uncertainty stealing hypothesis in which subjects, when uncertain about a perceptual comparison between a cued and uncued stimulus, tend to disproportionately choose the cued stimulus. The result is a psychometric function that mimics the results that would be measured if attention actually changed the appearance of the cued stimulus. In the present study, we measure uncertainty explicitly. In three separate experiments, subjects judged the relative appearance of two Gabor patches that differed in contrast. In the first two experiments, subjects performed a comparative judgment, reporting which stimulus had the higher contrast. In the third experiment, subjects performed an equality judgment, reporting whether the two stimuli had the same or different contrast. In the first comparative judgment experiment and in the equality judgment experiment, one of the two stimuli was pre-cued by an exogenous cue. In the second comparative judgment experiment, a decision bias was explicitly introduced: one stimulus was followed by a post-cue and the subjects were instructed, when uncertain, to choose the cued target. In all three experiments, subjects also indicated whether or not they were certain about each response. The results reveal that in the pre-cue comparative judgment, attention shifted the subjects’ uncertainty and made subjects more likely to report that the cued stimulus had higher contrast. In the post-cue biased comparative judgment, subjects also were more likely to report that the cued stimulus had higher contrast, but without a shift in uncertainty. In the equality judgment, attention did not affect the contrast judgment, and the subjects’ uncertainty remained aligned with their decision. We conclude that attention does not alter appearance but rather manipulates subjects’ uncertainty and decision mechanisms.

Toward realistic binaural auralizations – perceptual comparison between measurement and simulation-based auralizations and the real room for a classroom scenario

In order to make full use of their potential to replace experiments in real rooms, auralizations must be as realistic as possible. Recently, it has been shown that for speech, head-tracked binaural auralizations based on measured binaural room impulse responses (BRIRs) can be so realistic, that they become indistinguishable (or nearly so) from the real room [1, 2]. In the present contribution, perceptual comparisons between the auralized and the real room are reported for auralizations based both on measured and simulated BRIRs. In the experiment, subjects sitting in the real room rated the agreement between the real and the auralized room with respect to a number of attributes. The results indicate that for most attributes, the agreement between the auralized and the real room can be very convincing (better than 7.5 on a nine-point scale). This was not only observed for auralizations based on measured BRIRs, but also for those based on simulated BRIRs. In the scenario considered here, the use of individual head-related impulse responses (HRIRs) does not seem to offer any benefit over using HRIRs from a head-and-torso-simulator.

Residual Augmented Attentional U-Shaped Network for Spectral Reconstruction from RGB Images

Deep convolutional neural networks (CNNs) have been successfully applied to spectral reconstruction (SR) and acquired superior performance. Nevertheless, the existing CNN-based SR approaches integrate hierarchical features from different layers indiscriminately, lacking an investigation of the relationships of intermediate feature maps, which limits the learning power of CNNs. To tackle this problem, we propose a deep residual augmented attentional u-shape network (RA2UN) with several double improved residual blocks (DIRB) instead of paired plain convolutional units. Specifically, a trainable spatial augmented attention (SAA) module is developed to bridge the encoder and decoder to emphasize the features in the informative regions. Furthermore, we present a novel channel augmented attention (CAA) module embedded in the DIRB to rescale adaptively and enhance residual learning by using first-order and second-order statistics for stronger feature representations. Finally, a boundary-aware constraint is employed to focus on the salient edge information and recover more accurate high-frequency details. Experimental results on four benchmark datasets demonstrate that the proposed RA2UN network outperforms the state-of-the-art SR methods under quantitative measurements and perceptual comparison.

Policies or knowledge: priors differ between a perceptual and sensorimotor task

If the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g., movement vs. perception, should not matter. If, on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking whether a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn prior distribution in the sensorimotor estimation task, measured priors are consistently broader than sensorimotor priors in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors are more like policies than knowledge. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distributions. NEW & NOTEWORTHY We do not know whether the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of many Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.

Policies or Knowledge: Priors differ between perceptual and sensorimotor tasks

ABSTRACTIf the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g. movement vs perception, should not matter. If on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking if a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn the experimentally-imposed prior distribution in the sensorimotor estimation task, measured priors are consistently broader than expected in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors strongly resemble policies. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distribution.NEW AND NOTEWORTHYWe do not know if the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of may Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.