Induction of nitric oxide via humming does not improve short-term cognitive performance or influence emotional processing.

Nitric oxide (NO) is involved in a variety of biological functions including blood vessel dilation and neurotransmitter release. In animals, NO has been demonstrated to affect multiple behavioral outcomes, such as memory performance and arousal, whereas this link is less explored in humans. NO is created in the paranasal sinuses and studies show that humming releases paranasal NO to the nasal tract and that NO can then cross the blood brain barrier. Akin to animal models, we hypothesized that this NO may traverse into the brain and positively affect information processing. In contrast to our hypothesis, an articulatory suppression memory paradigm and a speeded detection task found deleterious effects of humming while performing the task. Likewise, we found no effect of humming on emotional processing of photos. In a fourth experiment, participants hummed before each trial in a speeded detection task, but we again found no effect on response time. In conclusion, either nasal NO does not travel to the brain, or NO in the brain does not have the expected impact on cognitive performance and emotional processing in humans. It remains possible that NO influences other cognitive processes not tested for here.

A replication and re-analysis of a classic texture segmentation study.

A classic finding reported in Beck (1966a) is that observers tend to indicate a more natural texture break between a set of T's and tilted T's than between a set of T's and backward L's. This finding has played a prominent role in discussions about the properties of texture segmentation and in the development of computational theories of texture segmentation. Due to the small sample size of the original study, we replicated the original experiment with a larger sample. Regrettably, we discovered that the description in Beck (1966a) is insufficient to allow us to reproduce the stimuli. For our replication, we created stimuli that seem consistent with the spirit of the original study. The results of the replication study partly match Beck's original data ([Formula: see text]), but conclusions indicated by these results deviate from some of Beck's key conclusions. We also further explored the influential claim derived from a second experiment in Beck (1966a) that texture segmentation was not related to the perceived similarity of elements. Contrary to this claim, a re-analysis of Beck's data indicates the counterintuitive conclusion that observers tend to indicate stronger texture segmentation between regions that are rated as being more similar. In a replication of the second experiment, this relation flipped so that the replication observers indicated stronger texture segmentation for regions with lower similarity ratings. Despite being influential, we conclude that there are substantial problems with the stimuli, analyses, and conclusions in Beck (1966a).

How the black hole illusion environment affects operational performance at different flight phases in aviation.

The black hole illusion (BHI) is a subtype of spatial disorientation that can result in fatal consequences in aviation. Research on the BHI has generally focused on altitude deviation, and few studies have examined the effect across different flight phases. In a simulation-based experiment, 18 participants performed 12 simulated approach and landing tasks in normal and BHI environments. Flight performance was analyzed with 14 flight parameters and was compared across five points and three phases, which were referenced from a National Transportation Safety Board report and other previous studies. Results showed that multiple flight parameters were significantly impaired and that their influences varied from the initial approach to the final touchdown. In the BHI environment, participants tended to descend aggressively during the approach phase and flew a lower but similar glidepath during the last approach phase. They might have realized the abnormal situation induced by the BHI but usually were unable to recover from the dangerous maneuver in time. Additionally, the result of glide path error, one of the most commonly used variables in previous BHI research, was only significant during the last approach phase. Flight stability was also impaired in the BHI environment. This is the first study to systematically analyze the BHI effects on multiple flight parameters at different flight phases. The use of this experimental paradigm could facilitate future research to evaluate and prevent the BHI in a more comprehensive way.

Perceptual grouping strategies and texture segmentation: Strategic connections and selection.

In a series of articles, Jacob Beck proposed that a variety of texture segmentation phenomena occurs due to emergent features that arise from "links" between elements with appropriate local properties, such as alignment, orientation, and proximity. His findings and ideas guided theoretical and computational models, and some of his demonstrations became textbook knowledge about visual perception. We build on this work in two ways. First, we provide a modern replication of a classic texture segmentation study using a much larger sample size. Overall, the replication agrees with Beck's original findings, although there are some quantitative differences. Second, we show how to apply a quantitative model of visual cortex to Beck's experiment and demonstrate that the model can explain many aspects of Beck's findings. Key to the model's success is cognitive control of connections between individual elements (akin to Beck's "links" between elements) and a selection mechanism that makes it easy to identify how well elements within a region connect and how well different regions are disconnected. Overall, the model supports Beck's claim that local properties can facilitate patterns of connections between stimulus elements and that some connection patterns allow an observer to easily distinguish textures.

COVID-19 infection does not seem to affect cognition in college students.

Emerging evidence indicates that COVID-19 damages the central nervous system and thereby might engender long-term cognitive impairment. Self-reports and some measures of cognitive ability suggest that long COVID can lead to substantial and frightening detriments in cognition. To further explore this issue, we used data from university courses on cognitive psychology where students participated in classic experiments that measure various aspects of cognition. Across 24 experiments we compared cognitive performance of students who had contracted COVID-19 against those who were uninfected. Using Bayes Factor analyses, we assessed both differences and invariances in performance as a function of infection status. Our findings suggest that COVID-19 infection has hardly any impact on cognition for university students.

Equivalent statistics for a one-sample t-test.

Recent insights into problems with common statistical practice in psychology have motivated scientists to consider alternatives to the traditional frequentist approach that compares p-values to a significance criterion. While these alternatives have worthwhile attributes, Francis (Behavior Research Methods, 40, 1524-1538, 2017) showed that many proposed test statistics for the situation of a two-sample t-test are based on precisely the same information in a given data set; and for a given sample size, one can convert from any statistic to the others. Here, we show that the same relationship holds for the equivalent of a one-sample t-test. We derive the relationships and provide an on-line app that performs the computations. A key conclusion of this analysis is that many types of tests are based on the same information, so the choice of which approach to use should reflect the intent of the scientist and the appropriateness of the corresponding inferential framework for that intent.

Perceptual Grouping Strategies in a Letter Identification Task: Strategic Connections, Selection, and Segmentation.

Although perceptual grouping has been widely studied, its mechanisms remain poorly understood. We propose a neural model of grouping that, through top-down control of its circuits, implements a grouping strategy involving both a connection strategy (which elements to connect) and a selection strategy (that defines spatiotemporal properties of a selection signal to segment target elements and facilitate identification). We apply the model to a letter discrimination task that investigated relationships among uniform connectedness and the grouping principles of proximity and shape similarity. Participants reported whether small circles formed a global letter E or H, and these circles could be connected by a line or be embedded in a matrix of squares. In the model, a good grouping strategy for this task consists of a connection strategy that connects circles but not squares for all conditions and a selection strategy that uses a selection signal of varying size, depending on whether squares were present. Consistent with empirical results, which were verified in two replication studies, model performance is worse with distractor squares, and line connectors improve performance only in the condition with squares. Rather than relying on abstract grouping principles, we show how the empirical results can be explained in terms of observers implementing a task-dependent grouping strategy that promotes overall performance.

Cortical circuits for top-down control of perceptual grouping.

A fundamental characteristic of human visual perception is the ability to group together disparate elements in a scene and treat them as a single unit. The mechanisms by which humans create such groupings remain unknown, but grouping seems to play an important role in a wide variety of visual phenomena, and a good understanding of these mechanisms might provide guidance for how to improve machine vision algorithms. Here, we build on a proposal that some groupings are the result of connections in cortical area V2 that join disparate elements, thereby allowing them to be selected and segmented together. In previous instantiations of this proposal, connection formation was based on the anatomy (e.g., extent) of receptive fields, which made connection formation obligatory when the stimulus conditions stimulate the corresponding receptive fields. We now propose dynamic circuits that provide greater flexibility in the formation of connections and that allow for top-down control of perceptual grouping. With computer simulations we explain how the circuits work and show how they can account for a wide variety of Gestalt principles of perceptual grouping, texture segmentation tasks, amodal illusory contours, and ratings of perceived groupings. We propose that human observers use such top-down control to implement task-dependent connection strategies that encourage particular groupings of stimulus elements in order to promote performance on various visual tasks.

Excess success in articles on object-based attention.

Twenty-five years of research has explored the object-based attention effect using the two-rectangles paradigm and closely related paradigms. While reading this literature, we noticed statistical attributes that are sometimes related to questionable research practices, which can undermine the reported conclusions. To quantify these attributes, we applied the Test for Excess Success (TES) individually to 37 articles that investigate various properties of object-based attention and comprise four or more experiments. A TES analysis estimates the probability that a direct replication of the experiments in a given article with the same sample sizes would have the same success (or better) as the original article. If the probability is low, then readers should be skeptical about the conclusions that are based on those experimental results. We find that 19 of the 37 analyzed articles (51%) seem too good to be true in that they have a replication probability below 0.1. In a new large sample study, we do find evidence for the basic object-based attention effect in the two-rectangles paradigm, which this literature builds on. A power analysis using this data shows that commonly used sample sizes in studies that investigate properties of object-based attention with the two-rectangles paradigm are, in fact, much too small to reliably detect even the basic effect.

Shrinking Bouma's window: How to model crowding in dense displays.

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma's law, only the target's nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model's outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.

Reversing Bonferroni.

It is common for conclusions of empirical studies to depend on multiple significant outcomes. This practice may seem reasonable, but it has some unintended effects. In particular, the compound Type I error rate for multiple studies (the likelihood of concluding that an effect exists when it does not) can be much lower than that of the individual studies. This in itself is not a problem since a low Type I error rate is desirable. However, there is also an accompanying drop in power, meaning that the probability of finding support for a true effect is low. Currently, there is no standard statistical method for dealing with the hyper-conservative error rate and accompanying low power that results from investigations requiring multiple significant outcomes. Here, we propose a novel solution to this problem: We show that it is sometimes appropriate to reverse the logic of the classic Bonferroni correction and increase the significance criterion in order to maintain an intended compound Type I error rate across multiple tests. This reverse Bonferroni approach dramatically improves statistical power and encourages careful planning of statistical analyses prior to data collection. To avoid adding to the list of questionable research practices that seem to contaminate some psychological research, we suggest that reverse Bonferroni be restricted to situations where authors pre-register their analysis plans.

When illusions merge.

We recently found only weak correlations between the susceptibility to various visual illusions. However, we observed strong correlations among different variants of an illusion, suggesting that the visual space of illusions includes several illusion-specific factors. Here, we specifically examined how factors for the vertical-horizontal, Müller-Lyer, and Ponzo illusions relate to each other. We measured the susceptibility to each illusion separately and to combinations of two illusions, which we refer to as a merged illusion; for example, we tested the Müller-Lyer illusion and the vertical-horizontal illusion, as well as a merged version of both illusions. We used an adjustment procedure in two experiments with 306 and 98 participants, respectively. Using path analyses, correlations, and exploratory factor analyses, we found that the susceptibility to a merged illusion is well predicted from the susceptibilities to the individual illusions. We suggest that there are illusion-specific factors that, by independent combinations, represent the whole visual structure underlying illusions.

Factors underlying visual illusions are illusion-specific but not feature-specific.

Common factors are ubiquitous. For example, there is a common factor, g, for intelligence. In vision, there is much weaker evidence for such common factors. For example, visual illusion magnitudes correlate only weakly with each other. Here, we investigated whether illusions are hyper-specific as in perceptual learning. First, we tested 19 variants of the Ebbinghaus illusion that differed in color, shape, or texture. Correlations between the illusion magnitudes of the different variants were mostly significant. Second, we reanalyzed a dataset from a previous experiment where 10 illusions were tested under four conditions of luminance and found significant correlations between the different luminance conditions of each illusion. However, there were only very weak correlations between the 10 different illusions. Third, five visual illusions were tested with four orientations. Again, there were significant correlations between the four orientations of each illusion, but not across different illusions. The weak inter-illusion correlations suggest that there is no unique common mechanism for the tested illusions. We suggest that most illusions make up their own factor.

The moon size illusion does not improve perceptual judgments.

Recent studies suggest that the accuracy of perceptual judgments can be influenced by the perceived illusory size of a stimulus, with judgments being more accurate for increased illusory size. This phenomenon seems consistent with recent neuroscientific findings that representations in early visual areas reflect the perceived (illusory) size of stimuli rather than the physical size. We further explored this idea with the moon illusion, in which the moon appears larger when it is close to the horizon and smaller when it is higher in the sky. Participants (n=230) adjusted the orientation of an image of the moon on a smartphone to match the perceived orientation of the moon in the sky. Contrary to previous studies that investigated accuracy and size illusions, we found slightly lower perceptual judgment accuracy when the moon appeared large (close to the horizon) compared to when it appeared small (high in the sky).

Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision - The Case of Visual Crowding.

Traditionally, human vision research has focused on specific paradigms and proposed models to explain very specific properties of visual perception. However, the complexity and scope of modern psychophysical paradigms undermine the success of this approach. For example, perception of an element strongly deteriorates when neighboring elements are presented in addition (visual crowding). As it was shown recently, the magnitude of deterioration depends not only on the directly neighboring elements but on almost all elements and their specific configuration. Hence, to fully explain human visual perception, one needs to take large parts of the visual field into account and combine all the aspects of vision that become relevant at such scale. These efforts require sophisticated and collaborative modeling. The Neurorobotics Platform (NRP) of the Human Brain Project offers a unique opportunity to connect models of all sorts of visual functions, even those developed by different research groups, into a coherently functioning system. Here, we describe how we used the NRP to connect and simulate a segmentation model, a retina model, and a saliency model to explain complex results about visual perception. The combination of models highlights the versatility of the NRP and provides novel explanations for inward-outward anisotropy in visual crowding.

Beyond Bouma's window: How to explain global aspects of crowding?

In crowding, perception of an object deteriorates in the presence of nearby elements. Although crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation, to date there exists no consensus on how to model it. Previous experiments showed that the global configuration of the entire stimulus must be taken into account. These findings rule out simple pooling or substitution models and favor models sensitive to global spatial aspects. In order to investigate how to incorporate global aspects into models, we tested a large number of models with a database of forty stimuli tailored for the global aspects of crowding. Our results show that incorporating grouping like components strongly improves model performance.

Retinal spatiotemporal dynamics on emergence of visual persistence and afterimages.

Visual persistence (stimulus perception that prolongs for a few milliseconds after the physical disappearance of the stimulus) and afterimages (an illusory percept that lingers after the physical disappearance of the stimulus at the retinotopic location of the preceding stimulus) are classic perceptual phenomena reflecting temporal characteristics of the visual system. These phenomena are modulated by some common stimulus aspects: A longer stimulus generates shorter persistence and a longer afterimage and a lower spatial-frequency stimulus generates shorter persistence and a stronger afterimage. The current study proposes that these spatiotemporal characteristics of visual persistence and afterimages can be explained by a generic retinal processing architecture. Wilson (1997) developed a neural network model of retinal circuitry and demonstrated that afterimages emerge due to a retinal light-adaptive gain control mechanism. In this study, we provide an overview of the retinal physiology to assess the feasibility of his retinal model, and simulate psychophysical experiments on persistence and afterimages in the same model to provide systematic explanations to the stimulus duration and spatial frequency effects. Our results suggest that these characteristics emerge from the spatiotemporal characteristics of each cell (response gain and time course, receptive-field structure) that comprises a part of the feedforward-feedback laminar network in the retina. The retinal circuitry performs short- and long-term adaptive operations as the signal transmission is recurrently regulated by various feedback mechanisms and consequently engenders complicated spatiotemporal dynamics in the ganglion cell responses that match the patterns of the perceptual phenomena. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Using a model of human visual perception to improve deep learning.

Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the human visual system represents information in a format that promotes good training and classification. To demonstrate this idea, we show how occluding objects can impair performance of a deep learning system that is trained to classify digits in the MNIST database. We describe a human inspired segmentation and interpolation algorithm that attempts to reconstruct occluded parts of an image, and we show that using this reconstruction algorithm to pre-process occluded images promotes training and classification performance.