Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing

Acknowledgments The authors thank Ruth Rosenholtz for her detailed comments on this manuscript and for sharing the code of the TTM. We thank both reviewers for their insightful comments. A.B. was supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). O.H.C. was supported by the Swiss National Science Foundation (SNF) 320030_176153 “Basics of visual processing: from elements to figures.” A.D. was supported by the Swiss National Science Foundation grants No. 176153 “Basics of visual processing: from elements to figures” and No. 191718 “Towards machines that see like us: human eye movements for robust deep recurrent neural networks.” D.W. was supported by the National Institutes of Health grant R01 CA236793.Peer reviewedPublisher PD

Specific Gestalt principles cannot explain (un)crowding

The standard physiological model has serious problems accounting for many aspects of vision, particularly when stimulus configurations become slightly more complex than the ones classically used, e.g., configurations of Gabors rather than only one or a few Gabors. For example, as shown in many publications, crowding cannot be explained with most models crafted in the spirit of the physiological approach. In crowding, a target is neighbored by flanking elements, which impair target discrimination. However, when more flankers are added, performance can improve for certain flanker configurations (uncrowding), which cannot be explained by classic models. As was shown, aspects of perceptual organization play a crucial role in uncrowding. For this reason, we tested here whether known principles of perceptual organization can explain crowding and uncrowding. The answer is negative. As shown with subjective tests, whereas grouping is indeed key in uncrowding, the four Gestalt principles examined here did not provide a clear explanation to this effect, as variability in performance was found between and within categories of configurations. We discuss the philosophical foundations of both the physiological and the classic Gestalt approaches and sketch a way to a happy marriage between the two

How do visual skills relate to action video game performance?

It has been claimed that video gamers possess increased perceptual and cognitive skills compared to non-video gamers. Here, we examined to which extent gaming performance in CS:GO (Counter-Strike: Global Offensive) correlates with visual performance.We tested 94 players ranging from beginners to experts with a battery of visual paradigms, such as visual acuity and contrast detection. In addition, we assessed performance in specific gaming skills, such as shooting and tracking, and administered personality traits. All measures together explained about 70% of the variance of the players’ rank. In particular, regression models showed that a few visual abilities, such as visual acuity in the periphery and the susceptibility to the Honeycomb illusion, were strongly associated with the players’ rank. Although the causality of the effect remains unknown, our results show that high-rank players perform better in certain visual skills compared to low-rank players

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Spatial and temporal integration of visual features

Visual processing can be seen as the integration and segmentation of features. Objects are composed of contours, integrated into shapes and segmented from other contours. Information also needs to be integrated to solve the ill-posed problems of vision. For example, in the "color" perception of an object, illuminance needs to be discounted, requiring large-scale integration of luminance values. Whereas there is little controversy about the crucial role of integration, very little is known about how it really works. In this thesis, I focused on large-scale spatiotemporal information using two paradigms. First, I used the Ternus-Pikler display (TPD) to understand non-retinotopic, temporal integration, and then I used crowding to understand spatial integration across, more or less, the entire visual field. Motions of object parts are perceived relative to the specific object. For example, a reflector on a bicycle wheel seems to rotate even though it is cycloidal in retinotopic coordinates. This is because the reflector's motion is subtracted from the bike's horizontal motion. Instead of bike motion, I used the TPD, which is perfectly suited to understand non-retinotopic processing. There are two possibilities of how information may be integrated non-retinotopically: either based on attentional tracking, e.g., of the reflector's motion, or relying on inbuilt automated mechanisms. I showed that attentional tracking does not play a major role for non-retinotopic processing in the TPD. Second, I showed that invisible retinotopic information can strongly modulate the visible, non-retinotopic percept, further supporting automated integration processes. Crowding occurs when the perception of a target deteriorates because of the surrounding elements. It is the standard situation in everyday vision, since elements are rarely encountered in isolation. The classic model of vision integrates information from low-level to high-level feature detectors. By adding flankers, this model can only predict performance deterioration. However, this prediction was proven wrong because flankers far from the target can even lead to a release of crowding. Integration across the entire visual field is crucial. Here, I systematically investigated the characteristics of this large-scale integration. First, I dissected complex multi-flanker configurations and showed that low-level aspects play only a minor role. Configural aspects and the Gestalt principle of Prägnanz seem to be involved instead. However, as I showed secondly, the basic Gestalt principles fail to explain our results. Lastly, I tested several computational models, including one-stage feedforward models that integrate information within a local area or across the whole visual field, and two-stage recursive models that integrate global information and then explicitly segment elements. I showed that all models fail, unless they take explicit grouping and segmentation processing into accounts, such as capsule networks and the Laminart model. Overall, spatial and temporal integration is rather a complex inbuilt automated mechanism, and integration occurs across the whole visual field, contrary to most classic and recent models in vision. Moreover, global integration can only be reproduced by two-stage models, which process grouping and segmentation. To better understand perception, we need to consider models that group elements by multiple processes and recursively segment other groups explicitly

Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task

When making a choice with limited information, we explore new features through trial-and-error to learn how they are related. However, few studies have investigated exploratory behaviour when information is limited. In this study, we address, at both the behavioural and neural level, how, when, and why humans explore new feature dimensions to learn a new policy for choosing a state-space. We designed a novel multi-dimensional reinforcement learning task to encourage participants to explore and learn new features, then used a reinforcement learning algorithm to model policy exploration and learning behaviour. Our results provide the first evidence that, when humans explore new feature dimensions, their values are transferred from the previous policy to the new online (active) policy, as opposed to being learned from scratch. We further demonstrated that exploration may be regulated by the level of cognitive ambiguity, and that this process might be controlled by the frontopolar cortex. This opens up new possibilities of further understanding how humans explore new features in an open-space with limited information

Basic gestalt laws cannot explain uncrowding

Visual crowding is the inability to perceive elements within clutter. Traditional crowding models, such as pooling, predict that performance deteriorates when flankers are added. However, this prediction has been disproved. For example, performance was found to deteriorate when a Vernier was surrounded by a single square but also to improve when more squares were added. This phenomenon is termed “uncrowding.” Previous studies showed that it is not the number of flankers that matters for uncrowding but the configuration. To understand how a configuration leads to crowding or uncrowding, we presented a Vernier surrounded by a square in the center of the screen. To that we added squares and stars that constructed different configurations according to the Gestalt laws of symmetry, closure, and good continuation. We did not find any evidence that the Gestalt laws we tested play an important role in crowding. To test for low-level factors, we also used a pixel-wise clustering method (k-means algorithm). However, we could not find evidence for the involvement of low-level factors either. We conclude that neither Gestalt laws nor basic processing can explain crowding and uncrowding. Likely, more complex aspects of display matter

Learning chemical intuition from humans in the loop

The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist\u27s career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased \textit{de novo} drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license