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.

What to Choose Next? A Paradigm for Testing Human Sequential Decision Making.

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm to test it. Here, we developed such a paradigm and investigated key components of reinforcement learning models: the eligibility trace (i.e., the memory trace of previous decision steps), the external reward, and the ability to exploit the statistics of the environment's structure (model-free vs. model-based mechanisms). We show that the eligibility trace decays not with sheer time, but rather with the number of discrete decision steps made by the participants. We further show that, unexpectedly, neither monetary rewards nor the environment's spatial regularity significantly modulate behavioral performance. Finally, we found that model-free learning algorithms describe human performance better than model-based algorithms.

About individual differences in vision.

In cognition, audition, and somatosensation, performance strongly correlates between different paradigms, which suggests the existence of common factors. In contrast, visual performance in seemingly very similar tasks, such as visual and bisection acuity, are hardly related, i.e., pairwise correlations between performance levels are low even though test-retest reliability is high. Here we show similar results for visual illusions. Consistent with previous findings, we found significant correlations between the illusion magnitude of the Ebbinghaus and Ponzo illusions, but this relationship was the only significant correlation out of 15 further comparisons. Similarly, we found a significant link for the Ponzo illusion with both mental imagery and cognitive disorganization. However, most other correlations between illusions and personality were not significant. The findings suggest that vision is highly specific, i.e., there is no common factor. While this proposal does not exclude strong and stable associations between certain illusions and between certain illusions and personality traits, these associations seem to be the exception rather than the rule.

Motion-based nearest vector metric for reference frame selection in the perception of motion.

We investigated how the visual system selects a reference frame for the perception of motion. Two concentric arcs underwent circular motion around the center of the display, where observers fixated. The outer (target) arc's angular velocity profile was modulated by a sine wave midflight whereas the inner (reference) arc moved at a constant angular speed. The task was to report whether the target reversed its direction of motion at any point during its motion. We investigated the effects of spatial and figural factors by systematically varying the radial and angular distances between the arcs, and their relative sizes. We found that the effectiveness of the reference frame decreases with increasing radial- and angular-distance measures. Drastic changes in the relative sizes of the arcs did not influence motion reversal thresholds, suggesting no influence of stimulus form on perceived motion. We also investigated the effect of common velocity by introducing velocity fluctuations to the reference arc as well. We found no effect of whether or not a reference frame has a constant motion. We examined several form- and motion-based metrics, which could potentially unify our findings. We found that a motion-based nearest vector metric can fully account for all the data reported here. These findings suggest that the selection of reference frames for motion processing does not result from a winner-take-all process, but instead, can be explained by a field whose strength decreases with the distance between the nearest motion vectors regardless of the form of the moving objects.

A computational model for reference-frame synthesis with applications to motion perception.

As discovered by the Gestaltists, in particular by Duncker, we often perceive motion to be within a non-retinotopic reference frame. For example, the motion of a reflector on a bicycle appears to be circular, whereas, it traces out a cycloidal path with respect to external world coordinates. The reflector motion appears to be circular because the human brain subtracts the horizontal motion of the bicycle from the reflector motion. The bicycle serves as a reference frame for the reflector motion. Here, we present a general mathematical framework, based on vector fields, to explain non-retinotopic motion processing. Using four types of non-retinotopic motion paradigms, we show how the theory works in detail. For example, we show how non-retinotopic motion in the Ternus-Pikler display can be computed.

Why vision is not both hierarchical and feedforward.

In classical models of object recognition, first, basic features (e.g., edges and lines) are analyzed by independent filters that mimic the receptive field profiles of V1 neurons. In a feedforward fashion, the outputs of these filters are fed to filters at the next processing stage, pooling information across several filters from the previous level, and so forth at subsequent processing stages. Low-level processing determines high-level processing. Information lost on lower stages is irretrievably lost. Models of this type have proven to be very successful in many fields of vision, but have failed to explain object recognition in general. Here, we present experiments that, first, show that, similar to demonstrations from the Gestaltists, figural aspects determine low-level processing (as much as the other way around). Second, performance on a single element depends on all the other elements in the visual scene. Small changes in the overall configuration can lead to large changes in performance. Third, grouping of elements is key. Only if we know how elements group across the entire visual field, can we determine performance on individual elements, i.e., challenging the classical stereotypical filtering approach, which is at the very heart of most vision models.

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception.

Experimentalists tend to classify models of visual perception as being either local or global, and involving either feedforward or feedback processing. We argue that these distinctions are not as helpful as they might appear, and we illustrate these issues by analyzing models of visual crowding as an example. Recent studies have argued that crowding cannot be explained by purely local processing, but that instead, global factors such as perceptual grouping are crucial. Theories of perceptual grouping, in turn, often invoke feedback connections as a way to account for their global properties. We examined three types of crowding models that are representative of global processing models, and two of which employ feedback processing: a model based on Fourier filtering, a feedback neural network, and a specific feedback neural architecture that explicitly models perceptual grouping. Simulations demonstrate that crucial empirical findings are not accounted for by any of the models. We conclude that empirical investigations that reject a local or feedforward architecture offer almost no constraints for model construction, as there are an uncountable number of global and feedback systems. We propose that the identification of a system as being local or global and feedforward or feedback is less important than the identification of a system's computational details. Only the latter information can provide constraints on model development and promote quantitative explanations of complex phenomena.

Deleterious effects of roving on learned tasks.

In typical perceptual learning experiments, one stimulus type (e.g., a bisection stimulus offset either to the left or right) is presented per trial. In roving, two different stimulus types (e.g., a 30' and a 20' wide bisection stimulus) are randomly interleaved from trial to trial. Roving can impair both perceptual learning and task sensitivity. Here, we investigate the relationship between the two. Using a bisection task, we found no effect of roving before training. We next trained subjects and they improved. A roving condition applied after training impaired sensitivity.

Trait anxiety and post-learning stress do not affect perceptual learning.

While it is well established that stress can modulate declarative learning, very few studies have investigated the influence of stress on non-declarative learning. Here, we studied the influence of post-learning stress, which effectively modulates declarative learning, on perceptual learning of a visual texture discrimination task (TDT). On day one, participants trained for one session with TDT and were instructed that they, at any time, could be exposed to either a high stressor (ice-water; Cold Pressor Test; CPT) or a low stressor (warm water). Participants did not know when or which stressor they would be exposed to. To determine the impact of the stressor on TDT learning, all participants returned the following day to perform another TDT session. Only participants exposed to the high stressor had significantly elevated cortisol levels. However, there was no difference in TDT improvements from day one to day two between the groups. Recent studies suggested that trait anxiety modulates visual perception under anticipation of stressful events. Here, trait anxiety did neither modulate performance nor influence responsiveness to stress. These results do not support a modulatory role for stress on non-declarative perceptual learning.

Hemifield asymmetry in the potency of exogenous auditory and visual cues.

Neurologically normal subjects misperceive the midpoints of lines (PSE) as reliably leftward of veridical center, a phenomenon known as pseudoneglect. This leftward bias reflects the dominance of the right cerebral hemisphere in deploying spatial attention. Transient visual cues, delivered to either the left or right endpoints of lines, modulate PSE such that leftward biases are increased by leftward cues, and are decreased by rightward cues, relative to a no-cue control condition. We ask whether lateralized auditory cues can similarly influence PSE in a tachistoscopic visual line bisection task, and describe how visual and auditory cues, in spatially synergistic or antagonistic combinations, jointly influence PSE. Our results demonstrate that whereas auditory and visual cues both modulate PSE, visual cues are overall more potent than auditory cues. Visual and auditory cues are weighted such that visual cues are significantly more potent than auditory cues when visual cues are delivered to left hemispace. Visual and auditory cues are equipotent when visual cues are delivered to right hemispace. These results are consistent with the existence of independent lateralized networks governing the deployment of visuospatial and audiospatial attention. An analysis of the weighting of unisensory visual and auditory cues which optimally predicts PSE in multisensory cue conditions shows that cues combine additively. There was no evidence for a superadditive multisensory cue combination.