What Fechner could not do: Separating perceptual encoding and decoding with difference scaling

A key question in perception research is how stimulus variations translate into perceptual magnitudes, that is, the perceptual encoding process. As experimenters, we cannot probe perceptual magnitudes directly, but infer the encoding process from responses obtained in a psychophysical experiment. The most prominent experimental technique to measure perceptual appearance is matching, where observers adjust a probe stimulus to match a target in its appearance along the dimension of interest. The resulting data quantify the perceived magnitude of the target in physical units of the probe, and are thus an indirect expression of the underlying encoding process. In this paper, we show analytically and in simulation that data from matching tasks do not sufficiently constrain perceptual encoding functions, because there exist an infinite number of pairs of encoding functions that generate the same matching data. We use simulation to demonstrate that maximum likelihood conjoint measurement (Ho, Landy, & Maloney, 2008; Knoblauch & Maloney, 2012) does an excellent job of recovering the shape of ground truth encoding functions from data that were generated with these very functions. Finally, we measure perceptual scales and matching data for White's effect (White, 1979) and show that the matching data can be predicted from the estimated encoding functions, down to individual differences.

Poster Session I: Combining spatial and quantitative models to account for the relationship between luminance, context and brightness

The apparent brightness of a target region is determined not only by its local luminance, but also by its surrounding context. This is well demonstrated by the many illusions where some surround context causes two physically identical targets to appear different in brightness (e.g. White's (1979) illusion). Here we use perceptual scales from Maximum Likelihood Conjoint Measurements to test the predictive power of computational brightness models as a function of surround context and local luminance. While the qualitative relationship between target brightness and surround context (i.e. direction of effect) is captured well by image-computable models of the spatial filtering type (e.g. Blakeslee and McCourt, 1997), these do not predict the quantitative aspects of that relationship (i.e. magnitude of effect). A different class of models (e.g. Whittle, 1992) makes quantitative predictions of brightness as a function of luminance, but requires labeling of target and background (i.e., is not image-computable), thus cannot by itself account for the spatial context. Hence we combine Whittle's model with spatial mechanisms to account for both spatial context and local luminance. A combined model using multiscale spatial filtering and calculating brightness at every spatial scale is able to provide both a correct qualitative prediction of direction of effect, as well as a quantitative prediction of apparent brightness that can be tested against perceptual scales for different stimuli.

Fixational eye movements enable robust edge detection

Human vision relies on mechanisms that respond to luminance edges in space and time. Most edge models use orientation-selective mechanisms on multiple spatial scales and operate on static inputs assuming that edge processing occurs within a single fixational instance. Recent studies, however, demonstrate functionally relevant temporal modulations of the sensory input due to fixational eye movements. Here we propose a spatiotemporal model of human edge detection that combines elements of spatial and active vision. The model augments a spatial vision model by temporal filtering and shifts the input images over time, mimicking an active sampling scheme via fixational eye movements. The first model test was White's illusion, a lightness effect that has been shown to depend on edges. The model reproduced the spatial-frequency-specific interference with the edges by superimposing narrowband noise (1–5 cpd), similar to the psychophysical interference observed in White's effect. Second, we compare the model's edge detection performance in natural images in the presence and absence of Gaussian white noise with human-labeled contours for the same (noise-free) images. Notably, the model detects edges robustly against noise in both test cases without relying on orientation-selective processes. Eliminating model components, we demonstrate the relevance of multiscale spatiotemporal filtering and scale-specific normalization for edge detection. The proposed model facilitates efficient edge detection in (artificial) vision systems and challenges the notion that orientation-selective mechanisms are required for edge detection.

BRENCH: An open-source framework for b(r)enchmarking brightness models

Various image-computable models have been proposed to describe the relationship between local luminance, visual context, and perceived luminance (brightness) or perceived reflectance (lightness). Classically, these models are tested on a subset of relevant stimuli. While specific failure cases have been shown for most models, a systematic overview is lacking. As a consequence, it is unclear how to favor any specific model of human brightness perception. Our goal is to work towards such a comprehensive overview. Towards that end, we are developing a stimulus benchmark, and evaluate various brightness models on these stimuli. For this, we provide publicly available re-implementations of several brightness models in Python, code for creating parameterized stimuli, and BRENCH - a framework to automate running and evaluating brightness models on (sets of) stimuli. With our framework, we can replicate previously published modeling results. Going beyond, the framework facilitates the comparison of models from multiple publications across all stimuli from those publications. BRENCH is flexible to allow for new model(parameterization)s and stimuli. Comparing a larger set of models and stimuli makes it possible to group models which perform similarly on certain stimuli, and group stimuli based on similar model performances. We hope BRENCH aids discussions about what stimuli should form a benchmark for brightness models, and what the interface and form of such models should be.

Conjoint measurement of perceived transparency and perceived contrast in variegated checkerboards

One fundamental question in vision research is how the retinal input is segmented into perceptually relevant variables. A striking example of this segmentation process is transparency perception, in which luminance information in one location contributes to two perceptual variables: the properties of the transparent medium itself and of what is being seen in the background. Previous work by Robilotto et al. (2002, 2004) suggested that perceived transparency is closely related to perceived contrast, but how these two relate to retinal luminance has not been established. Here we studied the relationship between perceived transparency, perceived contrast, and image luminance using maximum likelihood conjoint measurement (MLCM). Stimuli were rendered images of variegated checkerboards that were composed of multiple reflectances and partially covered by a transparent overlay. We systematically varied the transmittance and reflectance of the transparent medium and measured perceptual scales of perceived transparency. We also measured scales of perceived contrast using cut-outs of the transparency stimuli that did not contain any geometrical cues to transparency. Perceptual scales for perceived transparency and contrast followed a remarkably similar pattern across observers. We tested the empirically observed scales against predictions from various contrast metrics and found that perceived transparency and perceived contrast were equally well predicted by a metric based on the logarithm of Michelson or Whittle contrast. We conclude that judgments of perceived transparency and perceived contrast are likely to be supported by a common mechanism, which can be computationally captured as a logarithmic contrast.

Up to 5-year retention of abatacept in Belgian patients with moderate-to-severe rheumatoid arthritis: a sub-analysis of the international, observational ACTION study

Favorable efficacy and safety profiles have been demonstrated for abatacept in patients with rheumatoid arthritis (RA) in randomized controlled trials, but these data require validation during long-term follow-ups in routine clinical practice. This study explored long-term safety and retention rates in RA patients treated with intravenous abatacept in the Belgian cohort of the international AbataCepT In rOutiNe clinical practice (ACTION) study (NCT02109666). This non-interventional, observational, longitudinal study included Belgian patients aged ≥ 18 years with moderate-to-severe RA who started intravenous abatacept treatment as first- or second/further-line biologic therapy in routine clinical practice. Between October 2010 and December 2012, 141 patients were enrolled in this cohort, of whom 135 evaluable patients (6 biologic-naïve; 129 previously exposed to ≥ 1 prior biologic disease modifying anti-rheumatic drugs) were eligible for the descriptive analysis; 131/135 were included in the effectiveness analysis. Mean disease duration was 10.5 years (standard deviation 9.7) before abatacept initiation. RA patients presented with high disease activity and comorbidity rate, having failed multiple previous treatment options. In this cohort, the 5-year abatacept retention rate was 34% (95% confidence interval, 23-45%) per protocol, and 51% (95% confidence interval, 40-61%) when temporary discontinuations of abatacept > 84 days (n = 24) were not considered as treatment discontinuations. After 5 years of abatacept treatment, clinical outcomes were favorable [good/moderate European League Against Rheumatism (EULAR) responses in 91.7% patients]. No new safety signals were detected for abatacept in routine clinical practice. In this difficult-to-treat Belgian RA population, high retention rates, good clinical outcomes and favorable safety profile were observed with abatacept.

Toward reliable measurements of perceptual scales in multiple contexts

A central question in psychophysical research is how perceptual differences between stimuli translate into physical differences and vice versa. Characterizing such a psychophysical scale would reveal how a stimulus is converted into a perceptual event, particularly under changes in viewing conditions (e.g., illumination). Various methods exist to derive perceptual scales, but in practice, scale estimation is often bypassed by assessing appearance matches. Matches, however, only reflect the underlying perceptual scales but do not reveal them directly. Two recently developed methods, MLDS (Maximum Likelihood Difference Scaling) and MLCM (Maximum Likelihood Conjoint Measurement), promise to reliably estimate perceptual scales. Here we compared both methods in their ability to estimate perceptual scales across context changes in the domain of lightness perception. In simulations, we adopted a lightness constant, a contrast, and a luminance-based observer model to generate differential patterns of perceptual scales. MLCM correctly recovered all models. MLDS correctly recovered only the lightness constant observer model. We also empirically probed both methods with two types of stimuli: (a) variegated checkerboards that support lightness constancy and (b) center-surround stimuli that do not support lightness constancy. Consistent with the simulations, MLDS and MLCM provided similar scale estimates in the first case and divergent estimates in the second. In addition, scales from MLCM–and not from MLDS–accurately predicted asymmetric matches for both types of stimuli. Taking experimental and simulation results together, MLCM seems more apt to provide a valid estimate of the perceptual scales underlying judgments of lightness across viewing conditions.

Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment

Maximum likelihood difference scaling (MLDS) is a method for the estimation of perceptual scales based on the judgment of differences in stimulus appearance (Maloney & Yang, 2003). MLDS has recently also been used to estimate near-threshold discrimination performance (Devinck & Knoblauch, 2012). Using MLDS as a psychophysical method for sensitivity estimation is potentially appealing, because MLDS has been reported to need less data than forced-choice procedures, and particularly naive observers report to prefer suprathreshold comparisons to JND-style threshold tasks. Here we compare two methods, MLDS and two-interval forced-choice (2-IFC), regarding their capability to estimate sensitivity assuming an underlying signal-detection model. We first examined the theoretical equivalence between both methods using simulations. We found that they disagreed in their estimation only when sensitivity was low, or when one of the assumptions on which MLDS is based was violated. Furthermore, we found that the confidence intervals derived from MLDS had a low coverage; i.e., they were too narrow, underestimating the true variability. Subsequently we compared MLDS and 2-IFC empirically using a slant-from-texture task. The amount of agreement between sensitivity estimates from the two methods varied substantially across observers. We discuss possible reasons for the observed disagreements, most notably violations of the MLDS model assumptions. We conclude that in the present example MLDS and 2-IFC could equally be used to estimate sensitivity to differences in slant, with MLDS having the benefit of being more efficient and more pleasant, but having the disadvantage of unsatisfying coverage.

Maximum likelihood difference scales represent perceptual magnitudes and predict appearance matches

One central problem in perception research is to understand how internal experiences are linked to physical variables. Most commonly, this relationship is measured using the method of adjustment, but this has two shortcomings: The perceptual scales that relate physical and perceptual variables are not measured directly, and the method often requires perceptual comparisons between viewing conditions. To overcome these problems, we measured perceptual scales of surface lightness using maximum likelihood difference scaling, asking observers only to compare the lightness of surfaces presented in the same context. Observers were lightness constant, and the perceptual scales qualitatively and quantitatively predicted perceptual matches obtained in a conventional adjustment experiment. Additionally, we show that a contrast-based model of lightness perception predicted 98% of the variance in the scaling and 88% in the matching data. We suggest that the predictive power was higher for scales because they are closer to the true variables of interest.

Testing the role of Michelson contrast for the perception of surface lightness

It is still an unresolved question how the visual system perceives surface lightness given the ambiguity of the sensory input signal. We studied lightness perception using two-dimensional images of variegated checkerboards shown as perspective projections of three-dimensional objects. We manipulated the contrast of a target check relative to its surround either by rendering the image under different viewing conditions or by introducing noncoincidental changes of the reflectance of the surfaces adjacent to the target. We examined the predictive power of the normalized contrast model (Zeiner & Maertens, 2014) for the different viewing conditions (plain view vs. dark and light transparency) as well as for the noncoincidental surround changes (only high or only low reflectances in the surround). The model accounted for lightness matches across different viewing conditions but not for the surround changes. The observed simultaneous contrast effects were smaller than what would be predicted by the model. We evaluated two model extensions that-both relying on contrast-predicted the observed data well. Both model extensions point to the importance of contrast statistics across space and/or time for the computation of lightness, but it awaits future testing to evaluate whether and how the visual system could represent such statistics.

Measuring the Visual Salience of 3D Printed Objects

To investigate human viewing behavior on physical realizations of 3D objects, the authors use an eye tracker with scene camera and fiducial markers on 3D objects to gather fixations on the presented stimuli. They use this data to validate assumptions regarding visual saliency that so far have experimentally only been analyzed for flat stimuli. They provide a way to compare fixation sequences from different subjects and developed a model for generating test sequences of fixations unrelated to the stimuli. Their results suggest that human observers agree in their fixations for the same object under similar viewing conditions. They also developed a simple procedure to validate computational models for visual saliency of 3D objects and found that popular models of mesh saliency based on center surround patterns fail to predict fixations.

The direct impact of landslides on household income in tropical regions: A case study from the Rwenzori Mountains in Uganda

Landslides affect millions of people worldwide, but theoretical and empirical studies on the impact of landslides remain scarce, especially in Sub-Saharan Africa. This study proposes and applies a method to estimate the direct impact of landslides on household income and to investigate the presence of specific risk sharing and mitigation strategies towards landslides in a tropical and rural environment. An original cross-sectional household survey is used in combination with geographical data to acquire detailed information on livelihoods and on hazards in the Rwenzori mountains, Uganda. Ordinary least square regressions and probit estimations with village fixed effects are used to estimate the impact of landslides and the presence of mitigation strategies. Geographical information at household level allows to disentangle the direct impact from the indirect effects of landslides. We show that the income of affected households is substantially reduced during the first years after a landslide has occurred. We find that members of recently affected households participate more in wage-employment or in self-employed activities, presumably to address income losses following a landslide. Yet, we see that these jobs do not provide sufficient revenue to compensate for the loss of income from agriculture. Given that landslides cause localized shocks, finding a significant direct impact in our study indicates that no adequate risk sharing mechanisms are in place in the Rwenzori sub-region. These insights are used to derive policy recommendations for alleviating the impact of landslides in the region. By quantifying the direct impact of landslides on household income in an agricultural context in Africa this study draws the attention towards a problem that has been broadly underestimated so far and provides a sound scientific base for disaster risk reduction in the region. Both the methodology and the findings of this research are applicable to other tropical regions with high landslide densities.

Testing the role of luminance edges in White's illusion with contour adaptation

White's illusion is the perceptual effect that two equiluminant gray patches superimposed on a black-and-white square-wave grating appear different in lightness: A test patch placed on a dark stripe of the grating looks lighter than one placed on a light stripe. Although the effect does not depend on the aspect ratio of the test patches, and thus on the amount of border that is shared with either the dark or the light stripe, the context of each patch must, in a yet to be specified way, influence their lightness. We employed a contour adaptation paradigm (Anstis, 2013) to test the contribution of each of the test patches' edges to the perceived lightness of the test patches. We found that adapting to the edges that are oriented parallel to the grating slightly increased the lightness illusion, whereas adapting to the orthogonal edges abolished, or for some observers even reversed, the lightness illusion. We implemented a temporal adaptation mechanism in three spatial filtering models of lightness perception, and show that the models cannot account for the observed adaptation effects. We conclude that White's illusion is largely determined by edge contrast across the edge orthogonal to the grating, whereas the parallel edge has little or no influence. We suggest mechanisms that could explain this asymmetry.

Exploitation of environmental constraints in human and robotic grasping

We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints.

Context affects lightness at the level of surfaces

Visual perception of object attributes such as surface lightness is crucial for successful interaction with the environment. How the visual system assigns lightness to image regions is not yet understood. It has been shown that the context in which a surface is embedded influences its perceived lightness, but whether that influence involves predominantly low-, mid-, or high-level visual mechanisms has not been resolved. To answer this question, we measured whether perceptual attributes of target image regions affected their perceived lightness when they were placed in different contexts. We varied the sharpness of the edge while keeping total target flux fixed. Targets with a sharp edge were consistent with the perceptual interpretation of a surface, and in that case, observers perceived significant brightening or darkening of the target. Targets with blurred edges rather appeared to be spotlights instead of surfaces; for targets with blurred edges, there was much less of a contextual effect on target lightness. The results indicate that the effect of context on the lightness of an image region is not fixed but is strongly affected by image manipulations that modify the perceptual attributes of the target, implying that a mid-level scene interpretation affects lightness perception.

Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception

Spatial filtering models are currently a widely accepted mechanistic account of human lightness perception. Their popularity can be ascribed to two reasons: They correctly predict how human observers perceive a variety of lightness illusions, and the processing steps involved in the models bear an apparent resemblance with known physiological mechanisms at early stages of visual processing. Here, we tested the adequacy of these models by probing their response to stimuli that have been modified by adding narrowband noise. Psychophysically, it has been shown that noise in the range of one to five cycles per degree (cpd) can drastically reduce the strength of some lightness phenomena, while noise outside this range has little or no effect on perceived lightness. Choosing White's illusion (White, 1979) as a test case, we replicated and extended the psychophysical results, and found that none of the spatial filtering models tested was able to reproduce the spatial frequency specific effect of narrowband noise. We discuss the reasons for failure for each model individually, but we argue that the failure is indicative of the general inadequacy of this class of spatial filtering models. Given the present evidence we do not believe that spatial filtering models capture the mechanisms that are responsible for producing many of the lightness phenomena observed in human perception. Instead we think that our findings support the idea that low-level contributions to perceived lightness are primarily determined by the luminance contrast at surface boundaries.

Linking luminance and lightness by global contrast normalization

In the present experiment we addressed the question of how the visual system determines surface lightness from luminances in the retinal image. We measured the perceived lightness of target surfaces that were embedded in custom-made checkerboards. The checkerboards consisted of 10 by 10 checks of 10 different reflectance values that were arranged randomly across the board. They were rendered under six viewing conditions including plain view, with a shadow-casting cylinder, or with one of four different transparent media covering part of the board. For each reflectance we measured its corresponding luminance in the different viewing conditions. We then assessed the lightness matches of four observers for each of the reflectances in the different viewing conditions. We derived predictions of perceived lightness based on local luminance, Michelson contrast, edge integration, anchoring theory, and a normalized Michelson contrast measure. The normalized contrast measure was the best predictor of surface lightness and was almost as good as the actual reflectance values. The normalized contrast measure combines a local computation of Michelson contrast with a region-based normalization of contrast ranges with respect to the contrast range in plain view. How the segregation of image regions is accomplished remains to be elucidated.

When nonspecific histology can be a clue to the diagnosis: three cases of trigeminal trophic syndrome

Trigeminal trophic syndrome (TTS) is a rare cause of facial ulceration, which is usually associated with damage to the trigeminal nerve pathway, either centrally or peripherally, the most common causes being cerebrovascular accidents and trigeminal nerve ablation procedures. We present three cases of TTS, emphasizing the histopathological features. All three patients presented with facial ulceration. Two patients had a single lesion, and the third had several ulcers. However, in all cases, there was involvement of the nasal ala, and the lesions were strictly unilateral. Histology consistently showed ulceration with signs of severe chronic trauma: scarring, lichenification and/or pseudoepitheliomatous hyperplasia. Diagnosis of TTS can be difficult, and requires close clinicopathological correlation. Histology is important in excluding the majority of possible conditions included in the differential diagnosis, mainly malignancy and infectious processes. Several treatments have been described, but TTS is frequently refractory to treatment.

Local determinants of contour interpolation

Objects in our visual environment are perceived as integral wholes even when their retinal images are incomplete. We ask whether the perceptual precision of subjective interpolation between isolated image parts depends on the overall proportion of visible image information or rather on its geometrical arrangement. We used Varin-type subjective shapes that provide less physical stimulus information than Kanizsa-type figures because partially occluded solid inducers are replaced by partially occluded concentric arcs. We tested whether perceptual precision varies as a function of contour support, or alternatively, depends on the number of, and the distance between, line endings within the inducers. We measured performance in a probe localization task, where a small target is presented at different distances around a subjective boundary. Sensitivity, captured by the just noticeable position difference between in- and outside probes, crucially depended on the geometric arrangement of line ends in the Varin figures. This is objective evidence that the apparent subjective contour strength does not primarily depend on contour support but is determined by the number and the separation between inducers' line endings. The results suggest that neuronal mechanisms sensitive to highly localized 2D features are crucial for determining the perceived shape of visual objects.