Perceptual dimensions of wood materials.

Materials exhibit an extraordinary range of visual appearances. Characterizing and quantifying appearance is important not only for basic research on perceptual mechanisms but also for computer graphics and a wide range of industrial applications. Although methods exist for capturing and representing the optical properties of materials and how they vary across surfaces (Haindl & Filip, 2013), the representations are typically very high-dimensional, and how these representations relate to subjective perceptual impressions of material appearance remains poorly understood. Here, we used a data-driven approach to characterizing the perceived appearance characteristics of 30 samples of wood veneer using a "visual fingerprint" that describes each sample as a multidimensional feature vector, with each dimension capturing a different aspect of the appearance. Fifty-six crowd-sourced participants viewed triplets of movies depicting different wood samples as the sample rotated. Their task was to report which of the two match samples was subjectively most similar to the test sample. In another online experiment, 45 participants rated 10 wood-related appearance characteristics for each of the samples. The results reveal a consistent embedding of the samples across both experiments and a set of nine perceptual dimensions capturing aspects including the roughness, directionality, and spatial scale of the surface patterns. We also showed that a weighted linear combination of 11 image statistics, inspired by the rating characteristics, predicts perceptual dimensions well.

High-level aftereffects reveal the role of statistical features in visual shape encoding.

Visual shape perception is central to many everyday tasks, from object recognition to grasping and handling tools.1,2,3,4,5,6,7,8,9,10 Yet how shape is encoded in the visual system remains poorly understood. Here, we probed shape representations using visual aftereffects-perceptual distortions that occur following extended exposure to a stimulus.11,12,13,14,15,16,17 Such effects are thought to be caused by adaptation in neural populations that encode both simple, low-level stimulus characteristics17,18,19,20 and more abstract, high-level object features.21,22,23 To tease these two contributions apart, we used machine-learning methods to synthesize novel shapes in a multidimensional shape space, derived from a large database of natural shapes.24 Stimuli were carefully selected such that low-level and high-level adaptation models made distinct predictions about the shapes that observers would perceive following adaptation. We found that adaptation along vector trajectories in the high-level shape space predicted shape aftereffects better than simple low-level processes. Our findings reveal the central role of high-level statistical features in the visual representation of shape. The findings also hint that human vision is attuned to the distribution of shapes experienced in the natural environment.

Inferring shape transformations in a drawing task.

Many objects and materials in our environment are subject to transformations that alter their shape. For example, branches bend in the wind, ice melts, and paper crumples. Still, we recognize objects and materials across these changes, suggesting we can distinguish an object's original features from those caused by the transformations ("shape scission"). Yet, if we truly understand transformations, we should not only be able to identify their signatures but also actively apply the transformations to new objects (i.e., through imagination or mental simulation). Here, we investigated this ability using a drawing task. On a tablet computer, participants viewed a sample contour and its transformed version, and were asked to apply the same transformation to a test contour by drawing what the transformed test shape should look like. Thus, they had to (i) infer the transformation from the shape differences, (ii) envisage its application to the test shape, and (iii) draw the result. Our findings show that drawings were more similar to the ground truth transformed test shape than to the original test shape-demonstrating the inference and reproduction of transformations from observation. However, this was only observed for relatively simple shapes. The ability was also modulated by transformation type and magnitude but not by the similarity between sample and test shapes. Together, our findings suggest that we can distinguish between representations of original object shapes and their transformations, and can use visual imagery to mentally apply nonrigid transformations to observed objects, showing how we not only perceive but also 'understand' shape.

Homology judgements of pre-evolutionary naturalists explained by general human shape matching abilities.

Many biological homologies were discovered before Darwin and without agreed criteria. Paradigmatic examples include the phylogenetic homology of mammalian forelimb bones and the serial homology of floral organs in waterlilies. It is generally assumed that perceiving similarities intuitively was the first step towards establishing morphological homologies. However, this assumption has never been tested. We hypothesize that pre-evolutionary naturalists relied on the well-established ability of humans to find visual correspondences between differently shaped objects. By using images of homologous organs and applying an experimental paradigm from cognitive psychology, we found that (1) naïve participants utilised this ability when identifying "corresponding" locations. In addition, (2) these locations were statistically indistinguishable from the locations that pre-evolutionary naturalists and contemporary experts considered homologous. Furthermore, (3) presenting naïve participants with images of intermediate organs influenced their correspondence judgements. This influence was in line with historical reports according to which intermediate organs facilitated the pre-evolutionary recognition of homologies.

Animal eMotion, or the emotional evaluation of moving animals.

Responding adequately to the behaviour of human and non-human animals in our environment has been crucial for our survival. This is also reflected in our exceptional capacity to detect and interpret biological motion signals. However, even though our emotions have specifically emerged as automatic adaptive responses to such vital stimuli, few studies investigated the influence of biological motion on emotional evaluations. Here, we test how the motion of animals affects emotional judgements by contrasting static animal images and videos. We investigated this question (1) in non-fearful observers across many different animals, and (2) in observers afraid of particular animals across four types of animals, including the feared ones. In line with previous studies, we find an idiosyncratic pattern of evoked emotions across different types of animals. These emotions can be explained to different extents by regression models based on relevant predictor variables (e.g. familiarity, dangerousness). Additionally, our findings show a boosting effect of motion on emotional evaluations across all animals, with an additional increase in (negative) emotions for moving feared animals (except snakes). We discuss implications of our results for experimental and clinical research and applications, highlighting the importance of experiments with dynamic and ecologically valid stimuli.

Superordinate Categorization Based on the Perceptual Organization of Parts.

Plants and animals are among the most behaviorally significant superordinate categories for humans. Visually assigning objects to such high-level classes is challenging because highly distinct items must be grouped together (e.g., chimpanzees and geckos) while more similar items must sometimes be separated (e.g., stick insects and twigs). As both animals and plants typically possess complex multi-limbed shapes, the perceptual organization of shape into parts likely plays a crucial rule in identifying them. Here, we identify a number of distinctive growth characteristics that affect the spatial arrangement and properties of limbs, yielding useful cues for differentiating plants from animals. We developed a novel algorithm based on shape skeletons to create many novel object pairs that differ in their part structure but are otherwise very similar. We found that particular part organizations cause stimuli to look systematically more like plants or animals. We then generated other 110 sequences of shapes morphing from animal- to plant-like appearance by modifying three aspects of part structure: sprouting parts, curvedness of parts, and symmetry of part pairs. We found that all three parameters correlated strongly with human animal/plant judgments. Together our findings suggest that subtle changes in the properties and organization of parts can provide powerful cues in superordinate categorization.

One-shot generalization in humans revealed through a drawing task.

Humans have the amazing ability to learn new visual concepts from just a single exemplar. How we achieve this remains mysterious. State-of-the-art theories suggest observers rely on internal 'generative models', which not only describe observed objects, but can also synthesize novel variations. However, compelling evidence for generative models in human one-shot learning remains sparse. In most studies, participants merely compare candidate objects created by the experimenters, rather than generating their own ideas. Here, we overcame this key limitation by presenting participants with 2D 'Exemplar' shapes and asking them to draw their own 'Variations' belonging to the same class. The drawings reveal that participants inferred-and synthesized-genuine novel categories that were far more varied than mere copies. Yet, there was striking agreement between participants about which shape features were most distinctive, and these tended to be preserved in the drawn Variations. Indeed, swapping distinctive parts caused objects to swap apparent category. Our findings suggest that internal generative models are key to how humans generalize from single exemplars. When observers see a novel object for the first time, they identify its most distinctive features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants.

The Perception of "Intelligent" Design in Visual Structure.

Many objects in our visual environment will appear to us either as a consequence of "intelligent" design-the purposeful action of an animal mind-or as a consequence of self-organization in response to nature's forces-for example, wind or gravity. Here, the origin of this distinction is studied by collecting human judgements about skeletal representations of objects, that reduce objects to their basic visual structure. The results suggest that humans attribute an animate origin to visual objects with basic structures exhibiting straight lines and right angles.

Scale ambiguities in material recognition.

Many natural materials have complex, multi-scale structures. Consequently, the inferred identity of a surface can vary with the assumed spatial scale of the scene: a plowed field seen from afar can resemble corduroy seen up close. We investigated this 'material-scale ambiguity' using 87 photographs of diverse materials (e.g., water, sand, stone, metal, and wood). Across two experiments, separate groups of participants (N = 72 adults) provided judgements of the material category depicted in each image, either with or without manipulations of apparent distance (by verbal instructions, or adding objects of familiar size). Our results demonstrate that these manipulations can cause identical images to be assigned to completely different material categories, depending on the assumed scale. Under challenging conditions, therefore, the categorization of materials is susceptible to simple manipulations of apparent distance, revealing a striking example of top-down effects in the interpretation of image features.

Effects of visual and visual-haptic perception of material rigidity on reaching and grasping in the course of development.

The development of material property perception for grasping objects is not well explored during early childhood. Therefore, we investigated infants', 3-year-old children's, and adults' unimanual grasping behavior and reaching kinematics for objects of different rigidity using a 3D motion capture system. In Experiment 1, 11-month-old infants and for purposes of comparison adults, and in Experiment 2, 3-year old children were encouraged to lift relatively heavy objects with one of two handles differing in rigidity after visual (Condition 1) and visual-haptic exploration (Condition 2). Experiment 1 revealed that 11-months-olds, after visual object exploration, showed no significant material preference, and thus did not consider the material to facilitate grasping. After visual-haptic object exploration and when grasping the contralateral handles, infants showed an unexpected preference for the soft handles, which were harder to use to lift the object. In contrast, adults generally grasped the rigid handle exploiting their knowledge about efficient and functional grasping in both conditions. Reaching kinematics were barely affected by rigidity, but rather by condition and age. Experiment 2 revealed that 3-year-olds no longer exhibit a preference for grasping soft handles, but still no adult-like preference for rigid handles in both conditions. This suggests that material rigidity plays a minor role in infants' grasping behavior when only visual material information is available. Also, 3-year-olds seem to be on an intermediate level in the development from (1) preferring the pleasant sensation of a soft fabric, to (2) preferring the efficient rigid handle.

The role of contextual materials in object recognition.

While scene context is known to facilitate object recognition, little is known about which contextual "ingredients" are at the heart of this phenomenon. Here, we address the question of whether the materials that frequently occur in scenes (e.g., tiles in a bathroom) associated with specific objects (e.g., a perfume) are relevant for the processing of that object. To this end, we presented photographs of consistent and inconsistent objects (e.g., perfume vs. pinecone) superimposed on scenes (e.g., a bathroom) and close-ups of materials (e.g., tiles). In Experiment 1, consistent objects on scenes were named more accurately than inconsistent ones, while there was only a marginal consistency effect for objects on materials. Also, we did not find any consistency effect for scrambled materials that served as color control condition. In Experiment 2, we recorded event-related potentials and found N300/N400 responses-markers of semantic violations-for objects on inconsistent relative to consistent scenes. Critically, objects on materials triggered N300/N400 responses of similar magnitudes. Our findings show that contextual materials indeed affect object processing-even in the absence of spatial scene structure and object content-suggesting that material is one of the contextual "ingredients" driving scene context effects.

An image-computable model of human visual shape similarity.

Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model ('ShapeComp'), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

Response control by primes, targets, and distractors: from feedforward activation to controlled inhibition.

The visual system has to distinguish between information that is relevant and irrelevant for current behavioral goals. This is especially important in automatized responses. Here, we study how task-irrelevant distractors with task-relevant features gain access to speeded, automatized motor responses in a response-priming paradigm. In two tasks, we present distractors either together with primes or with targets, and vary the consistency between primes/targets and distractors as well as the number and saturation of distractors. Our findings are consistent with accounts where primes, targets, and distractors contribute to response activations by sequential feedforward response activation. In addition, conditions with especially salient target-distractors seem to lead to active inhibition of the primed response that occurs late in the trial. We replicate all main findings in a control experiment. Together, our findings show that the effects of distractors depend on (i) their stimulus characteristics as well as on (ii) the phase at which they enter visual processing-with effects ranging from feedforward activation to controlled inhibition of responses.

Testing a gamified Spider App to reduce spider fear and avoidance.

Mobile applications are increasingly part of mental health programs and various apps have been developed for treating anxiety disorders. Typically, they aim to improve anxiety symptoms via established CBT techniques, such as exposure principles, which are considered extremely unpleasant for fearful individuals. We combined in a mobile application exposure principles with gamification elements (e.g. narrative background, level progression, points, and feedback). These elements should increase the motivation for confronting spider images and decrease the experienced distress. To evaluate the application, two groups of spider-fearful individuals played either the Spider App (experimental group) or a non-spider associated app (control group) twice a day for approximately 12 min for 7 days. After this week, participants of the experimental group showed less avoidance behavior of spiders (BAT), as well as lower anxiety of spiders (SPQ, FAS). Groups were not different in measures of depression or psychological distress. Interestingly, participants playing the Spider App reported higher anxiety, disgust and arousal ratings shortly after playing the app. However, anxiety, disgust, and arousal ratings decreased from day to day. We discuss our findings with respect to implications for the clinical practice.

The role of semantics in the perceptual organization of shape.

Establishing correspondence between objects is fundamental for object constancy, similarity perception and identifying transformations. Previous studies measured point-to-point correspondence between objects before and after rigid and non-rigid shape transformations. However, we can also identify 'similar parts' on extremely different objects, such as butterflies and owls or lizards and whales. We measured point-to-point correspondence between such object pairs. In each trial, a dot was placed on the contour of one object, and participants had to place a dot on 'the corresponding location' of the other object. Responses show correspondence is established based on similarities between semantic parts (such as head, wings, or legs). We then measured correspondence between ambiguous objects with different labels (e.g., between 'duck' and 'rabbit' interpretations of the classic ambiguous figure). Despite identical geometries, correspondences were different across the interpretations, based on semantics (e.g., matching 'Head' to 'Head', 'Tail' to 'Tail'). We present a zero-parameter model based on labeled semantic part data (obtained from a different group of participants) that well explains our data and outperforms an alternative model based on contour curvature. This demonstrates how we establish correspondence between very different objects by evaluating similarity between semantic parts, combining perceptual organization and cognitive processes.

Softness and weight from shape: Material properties inferred from local shape features.

Object shape is an important cue to material identity and for the estimation of material properties. Shape features can affect material perception at different levels: at a microscale (surface roughness), mesoscale (textures and local object shape), or megascale (global object shape) level. Examples for local shape features include ripples in drapery, clots in viscous liquids, or spiraling creases in twisted objects. Here, we set out to test the role of such shape features on judgments of material properties softness and weight. For this, we created a large number of novel stimuli with varying surface shape features. We show that those features have distinct effects on softness and weight ratings depending on their type, as well as amplitude and frequency, for example, increasing numbers and pointedness of spikes makes objects appear harder and heavier. By also asking participants to name familiar objects, materials, and transformations they associate with our stimuli, we can show that softness and weight judgments do not merely follow from semantic associations between particular stimuli and real-world object shapes. Rather, softness and weight are estimated from surface shape, presumably based on learned heuristics about the relationship between a particular expression of surface features and material properties. In line with this, we show that correlations between perceived softness or weight and surface curvature vary depending on the type of surface feature. We conclude that local shape features have to be considered when testing the effects of shape on the perception of material properties such as softness and weight.

A dataset for evaluating one-shot categorization of novel object classes.

With the advent of deep convolutional neural networks, machines now rival humans in terms of object categorization. The neural networks solve categorization with a hierarchical organization that shares a striking resemblance to their biological counterpart, leading to their status as a standard model of object recognition in biological vision. Despite training on thousands of images of object categories, however, machine-learning networks are poorer generalizers, often fooled by adversarial images with very simple image manipulations that humans easily distinguish as a false image. Humans, on the other hand, can generalize object classes from very few samples. Here we provide a dataset of novel object classifications in humans. We gathered thousands of crowd-sourced human responses to novel objects embedded either with 1 or 16 context sample(s). Human decisions and stimuli together have the potential to be re-used (1) as a tool to better understand the nature of the gap in category learning from few samples between human and machine, and (2) as a benchmark of generalization across machine learning networks.

Analyzing Response Times and Other Types of Time-to-Event Data Using Event History Analysis: A Tool for Mental Chronometry and Cognitive Psychophysiology.

In this Methods article, we discuss and illustrate a unifying, principled way to analyze response time data from psychological experiments-and all other types of time-to-event data. We advocate the general application of discrete-time event history analysis (EHA) which is a well-established, intuitive longitudinal approach to statistically describe and model the shape of time-to-event distributions. After discussing the theoretical background behind the so-called hazard function of event occurrence in both continuous and discrete time units, we illustrate how to calculate and interpret the descriptive statistics provided by discrete-time EHA using two example data sets (masked priming, visual search). In case of discrimination data, the hazard analysis of response occurrence can be extended with a microlevel speed-accuracy trade-off analysis. We then discuss different approaches for obtaining inferential statistics. We consider the advantages and disadvantages of a principled use of discrete-time EHA for time-to-event data compared to (a) comparing means with analysis of variance, (b) other distributional methods available in the literature such as delta plots and continuous-time EHA methods, and (c) only fitting parametric distributions or computational models to empirical data. We conclude that statistically controlling for the passage of time during data analysis is equally important as experimental control during the design of an experiment, to understand human behavior in our experimental paradigms.

One-shot categorization of novel object classes in humans.

One aspect of human vision unmatched by machines is the capacity to generalize from few samples. Observers tend to know when novel objects are in the same class despite large differences in shape, material or viewpoint. A major challenge in studying such generalization is that participants can see each novel sample only once. To overcome this, we used crowdsourcing to obtain responses from 500 human observers on 20 novel object classes, with each stimulus compared to 1 or 16 related objects. The results reveal that humans generalize from sparse data in highly systematic ways with the number and variance of the samples. We compared human responses to 'ShapeComp', an image-computable model based on >100 shape descriptors, and 'AlexNet', a convolution neural network that roughly matches humans at recognizing 1000 categories of real-world objects. With 16 samples, the models were consistent with human responses without free parameters. Thus, when there are a sufficient number of samples, observers rely on shallow but efficient processes based on a fixed set of features. With 1 sample, however, the models required different feature weights for each object. This suggests that one-shot categorization involves more sophisticated processes that actively identify the unique characteristics underlying each object class.

Interpreting and responding to ambiguous natural images in spider phobia.

BACKGROUND AND OBJECTIVES: The fast detection of and response to threatening stimuli is an important task of the human visual and motor systems, and is especially challenging when stimuli are ambiguous. This study investigates the perception, evaluation and fast response to ambiguous natural spider stimuli in spider-fearful and non-anxious participants. METHODS: Stimuli were created by gradually morphing natural images of spiders and non-spiders (a crab, a starfish, a bunch of keys, and a flower). In Study 1, participants rated the images on perceptual and emotional dimensions and responded to them in a response priming task to measure rapid information processing. In Study 2, results were validated and extended in a different paradigm by using a go/no-go task. RESULTS: As expected, spider-fearful participants showed an interpretative bias for ambiguous stimuli (i.e., perceived them as more similar to spiders) and rated spider(-like) stimuli as more unpleasant, arousing, and disgusting. In Study 1, spider stimuli were preferentially processed in spider-fearful participants as observed in faster responses to spider targets-however, responses were not different to comparison participants for ambiguous stimuli. Study 2 suggests that this finding can be explained by differences in stimulus duration. LIMITATIONS: No participants with positive attitudes towards spiders or a second fearful comparison group were included. CONCLUSIONS: We suggest that these findings can be explained by the nature of the applied tasks that tap into early phases of visual processing, thereby relying on feedforward-mediated low-spatial-frequency information extracted via the fast, subcortical path to the amygdala.