Turning visual search time on its head

Target-distractor similarity predicts visual search efficiency but only for highly similar features

A major constraining factor for attentional selection is the similarity between targets and distractors. When similarity is low, target items can be identified quickly and efficiently, whereas high similarity can incur large costs on processing speed. Models of visual search contrast a fast, efficient parallel stage with a slow serial processing stage where search times are strongly modulated by the number of distractors in the display. In particular, recent work has argued that the magnitude of search slopes should be inversely proportional to target-distractor similarity. Here, we assessed the relationship between target-distractor similarity and search slopes. In our visual search tasks, participants detected an oddball color target among distractors (Experiments 1 & 2) or discriminated the direction of a triangle in the oddball color (Experiment 3). We systematically varied the similarity between target and distractor colors (along a circular CIELAB color wheel) and the number of distractors in the search array, finding logarithmic search slopes that were inversely proportional to the number of items in the array. Surprisingly, we also found that searches were highly efficient (i.e., near-zero slopes) for targets and distractors that were extremely similar (≤20° in color space). These findings indicate that visual search is systematically influenced by target-distractor similarity across different processing stages. Importantly, we found that search can be highly efficient and entirely unaffected by the number of distractors despite high perceptual similarity, in contrast to the general assumption that high similarity must lead to slow and serial search behavior.

Predicting Cued and Oddball Visual Search Performance from fMRI, MEG, and DNN Neural Representational Similarity

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing.

General object-based features account for letter perception

After years of experience, humans become experts at perceiving letters. Is this visual capacity attained by learning specialized letter features, or by reusing general visual features previously learned in service of object categorization? To explore this question, we first measured the perceptual similarity of letters in two behavioral tasks, visual search and letter categorization. Then, we trained deep convolutional neural networks on either 26-way letter categorization or 1000-way object categorization, as a way to operationalize possible specialized letter features and general object-based features, respectively. We found that the general object-based features more robustly correlated with the perceptual similarity of letters. We then operationalized additional forms of experience-dependent letter specialization by altering object-trained networks with varied forms of letter training; however, none of these forms of letter specialization improved the match to human behavior. Thus, our findings reveal that it is not necessary to appeal to specialized letter representations to account for perceptual similarity of letters. Instead, we argue that it is more likely that the perception of letters depends on domain-general visual features.

For over a century, scientists have conducted behavioral experiments to investigate how the visual system recognizes letters, but it has proven difficult to propose a model of the feature space underlying this capacity. Here we leveraged recent advances in machine learning to model a wide variety of features ranging from specialized letter features to general object-based features. Across two large-scale behavioral experiments we find that general object-based features account well for letter perception, and that adding letter specialization did not improve the correspondence to human behavior. It is plausible that the ability to recognize letters largely relies on general visual features unaltered by letter learning.

Feature similarity is non-linearly related to attentional selection: Evidence from visual search and sustained attention tasks

Although many theories of attention highlight the importance of similarity between target and distractor items for selection, few studies have directly quantified the function underlying this relationship. Across two commonly used tasks-visual search and sustained attention-we investigated how target-distractor similarity impacts feature-based attentional selection. Importantly, we found comparable patterns of performance in both visual search and sustained feature-based attention tasks, with performance (response times and d', respectively) plateauing at medium target-distractor distances (40°-50° around a luminance-matched color wheel). In contrast, visual search efficiency, as measured by search slopes, was affected by a much more narrow range of similarity levels (10°-20°). We assessed the relationship between target-distractor similarity and attentional performance using both a stimulus-based and psychologically-based measure of similarity and found this nonlinear relationship in both cases. However, psychological similarity accounted for some of the nonlinearities observed in the data, suggesting that measures of psychological similarity are more appropriate when studying effects of target-distractor similarities. These findings place novel constraints on models of selective attention and emphasize the importance of considering the similarity structure of the feature space over which attention operates. Broadly, the nonlinear effects of similarity on attention are consistent with accounts that propose attention exaggerates the distance between competing representations, possibly through enhancement of off-tuned neurons.

Trailblazers in Neuroscience: Using compositionality to understand how parts combine in whole objects

A fundamental question for any visual system is whether its image representation can be understood in terms of its components. Decomposing any image into components is challenging because there are many possible decompositions with no common dictionary, and enumerating them leads to a combinatorial explosion. Even in perception, many objects are readily seen as containing parts, but there are many exceptions. These exceptions include objects that are not perceived as containing parts, properties like symmetry that cannot be localized to any single part, and also special categories like words and faces whose perception is widely believed to be holistic. Here, I describe a novel approach we have used to address these issues and evaluate compositionality at the behavioral and neural levels. The key design principle is to create a large number of objects by combining a small number of pre-defined components in all possible ways. This allows for building component-based models that explain whole objects using a combination of these components. Importantly, any systematic error in model fits can be used to detect the presence of emergent or holistic properties. Using this approach, we have found that whole object representations are surprisingly predictable from their components, that some components are preferred to others in perception, and that emergent properties can be discovered or explained using compositional models. Thus, compositionality is a powerful approach for understanding how whole objects relate to their parts.

Improving Machine Vision Using Human Perceptual Representations: The Case of Planar Reflection Symmetry for Object Classification

Achieving human-like visual abilities is a holy grail for machine vision, yet precisely how insights from human vision can improve machines has remained unclear. Here, we demonstrate two key conceptual advances: First, we show that most machine vision models are systematically different from human object perception. To do so, we collected a large dataset of perceptual distances between isolated objects in humans and asked whether these perceptual data can be predicted by many common machine vision algorithms. We found that while the best algorithms explain  ∼ 70 percent of the variance in the perceptual data, all the algorithms we tested make systematic errors on several types of objects. In particular, machine algorithms underestimated distances between symmetric objects compared to human perception. Second, we show that fixing these systematic biases can lead to substantial gains in classification performance. In particular, augmenting a state-of-the-art convolutional neural network with planar/reflection symmetry scores along multiple axes produced significant improvements in classification accuracy (1-10 percent) across categories. These results show that machine vision can be improved by discovering and fixing systematic differences from human vision.

Qualitative similarities and differences in visual object representations between brains and deep networks

Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

Deep neural networks are widely considered as good models for biological vision. Here, we describe several qualitative similarities and differences in object representations between brains and deep networks that elucidate when deep networks can be considered good models for biological vision and how they can be improved.

Predicting how surface texture and shape combine in the human visual system to direct attention

Objects differ from one another along a multitude of visual features. The more distinct an object is from other objects in its surroundings, the easier it is to find it. However, it is still unknown how this distinctiveness advantage emerges in human vision. Here, we studied how visual distinctiveness signals along two feature dimensions—shape and surface texture—combine to determine the overall distinctiveness of an object in the scene. Distinctiveness scores between a target object and distractors were measured separately for shape and texture using a search task. These scores were then used to predict search times when a target differed from distractors along both shape and texture. Model comparison showed that the overall object distinctiveness was best predicted when shape and texture combined using a Euclidian metric, confirming the brain is computing independent distinctiveness scores for shape and texture and combining them to direct attention.

How the forest interacts with the trees: Multiscale shape integration explains global and local processing

Hierarchical stimuli have been widely used to study global and local processing. Two classic phenomena have been observed using these stimuli: the global advantage effect (we identify the global shape faster) and an interference effect (we identify shape slower when the global and local shapes are different). Because these phenomena have been observed during shape categorization tasks, it is unclear whether they reflect the categorical judgment or the underlying shape representation. Understanding the underlying shape representation is also critical because both global and local processing are modulated by stimulus properties. We performed two experiments to investigate these issues. In Experiment 1, we show that these phenomena can be observed in a same-different task, and that participants show systematic variation in response times across image pairs. We show that the response times to any pair of images can be accurately predicted using two factors: their dissimilarity and their distinctiveness relative to other images. In Experiment 2, we show that these phenomena can also be observed in a visual search task where participant did not have to make any categorical shape judgments. Here too, participants showed highly systematic variations in response time that could be explained as a linear sum of shape comparisons across global and local scales. Finally, the dissimilarity and distinctiveness factors estimated from the same-different task were systematically related to the search dissimilarities observed during visual search. In sum, our results show that global and local processing phenomena are properties of a systematic shape representation governed by simple rules.

A compositional neural code in high-level visual cortex can explain jumbled word reading

We read jubmled wrods effortlessly, but the neural correlates of this remarkable ability remain poorly understood. We hypothesized that viewing a jumbled word activates a visual representation that is compared to known words. To test this hypothesis, we devised a purely visual model in which neurons tuned to letter shape respond to longer strings in a compositional manner by linearly summing letter responses. We found that dissimilarities between letter strings in this model can explain human performance on visual search, and responses to jumbled words in word reading tasks. Brain imaging revealed that viewing a string activates this letter-based code in the lateral occipital (LO) region and that subsequent comparisons to stored words are consistent with activations of the visual word form area (VWFA). Thus, a compositional neural code potentially contributes to efficient reading.

Reading Increases the Compositionality of Visual Word Representations

Reading causes widespread changes in the brain, but its effect on visual word representations is unknown. Learning to read may facilitate visual processing by forming specialized detectors for longer strings or by making word responses more predictable from single letters—that is, by increasing compositionality. We provided evidence for the latter hypothesis using experiments that compared nonoverlapping groups of readers of two Indian languages (Telugu and Malayalam). Readers showed increased single-letter discrimination and decreased letter interactions for bigrams during visual search. Importantly, these interactions predicted subjects’ overall reading fluency. In a separate brain-imaging experiment, we observed increased compositionality in readers, whereby responses to bigrams were more predictable from single letters. This effect was specific to the anterior lateral occipital region, where activations best matched behavior. Thus, learning to read facilitates visual processing by increasing the compositionality of visual word representations.

Symmetric Objects Become Special in Perception Because of Generic Computations in Neurons

Symmetry is a salient visual property: It is easy to detect and influences perceptual phenomena from segmentation to recognition. Yet researchers know little about its neural basis. Using recordings from single neurons in monkey IT cortex, we asked whether symmetry—being an emergent property—induces nonlinear interactions between object parts. Remarkably, we found no such deviation: Whole-object responses were always the sum of responses to the object’s parts, regardless of symmetry. The only defining characteristic of symmetric objects was that they were more distinctive compared with asymmetric objects. This was a consequence of neurons preferring the same part across locations within an object. Just as mixing diverse paints produces a homogeneous overall color, adding heterogeneous parts within an asymmetric object renders it indistinct. In contrast, adding identical parts within a symmetric object renders it distinct. This distinctiveness systematically predicted human symmetry judgments, and it explains many previous observations about symmetry perception. Thus, symmetry becomes special in perception despite being driven by generic computations at the level of single neurons.

Look before you seek: Preview adds a fixed benefit to all searches

Knowing in advance what to look for speeds up search, but how this knowledge guides search is poorly understood. The little available evidence suggests that previewing the target produces larger reductions in search times for harder searches. To investigate this issue further, we performed seven experiments in which subjects searched for an oddball target after previewing the target, distracter, or an unrelated square. Consistent with previous studies, harder searches showed bigger reductions in search time for an informative preview. However, the same data replotted using the reciprocal of search time showed a remarkably different result: The informative preview showed a fixed additive increase in reciprocal search time across all searches regardless of difficulty. This is a nontrivial outcome because it cannot be explained using a simple relationship between search times in the informative and uninformative preview conditions. We interpret our findings by proposing that the reciprocal of search time reflects the strength of an underlying accumulating signal related to the distinctiveness or salience of the target over the distracters and that preview additively increases this signal for all searches. This in turn implies that the top-down signals related to target preview and bottom-up signals related to target-distracter salience sum linearly.

Object attributes combine additively in visual search

We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.

Texture discriminability in monkey inferotemporal cortex predicts human texture perception

Shape and texture are both important properties of visual objects, but texture is relatively less understood. Here, we characterized neuronal responses to discrete textures in monkey inferotemporal (IT) cortex and asked whether they can explain classic findings in human texture perception. We focused on three classic findings on texture discrimination: 1) it can be easy or hard depending on the constituent elements; 2) it can have asymmetries, and 3) it is reduced for textures with randomly oriented elements. We recorded neuronal activity from monkey inferotemporal (IT) cortex and measured texture perception in humans for a variety of textures. Our main findings are as follows: 1) IT neurons show congruent selectivity for textures across array size; 2) textures that were easy for humans to discriminate also elicited distinct patterns of neuronal activity in monkey IT; 3) texture pairs with asymmetries in humans also exhibited asymmetric variation in firing rate across monkey IT; and 4) neuronal responses to randomly oriented textures were explained by an average of responses to homogeneous textures, which rendered them less discriminable. The reduction in discriminability of monkey IT neurons predicted the reduced discriminability in humans during texture discrimination. Taken together, our results suggest that texture perception in humans is likely based on neuronal representations similar to those in monkey IT.

Features in visual search combine linearly

Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features-in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search.

Does linear separability really matter? Complex visual search is explained by simple search

Visual search in real life involves complex displays with a target among multiple types of distracters, but in the laboratory, it is often tested using simple displays with identical distracters. Can complex search be understood in terms of simple searches? This link may not be straightforward if complex search has emergent properties. One such property is linear separability, whereby search is hard when a target cannot be separated from its distracters using a single linear boundary. However, evidence in favor of linear separability is based on testing stimulus configurations in an external parametric space that need not be related to their true perceptual representation. We therefore set out to assess whether linear separability influences complex search at all. Our null hypothesis was that complex search performance depends only on classical factors such as target-distracter similarity and distracter homogeneity, which we measured using simple searches. Across three experiments involving a variety of artificial and natural objects, differences between linearly separable and nonseparable searches were explained using target-distracter similarity and distracter heterogeneity. Further, simple searches accurately predicted complex search regardless of linear separability (r = 0.91). Our results show that complex search is explained by simple search, refuting the widely held belief that linear separability influences visual search.