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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 the components 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 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 behavioural 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 neural and behavioural responses to 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. [ABSTRACT FROM AUTHOR]

Regions in the ventral visual pathway, such as the fusiform face area (FFA) and parahippocampal place area (PPA) are selective for images from specific object categories. Yet images from different object categories differ in their image properties. To investigate how these image properties are represented in the FFA and PPA, we compared neural responses to locally‐SCRAMBLED images (in which mid‐level, spatial properties are preserved) and globally‐SCRAMBLED images (in which mid‐level, spatial properties are not preserved). There was a greater response in the FFA and PPA to images from the preferred CATEGORY relative to their non‐preferred category for the scrambled conditions. However, there was a greater selectivity for locally‐scrambled compared to globally‐scrambled images. Next, we compared the magnitude of fMR‐adaptation to intact and scrambled images. fMR‐adaptation was evident to locally‐scrambled images from the preferred category. However, there was no adaptation to globally‐scrambled images from the preferred category. These results show that the selectivity to faces and places in the FFA and PPA is dependent on mid‐level properties of the image that are preserved by local‐scrambling. [ABSTRACT FROM AUTHOR]

Visual object information is conveyed from V1 to area TE along the ventral visual pathway with increasing receptive field (RF) sizes. The RFs of TE neurons are known to be large, but it is largely unknown how large RFs are shaped along the ventral visual pathway. In this study, we addressed this question in two aspects, static and dynamic mechanisms, by recording neural responses from macaque area TE and V4 to object stimuli presented at various locations in the visual field. As a component related to static mechanisms, we found that in area TE, but not in V4, response latency to objects presented at fovea were different from objects in periphery. As a component of the dynamic mechanisms, we examined effects of spatial attention on the RFs of TE neurons. Spatial attention did not affect response latency but modulated response magnitudes depending on attended location, shifting of the longitudinal axis of RFs toward the attended locations. In standard models of large RF formation, downstream neurons pool information from nearby RFs, and this process is repeated across the visual field and at each step along the ventral visual pathway. The present study revealed that this mechanism is not that simple: 1) different circuit mechanisms for foveal and peripheral visual fields may be situated between V4 and area TE, and 2) spatial attention dynamically changes the shape of RFs. NEW & NOTEWORTHY Receptive fields (RFs) of neurons are progressively increased along the ventral visual pathway so that an RF at the final stage, area TE, covers a large area of the visual field. We explored the mechanism and suggested involvement of parallel circuit mechanisms between V4 and TE for foveal and peripheral parts of visual field. We also found a dynamic component of RF shape formation through attentional modulation of responses in a location-dependent manner. [ABSTRACT FROM AUTHOR]

The primate visual system contains two major cortical pathways: a ventral-temporal pathway that has been associated with object processing and recognition, and a dorsal-parietal pathway that has been associated with spatial processing and action guidance. Our understanding of the role of the dorsal pathway, in particular, has greatly evolved within the framework of the two-pathway hypothesis since its original conception. Here, we present a comparative review of the primate dorsal pathway in humans and monkeys based on electrophysiological, neuroimaging, neuropsychological, and neuroanatomical studies. We consider similarities and differences across species in terms of the topographic representation of visual space; specificity for eye, reaching, or grasping movements; multi-modal response properties; and the representation of objects and tools. We also review the relative anatomical location of functionally- and topographically-defined regions of the posterior parietal cortex. An emerging theme from this comparative analysis is that non-spatial information is represented to a greater degree, and with increased complexity, in the human dorsal visual system. We propose that non-spatial information in the primate parietal cortex contributes to the perception-to-action system aimed at manipulating objects in peripersonal space. In humans, this network has expanded in multiple ways, including the development of a dorsal object vision system mirroring the complexity of the ventral stream, the integration of object information with parietal working memory systems, and the emergence of tool-specific object representations in the anterior intraparietal sulcus and regions of the inferior parietal lobe. We propose that these evolutionary changes have enabled the emergence of human-specific behaviors, such as the sophisticated use of tools. [ABSTRACT FROM AUTHOR]

In the cerebral cortex, membrane currents, i.e., action potentials and other membrane currents, express many forms of space-time dynamics. In the spontaneous asynchronous irregular state, their space-time dynamics are local non-propagating fluctuations and sparse spiking appearing at unpredictable positions. After transition to active spiking states, larger structured zones with active spiking neurons appear, propagating through the cortical network, driving it into various forms of widespread excitation, and engaging the network from microscopic scales to whole cortical areas. At each engaged cortical site, the amount of excitation in the network, after a delay, becomes matched by an equal amount of space-time fine-tuned inhibition that might be instrumental in driving the dynamics toward perception and action. [ABSTRACT FROM AUTHOR]

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might "spread" from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. [ABSTRACT FROM AUTHOR]

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as “human”, “mammal”, and “animal”). The feature-based model includes both object parts (such as “eye”, “tail”, and “handle”) and other descriptive features (such as “circular”, “green”, and “stubbly”). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation. [ABSTRACT FROM AUTHOR]

Abstract: This article reports two human experiments to investigate the effects of visual cues and sustained attention on spatial presence over a period of prolonged exposure in virtual environments. Inspired by the two functional subsystems subserving spatial and object vision in the human brain, visual cues and sustained attention were each classified into spatial and object cues, and spatial and non-spatial attention, respectively. In the first experiment, the effects of visual cues on spatial presence were examined when subjects were exposed to virtual environments configured with combinations of spatial and object cues. It was found that both types of visual cues enhanced spatial presence with saturation over a period of prolonged exposure, but the contribution of spatial cues became more relevant with longer exposure time. In the second experiment, subjects were asked to carry out two tasks involving sustained spatial attention and sustained non-spatial attention. We observed that spatially directed attention improved spatial presence more than non-spatially directed attention did. Furthermore, spatial attention had a positive interaction with detailed object cues. [Copyright &y& Elsevier]

Despite the lack of direct evidence, it is generally believed that top-down signals are mediated by the abundant feedback connections from higher- to lower-order sensory areas. Here we provide direct evidence for a top-down mechanism. We stained the visual cortex of the ferret with a voltage-sensitive dye and presented a short-duration contrast square. This elicited an initial feedforward and lateral spreading depolarization at the square representation in areas 17 and 18. After a delay, a broad feedback wave (FBW) of neuron peak depolarization traveled from areas 21 and 19 toward areas 18 and 17. In areas 18 and 17, the FBW contributed the peak depolarization of dendrites of the neurons representing the square, after which the neurons decreased their depolarization and firing. Thereafter, the peak depolarization surrounded the figure representation over most of areas 17 and 18 representing the background. Thus, the FBW is an example of a well behaved long-range communication from higher-order visual areas to areas 18 and 17, collectively addressing very large populations of neu- rons representing the visual scene. Through local interaction with feedforward and lateral spreading depolarization, the FBW differentially activates neurons representing the object and neurons representing the background. [ABSTRACT FROM AUTHOR]

Abstract It is generally believed that the acuity of the peripheral visual field is too poor to allow accurate object recognition and, that to be identified, most objects need to be brought into foveal vision by using saccadic eye movements. However, most measures of form vision in the periphery have been done at eccentricities below 10° and have used relatively artificial stimuli such as letters, digits and compound Gabor patterns. Little is known about how such data would apply in the case of more naturalistic stimuli. Here humans were required to categorize briefly flashed (28 ms) unmasked photographs of natural scenes (39° high, and 26° across) on the basis of whether or not they contained an animal. The photographs appeared randomly in nine locations across virtually the entire extent of the horizontal visual field. Accuracy was 93.3% for central vision and decreased almost linearly with increasing eccentricity (89.8% at 13°, 76.1% at 44.5° and 71.2% at 57.5°). Even at the most extreme eccentricity, where the images were centred at 70.5°, subjects scored 60.5% correct. No evidence was found for hemispheric specialization. This level of performance was achieved despite the fact that the position of the image was unpredictable, ruling out the use of precued attention to target locations. The results demonstrate that even high-level visual tasks involving object vision can be performed using the relatively coarse information provided by the peripheral retina. [ABSTRACT FROM AUTHOR]

Several fundamental aspects of motion vision circuitry are prevalent across flies and mice. Both taxa segregate ON and OFF signals. For any given spatial pattern, motion detectors in both taxa are tuned to speed, selective for one of four cardinal directions, and modulated by catecholamine neurotransmitters. These similarities represent conserved, canonical properties of the functional circuits and computational algorithms for motion vision. Less is known about feature detectors, including how receptive field properties differ from the motion pathway or whether they are under neuromodulatory control to impart functional plasticity for the detection of salient objects from a moving background. Here, we investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. We identified LC12 and LC15 as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. We show that the onset of background motion suppresses object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. Our results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection. • LC12 and LC15 are ON-OFF visual feature detectors • LC15 responds to moving bars, whereas LC12 responds to objects of any size • Object responses from both LCs are suppressed when the background is moving • Octopamine restores object responses in LC12 and LC15 against a moving background What are the neuronal mechanisms for extracting visual features? Städele et al. examine a group of 19 neuron classes of the fly brain and highlight two that detect visual features with response properties very different from those that characterize motion vision. Feature detection by these cells is under neuromodulatory influence by octopamine. [ABSTRACT FROM AUTHOR]

Female Aedes aegypti mosquitoes use multiple sensory modalities to hunt human hosts and obtain a blood meal for egg production. Attractive cues include carbon dioxide (CO 2), a major component of exhaled breath [ 1, 2 ]; heat elevated above ambient temperature, signifying warm-blooded skin [ 3, 4 ]; and dark visual contrast [ 5, 6 ], proposed to bridge long-range olfactory and short-range thermal cues [ 7 ]. Any of these sensory cues in isolation is an incomplete signal of a human host, and so a mosquito must integrate multimodal sensory information before committing to approaching and biting a person [ 8 ]. Here, we study the interaction of visual cues, heat, and CO 2 to investigate the contributions of human-associated stimuli to host-seeking decisions. We show that tethered flying mosquitoes strongly orient toward dark visual contrast, regardless of CO 2 stimulation and internal host-seeking status. This suggests that attraction to visual contrast is general and not contingent on other host cues. In free-flight experiments with CO 2 , adding a dark contrasting visual cue to a warmed surface enhanced attraction. Moderate warmth became more attractive to mosquitoes, and mosquitoes aggregated on the cue at all non-noxious temperatures. Gr3 mutants, unable to detect CO 2 , were lured to the visual cue at ambient temperatures but fled and did not return when the surface was warmed to host-like temperatures. This suggests that attraction to thermal cues is contingent on the presence of the additional sensory cue CO 2. Our results illustrate that mosquitoes integrate general attractive visual stimuli with context-dependent thermal stimuli to seek promising sites for blood feeding. • Mosquito attraction to dark visual contrast is not contingent on other host cues • Attractive visual cues enhance the attractiveness of warm stimuli • Carbon dioxide contingently unlocks thermotaxis toward potential hosts Female Aedes aegypti mosquitoes are nature's human-seeking missiles, and they use multiple sensory modalities to hunt us. Liu and Vosshall study how CO 2 from breath, body heat, and visual cues contributes to host pursuit. They find that mosquito visual responses are unaffected by CO 2 , but mosquitoes must sense CO 2 to unlock their attraction to heat. [ABSTRACT FROM AUTHOR]