Visual Cortical Areas V1 Research Articles

Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance.

Recent visual experience heavily influences our visual perception, but how neuronal activity is reshaped to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while two male rhesus macaque monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in the mid-level visual cortex.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance.

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here, we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action.

Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling.

The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.

Adaptive processing and perceptual learning in visual cortical areas V1 and V4

Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.

Laminar microcircuitry of visual cortex producing attention-associated electric fields

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.

Perceptual Texture Dimensions Modulate Neuronal Response Dynamics in Visual Cortical Area V4.

Texture is an important visual attribute for surface pattern discrimination and therefore object segmentation, but the neural bases of texture perception are largely unknown. Previously, we demonstrated that the responses of V4 neurons to naturalistic texture patches are sensitive to four key features of human texture perception: coarseness, directionality, regularity, and contrast. To begin to understand how distinct texture perception emerges from the dynamics of neuronal responses, in 2 macaque monkeys (1 male, 1 female), we investigated the relative contribution of the four texture attributes to V4 responses in terms of the strength and timing of response modulation. We found that the different feature dimensions are associated with different temporal dynamics. Specifically, the response modulation associated with directionality and regularity was significantly delayed relative to that associated with coarseness and contrast, suggesting that the latter are fundamentally simpler feature dimensions. The population of texture-selective neurons could be grouped into multiple clusters based on the combination of feature dimensions encoded, and those subpopulations displayed distinct temporal dynamics characterized by the weighted combinations of multiple features. Finally, we applied a population decoding approach to demonstrate that texture category information can be obtained from short temporal windows across time. These results demonstrate that the representation of different perceptually relevant texture features emerge over time in the responses of V4 neurons. The observed temporal organization provides a framework to interpret how the processing of surface features unfolds in early and midlevel cortical stages, and could ultimately inform the interpretation of perceptual texture dynamics.SIGNIFICANCE STATEMENT To delineate how neuronal responses underlie our ability to perceive visual textures, we related four key perceptual dimensions (coarseness, directionality, regularity, and contrast) of naturalistic textures to the strength and timing of modulation of neuronal responses in area V4, an intermediate stage in the form-processing, ventral visual pathway. Our results provide the first characterization of V4 temporal dynamics for texture encoding along perceptually defined axes.

A Stable Population Code for Attention in Prefrontal Cortex Leads a Dynamic Attention Code in Visual Cortex.

Attention often requires maintaining a stable mental state over time while simultaneously improving perceptual sensitivity. These requirements place conflicting demands on neural populations, as sensitivity implies a robust response to perturbation by incoming stimuli, which is antithetical to stability. Functional specialization of cortical areas provides one potential mechanism to resolve this conflict. We reasoned that attention signals in executive control areas might be highly stable over time, reflecting maintenance of the cognitive state, thereby freeing up sensory areas to be more sensitive to sensory input (i.e., unstable), which would be reflected by more dynamic attention signals in those areas. To test these predictions, we simultaneously recorded neural populations in prefrontal cortex (PFC) and visual cortical area V4 in rhesus macaque monkeys performing an endogenous spatial selective attention task. Using a decoding approach, we found that the neural code for attention states in PFC was substantially more stable over time compared with the attention code in V4 on a moment-by-moment basis, in line with our guiding thesis. Moreover, attention signals in PFC predicted the future attention state of V4 better than vice versa, consistent with a top-down role for PFC in attention. These results suggest a functional specialization of attention mechanisms across cortical areas with a division of labor. PFC signals the cognitive state and maintains this state stably over time, whereas V4 responds to sensory input in a manner dynamically modulated by that cognitive state.SIGNIFICANCE STATEMENT Attention requires maintaining a stable mental state while simultaneously improving perceptual sensitivity. We hypothesized that these two demands (stability and sensitivity) are distributed between prefrontal and visual cortical areas, respectively. Specifically, we predicted attention signals in visual cortex would be less stable than in prefrontal cortex, and furthermore prefrontal cortical signals would predict attention signals in visual cortex in line with the hypothesized role of prefrontal cortex in top-down executive control. Our results are consistent with suggestions deriving from previous work using separate recordings in the two brain areas in different animals performing different tasks and represent the first direct evidence in support of this hypothesis with simultaneous multiarea recordings within individual animals.

Timing of response onset and offset in macaque V4: stimulus and task dependence.

In the primate visual cortex, both the magnitude of the neuronal response and its timing can carry important information about the visual world, but studies typically focus only on response magnitude. Here, we examine the onset and offset latency of the responses of neurons in area V4 of awake, behaving macaques across several experiments in the context of a variety of stimuli and task paradigms. Our results highlight distinct contributions of stimuli and tasks to V4 response latency. We found that response onset latencies are shorter than typically cited (median = 75.5 ms), supporting a role for V4 neurons in rapid object and scene recognition functions. Moreover, onset latencies are longer for smaller stimuli and stimulus outlines, consistent with the hypothesis that longer latencies are associated with higher spatial frequency content. Strikingly, we found that onset latencies showed no significant dependence on stimulus occlusion, unlike in inferotemporal cortex, nor on task demands. Across the V4 population, onset latencies had a broad distribution, reflecting the diversity of feedforward, recurrent, and feedback connections that inform the responses of individual neurons. Response offset latencies, on the other hand, displayed the opposite tendency in their relationship to stimulus and task attributes: they are less influenced by stimulus appearance but are shorter in guided saccade tasks compared with fixation tasks. The observation that response latency is influenced by stimulus- and task-associated factors emphasizes a need to examine response timing alongside firing rate in determining the functional role of area V4.NEW & NOTEWORTHY Onset and offset timing of neuronal responses can provide information about visual environment and neuron's role in visual processing and its anatomical connectivity. In the first comprehensive examination of onset and offset latencies in the intermediate visual cortical area V4, we find neurons respond faster than previously reported, making them ideally suited to contribute to rapid object and scene recognition. While response onset reflects stimulus characteristics, timing of response offset is influenced more by behavioral task.

Priming of Attentional Selection in Macaque Visual Cortex: Feature-Based Facilitation and Location-Based Inhibition of Return.

Visual search performance varies with stimulus and response history. Priming of pop-out refers to increased accuracy and reduced response time with repeated presentation of particular singleton and distractor features (e.g., a red target among green distractor stimuli), which are abruptly impaired when singleton and distractor features swap (e.g., green target among red distractors). Meanwhile, inhibition of return refers to the slowing of response time when target location repeats. Neurophysiological correlates of both these phenomena have been reported in the frontal eye field (FEF), an area in the frontal lobe contributing to attentional selection and eye movement planning. To understand the mechanistic origin of these adaptive behaviors, we investigated visual cortical area V4, an area providing input to and receiving feedback from FEF, during feature-based priming of pop-out and location-based inhibition of return. Performing a color pop-out task, monkeys exhibited pronounced priming of pop-out and inhibition of return. Neural spiking from V4 revealed earlier target selection associated with priming of pop-out and delayed selection associated with inhibition of return. These results demonstrate substantial involvement of extrastriate visual cortex in behavioral priming and inhibition of return.

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models.

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface–shroud resonances support conscious seeing and action, whereas feature–category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure–ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive–emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

A Flexible 202-Channel Epidural ECoG Array With PEDOT: PSS Coated Electrodes for Chronic Recording of the Visual Cortex

Electrocorticography (ECoG) electrode arrays for epidural implantation allow establishing neural interfaces, which promote investigation of complex distributed brain functions and development of neuroprosthetic systems while having reduced invasiveness in comparison to intracortical or subdural electrode placement. In this paper, we present the design, fabrication, and both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vitro</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> evaluation of a flexible ECoG array with 202 closely spaced electrodes of 560- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter, each coated with the polymer poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT: PSS) for chronic recording in two visual cortical areas V1 and V4 of macaque monkeys. The specific layout of the array provides a close fit to the curved surface of the brain. We confirm its chronic recording functionality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> by estimating visual receptive fields for the individual electrodes.

Theta Rhythmic Neuronal Activity and Reaction Times Arising from Cortical Receptive Field Interactions during Distributed Attention

SummaryGrowing evidence suggests that distributed spatial attention may invoke theta (3–9 Hz) rhythmic sampling processes. The neuronal basis of such attentional sampling is, however, not fully understood. Here we show using array recordings in visual cortical area V4 of two awake macaques that presenting separate visual stimuli to the excitatory center and suppressive surround of neuronal receptive fields (RFs) elicits rhythmic multi-unit activity (MUA) at 3–6 Hz. This neuronal rhythm did not depend on small fixational eye movements. In the context of a distributed spatial attention task, during which the monkeys detected a spatially and temporally uncertain target, reaction times (RTs) exhibited similar rhythmic fluctuations. RTs were fast or slow depending on the target occurrence during high or low MUA, resulting in rhythmic MUA-RT cross-correlations at theta frequencies. These findings show that theta rhythmic neuronal activity can arise from competitive RF interactions and that this rhythm may result in rhythmic RTs potentially subserving attentional sampling.

Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion.

The primate visual system has an exquisite ability to discriminate partially occluded shapes. Recent electrophysiological recordings suggest that response dynamics in intermediate visual cortical area V4, shaped by feedback from prefrontal cortex (PFC), may play a key role. To probe the algorithms that may underlie these findings, we build and test a model of V4 and PFC interactions based on a hierarchical predictive coding framework. We propose that probabilistic inference occurs in two steps. Initially, V4 responses are driven solely by bottom-up sensory input and are thus strongly influenced by the level of occlusion. After a delay, V4 responses combine both feedforward input and feedback signals from the PFC; the latter reflect predictions made by PFC about the visual stimulus underlying V4 activity. We find that this model captures key features of V4 and PFC dynamics observed in experiments. Specifically, PFC responses are strongest for occluded stimuli and delayed responses in V4 are less sensitive to occlusion, supporting our hypothesis that the feedback signals from PFC underlie robust discrimination of occluded shapes. Thus, our study proposes that area V4 and PFC participate in hierarchical inference, with feedback signals encoding top-down predictions about occluded shapes.

Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices.

Background:The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions.Methods:This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions.Results:Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.Conclusion:This article hereby proposes a unified neural theory of prefrontal cortex and its functions.

Latent binocular function in amblyopia

Recently, psychophysical studies have shown that humans with amblyopia do have binocular function that is not normally revealed due to dominant suppressive interactions under normal viewing conditions. Here we use magnetoencephalography (MEG) combined with dichoptic visual stimulation to investigate the underlying binocular function in humans with amblyopia for stimuli that, because of their temporal properties, would be expected to bypass suppressive effects and to reveal any underlying binocular function.We recorded contrast response functions in visual cortical area V1 of amblyopes and normal observers using a steady state visually evoked responses (SSVER) protocol. We used stimuli that were frequency-tagged at 4Hz and 6Hz that allowed identification of the responses from each eye and were of a sufficiently high temporal frequency (>3Hz) to bypass suppression. To characterize binocular function, we compared dichoptic masking between the two eyes in normal and amblyopic participants as well as interocular phase differences in the two groups.We observed that the primary visual cortex responds less to the stimulation of the amblyopic eye compared to the fellow eye. The pattern of interaction in the amblyopic visual system however was not significantly different between the amblyopic and fellow eyes. However, the amblyopic suppressive interactions were lower than those observed in the binocular system of our normal observers. Furthermore, we identified an interocular processing delay of approximately 20ms in our amblyopic group.To conclude, when suppression is greatly reduced, such as the case with our stimulation above 3Hz, the amblyopic visual system exhibits a lack of binocular interactions.

Interactions between feedback and lateral connections in the primary visual cortex

Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.

The Formation of Hierarchical Decisions in the Visual Cortex

Intelligence relies on our ability to find appropriate sequences of decisions in complex problem spaces. The efficiency of a problem solver depends on the speed of its individual decisions and the number of decisions it can explore in parallel. It remains unknown whether the primate brain can consider multiple decisions at the same time. We therefore trained monkeys to navigate through a decision tree with stochastic sensory evidence at multiple branching points and recorded neuronal activity in visual cortical areas V1 and V4. We found a first phase of decision making in which neuronal activity increased in parallel along multiple branches of the decision tree. This was followed by an integration phase where the optimal overall strategy crystallized as the result of interactions between local decisions. The results reveal how sensory evidence is integrated efficiently for hierarchical decisions and contribute to our understanding of the brain mechanisms that implement complex mental programs.

Computation of Object Size in Visual Cortical Area V4 as a Neural Basis for Size Constancy.

We perceive the size of an object to be relatively stable despite changes in the size of its retinal image that accompany changes in viewing distance. This phenomenon, called size constancy, is accomplished by combining retinal image size and distance information in our brain. We demonstrate that a large population of V4 neurons changes their size tuning depending on the perceived distance of a visual stimulus derived from binocular disparity. They prefer larger or smaller retinal image sizes when stimuli are stereoscopically presented nearer or farther away, respectively. This property makes V4 neurons suitable for encoding the actual size of objects, not simply the retinal image sizes, and providing a possible mechanism for perceptual size constancy.