Newborn chickens generate invariant object representations at the onset of visual object experience

Parallel development of social behavior in biological and artificial fish

Our algorithmic understanding of vision has been revolutionized by a reverse engineering paradigm that involves building artificial systems that perform the same tasks as biological systems. Here, we extend this paradigm to social behavior. We embodied artificial neural networks in artificial fish and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of biological fish. When artificial fish had deep reinforcement learning and curiosity-derived rewards, they spontaneously developed fish-like social behaviors, including collective behavior and social preferences (favoring in-group over out-group members). The artificial fish also developed social behavior in naturalistic ocean worlds, showing that these embodied models generalize to real-world learning contexts. Thus, animal-like social behaviors can develop from generic learning algorithms (reinforcement learning and intrinsic motivation). Our study provides a foundation for reverse-engineering the development of social behavior using image-computable models from artificial intelligence, bridging the divide between high-dimensional sensory inputs and collective action.

Parallel development of object recognition in newborn chicks and deep neural networks

How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distributions in the newborn's environment. To test whether space-time fitting is a viable theory for learning how to see, we performed parallel controlled-rearing experiments on newborn chicks and deep neural networks (DNNs), including CNNs and transformers. First, we raised newborn chicks in impoverished environments containing a single object, then simulated those environments in a video game engine. Second, we recorded first-person images from agents moving through the virtual animal chambers and used those images to train DNNs. Third, we compared the viewpoint-invariant object recognition performance of the chicks and DNNs. When DNNs received the same visual diet (training data) as chicks, the models developed common object recognition skills as chicks. DNNs that used time as a teaching signal-space-time fitters-also showed common patterns of successes and failures across the test viewpoints as chicks. Thus, DNNs can learn object recognition in the same impoverished environments as newborn animals. We argue that space-time fitters can serve as formal scientific models of newborn visual systems, providing image-computable models for studying how newborns learn to see from raw visual experiences.

Digital Twin Studies for Reverse Engineering the Origins of Visual Intelligence

What are the core learning algorithms in brains? Nativists propose that intelligence emerges from innate domain-specific knowledge systems, whereas empiricists propose that intelligence emerges from domain-general systems that learn domain-specific knowledge from experience. We address this debate by reviewing digital twin studies designed to reverse engineer the learning algorithms in newborn brains. In digital twin studies, newborn animals and artificial agents are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning abilities. Supporting empiricism, digital twin studies show that domain-general algorithms learn animal-like object perception when trained on the first-person visual experiences of newborn animals. Supporting nativism, digital twin studies show that domain-general algorithms produce innate domain-specific knowledge when trained on prenatal experiences (retinal waves). We argue that learning across humans, animals, and machines can be explained by a universal principle, which we call space-time fitting. Space-time fitting explains both empiricist and nativist phenomena, providing a unified framework for understanding the origins of intelligence.

Spontaneous biases enhance generalization in the neonate brain

Inductive generalization is adaptive in novel contexts for both biological and artificial intelligence. Spontaneous generalization in inexperienced animals raises questions on whether predispositions (evolutionarily acquired biases, or priors) enable generalization from sparse data, without reinforcement. We exposed neonate chicks to an artificial social partner of a specific color, and then looked at generalization on the red-yellow or blue-green ranges. Generalization was inconsistent with an unbiased model. Biases included asymmetrical generalization gradients, some preferences for unfamiliar stimuli, different speed of learning, faster learning for colors infrequent in the natural spectrum. Generalization was consistent with a Bayesian model that incorporates predispositions as initial preferences and treats the learning process as an update of predispositions. Newborn chicks are evolutionarily prepared for generalization, via biases independent from experience, reinforcement, or supervision. To solve the problem of induction, biological and artificial intelligence can use biases tuned to infrequent stimuli, such as the red and blue colors.

The Development of Object Recognition Requires Experience with the Surface Features of Objects

What role does visual experience play in the development of object recognition? Prior controlled-rearing studies suggest that newborn animals require slow and smooth visual experiences to develop object recognition. Here, we examined whether the development of object recognition also requires experience with the surface features of objects. We raised newborn chicks in automated controlled-rearing chambers that contained a single virtual object, then tested their ability to recognize that object from familiar and novel viewpoints. When chicks were reared with an object that had surface features, the chicks developed view-invariant object recognition. In contrast, when chicks were reared with a line drawing of an object, the chicks failed to develop object recognition. The chicks reared with line drawings performed at chance level, despite acquiring over 100 h of visual experience with the object. These results indicate that the development of object recognition requires experience with the surface features of objects.

Invariance of object detection in untrained deep neural networks

The ability to perceive visual objects with various types of transformations, such as rotation, translation, and scaling, is crucial for consistent object recognition. In machine learning, invariant object detection for a network is often implemented by augmentation with a massive number of training images, but the mechanism of invariant object detection in biological brains—how invariance arises initially and whether it requires visual experience—remains elusive. Here, using a model neural network of the hierarchical visual pathway of the brain, we show that invariance of object detection can emerge spontaneously in the complete absence of learning. First, we found that units selective to a particular object class arise in randomly initialized networks even before visual training. Intriguingly, these units show robust tuning to images of each object class under a wide range of image transformation types, such as viewpoint rotation. We confirmed that this “innate” invariance of object selectivity enables untrained networks to perform an object-detection task robustly, even with images that have been significantly modulated. Our computational model predicts that invariant object tuning originates from combinations of non-invariant units via random feedforward projections, and we confirmed that the predicted profile of feedforward projections is observed in untrained networks. Our results suggest that invariance of object detection is an innate characteristic that can emerge spontaneously in random feedforward networks.

Visual Field Analysis: A reliable method to score left and right eye use using automated tracking

Brain and behavioural asymmetries have been documented in various taxa. Many of these asymmetries involve preferential left and right eye use. However, measuring eye use through manual frame-by-frame analyses from video recordings is laborious and may lead to biases. Recent progress in technology has allowed the development of accurate tracking techniques for measuring animal behaviour. Amongst these techniques, DeepLabCut, a Python-based tracking toolbox using transfer learning with deep neural networks, offers the possibility to track different body parts with unprecedented accuracy. Exploiting the potentialities of DeepLabCut, we developed Visual Field Analysis, an additional open-source application for extracting eye use data. To our knowledge, this is the first application that can automatically quantify left-right preferences in eye use. Here we test the performance of our application in measuring preferential eye use in young domestic chicks. The comparison with manual scoring methods revealed a near perfect correlation in the measures of eye use obtained by Visual Field Analysis. With our application, eye use can be analysed reliably, objectively and at a fine scale in different experimental paradigms.

Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions

Soon after hatching, the young of precocial species, such as domestic chicks or ducklings, learn to recognize their social partner by simply being exposed to it (imprinting process). Even artificial objects or stimuli displayed on monitor screens can effectively trigger filial imprinting, though learning is canalized by spontaneous preferences for animacy signals, such as certain kinds of motion or a face-like appearance. Imprinting is used as a behavioural paradigm for studies on memory formation, early learning and predispositions, as well as number and space cognition, and brain asymmetries. Here, we present an automatized setup to expose and/or test animals for a variety of imprinting experiments. The setup consists of a cage with two high-frequency screens at the opposite ends where stimuli are shown. Provided with a camera covering the whole space of the cage, the behaviour of the animal is recorded continuously. A graphic user interface implemented in Matlab allows a custom configuration of the experimental protocol, that together with Psychtoolbox drives the presentation of images on the screens, with accurate time scheduling and a highly precise framerate. The setup can be implemented into a complete workflow to analyse behaviour in a fully automatized way by combining Matlab (and Psychtoolbox) to control the monitor screens and stimuli, DeepLabCut to track animals' behaviour, Python (and R) to extract data and perform statistical analyses. The automated setup allows neuro-behavioural scientists to perform standardized protocols during their experiments, with faster data collection and analyses, and reproducible results.

Distorting Face Representations in Newborn Brains

What role does experience play in the development of face recognition? A growing body of evidence indicates that newborn brains need slowly changing visual experiences to develop accurate visual recognition abilities. All of the work supporting this "slowness constraint" on visual development comes from studies testing basic-level object recognition. Here, we present the results of controlled-rearing experiments that provide evidence for a slowness constraint on the development of face recognition, a prototypical subordinate-level object recognition task. We found that (1) newborn chicks can rapidly develop view-invariant face recognition and (2) the development of this ability relies on experience with slowly moving faces. When chicks were reared with quickly moving faces, they built distorted face representations that largely lacked invariance to viewpoint changes, effectively "breaking" their face recognition abilities. These results provide causal evidence that slowly changing visual experiences play a critical role in the development of face recognition, akin to basic-level object recognition. Thus, face recognition is not a hardwired property of vision but is learned rapidly as the visual system adapts to the temporal structure of the animal's visual environment.

No evidence of spontaneous preference for slowly moving objects in visually naïve chicks

It has been recently reported that young chicks that have received equal exposure to slowly- and fast-rotating objects showed a preference for slowly-rotating objects. This would suggest that visual experience with slowly moving objects is necessary for object recognition in newborns. I attempted to duplicate this finding in newborn chicks using a simple rotating blue cube. No significant preference was found. Using objects similar to the ones used in the previous study (digital embryos), I observed a strong and robust preference for the fast- (not for the slow-) rotating object. To clarify whether the discrepancies with the previous study could be due to the stimuli frame-frequency used (the chicks' visual system is characterized by high temporal resolution), I repeated the experiments by presenting the stimuli with a lower-frame frequency (from 120 fps to 24 fps). However, similar preferences for the fast-rotating objects were found, this time also for the rotating blue cube. These results suggest a preference for fast-rotating objects that is modulated by the shape and, in part, by the frame-frequency. It remains to be established whether the discrepancies between this study and the previous study can be explained by differences related to strains or artefacts due to the use of monitors with a low-refresh rate.

One-shot learning of view-invariant object representations in newborn chicks

Can newborn brains perform one-shot learning? To address this question, we reared newborn chicks in strictly controlled environments containing a single view of a single object, then tested their object recognition performance across 24 uniformly-spaced viewpoints. We found that chicks can build view-invariant object representations from a single view of an object: a case of one-shot learning in newborn brains. Chicks can also build the same view-invariant object representation from different views of an object, showing that newborn brains converge on common object representations from different sets of sensory inputs. Finally, by rearing chicks with larger numbers of object views, we found that chicks develop enhanced recognition for familiar views. These results illuminate the earliest stages of object recognition, revealing (1) powerful one-shot learning that builds invariant object representations from the first views of an object and (2) view-based learning that enriches object representations, producing enhanced recognition for familiar views.

Automated Study Challenges the Existence of a Foundational Statistical-Learning Ability in Newborn Chicks

What mechanisms underlie learning in newborn brains? Recently, researchers reported that newborn chicks use unsupervised statistical learning to encode the transitional probabilities (TPs) of shapes in a sequence, suggesting that TP-based statistical learning can be present in newborn brains. Using a preregistered design, we attempted to reproduce this finding with an automated method that eliminated experimenter bias and allowed more than 250 times more data to be collected per chick. With precise measurements of each chick's behavior, we were able to perform individual-level analyses and substantially reduce measurement error for the group-level analyses. We found no evidence that newborn chicks encode the TPs between sequentially presented shapes. None of the chicks showed evidence for this ability. Conversely, we obtained strong evidence that newborn chicks encode the shapes of individual objects, showing that this automated method can produce robust results. These findings challenge the claim that TP-based statistical learning is present in newborn brains.

Skeletal descriptions of shape provide unique perceptual information for object recognition

With seemingly little effort, humans can both identify an object across large changes in orientation and extend category membership to novel exemplars. Although researchers argue that object shape is crucial in these cases, there are open questions as to how shape is represented for object recognition. Here we tested whether the human visual system incorporates a three-dimensional skeletal descriptor of shape to determine an object's identity. Skeletal models not only provide a compact description of an object's global shape structure, but also provide a quantitative metric by which to compare the visual similarity between shapes. Our results showed that a model of skeletal similarity explained the greatest amount of variance in participants' object dissimilarity judgments when compared with other computational models of visual similarity (Experiment 1). Moreover, parametric changes to an object's skeleton led to proportional changes in perceived similarity, even when controlling for another model of structure (Experiment 2). Importantly, participants preferentially categorized objects by their skeletons across changes to local shape contours and non-accidental properties (Experiment 3). Our findings highlight the importance of skeletal structure in vision, not only as a shape descriptor, but also as a diagnostic cue of object identity.

Using automated controlled rearing to explore the origins of object permanence

What are the origins of object permanence? Despite widespread interest in this question, methodological barriers have prevented detailed analysis of how experience shapes the development of object permanence in newborn organisms. Here, we introduce an automated controlled-rearing method for studying the emergence of object permanence in strictly controlled virtual environments. We used newborn chicks as an animal model and recorded their behavior continuously (24/7) from the onset of vision. Across four experiments, we found that object permanence can develop rapidly, within the first few days of life. This ability developed even when chicks were reared in impoverished visual environments containing no object occlusion events. Object permanence failed to develop, however, when chicks were reared in environments containing temporally non-smooth objects (objects moving on discontinuous spatiotemporal paths). These results suggest that experience with temporally smooth objects facilitates the development of object permanence, confirming a key prediction of temporal learning models in computational neuroscience.

Constraints on Statistical Learning Across Species

Both human and nonhuman organisms are sensitive to statistical regularities in sensory inputs that support functions including communication, visual processing, and sequence learning. One of the issues faced by comparative research in this field is the lack of a comprehensive theory to explain the relevance of statistical learning across distinct ecological niches. In the current review we interpret cross-species research on statistical learning based on the perceptual and cognitive mechanisms that characterize the human and nonhuman models under investigation. Considering statistical learning as an essential part of the cognitive architecture of an animal will help to uncover the potential ecological functions of this powerful learning process.

A Developmental Approach to Machine Learning?

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order - with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines.

Spontaneous Preference for Slowly Moving Objects in Visually Naïve Animals

To perceive the world successfully, newborns need certain types of visual experiences. The development of object recognition, for example, requires visual experience with slowly moving objects. To date, however, it is unknown whether newborns actively seek out the best visual experiences for developing object recognition. To address this question, I used an automated controlled-rearing method to examine whether visually naïve animals (newborn chicks) seek out slowly moving objects. Despite receiving equal exposure to slowly and to quickly rotating objects, the majority of the chicks developed a preference for slowly rotating objects. This preference was robust, producing large effect sizes across objects, experiments, and successive test days. These results indicate that newborn brains rapidly develop mechanisms for orienting young animals toward optimal visual experiences, thus facilitating the development of object recognition. This study also demonstrates that automation can be a valuable tool for studying the origins and development of visual preferences.

Spontaneous generalization of abstract multimodal patterns in young domestic chicks

From the early stages of life, learning the regularities associated with specific objects is crucial for making sense of experiences. Through filial imprinting, young precocial birds quickly learn the features of their social partners by mere exposure. It is not clear though to what extent chicks can extract abstract patterns of the visual and acoustic stimuli present in the imprinting object, and how they combine them. To investigate this issue, we exposed chicks (Gallus gallus) to three days of visual and acoustic imprinting, using either patterns with two identical items or patterns with two different items, presented visually, acoustically or in both modalities. Next, chicks were given a choice between the familiar and the unfamiliar pattern, present in either the multimodal, visual or acoustic modality. The responses to the novel stimuli were affected by their imprinting experience, and the effect was stronger for chicks imprinted with multimodal patterns than for the other groups. Interestingly, males and females adopted a different strategy, with males more attracted by unfamiliar patterns and females more attracted by familiar patterns. Our data show that chicks can generalize abstract patterns by mere exposure through filial imprinting and that multimodal stimulation is more effective than unimodal stimulation for pattern learning.

Newborn chicks show inherited variability in early social predispositions for hen-like stimuli

Predispositions of newborn vertebrates to preferentially attend to living beings and learn about them are pervasive. Their disturbance (e.g. in neonates at risk for autism), may compromise the proper development of a social brain. The genetic bases of such predispositions are unknown. We use the well-known visual preferences of newly-hatched chicks (Gallus gallus) for the head/neck region of the hen to investigate the presence of segregating variation in the predispositions to approach a stuffed hen vs. a scrambled version of it. We compared the spontaneous preferences of three breeds maintained genetically isolated for at least eighteen years while identically raised. Visually-naïve chicks of all breeds (Padovana, Polverara and Robusta maculata) showed the same initial preference for the predisposed stimulus, suggesting that the direction of the initial preference might be genetically fixed. A few minutes later though, striking differences emerged between breeds, which could indicate different strategies of dealing with affiliative objects: while the Polverara breed maintained a constant preference across the entire test, the Padovana and Robusta breeds progressively explored the alternative stimulus more. We hence documented the presence of inherited genetic variability in the expression of early social predispositions in interaction with environmental stimuli.

A smoothness constraint on the development of object recognition

Understanding how the brain learns to recognize objects is one of the ultimate goals in the cognitive sciences. To date, however, we have not yet characterized the environmental factors that cause object recognition to emerge in the newborn brain. Here, I present the results of a high-throughput controlled-rearing experiment that examined whether the development of object recognition requires experience with temporally smooth visual objects. When newborn chicks (Gallus gallus) were raised with virtual objects that moved smoothly over time, the chicks developed accurate color recognition, shape recognition, and color-shape binding abilities. In contrast, when newborn chicks were raised with virtual objects that moved non-smoothly over time, the chicks' object recognition abilities were severely impaired. These results provide evidence for a "smoothness constraint" on newborn object recognition. Experience with temporally smooth objects facilitates the development of object recognition.

The development of newborn object recognition in fast and slow visual worlds

Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world.

Enhanced learning of natural visual sequences in newborn chicks

To what extent are newborn brains designed to operate over natural visual input? To address this question, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) show enhanced learning of natural visual sequences at the onset of vision. We took the same set of images and grouped them into either natural sequences (i.e., sequences showing different viewpoints of the same real-world object) or unnatural sequences (i.e., sequences showing different images of different real-world objects). When raised in virtual worlds containing natural sequences, newborn chicks developed the ability to recognize familiar images of objects. Conversely, when raised in virtual worlds containing unnatural sequences, newborn chicks' object recognition abilities were severely impaired. In fact, the majority of the chicks raised with the unnatural sequences failed to recognize familiar images of objects despite acquiring over 100 h of visual experience with those images. Thus, newborn chicks show enhanced learning of natural visual sequences at the onset of vision. These results indicate that newborn brains are designed to operate over natural visual input.

Unraveling the nature of autism: finding order amid change

In this article, we hypothesize that individuals with autism spectrum disorder (ASD) are born with a deficit in invariance detection, which is a learning process whereby people and animals come to attend the relatively stable patterns or structural regularities in the changing stimulus array. This paper synthesizes a substantial body of research which suggests that a deficit in the domain-general perceptual learning process of invariant detection in ASD can lead to a cascade of consequences in different developmental domains. We will outline how this deficit in invariant detection can cause uncertainty, unpredictability, and a lack of control for individuals with ASD and how varying degrees of impairments in this learning process can account for the heterogeneity of the ASD phenotype. We also describe how differences in neural plasticity in ASD underlie the impairments in perceptual learning. The present account offers an alternative to prior theories and contributes to the challenge of understanding the developmental trajectories that result in the variety of autistic behaviors.

A chicken model for studying the emergence of invariant object recognition

“Invariant object recognition” refers to the ability to recognize objects across variation in their appearance on the retina. This ability is central to visual perception, yet its developmental origins are poorly understood. Traditionally, nonhuman primates, rats, and pigeons have been the most commonly used animal models for studying invariant object recognition. Although these animals have many advantages as model systems, they are not well suited for studying the emergence of invariant object recognition in the newborn brain. Here, we argue that newly hatched chicks (Gallus gallus) are an ideal model system for studying the emergence of invariant object recognition. Using an automated controlled-rearing approach, we show that chicks can build a viewpoint-invariant representation of the first object they see in their life. This invariant representation can be built from highly impoverished visual input (three images of an object separated by 15° azimuth rotations) and cannot be accounted for by low-level retina-like or V1-like neuronal representations. These results indicate that newborn neural circuits begin building invariant object representations at the onset of vision and argue for an increased focus on chicks as an animal model for studying invariant object recognition.

What can fish brains tell us about visual perception?

Fish are a complex taxonomic group, whose diversity and distance from other vertebrates well suits the comparative investigation of brain and behavior: in fish species we observe substantial differences with respect to the telencephalic organization of other vertebrates and an astonishing variety in the development and complexity of pallial structures. We will concentrate on the contribution of research on fish behavioral biology for the understanding of the evolution of the visual system. We shall review evidence concerning perceptual effects that reflect fundamental principles of the visual system functioning, highlighting the similarities and differences between distant fish groups and with other vertebrates. We will focus on perceptual effects reflecting some of the main tasks that the visual system must attain. In particular, we will deal with subjective contours and optical illusions, invariance effects, second order motion and biological motion and, finally, perceptual binding of object properties in a unified higher level representation.

Feedforward object-vision models only tolerate small image variations compared to human

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex.

Newly Hatched Chicks Solve the Visual Binding Problem

For an organism to perceive coherent and unified objects, its visual system must bind color and shape features into integrated color-shape representations in memory. However, the origins of this ability have not yet been established. To examine whether newborns can build an integrated representation of the first object they see, I raised newly hatched chicks ( Gallus gallus) in controlled-rearing chambers that contained a single virtual object. This object rotated continuously, revealing a different color and shape combination on each of its two faces. Chicks were able to build an integrated representation of this object. For example, they reliably distinguished an object defined by a purple circle and yellow triangle from an object defined by a purple triangle and yellow circle. This result shows that newborns can begin binding color and shape features into integrated representations at the onset of their experience with visual objects.