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.

One-shot object parsing in newborn chicks.

Controlled-rearing studies provide the unique opportunity to examine which psychological mechanisms are present at birth and which mechanisms emerge from experience. Here we show that one core component of visual perception-the ability to parse objects from backgrounds-is present when newborn animals see their first object. We reared newborn chicks in strictly controlled environments containing a single object on a single background, then tested the chicks' object parsing and recognition abilities. We found that chicks can parse objects from natural backgrounds at the onset of vision, allowing chicks to recognize objects equally well across familiar and novel backgrounds. We also found that the development of object parsing requires motion cues, akin to the development of object parsing in human infants and newly sighted blind patients. These results demonstrate that newborn brains are capable of "one-shot object parsing" and show that motion cues scaffold object perception from the earliest stages of learning. We conclude that prenatal developmental programs build brain architectures with an object-based inductive bias, allowing animals to solve object perception tasks immediately without extensive experience with objects. We discuss the implications of this finding for developmental psychology, computational neuroscience, and artificial intelligence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Using automation to combat the replication crisis: A case study from controlled-rearing studies of newborn chicks.

The accuracy of science depends on the precision of its methods. When fields produce precise measurements, the scientific method can generate remarkable gains in knowledge. When fields produce noisy measurements, however, the scientific method is not guaranteed to work - in fact, noisy measurements are now regarded as a leading cause of the replication crisis in psychology. Scientists should therefore strive to improve the precision of their methods, especially in fields with noisy measurements. Here, we show that automation can reduce measurement error by ∼60% in one domain of developmental psychology: controlled-rearing studies of newborn chicks. Automated studies produce measurements that are 3-4 times more precise than non-automated studies and produce effect sizes that are 3-4 times larger than non-automated studies. Automation also eliminates experimenter bias and allows replications to be performed quickly and easily. We suggest that automation can be a powerful tool for improving measurement precision, producing high powered experiments, and combating the replication crisis.

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.

The Development of Invariant Object Recognition Requires Visual Experience With Temporally Smooth Objects.

How do newborns learn to recognize objects? According to temporal learning models in computational neuroscience, the brain constructs object representations by extracting smoothly changing features from the environment. To date, however, it is unknown whether newborns depend on smoothly changing features to build invariant object representations. Here, we used an automated controlled-rearing method to examine whether visual experience with smoothly changing features facilitates the development of view-invariant object recognition in a newborn animal model-the domestic chick (Gallus gallus). When newborn chicks were reared with a virtual object that moved smoothly over time, the chicks created view-invariant representations that were selective for object identity and tolerant to viewpoint changes. Conversely, when newborn chicks were reared with a temporally non-smooth object, the chicks developed less selectivity for identity features and less tolerance to viewpoint changes. These results provide evidence for a "smoothness constraint" on the development of invariant object recognition and indicate that newborns leverage the temporal smoothness of natural visual environments to build abstract mental models of objects.

Measuring the speed of newborn object recognition in controlled visual worlds.

How long does it take for a newborn to recognize an object? Adults can recognize objects rapidly, but measuring object recognition speed in newborns has not previously been possible. Here we introduce an automated controlled-rearing method for measuring the speed of newborn object recognition in controlled visual worlds. We raised newborn chicks (Gallus gallus) in strictly controlled environments that contained no objects other than a single virtual object, and then measured the speed at which the chicks could recognize that object from familiar and novel viewpoints. The chicks were able to recognize the object rapidly, at presentation rates of 125 ms per image. Further, recognition speed was equally fast whether the object was presented from familiar viewpoints or novel viewpoints (30° and 60° azimuth rotations). Thus, newborn chicks can recognize objects across novel viewpoints within a fraction of a second. These results demonstrate that newborns are capable of both rapid and invariant object recognition at the onset of vision.

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.

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.

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.

Face recognition in newly hatched chicks at the onset of vision.

How does face recognition emerge in the newborn brain? To address this question, we used an automated controlled-rearing method with a newborn animal model: the domestic chick (Gallus gallus). This automated method allowed us to examine chicks' face recognition abilities at the onset of both face experience and object experience. In the first week of life, newly hatched chicks were raised in controlled-rearing chambers that contained no objects other than a single virtual human face. In the second week of life, we used an automated forced-choice testing procedure to examine whether chicks could distinguish that familiar face from a variety of unfamiliar faces. Chicks successfully distinguished the familiar face from most of the unfamiliar faces-for example, chicks were sensitive to changes in the face's age, gender, and orientation (upright vs. inverted). Thus, chicks can build an accurate representation of the first face they see in their life. These results show that the initial state of face recognition is surprisingly powerful: Newborn visual systems can begin encoding and recognizing faces at the onset of vision.

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.

An automated controlled-rearing method for studying the origins of movement recognition in newly hatched chicks.

Movement recognition is central to visual perception and cognition, yet its origins are poorly understood. Can newborn animals encode and recognize movements at the onset of vision, or does this ability have a protracted developmental trajectory? To address this question, we used an automated controlled-rearing method with a newborn animal model: the domestic chick (Gallus gallus). This automated method made it possible to collect over 150 test trials from each subject. In their first week of life, chicks were raised in controlled-rearing chambers that contained a single virtual agent who repeatedly performed three movements. In their second week of life, we tested whether chicks could recognize the agent's movements. Chicks successfully recognized both individual movements and sequences of movements. Further, chicks successfully encoded the order that movements occurred within a sequence. These results indicate that newborn visual systems can encode and recognize movements at the onset of vision and argue for an increased focus on automated controlled-rearing methods for studying the emergence of perceptual and cognitive abilities.

Characterizing the information content of a newly hatched chick's first visual object representation.

How does object recognition emerge in the newborn brain? To address this question, I examined the information content of the first visual object representation built by newly hatched chicks (Gallus gallus). In their first week of life, chicks were raised in controlled-rearing chambers that contained a single virtual object rotating around a single axis. In their second week of life, I tested whether subjects had encoded information about the identity and viewpoint of the virtual object. The results showed that chicks built object representations that contained both object identity information and view-specific information. However, there was a trade-off between these two types of information: subjects who were more sensitive to identity information were less sensitive to view-specific information, and vice versa. This pattern of results is predicted by iterative, hierarchically organized visual processing machinery, the machinery that supports object recognition in adult primates. More generally, this study shows that invariant object recognition is a core cognitive ability that can be operational at the onset of visual object experience.

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.

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

To recognize objects quickly and accurately, mature visual systems build invariant object representations that generalize across a range of novel viewing conditions (e.g., changes in viewpoint). To date, however, the origins of this core cognitive ability have not yet been established. To examine how invariant object recognition develops in a newborn visual system, I raised chickens from birth for 2 weeks within controlled-rearing chambers. These chambers provided complete control over all visual object experiences. In the first week of life, subjects' visual object experience was limited to a single virtual object rotating through a 60° viewpoint range. In the second week of life, I examined whether subjects could recognize that virtual object from novel viewpoints. Newborn chickens were able to generate viewpoint-invariant representations that supported object recognition across large, novel, and complex changes in the object's appearance. Thus, newborn visual systems can begin building invariant object representations at the onset of visual object experience. These abstract representations can be generated from sparse data, in this case from a visual world containing a single virtual object seen from a limited range of viewpoints. This study shows that powerful, robust, and invariant object recognition machinery is an inherent feature of the newborn brain.

Binding actions and scenes in visual long-term memory.

How does visual long-term memory store representations of different entities (e.g., objects, actions, and scenes) that are present in the same visual event? Are the different entities stored as an integrated representation in memory, or are they stored separately? To address this question, we asked observers to view a large number of events; in each event, an action was performed within a scene. Afterward, the participants were shown pairs of action-scene sets and indicated which of the two they had seen. When the task required recognizing the individual actions and scenes, performance was high (80%). Conversely, when the task required remembering which actions had occurred within which scenes, performance was significantly lower (59%). We observed this dissociation between memory for individual entities and memory for entity bindings across multiple testing conditions and presentation durations. These experiments indicate that visual long-term memory stores information about actions and information about scenes separately from one another, even when an action and scene were observed together in the same visual event. These findings also highlight an important limitation of human memory: Situations that require remembering actions and scenes as integrated events (e.g., eyewitness testimony) may be particularly vulnerable to memory errors.

Visual long-term memory stores high-fidelity representations of observed actions.

The ability to remember others' actions is fundamental to social cognition, but the precision of action memories remains unknown. To probe the fidelity of the action representations stored in visual long-term memory, we asked observers to view a large number of computer-animated actions. Afterward, observers were shown pairs of actions and indicated which of the two actions they had seen for each pair. On some trials, the previously viewed action was paired with an action from a different action category, and on other trials, it was paired with an action from the same category. Accuracy on both types of trials was remarkably high (81% and 82%, respectively). Further, results from a second experiment showed that the action representations maintained in visual long-term memory can be nearly as precise as the action representations maintained in visual working memory. Together, these findings provide evidence for a mechanism in visual long-term memory that maintains high-fidelity representations of observed actions.

From movements to actions: two mechanisms for learning action sequences.

When other individuals move, we interpret their movements as discrete, hierarchically-organized, goal-directed actions. However, the mechanisms that integrate visible movement features into actions are poorly understood. Here, we consider two sequence learning mechanisms - transitional probability-based (TP) and position-based encoding computations - that have been studied extensively in the domain of language learning, and investigate their potential for integrating movements into actions. If these learning mechanisms integrate movements into actions, then they should create memory units that contain (i) movement information, (ii) information about the order in which movements occurred, and (iii) information allowing actions to be recognized from different viewpoints. We show that both mechanisms retain movement information. However, only the position-based mechanism creates movement representations that are view-invariant and contain order information. The TP-based mechanism creates movement representations that are view-dependent and contain no order information. We therefore suggest that the TP-based mechanism is unlikely to play an important role for integrating movements into actions. In contrast, the position-based mechanism retains some of the types of information needed to represent goal-directed actions, which makes it an attractive target for further research to explore what, if any, role it plays in the perception of goal-directed actions.