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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

A core knowledge architecture of visual working memory.

Visual working memory (VWM) is widely thought to contain specialized buffers for retaining spatial and object information: a 'spatial-object architecture.' However, studies of adults, infants, and nonhuman animals show that visual cognition builds on core knowledge systems that retain more specialized representations: (1) spatiotemporal representations for object tracking, (2) object identity representations for object recognition, and (3) view-dependent snapshots for place recognition. In principle, these core knowledge systems may retain information separately from one another.