View-invariance learning in object recognition by pigeons depends on error-driven associative learning processes

Why is the Rescorla-Wagner model so influential?

The influence of the Rescorla-Wagner model cannot be overestimated, despite that (1) the model does not differ much computationally from its predecessors and competitors, and (2) its shortcomings are well-known in the learning community. Here we discuss the reasons behind its widespread influence in the cognitive and neural sciences, and argue that it is the constant search for general-process theories by learning scholars which eventually produced a model whose application spans many different areas of research to this day. We focus on the theoretical and empirical background of the model, the theoretical connections that it has with later developments across Marr's levels of analysis, as well as the broad variety of research that it has guided and inspired.

Subsampling of cues in associative learning

Theories of learning distinguish between elemental and configural stimulus processing depending on whether stimuli are processed independently or as whole configurations. Evidence for elemental processing comes from findings of summation in animals where a compound of two dissimilar stimuli is deemed to be more predictive than each stimulus alone, whereas configural processing is supported by experiments using similar stimuli in which summation is not found. However, in humans the summation effect is robust and impervious to similarity manipulations. In three experiments in human predictive learning, we show that summation can be obliterated when partially reinforced cues are added to the summands in training and tests. This lack of summation only holds when the partially reinforced cues are similar to the reinforced cues (experiment 1) and seems to depend on participants sampling only the most salient cue in each trial (experiments 2a and 2b) in a sequential visual search process. Instead of attributing our and others' instances of lack of summation to the customary idea of configural processing, we offer a formal subsampling rule that might be applied to situations in which the stimuli are hard to parse from each other.

Beyond the "Conceptual Nervous System": Can computational cognitive neuroscience transform learning theory?

In the last century, learning theory has been dominated by an approach assuming that associations between hypothetical representational nodes can support the acquisition of knowledge about the environment. The similarities between this approach and connectionism did not go unnoticed to learning theorists, with many of them explicitly adopting a neural network approach in the modeling of learning phenomena. Skinner famously criticized such use of hypothetical neural structures for the explanation of behavior (the "Conceptual Nervous System"), and one aspect of his criticism has proven to be correct: theory underdetermination is a pervasive problem in cognitive modeling in general, and in associationist and connectionist models in particular. That is, models implementing two very different cognitive processes often make the exact same behavioral predictions, meaning that important theoretical questions posed by contrasting the two models remain unanswered. We show through several examples that theory underdetermination is common in the learning theory literature, affecting the solvability of some of the most important theoretical problems that have been posed in the last decades. Computational cognitive neuroscience (CCN) offers a solution to this problem, by including neurobiological constraints in computational models of behavior and cognition. Rather than simply being inspired by neural computation, CCN models are built to reflect as much as possible about the actual neural structures thought to underlie a particular behavior. They go beyond the "Conceptual Nervous System" and offer a true integration of behavioral and neural levels of analysis.

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.

Colour, vision and coevolution in avian brood parasitism

The coevolutionary interactions between avian brood parasites and their hosts provide a powerful system for investigating the diversity of animal coloration. Specifically, reciprocal selection pressure applied by hosts and brood parasites can give rise to novel forms and functions of animal coloration, which largely differ from those that arise when selection is imposed by predators or mates. In the study of animal colours, avian brood parasite-host dynamics therefore invite special consideration. Rapid advances across disciplines have paved the way for an integrative study of colour and vision in brood parasite-host systems. We now know that visually driven host defences and host life history have selected for a suite of phenotypic adaptations in parasites, including mimicry, crypsis and supernormal stimuli. This sometimes leads to vision-based host counter-adaptations and increased parasite trickery. Here, we review vision-based adaptations that arise in parasite-host interactions, emphasizing that these adaptations can be visual/sensory, cognitive or phenotypic in nature. We highlight recent breakthroughs in chemistry, genomics, neuroscience and computer vision, and we conclude by identifying important future directions. Moving forward, it will be essential to identify the genetic and neural bases of adaptation and to compare vision-based adaptations to those arising in other sensory modalities.This article is part of the themed issue 'Animal coloration: production, perception, function and application'.

Promoting rotational-invariance in object recognition despite experience with only a single view

Different processes are assumed to underlie invariant object recognition across affine transformations, such as changes in size, and non-affine transformations, such as rotations in depth. From this perspective, promoting invariant object recognition across rotations in depth requires visual experience with the object from multiple viewpoints. One learning mechanism potentially contributing to invariant recognition is the error-driven learning of associations between relatively view-invariant visual properties and motor responses or object labels. This account uniquely predicts that experience with affine transformations of a single object view may also promote view-invariance, if view-invariant properties are also invariant across such affine transformations. We empirically confirmed this prediction in both people and pigeons, thereby suggesting that: (a) the hypothesized mechanism participates in view-invariance learning, (b) this mechanism is present across distantly-related vertebrates, and (c) the distinction between affine and non-affine transformations may not be fundamental for biological visual systems, as previously assumed.

Spontaneous object recognition: a promising approach to the comparative study of memory

Spontaneous recognition of a novel object is a popular measure of exploratory behavior, perception and recognition memory in rodent models. Because of its relative simplicity and speed of testing, the variety of stimuli that can be used, and its ecological validity across species, it is also an attractive task for comparative research. To date, variants of this test have been used with vertebrate and invertebrate species, but the methods have seldom been sufficiently standardized to allow cross-species comparison. Here, we review the methods necessary for the study of novel object recognition in mammalian and non-mammalian models, as well as the results of these experiments. Critical to the use of this test is an understanding of the organism's initial response to a novel object, the modulation of exploration by context, and species differences in object perception and exploratory behaviors. We argue that with appropriate consideration of species differences in perception, object affordances, and natural exploratory behaviors, the spontaneous object recognition test can be a valid and versatile tool for translational research with non-mammalian models.

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.

Mechanisms of object recognition: what we have learned from pigeons

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the "simple" brains of pigeons.

Object-specific and relational learning in pigeons

Abstract or relational stimulus processing requires an organism to appreciate the interrelations between or among two or more stimuli (e.g., same or different, less than or greater than). In the current study, we explored the role of concrete and abstract information processing in pigeons performing a visual categorization task which could be solved by attending to either the specific objects presented or the relation among the objects. In Experiment 1, we gave pigeons three training phases in which we gradually increased the variability (that is, the number of object arrays) in the training set. In Experiment 2, we trained a second group of pigeons with an even larger number of object arrays from the outset. We found that, the larger the variability in the training exemplars, the lesser the pigeons' attention to object-specific information and the greater their attention to relational information; nevertheless, the contribution of object-specific information to categorization performance was never completely eliminated. This pervasive influence of object-specific information is not peculiar to animals, but has been observed in young children and human adults as well.

Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures

Pattern-based identity signatures are commonplace in the animal kingdom, but how they are recognized is poorly understood. Here we develop a computer vision tool for analysing visual patterns, NATUREPATTERNMATCH, which breaks new ground by mimicking visual and cognitive processes known to be involved in recognition tasks. We apply this tool to a long-standing question about the evolution of recognizable signatures. The common cuckoo (Cuculus canorus) is a notorious cheat that sneaks its mimetic eggs into nests of other species. Can host birds fight back against cuckoo forgery by evolving highly recognizable signatures? Using NATUREPATTERNMATCH, we show that hosts subjected to the best cuckoo mimicry have evolved the most recognizable egg pattern signatures. Theory predicts that effective pattern signatures should be simultaneously replicable, distinctive and complex. However, our results reveal that recognizable signatures need not incorporate all three of these features. Moreover, different hosts have evolved effective signatures in diverse ways.

The effect of spatial frequency on perceptual learning of inverted faces

We investigated the efficacy of training adults to recognize full spectrum inverted faces presented with different viewpoints. To examine the role of different spatial frequencies in any learning, we also used high-pass filtered faces that preserved featural information and low-pass filtered faces that severely reduced that featural information. Although all groups got faster over the 2 days of training, there was more improvement in accuracy for the group exposed to full spectrum faces than in the two groups exposed to filtered faces, both of which improved more modestly and only when the same faces were shown on the 2 days of training. For the group exposed to the full spectrum range and, to a lesser extent, for those exposed to high frequency faces, training generalized to a new set of full spectrum faces of a different size in a different task, but did not lead to evidence of holistic processing or improved sensitivity to feature shape or spacing in inverted faces. Overall these results demonstrate that only 2h of practice in recognizing full-spectrum inverted faces presented from multiple points of view is sufficient to improve recognition of the trained faces and to generalize to novel instances. Perceptual learning also occurred for low and high frequency faces but to a smaller extent.