Orthographic processing in pigeons (Columba livia)

Parallel executive pallio-motor loops in the pigeon brain

A core component of the avian pallial cognitive network is the multimodal nidopallium caudolaterale (NCL) that is considered to be analogous to the mammalian prefrontal cortex (PFC). The NCL plays a key role in a multitude of executive tasks such as working memory, decision-making during navigation, and extinction learning in complex learning environments. Like the PFC, the NCL is positioned at the transition from ascending sensory to descending motor systems. For the latter, it sends descending premotor projections to the intermediate arcopallium (AI) and the medial striatum (MSt). To gain detailed insight into the organization of these projections, we conducted several retrograde and anterograde tracing experiments. First, we tested whether NCL neurons projecting to AI (NCLarco neurons) and MSt (NCLMSt neurons) are constituted by a single neuronal population with bifurcating neurons, or whether they form two distinct populations. Here, we found two distinct projection patterns to both target areas that were associated with different morphologies. Second, we revealed a weak topographic projection toward the medial and lateral striatum and a strong topographic projection toward AI with clearly distinguishable sensory termination fields. Third, we investigated the relationship between the descending NCL pathways to the arcopallium with those from the hyperpallium apicale, which harbors a second major descending pathway of the avian pallium. We embed our findings within a system of parallel pallio-motor loops that carry information from separate sensory modalities to different subpallial systems. Our results also provide insights into the evolution of the avian motor system from which, possibly, the song system has emerged.

Jackdaws form categorical prototypes based on experience with category exemplars

Categorization represents one cognitive ability fundamental to animal behavior. Grouping of elements based on perceptual or semantic features helps to reduce processing resources and facilitates appropriate behavior. Corvids master complex categorization, yet the detailed categorization learning strategies are less well understood. We trained two jackdaws on a delayed match to category paradigm using a novel, artificial stimulus type, RUBubbles. Both birds learned to differentiate between two session-unique categories following two distinct learning protocols. Categories were either introduced via central category prototypes (low variability approach) or using a subset of diverse category exemplars from which diagnostic features had to be identified (high variability approach). In both versions, the stimulus similarity relative to a central category prototype explained categorization performance best. Jackdaws consistently used a central prototype to judge category membership, regardless of whether this prototype was used to introduce distinct categories or had to be inferred from multiple exemplars. Reliance on a category prototype occurred already after experiencing only a few trials with different category exemplars. High stimulus set variability prolonged initial learning but showed no consistent beneficial effect on later generalization performance. High numbers of stimuli, their perceptual similarity, and coherent category structure resulted in a prototype-based strategy, reflecting the most adaptive, efficient, and parsimonious way to represent RUBubble categories. Thus, our birds represent a valuable comparative animal model that permits further study of category representations throughout learning in different regions of a brain producing highly cognitive behavior.

Why birds are smart

Many cognitive neuroscientists believe that both a large brain and an isocortex are crucial for complex cognition. Yet corvids and parrots possess non-cortical brains of just 1-25 g, and these birds exhibit cognitive abilities comparable with those of great apes such as chimpanzees, which have brains of about 400 g. This opinion explores how this cognitive equivalence is possible. We propose four features that may be required for complex cognition: a large number of associative pallial neurons, a prefrontal cortex (PFC)-like area, a dense dopaminergic innervation of association areas, and dynamic neurophysiological fundaments for working memory. These four neural features have convergently evolved and may therefore represent 'hard to replace' mechanisms enabling complex cognition.

Convergent Circuit Computation for Categorization in the Brains of Primates and Songbirds

Categorization is crucial for behavioral flexibility because it enables animals to group stimuli into meaningful classes that can easily be generalized to new circumstances. A most abstract quantitative category is set size, the number of elements in a set. This review explores how categorical number representations are realized by the operations of excitatory and inhibitory neurons in associative telencephalic microcircuits in primates and songbirds. Despite the independent evolution of the primate prefrontal cortex and the avian nidopallium caudolaterale, the neuronal computations of these associative pallial circuits show surprising correspondence. Comparing cellular functions in distantly related taxa can inform about the evolutionary principles of circuit computations for cognition in distinctly but convergently realized brain structures.

Categorical representation of abstract spatial magnitudes in the executive telencephalon of crows

The ability to group abstract continuous magnitudes into meaningful categories is cognitively demanding but key to intelligent behavior. To explore its neuronal mechanisms, we trained carrion crows to categorize lines of variable lengths into arbitrary "short" and "long" categories. Single-neuron activity in the nidopallium caudolaterale (NCL) of behaving crows reflected the learned length categories of visual stimuli. The length categories could be reliably decoded from neuronal population activity to predict the crows' conceptual decisions. NCL activity changed with learning when a crow was retrained with the same stimuli assigned to more categories with new boundaries ("short", "medium," and "long"). Categorical neuronal representations emerged dynamically so that sensory length information at the beginning of the trial was transformed into behaviorally relevant categorical representations shortly before the crows' decision making. Our data show malleable categorization capabilities for abstract spatial magnitudes mediated by the flexible networks of the crow NCL.

Visual attention modulates the transition from fine-grained, serial processing to coarser-grained, more parallel processing: A computational modeling study

During reading acquisition, beginning readers transition from serial to more parallel processing. The acquisition of word specific knowledge through orthographic learning is critical for this transition. However, the processes by which orthographic representations are acquired and fine-tuned as learning progresses are not well understood. Our aim was to explore the role of visual attention in this transition through computational modeling. We used the BRAID-Learn model, a Bayesian model of visual word recognition, to simulate the orthographic learning of 700 4-to 10-letter English known words and novel words, presented 5 times each to the model. The visual attention quantity available for letter identification was manipulated in the simulations to assess its influence on the learning process. We measured the overall processing time and number of attentional fixations simulated by the model across exposures and their impact on two markers of serial processing, the lexicality and length effects, depending on visual attention quantity. Results showed that the two lexicality and length effects were modulated by visual attention quantity. The quantity of visual attention available for processing further modulated novel word orthographic learning and the evolution of the length effect on processing time and number of attentional fixations across repeated exposures to novel words. The simulated patterns are consistent with behavioral data and the developmental trajectories reported during reading acquisition. Overall, the model predicts that the efficacy of orthographic learning depends on visual attention quantity and that visual attention may be critical to explain the transition from serial to more parallel processing.

Supervised machine learning aided behavior classification in pigeons

Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.

Resolving the associative learning paradox by category learning in pigeons

A wealth of evidence indicates that humans can engage two types of mechanisms to solve category-learning tasks: declarative mechanisms, which involve forming and testing verbalizable decision rules, and associative mechanisms, which involve gradually linking stimuli to appropriate behavioral responses.1,2,3 In contrast to declarative mechanisms, associative mechanisms have received surprisingly little attention in the broader category-learning literature. Although various forms of associatively driven artificial intelligence (AI) have matched-and even surpassed-humans' performance on several challenging problems,3,4,5,6 associative learning is routinely dismissed as being too simple to power the impressive cognitive achievements of both humans and non-human species.6,7,8,9 Here, we attempt to resolve this paradox by demonstrating that pigeons-which appear to rely solely on associative learning mechanisms in several tasks that promote declarative rule use by humans3,10,11,12-succeed at learning a novel, highly demanding category structure that ought to hinder declarative rule use: the sectioned-rings task. Our findings highlight the power and flexibility that associative mechanisms afford in the realm of category learning.

Visual categories and concepts in the avian brain

Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research-at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian 'visual cortex'. Third, we focus on the neurocomputational basis of perceptual categorization in the bird's visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

Reading about a RELO-VUTION

Pseudowords created by transposing two letters of words (e.g., MOHTER; CHOLOCATE) are highly confusable with their base word; this is known as the transposed-letter similarity effect. In this work, we examined whether transposed-letter effects occur when words span more than one line (e.g., CHOLO- in one line and CATE in another line; note that the transposed letters L and C are in different lines). While this type of presentation is not the canonical format for reading in alphabetic languages, it is widely used in advertising, billboards, and street signs. Transposed-letter pseudowords and their replacement-letter controls were written in the standard one-line format versus a two-line format (Experiments 1-2) or a syllable-per-line format (Experiment 3). While results showed some decrease in the transposed-letter effect in the two-line and syllabic formats, the transposed-letter effect was still substantial in the accuracy of responses. These findings demonstrate that even when the letters being transposed are relatively far apart in space, the transposed-letter effect is still robust. Thus, a major component of letter position coding occurs at an abstract level.

Regional Patterning of Adult Neurogenesis in the Homing Pigeon’s Brain

In the avian brain, adult neurogenesis has been reported in the telencephalon of several species, but the functional significance of this trait is still ambiguous. Homing pigeons (Columba livia f.d.) are well-known for their navigational skills. Their brains are functionally adapted to homing with, e.g., larger hippocampi. So far, no comprehensive mapping of adult neuro- and gliogenesis or studies of different developmental neuronal stages in the telencephalon of homing pigeons exists, although comprehensive analyses in various species surely will result in a higher understanding of the functional significance of adult neurogenesis. Here, adult, free flying homing pigeons were treated with 5-bromo-deoxyuridine (BrdU) to label adult newborn cells. Brains were dissected and immunohistochemically processed with several markers (GFAP, Sox2, S100ß, Tbr2, DCX, Prox1, Ki67, NeuN, Calbindin, Calretinin) to study different stages of adult neurogenesis in a quantitative and qualitative way. Therefore, immature and adult newborn neurons and glial cells were analyzed along the anterior–posterior axis. The analysis proved the existence of different neuronal maturation stages and showed that immature cells, migrating neurons and adult newborn neurons and glia were widely and regionally unequally distributed. Double- and triple-labelling with developmental markers allowed a stage classification of adult neurogenesis in the pigeon brain (1: continuity of stem cells/proliferation, 2: fate specification, 3: differentiation/maturation, 4: integration). The most adult newborn neurons and glia were found in the intercalated hyperpallium (HI) and the hippocampal formation (HF). The highest numbers of immature (DCX+) cells were detected in the nidopallium (N). Generally, the number of newborn glial cells exceeded the number of newborn neurons. Individual structures (e.g., HI, N, and HF) showed further variations along the anterior–posterior axis. Our qualitative classification and the distribution of maturing cells in the forebrain support the idea that there is a functional specialization, respectively, that there is a link between brain-structure and function, species-specific requirements and adult neurogenesis. The high number of immature neurons also suggests a high level of plasticity, which points to the ability for rapid adaption to environmental changes through additive mechanisms. Furthermore, we discuss a possible influence of adult neurogenesis on spatial cognition.

Seeing the Forest for the Trees, and the Ground Below My Beak: Global and Local Processing in the Pigeon's Visual System

Non-human animals tend to solve behavioral tasks using local information. Pigeons are particularly biased toward using the local features of stimuli to guide behavior in small-scale environments. When behavioral tasks are performed in large-scale environments, pigeons are much better global processors of information. The local and global strategies are mediated by two different fovea in the pigeon retina that are associated with the tectofugal and thalamofugal pathways. We discuss the neural mechanisms of pigeons' bias for local information within the tectofugal pathway, which terminates at an intermediate stage of extracting shape complexity. We also review the evidence suggesting that the thalamofugal pathway participates in global processing in pigeons and is primarily engaged in constructing a spatial representation of the environment in conjunction with the hippocampus.

Probabilistic modeling of orthographic learning based on visuo-attentional dynamics

How is orthographic knowledge acquired? In line with the self-teaching hypothesis, most computational models assume that phonological recoding has a pivotal role in orthographic learning. However, these models make simplifying assumptions on the mechanisms involved in visuo-orthographic processing. Against evidence from eye movement data during orthographic learning, they assume that orthographic information on novel words is immediately available and accurately encoded after a single exposure. In this paper, we describe BRAID-Learn, a new computational model of orthographic learning. BRAID-Learn is a probabilistic and hierarchical model that incorporates the mechanisms of visual acuity, lateral interference, and visual attention involved in word recognition. Orthographic learning in the model rests on three main mechanisms: first, visual attention moves over the input string to optimize the gain of information on letter identity at each fixation; second, top-down lexical influence is modulated as a function of stimulus familiarity; third, after exploration, perceived information is used to create a new orthographic representation or stabilize a better-specified representation of the input word. BRAID-Learn was challenged on its capacity to simulate the eye movement patterns reported in humans during incidental orthographic learning. In line with the behavioral data, the model predicts a larger decline with exposures in number of fixations and processing time for novel words than for known words. For novel words, most changes occur between the first and second exposure, that is to say, after creation in memory of a new orthographic representation. Beyond phonological recoding, our results suggest that visuo-attentional exploration is an intrinsic portion of orthographic learning seldom taken into consideration by models or theoretical accounts.

High associative neuron numbers could drive cognitive performance in corvid species

Corvids possess cognitive skills, matching those of non-human primates. However, how these species with their small brains achieve such feats remains elusive. Recent studies suggest that cognitive capabilities could be based on the total numbers of telencephalic neurons. Here we extend this hypothesis further and posit that especially high neuron counts in associative pallial areas drive flexible, complex cognition. If true, avian species like corvids should specifically accumulate neurons in the avian associative areas meso- and nidopallium. To test the hypothesis, we analyzed the neuronal composition of telencephalic areas in corvids and non-corvids (chicken, pigeons, and ostriches - the species with the largest bird brain). The overall number of pallial neurons in corvids was much higher than in chicken and pigeons and comparable to those of ostriches. However, neuron numbers in the associative mesopallium and nidopallium were twice as high in corvids and, in correlation with these associative areas, the corvid subpallium also contained high neuron numbers. These findings support our hypothesis that large absolute numbers of associative pallial neurons contribute to cognitive flexibility and complexity and are key to explain why crows are smart. Since meso/nidopallial and subpallial areas scale jointly, it is conceivable that associative pallio-striatal loops play a similar role in executive decision-making as described in primates. This article is protected by copyright. All rights reserved.

Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

What makes cognition ‘advanced’ is an open and not precisely defined question. In this study, a cognitive model of cleaner fish learning the ephemeral-reward task demonstrates how a critical step in cognitive evolution may be understood as the evolution of chunking and its tuning to fit ecological conditions.

Vision: What's so special about words?

Readers are sensitive to the statistics of written language. New research suggests that this sensitivity may be driven by the same domain-general mechanisms that enable the visual system to detect statistical regularities in the visual environment.

Pigeons show how meta-control enables decision-making in an ambiguous world

In situations where the left and right brain sides receive conflicting information that leads to incompatible response options, the brain requires efficient problem-solving mechanisms. This problem is particularly significant in lateralized brains, in which the hemispheres differ in encoding strategies or attention focus and hence, consider different information for decision-making. Meta-control, in which one hemisphere dominates ambiguous decisions, can be a mechanism that ensures fast behavioral reactions. We therefore confronted pigeons with a task in which two stimulus classes were brought into conflict. To this end, we trained pigeons simultaneously on two categories (cats or dogs) whereby each hemisphere learnt only one of the categories respectively. After learning, the birds were confronted with stimulus pairs that combined a picture with a cat (positive for one hemisphere) and a picture with a dog (positive for the other hemisphere). Pecking responses indicated the hemisphere dominating response selection. Pigeons displayed individual meta-control despite equal categorization performances of both brain hemispheres. This means that hemispheric dominance only emerged in interhemispheric conflict situations. The analysis of response latencies indicate that conflict decisions relied on intrahemispheric processes. Interhemispheric components played a role for more complex decisions. This flexibility could be a crucial building block for the evolutionary success of a lateralized brain.

A general-purpose mechanism of visual feature association in visual word identification and beyond

As writing systems are a relatively novel invention (slightly over 5 kya),¹ they could not have influenced the evolution of our species. Instead, reading might recycle evolutionary older mechanisms that originally supported other tasks^2,3 and preceded the emergence of written language. Accordingly, it has been shown that baboons and pigeons can be trained to distinguish words from nonwords based on orthographic regularities in letter co-occurrence.^4,5 This suggests that part of what is usually considered reading-specific processing could be performed by domain-general visual mechanisms. Here, we tested this hypothesis in humans: if the reading system relies on domain-general visual mechanisms, some of the effects that are often found with orthographic material should also be observable with non-orthographic visual stimuli. We performed three experiments using the same exact design but with visual stimuli that progressively departed from orthographic material. Subjects were passively familiarized with a set of composite visual items and tested in an oddball paradigm for their ability to detect novel stimuli. Participants showed robust sensitivity to the co-occurrence of features ("bigram" coding) with strings of letter-like symbols but also with made-up 3D objects and sinusoidal gratings. This suggests that the processing mechanisms involved in the visual recognition of novel words also support the recognition of other novel visual objects. These mechanisms would allow the visual system to capture statistical regularities in the visual environment.^6-9 We hope that this work will inspire models of reading that, although addressing its unique aspects, place it within the broader context of vision.

Are There Differences in "Intelligence" Between Nonhuman Species? The Role of Contextual Variables

We review evidence for Macphail’s (1982, 1985, 1987)Null Hypothesis, that nonhumans animals do not differ either qualitatively or quantitatively in their cognitive capacities. Our review supports the Null Hypothesis in so much as there are no qualitative differences among nonhuman vertebrate animals, and any observed differences along the qualitative dimension can be attributed to failures to account for contextual variables. We argue species do differ quantitatively, however, and that the main difference in “intelligence” among animals lies in the degree to which one must account for contextual variables.

Statistical learning in domestic chicks is modulated by strain and sex

Statistical learning is a key mechanism for detecting regularities from a variety of sensory inputs. Precocial newborn domestic chicks provide an excellent model for (1) exploring unsupervised forms of statistical learning in a comparative perspective, and (2) elucidating the ecological function of statistical learning using imprinting procedures. Here we investigated the role of the sex of the chicks in modulating the direction of preference (for familiarity or novelty) in a visual statistical learning task already employed with chicks and human infants. Using both automated tracking and direct human coding, we confirmed chicks' capacity to recognize the presence of a statistically defined structure underlying a continuous stream of shapes. Using a different chicken strain than previous studies, we were also able to highlight sex differences in chicks' propensity to approach the familiar or novel sequence. This could also explain a previous failure to reveal statistical learning in chicks which sex was however not determined. Our study confirms chicks' ability to track visual statistics. The pivotal role of sex in determining familiarity or novelty preferences in this species and the interaction with the animals' strain highlight the importance to contextualize comparative research within the ecology of each species.

Working Memory as an Indicator for Comparative Cognition - Detecting Qualitative and Quantitative Differences

Working memory (WM), the representation of information held accessible for manipulation over time, is an essential component of all higher cognitive abilities. It allows for complex behaviors that go beyond simple stimulus-response associations and inflexible behavioral patterns. WM capacity determines how many different pieces of information (items) can be used for these cognitive processes, and in humans, it correlates with fluid intelligence. As such, WM might be a useful tool for comparison of cognition across species. WM can be tested using comparatively simple behavioral protocols, based on operant conditioning, in a multitude of different species. Species-specific contextual variables that influence an animal’s performance on a non-cognitive level are controlled by adapting the WM paradigm. The neuronal mechanisms by which WM emerges in the brain, as sustained neuronal activity, are comparable between the different species studied (mammals and birds), as are the areas of the brain in which WM activity can be measured. Thus WM is comparable between vastly different species within their respective niches, accounting for specific contextual variables and unique adaptations. By approaching the question of “general cognitive abilities” or “intelligence” within the animal kingdom from the perspective of WM, the complexity of the core question at hand is reduced to a fundamental memory system required to allow for complex cognitive abilities. This article argues that measuring WM can be a suitable addition to the toolkit of comparative cognition. By measuring WM on a behavioral level and going beyond behavior to the underlying physiological processes, qualitative and quantitative differences in cognition between different animal species can be identified, free of contextual restraints.

Taking pigeons to heart: Birds proficiently diagnose human cardiac disease

In two experiments, we trained pigeons (Columba livia) to sort visual images (obtained by clinical myocardial perfusion imaging techniques) depicting different degrees of human cardiac disfunction (myocardial hypoperfusion of the left ventricle) into normal and abnormal categories by providing food reward only after correct choice responses. Pigeons proved to be highly proficient at categorizing pseudo-colorized images as well as highly sensitive to the degree of the perfusion deficit depicted in the abnormal images. In later testing, the pigeons completely transferred discriminative responding to novel stimuli, demonstrating that they had fully learned the normal and abnormal categories. Yet, these pigeons failed to transfer discriminative responding to grayscale images containing no color information. We therefore trained a second cohort of pigeons to categorize grayscale image sets from the outset. These birds required substantially more training to achieve similar levels of performance. Yet, they too completely transferred discriminative responding to novel stimuli by relying on both global and local disparities in brightness between the normal and abnormal images. These results confirm that pseudo-colorization can enhance pigeons' categorization of human cardiac images, a result also found with human observers. Overall, our findings further document the potential of the pigeon as a useful aide in studies of medical image perception.

Columban Simulation Project 2.0: Numerical Competence and Orthographic Processing in Pigeons and Primates

Thirty years ago Burrhus Frederic Skinner and Robert Epstein began what is known as the Columban Simulation Project. With pigeons as their subjects, they simulated a series of studies that purportedly demonstrated insight, self-recognition, and symbolic communication in chimpanzees. In each case, with the appropriate training, they demonstrated that pigeons performed in a comparable manner to chimpanzees. When discussing these studies in the context of his Null Hypothesis, Macphail paid little attention to how the pigeons and chimpanzees solved the tasks and simply assumed that successful performance on the tasks reflected a similar underlying mechanism. Here, following a similar process to the original Columban Simulation Project, we go beyond this success testing and employ the signature testing approach to assess whether pigeons and primates employ a similar mechanism on tasks that tap numerical competence and orthographic processing. Consistent with the Null Hypothesis, pigeons and primates successfully passed novel transfer tests and, critically, displayed comparable cognitive signatures. While these findings demonstrate the absence of a qualitative difference, the time taken to train pigeons on these tasks revealed a clear quantitative difference.

Avian Brains: Primate-like Functions of Neurons in the Crow Brain

Despite the negative connotations of the term 'birdbrain', birds possess cognitive abilities on par with primates. A new study finds that neurons in the crow's brain display characteristics similar to those displayed by neurons in the primate's brain.

Positional encoding in cotton-top tamarins (Saguinus oedipus)

Strategies used in artificial grammar learning can shed light into the abilities of different species to extract regularities from the environment. In the A(X)ⁿB rule, A and B items are linked, but assigned to different positional categories and separated by distractor items. Open questions are how widespread is the ability to extract positional regularities from A(X)ⁿB patterns, which strategies are used to encode positional regularities and whether individuals exhibit preferences for absolute or relative position encoding. We used visual arrays to investigate whether cotton-top tamarins (Saguinusoedipus) can learn this rule and which strategies they use. After training on a subset of exemplars, two of the tested monkeys successfully generalized to novel combinations. These tamarins discriminated between categories of tokens with different properties (A, B, X) and detected a positional relationship between non-adjacent items even in the presence of novel distractors. The pattern of errors revealed that successful subjects used visual similarity with training stimuli to solve the task and that successful tamarins extracted the relative position of As and Bs rather than their absolute position, similarly to what has been observed in other species. Relative position encoding appears to be favoured in different tasks and taxa. Generalization, though, was incomplete, since we observed a failure with items that during training had always been presented in reinforced arrays, showing the limitations in grasping the underlying positional rule. These results suggest the use of local strategies in the extraction of positional rules in cotton-top tamarins.

The face of animal cognition

As an increasing number of researchers investigate the cognitive abilities of an ever-wider range of animals, animal cognition is currently among the most exciting fields within animal behavior. Tinbergen would be proud: all four of his approaches are being pursued and we are learning much about how animals collect information and how they use that information to make decisions for their current and future states as well as what animals do not perceive or choose to ignore. Here I provide an overview of this productivity, alighting only briefly on any single example, to showcase the diversity of species, of approaches and the sheer mass of research effort currently under way. We are getting closer to understanding the minds of other animals and the evolution of cognition at an increasingly rapid rate.

A visual familiarity account of evidence for orthographic processing in pigeons (Columbia livia): a reply to Scarf, Corballis, Güntürkün, and Colombo (2017)

Scarf et al. (Proc Natl Acad Sci 113(40):11272-11276, 2016) demonstrated that pigeons, as with baboons (Grainger et al. in Science 336(6078):245-248, 2012; Ziegler in Psychol Sci. https://doi.org/10.1177/0956797612474322 , 2013), can be trained to display several behavioural hallmarks of human orthographic processing. But, Vokey and Jamieson (Psychol Sci 25(4):991-996, 2014) demonstrated that a standard, autoassociative neural network model of memory applied to pixel maps of the words and nonwords reproduces all of those results. In a subsequent report, Scarf et al. (Anim Cognit 20(5):999-1002, 2017) demonstrated that pigeons can reproduce one more marker of human orthographic processing: the ability to discriminate visually presented four-letter words from their mirror-reversed counterparts (e.g. "LEFT" vs. " "). The current report shows that the model of Vokey and Jamieson (2014) reproduces the results of Scarf et al. (2017) and reinforces the original argument: the recent results thought to support a conclusion of orthographic processing in pigeons and baboons are consistent with but do not force that conclusion.

Unsupervised learning of complex associations in an animal model

Supervised learning results from explicit corrective feedback, whereas unsupervised learning results from statistical co-occurrence. In an initial training phase, we gave pigeons an unsupervised learning task to see if mere pairing could establish associations between multiple pairs of visual images. To assess learning, we administered occasional testing trials in which pigeons were shown an object and had to choose between previously paired and unpaired tokens. Learning was evidenced by preferential choice of the previously unpaired token. In a subsequent supervised training phase, learning was facilitated if the object and token had previously been paired. These results document unsupervised learning in pigeons and resemble statistical learning in infants, suggesting an important parallel between human and animal cognition.

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.

Are baboons learning "orthographic" representations? Probably not

The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

Do 'literate' pigeons (Columba livia) show mirror-word generalization?

Many children pass through a mirror stage in reading, where they write individual letters or digits in mirror and find it difficult to correctly utilize letters that are mirror images of one another (e.g., b and d). This phenomenon is thought to reflect the fact that the brain does not naturally discriminate left from right. Indeed, it has been argued that reading acquisition involves the inhibition of this default process. In the current study, we tested the ability of literate pigeons, which had learned to discriminate between 30 and 62 words from 7832 nonwords, to discriminate between words and their mirror counterparts. Subjects were sensitive to the left-right orientation of the individual letters, but not the order of letters within a word. This finding may reflect the fact that, in the absence of human-unique top-down processes, the inhibition of mirror generalization may be limited.

In vivo measurement of T1 and T2 relaxation times in awake pigeon and rat brains at 7T

PURPOSE

Establishment of regional longitudinal (T₁ ) and transverse (T₂ ) relaxation times in awake pigeons and rats at 7T field strength. Regional differences in relaxation times between species and between two different pigeon breeds (homing pigeons and Figurita pigeons) were investigated.

METHODS

T₁ and T₂ relaxation times were determined for nine functionally equivalent brain regions in awake pigeons and rats using a multiple spin-echo saturation recovery method with variable repetition time and a multi-slice/multi-echo sequence, respectively. Optimized head fixation and habituation protocols were applied to accustom animals to the scanning conditions and to minimize movement.

RESULTS

The habituation protocol successfully limited movement of the awake animals to a negligible minimum, allowing reliable measurement of T₁ and T₂ values within all regions of interest. Significant differences in relaxation times were found between rats and pigeons but not between different pigeon breeds.

CONCLUSION

The obtained T₁ and T₂ values for awake pigeons and rats and the optimized habituation protocol will augment future MRI studies with awake animals. The differences in relaxation times observed between species underline the importance of the acquisition of T₁ /T₂ values as reference points for specific experiments. Magn Reson Med 79:1090-1100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.