AbstractMany 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.
- Dicke U.
- Roth G.
Neuronal factors determining high intelligence. ]. This is visible in primates [
- Deaner R.O.
- et al.
Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. ], Cetacea [
- Marino L.
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Cetaceans have complex brains for complex cognition. ], and birds [
- Cnotka J.
- et al.
Extraordinary large brains in tool-using Caledonian crows (Corvus moneduloides). ,
- Sol D.
- et al.
Neuron numbers link innovativeness with both absolute and relative brain size in birds. ], but has also been shown in insects [
- Collado M.Á.
- et al.
Brain size predicts learning abilities in bees. ]. Although details about relative or absolute brain size and similar variables are debated [
- Sol D.
- et al.
Neuron numbers link innovativeness with both absolute and relative brain size in birds. ], the idea that larger brains can in principle provide more neurons and thus more computing power is generally accepted [
- Sol D.
- et al.
Neuron numbers link innovativeness with both absolute and relative brain size in birds. ,
- Herculano-Houzel S.
Numbers of neurons as biological correlates of cognitive capability. ]. The second common prerequisite for complex cognition is the mammalian cerebral cortex (
- Luhmann H.J.
Dynamics of neocortical networks: connectivity beyond the canonical microcircuit. ]. This design allows the various attributes of a currently perceived object to be linked with each other to form a single percept that can be associated with an internal representation [
- Larkum M.
A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. ].
- Herculano-Houzel S.
The human brain in numbers: a linearly scaled-up primate brain. ]. In primates, about 90% of the cerebral cortex comprises the six-layered isocortex. The Ancient Greek term ‘isos’ means ‘equal’ or ‘same’. All areas of the isocortex have a similar appearance, although detailed analyses uncover regional differences that are used to subdivide the isocortex into different areas. The isocortex is sometimes also called the ‘neocortex’ because it is an evolutionary novelty of mammals. The remaining 10% of the cerebral cortex comprises the ‘allocortex’, with the Ancient Greek term ‘allos’ meaning ‘all others’ or ‘rest’. These others are traditionally subdivided into the ‘archicortex’, with the Ancient Greek term ‘arche’ meaning ‘origin’, and the ‘paleocortex’, from the Ancient Greek ‘palaios’ meaning ‘old’. The archicortex contains, among others, the hippocampal formation, the entorhinal cortex, and the retrosplenial cortex. The paleocortex contains many structures, some of which have six layers while others are hardly layered at all. To list a few, the paleocortex contains the olfactory bulb and tubercle, the cortical nucleus of the amygdala, the septum, and the prepiriform cortex [
- Zilles K.
- et al.
Cytoarchitecture and maps of the human cerebral cortex. ]. Previously, it was assumed that paleo-, archi-, and isocortex constitute a phylogenetic sequence that moves from old to novel. This view is no longer accepted.When compared with large mammals, birds have very small brains that comprise seemingly homogeneous nuclear clusters. These glaring anatomical differences should cast a dim prospect on avian cognition. However, across an array of cognitive tasks, several avian taxa perform similarly to great apes and appear to rely on similar cognitive algorithms. How is that possible? Our inability to answer this question shows that we are still far from a generic understanding of the link between brain structure and cognition.This opinion attempts to partially solve this riddle by identifying features that characterize the brains of both ‘smart’ mammals and ‘smart’ birds. Such an approach might show which neural mechanisms are necessary for complex cognition. To this end, we start by first characterizing avian cognition by providing several examples. Then, we question the assumption that big brains are a useful measure of processing capacity, proceed to provide a novel view on the organization of the bird forebrain, and review the first studies on large-scale neural networks that could constitute the substrate for avian cognition. In the end, we argue that four features are currently excellent candidates for complex cognition in corvids, parrots, and great apes. These are: (i) large quantities of associative pallial neurons; (ii) a PFC-like area; (iii) dense dopaminergic innervation of association areas; and (iv) dynamic neurophysiological properties of neuron groups that hold information in working memory. There are strong arguments that these have evolved independently in birds and mammals and thus could constitute ‘hard to replace’ mechanisms for brains that perform complex cognition.
Associative learning edit
Birds have been trained to identify and discern intricate shapes, and their relationship with food and other incentives has been extensively researched in relation to visual or auditory cues. [9] This might be a crucial skill that helps them survive. [clarification needed][10].
Animals are frequently subjected to associative learning as a means of cognitive assessment. [11] Bebus et al. “Learning about a predictive or causal relationship (association) between two stimuli, responses, or events” is the definition of associative learning. “[12] A classic example of associative learning is Pavlovian conditioning. Simple associative learning tasks can be used in avian research to evaluate how cognitive abilities vary with experimental measures.
Bebus et al. showed that the associative learning of Florida scrub-jays was correlated with personality, baseline hormone levels, and reversal learning. [12] Food rewards were linked to colored rings in order to assess associative learning abilities. The rewarding and non-rewarding colors were simply switched, and the researchers tested reversal learning by seeing how quickly the scrub-jays would adjust to the new association. According to their findings, reversal learning and associative learning are inversely connected. Put differently, [12] birds that quickly picked up the first association took longer to pick up the new association when it was reversed. The authors come to the conclusion that acquiring a new association and adjusting to an existing one must come at a cost. [12].
Bebus et al. additionally demonstrated a correlation between neophobia and reversal learning, with birds more adept at reversal learning when they were terrified of a novel environment the researchers had previously created. [12] The inverse correlation was measured, but it was not statistically significant. Less neophobic birds did better on the associative learning task. Opposite results were found by Guido et al. ,[13] demonstrated a negative correlation between reversal learning and neophobia in the South American predatory bird Milvago chimango. To put it another way, neophobic birds learned reversal lessons more slowly. The researchers proposed a contemporary explanation for this discrepancy: since birds that live close to cities benefit from being flexible learners due to fluctuations in human activity and from being less neophobic to feed on human resources (such as detritus), it is possible that high reversal learning ability and low neophobia coevolved. Because of these contextual variations, personality may not be enough to predict associative learning on its own [13].
Bebus et al. found a correlation between baseline hormone levels and associative learning. Their research found that better associative learning was predicted by lower baseline levels of corticosterone (CORT), a hormone involved in stress response. On the other hand, superior reversal learning was predicted by high baseline CORT levels [12]. [12] In summary, Bebus et al. discovered that higher associative learning capacities were predicted by lower baseline CORT levels and less neophobia (not statistically significant). Conversely, greater reversal learning abilities were predicted by higher baseline CORT levels and higher neophobia. [12].
Apart from reversal learning, hormone levels, and personality, additional research indicates that diet might also be correlated with associative learning capabilities. Bonaparte et al. showed that improved associative learning was correlated with high-protein diets in zebra finches. [14] The researchers demonstrated that in treated males, a high-diet regimen was linked to greater head width, tarsus length, and body mass. [14] Subsequent testing revealed a correlation between improved performance on an associative learning task and a high diet and larger head-to-tarsus ratio. [14] The researchers supported the idea that nutritional stress during development can have a deleterious effect on cognitive development, which may then lower the likelihood of successful reproduction, by using associative learning as a correlate of cognition. [14] Learning songs is one way that an unhealthy diet may impact the success of reproduction. The developmental stress hypothesis states that zebra finches acquire songs during a stressful stage of development and that their aptitude for learning intricate songs is a sign of their sufficient development. [15].
Contradicting results by Kriengwatana et al. [16] discovered that zebra finches fed a low-food diet before they reached nutritional independence—that is, before they can feed themselves—had no effect on neophobia but improved spatial associative learning and memory impairment. Additionally, they were unable to discover a link between associative learning and physiological growth. [16] Though Bonaparte et al. focused on protein content whereas Kriengwatana et al. focused on quantity of food, the results seem contradictory. It is necessary to carry out more research to fully understand the connection between associative learning and diet.
Associative learning may vary across species depending on their ecology. Food-storing and non-storing birds differ in their associative learning and memory, according to Clayton and Krebs. [17] They exposed non-storing jackdaws and blue tits and food-storing jays and marsh tits to seven sites, one of which had a food reward. In the initial stage of the trial, the bird was permitted to partially consume the food item after it randomly searched each of the seven locations for the reward. All species performed equally well in this first task. In order for the birds to get the remaining food item in the second phase of the experiment, they had to find their way back to the previously rewarding site where the sites had been hidden. In phase two, food-storing birds outperformed non-storing birds, according to the researchers. [17] Regardless of the availability of a reward, non-storing birds preferred to return to previously visited sites, whereas food-storing birds preferred to return to rewarding sites. [17] There was no performance difference between storers and non-storers if the food reward was visible in phase one. [17] These findings demonstrate that an ecological lifestyle can affect memory after associative learning rather than just learning.
In Australian magpies, associative learning correlates with age (Mirville et al. [18] The researchers’ original goal in conducting this study was to examine how group size affects learning. They did discover, however, that group size was only correlated with the task’s likelihood of interaction rather than associative learning itself. Rather, they discovered that age affected performance: adults were less likely to approach the task at first but more successful at finishing the associative learning task. Juveniles, on the other hand, approached the task more frequently but were less successful at finishing it. Because they were more likely to approach and succeed at the task, adults in larger groups were therefore the most likely to finish it. [18].
While being a quick learner may seem advantageous to everyone, Madden et al. indicated that whether or not associative learning was adaptive depended on an individual’s weight. [19] Using common pheasants, the researchers observed that heavy birds doing well on associative tasks were more likely to survive to be four months old after being released into the wild, while light birds doing well on associative tasks were less likely to do so. [19] The researchers offer two explanations for how weight affects the results: either larger individuals are more apt to be dominant and to benefit from new resources than smaller individuals, or they simply have a higher survival rate than smaller individuals because of things like larger food reserves, increased motility, and difficulty being killed by predators. [19] Alternatively, ecological pressures may affect smaller individuals differently. Smaller people may find associative learning more costly, which would lower their fitness and result in maladaptive behaviors. [19] Additionally, Madden et al. discovered that low survival rate in both groups was correlated with slow reversal learning. [19] The researchers proposed a trade-off theory in which the development of other cognitive capacities would be hampered by the expense of reversal learning. According to Bebus et al. associative learning and reversal learning are negatively correlated. [12] Perhaps because associative learning is improved, low reversal learning is correlated with better survival. Madden et al. also proposed this theory, although it should be noted that they were skeptical because they were unable to demonstrate the same inverse relationship between associative and reversal learning that Bebus et al.
In their research, Veit et al. demonstrate how associative learning altered the neural activity of crows’ NCL (nidopallium caudolaterale) neurons. [20] Visual cues were displayed on a screen for 600 ms, then there was a 1000 ms pause to test this. Following the pause, the crows had to select the right stimulus out of two that were presented at once, one of which was red and the other blue. Choosing the correct stimulus was rewarded with a food item. NCL neurons exhibited increased selective activity for the rewarding stimulus as the crows made their way through the learning process. Put differently, when the crow had to select the red stimulus, a particular NCL neuron that fired when the correct stimulus was the red one increased its firing rate selectively. During the delay period, when the crow was presumably considering which stimulus to select, there was an increase in firing. Additionally, increased NCL activity reflected the crows increased performance. According to the researchers, NCL neurons play a role in both learning associations and choosing which rewarding stimulus to respond to in the future. [20].
Slater and Hauber demonstrated that birds of prey can also learn associations through olfactory cues, despite the majority of research focusing on visual associative learning. Nine individuals from five different species of preying birds were trained to associate a neutral olfactory cue with a food reward in this study [21].
Brain anatomy editFurther information:
Scientists claimed at the start of the 20th century that birds’ basal ganglia were hyperdeveloped and had small, mammalian-like telencephalon structures. [46] Modern studies have refuted this view. [47] In birds, the basal ganglia make up a very tiny portion of the brain. Birds’ intelligence appears to reside in a different area of the brain, the medio-rostral neostriatum/hyperstriatum ventrale (also see nidopallium). In fact, the brain-to-body size ratio of higher primates is similar in psittacines (parrots) and corvines (birds of the crow family). [48] For a higher unit mass per volume, birds can also have twice the neuron packing density of primate brains, or in some cases comparable to the total number of neurons in much larger mammal brains. It has also been proposed that the avian pallium serves as an analogous neural foundation for consciousness [49][50][51]. [52][53].
Research conducted on captive birds has provided information about the smartest birds. Although parrots are unique in that they can mimic human speech, research on grey parrots has revealed that some of them can also form simple sentences and link words to their meanings (see Alex). The corvid family, which includes crows, ravens, and jays, and parrots, is thought to be the smartest group of birds. These species typically have the biggest high vocal centers, according to research Dr. Harvey J. Karten, a neuroscientist at UCSD who has researched bird physiology, has found similarities between human and avian brain anatomy in their lower regions. [citation needed].