A paper just published in Psychological Science:
Corvids (birds of the family Corvidae) display intelligent behavior previously ascribed only to primates, but such feats are not directly comparable across species. To make direct species comparisons, we used a same/different task in the laboratory to assess abstract-concept learning in black-billed magpies (Pica hudsonia). Concept learning was tested with novel pictures after training. Concept learning improved with training-set size, and test accuracy eventually matched training accuracy—full concept learning—with a 128-picture set; this magpie performance was equivalent to that of Clark’s nutcrackers (a species of corvid) and monkeys (rhesus, capuchin) and better than that of pigeons. Even with an initial 8-item picture set, both corvid species showed partial concept learning, outperforming both monkeys and pigeons. Similar corvid performance refutes the hypothesis that nutcrackers’ prolific cache-location memory accounts for their superior concept learning, because magpies rely less on caching. That corvids with “primitive” neural architectures evolved to equal primates in full concept learning and even to outperform them on the initial 8-item picture test is a testament to the shared (convergent) survival importance of abstract-concept learning.
The article’s conclusion contains this passage:
So, how did the apparently primitive bird brain that evolved from dinosaurs become competitive with, and even initially outperform, the abilities of what has been considered a more elaborate primate brain to perform abstract-concept learning, which involves thoughts and processes considered to be of the highest cognitive order? The answer most certainly lies in evolution itself, a multimillion-year process. Environmental pressures (social and otherwise) undoubtedly selected for and shaped these different neural architectures to successfully accomplish many of the same essential and intelligent behaviors for survival, an example of convergent evolution in which organisms not closely related (i.e., not monophyletic) independently evolved similar traits or functions as a result of having to adapt to similar environments or ecological niches. But the example of convergent evolution presented in the current study is comparatively novel and unique because its identification required special tests of the cognitive ability (trait) for the cognitive function of fully learning a same/different abstract concept to be revealed. Other examples of convergent evolution have been based on some obvious physical trait, such as wings, which typically can be identified from fossil records and have an obvious function of flying (some insects, birds, and bats).
Neural oscillation refers to the rhythmic activity of large numbers of the brains neurons. It is these oscillations that produce the brain waves that are measured on a EEG. Here’s a recent paper suggesting that dyslexia may be caused by abnormal neural oscillation in parts of the brain related to auditory and visual processing. Here is the abstract:
It has been proposed that atypical neural oscillations in both the auditory and the visual modalities could explain why some individuals fail to learn to read and suffer from developmental dyslexia. However, the role of specific oscillatory mechanisms in reading acquisition is still under debate. In this article, we take a cross-linguistic approach and argue that both the phonological and orthographic specifics of a language (e.g., linguistic rhythm, orthographic depth) shape the oscillatory activity thought to contribute to reading development. The proposed theoretical framework should allow future research to test cross-linguistic hypotheses that will shed light on the heterogeneity of auditory and visual disorders and their underlying brain dysfunction(s) in developmental dyslexia, and inform clinical practice by helping us to diagnose dyslexia across languages.
Another interesting story about invertebrate brains, in this case the spider:
“Spiders are very smart, that’s why we’re studying them,” says Ronald Hoy, a professor of neurobiology and behavior at Cornell University. “They use visual cues to steer by, and the kind of mazes that they can solve is considered to be pretty impressive for an invertebrate.”
A bee brain is tiny, yet it has amazing computational power.
Using a technique called micro-computed tomography, a group of researches have produced CAT scan images of the brain of a bumble bee. You can see them here.
Why is this important? The authors explain:
Despite their comparatively small size, insect brains are capable of rapidly detecting and responding to a plethora of diverse stimuli in a wide range of sensory modalities, facilitating their global ecological success and establishing them as an essential model system for cognitive biology and neuroscience. Although insect brains are smaller and simpler than their vertebrate counterparts, there is increasing evidence that insect cognitive performance can be impressive. For instance, foraging insects must learn and memorise navigation routes in complex landscapes requiring the ability to detect, distinguish and integrate a multitude of chemical, visual, landmark and celestial cues. Therefore, knowledge of insect brain structure allows us to understand how comparatively small (and simple) brains can generate complex patterns of behaviour and act as a gateway to understanding more complex brains and their evolutionary development. Indeed, variation in the volume of brain regions (examined using histological techniques) has been reported to be linked to differences in innate responses to stimuli, age/experience related behavioural transitions behavioural syndromes and rates of learning and performance in cognitive tasks. Yet, there remains much to discover about how insect brain structure relates to individual behaviour. Closing such a fundamental knowledge gap requires the development of new imaging protocols and the application of novel strategies to measure, record and robustly quantify aspects of brain morphology across multiple individuals.
It was brought to my attention that education secretary nominee Betsy DeVos is an investor in a company that promises “brain enhancement. ” While visiting the site I found this amazing claim:
The cavemen had it right all along! Because bone broth is full of collagen (and 30% of our bodies’ protein consists of this), it acts as a “gut healer.” According to research by clinical nutritionist Dr. Josh Axe, gut health and brain health are highly connected to each other. And, gut-healing is said to help lower anxiety and other mood-related disorders.
I am almost speechless. Where to begin? I guess we could start by asking who the heck is Josh Axe? He is
a certified doctor of natural medicine, doctor of chiropractic and clinical nutritionist with a passion to help people get healthy by using food as medicine.
I have no special prejudice against chiropractors, but the DeVos affiliated website claims that he has conducted research. If he has, why aren’t links provided?
It is true that there is collagen in the brain, but it doesn’t follow from that that consuming collagen helps brain performance. Moreover there is evidence that bone broth may have high levels of the neurotoxin lead.
Don’t get your hopes up yet. The findings reflect animal research, but here is the story from ScienceDaily:
Using LED lights flickering at a specific frequency, researchers have shown that they can significantly reduce the beta amyloid plaques seen in Alzheimer’s disease in the visual cortex of mice. This treatment appears to work by stimulating brain waves known as gamma oscillations, which the researchers discovered help the brain suppress beta amyloid production and invigorate cells responsible for destroying the plaques.
In developmental psychology, “sensitive period” refers to an age range where the the brain is especially sensitive to specific environmental stimuli. The most famous example of this is the sensitive period for language development during early childhood.
Now, a paper in Psychological Science reports on evidence for a sensitive period during adolescence and early adulthood:
In the current study, we investigated windows for enhanced learning of cognitive skills during adolescence. Six hundred thirty-three participants (11–33 years old) were divided into four age groups, and each participant was randomly allocated to one of three training groups. Each training group completed up to 20 days of online training in numerosity discrimination (i.e., discriminating small from large numbers of objects), relational reasoning (i.e., detecting abstract relationships between groups of items), or face perception (i.e., identifying differences in faces). Training yielded some improvement in performance on the numerosity-discrimination task, but only in older adolescents or adults. In contrast, training in relational reasoning improved performance on that task in all age groups, but training benefits were greater for people in late adolescence and adulthood than for people earlier in adolescence. Training did not increase performance on the face-perception task for any age group. Our findings suggest that for certain cognitive skills, training during late adolescence and adulthood yields greater improvement than training earlier in adolescence, which highlights the relevance of this late developmental stage for education.