Much of our behavior is shaped by feedback from the environment. We repeat behaviors that previously led to rewards and avoid those with negative outcomes. At the same time, we can learn in many situations without such feedback. Our ability to perceive sensory stimuli, for example, improves with training even in the absence of external feedback.
Feedback signals derived from the participants’ confidence reports activated the same brain areas typically engaged for external feedback or reward. Moreover, just as these regions were previously found to signal the difference between actual and expected rewards, so did they signal the difference between actual confidence levels and those expected on the basis of previous confidence levels. This parallel suggests that confidence may take over the role of external feedback in cases where no such feedback is available. Finally, the extent to which an individual exhibited these signals predicted overall learning success.
Broca [1, 2] identified a cortical lesion site in the left inferior frontal gyrus of a stroke patient who could only repetitively speak the syllable ‘Tan-tan’. (…)
Dronkers et al.  could study this historical brain. The images show that the lesion site did not only involve the gray matter of the posterior portion of the inferior frontal cortex, but also the underlying white matter as well as major parts of the basal ganglia and neighboring cortices. It became also clear, that the original concept of Broca’s region cannot elucidate the relationship between a lesion of well-defined cortical areas and specific speech deficits.
The widely used approach to quantify our beliefs and their relation to observations is Bayesian statistics. For example, I can belive that the average height of men in China is 160 cm, most of the men being between 150 and 170 cm. Then I go to China and start measuring men there. I look at my results and see that they are distributed not exactly as in my prior belief. How should I update my belief? A clear non-mathematical introduction on Bayesian statistics explains the idea of the method: https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
At Aeon, Nevin Climenhaga makes some interesting points about probability. After describing different interpretations of probability, one involving the frequency with which an event will occur, another involving its propensity to occur, and a third involving our confidence it will occur, he describes how, given a set of identical facts, each of these interpretations can lead to different numbers for the probability. He also describes how each interpretation has its problems.
He then proposes what he calls the “degree of support” interpretation. This recognizes that probabilities are relative to the information we consider. That is, when we express a probability of X, we are expressing that probability in relation to some set of data. If we take away or add new data, the probability will change.
This largely matches my own intuition of probability, that it is always (or almost always) relative to a certain perspective, to a particular…
Popular science is a well-established way of spreading the scientific culture and simplifying the meaning, results and consequences of scientific works. Often appearing to be a fruitful portal, popular science allows the general public to get an inkling of what is going on in the scientific world. In addition, it is also a good method of “translating” science for non-scientific experts working in the field of the humanities – for example, philosophers. Indeed, writing popular science articles can be seen as equivalent to translating texts between languages of different ethnic groups. However, as with every translation effort, attention has to be paid to the fact that translation is an imperfect process. It gives only a general picture. Moreover, for a successful translation from one language to another, we need not only an expert’s understanding of both languages but also someone with expertise in translation itself.
Likewise, it is possible for non-scientific people or students of the humanities to find through popular science a way to build a vision of scientific knowledge. Unfortunately, on many occasions, I have happened to read stories that have been badly popularized, where the author misunderstood the scientific study and, in the translation, has misled the general public and also non-expert scholars. Moreover, a lack of global vision and of deep knowledge can inevitably lead to erroneous interpretations and the consequences could be disastrous.
To avoid the pseudo-knowledge of non-experts, scientific knowledge should be popularized by experts in the field who have at the same time mastered a good method of explaining it to the general public. Indeed, popular science is a powerful and useful tool for scientists to achieve these important goals (*):
To translate complex scientific works for people far from the scientific field.
To disentangle some misunderstood concepts or ideas used by some pseudoscientific books or magazines.
To explain to the general public that some ideas are not really scientific ones and provide ways and tools in order for them to comprehend the unadulterated reality of science.
Thus, having a large population getting a clear picture of scientific knowledge will empower citizens able to make reasoned decisions on issues related to, for example, science and the medical field and will prevent society from reverting to the dark ages of history.
*Laura Bonetta. 2007. Scientists Enter the Blogosphere. Cell 129, pp. 443-445.
Modern scientific breakthroughs have raised many philosophical questions covering several domains, such as mathematics, genetics, quantum physics, artificial intelligence, psychosurgery and cognitive neuroscience. For example, the complexity of the brain, as revealed by the new techniques of imaging and other technology, demonstrates a level of organization that even scientists are only starting to understand and opens an immensely rich field of thought that philosophers are struggling with. Contemporary philosophers, such as Gaston Bachelard, regretted that a lot of philosophers, like Emile Meyerson, did not take much serious consideration of the diversity of scientific knowledge, a necessary step to conceive any philosophy of science. More specifically, Meyerson did not foresee that changes in scientific paradigms would lead to changes in the conception of epistemology itself (*).
Indeed, the considerable development of science, the complexity and richness of its terminology and its myriad concepts in our modern period make it more and more difficult to those who do not have (at least not yet) a scientific background to grasp what happens in several interconnected fields of science. How could a student of philosophy provide the analysis of scientific knowledge (epistemological analysis) without being able to understand at least one of the scientific areas, without being primarily a scientist? How is it possible in this condition to be able to construct and develop a critical analysis of the scientific method, its inferences and logical forms, if one does not have scientific training? It is certainly almost impossible to approach this goal with just a rudimentary scientific knowledge. Would that mean that modern science is destroying the philosophers’ field of thinking and reasoning? Certainly not. In my opinion, the access to the philosophy of science is always possible if one has a minimum of scientific background and an understanding of the basics of scientific methodology and recent advances. Students of philosophy already have a highly charged curriculum, so a good alternative would be for the scientists themselves to try to approach philosophy in search of a deeper or more general understanding of scientific problems and controversies.
* Frédéric Fruteau de Laclos. 2008. « Le bergsonisme, point aveugle de la critique bachelardienne du continuisme d’Émile Meyerson ». In Bachelard et Bergson (2008), pp. 109-122. Ed. Presses Universitaires de France.