How are beliefs formed?

According to The New York Times, citing the study, some students, while studying suicide notes, discovered that they had a talent for determining who really committed suicide. Out of twenty-five pairs of notes, this group of students correctly identified the real one twenty-four times. The others realized they were hopeless – they identified the real note only ten times.

As is often the case with psychological research, the whole set-up was a sham. While half of the notes were indeed genuine-they came from the Los Angeles County coroner’s office-the assessments were fictitious. Students who were told they were almost always right were, on average, no more astute than those who were told they were mostly wrong.

In the second phase of the study, the deception was exposed. The students were told that the real purpose of the experiment was to gauge their reactions to being told they thought they were right or wrong. (This, as it turned out, was also a hoax.) Finally, the students were asked to estimate how many suicide notes they actually categorized correctly and how many they thought the average student had identified.

At this point, something curious happened: the students in the high-scoring group said that they thought they had actually done quite well – significantly better than the average student – even though, as they had just been told, they had no reason to think so. Conversely – those students in the low-scoring group reported that they had, in their own opinion, done significantly worse than the average student – a conclusion that was equally unwarranted. So what’s the point?

A few years later, a new set of Stanford students were recruited for a similar study. This time they were handed packets of information about a pair of firefighters, Frank K. and George H. Frank had a young daughter and loved to scuba dive. George had a young son and played golf. The packets also included the men’s responses to what the researchers called a “risky-conservative choice test.” In one packet of information, Frank was a successful firefighter who almost always chose the safest option. In another version, Frank also chose the safest option, but was a lousy firefighter who received several warnings from his superiors.

In the middle of the study, the students were told that they had been deliberately misled and that the information they had received was completely fictitious. They were then asked to describe their own beliefs: how do they think a firefighter should view risk? Students who received the first packet thought that a firefighter would try to avoid risk, while students in the second group believed that a firefighter would take risks.

It turns out that even after “the evidence for their beliefs has been completely refuted, people are unable to make appropriate changes to those beliefs,” the researchers wrote. In this case, the failure was “particularly impressive” because two data points would never be enough to generalize the information.

Ultimately, the Stanford study became famous. The claim made by a group of scientists in the seventies that humans could not think straight sounded shocking. Today it is not – thousands of subsequent experiments have confirmed the discovery of American scientists. Today, any graduate student with a tablet can demonstrate that seemingly reasonable people are often completely irrational. Rarely has this insight seemed more relevant than it does today, has it not?

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When asked to recommend a travel itinerary for a city you’ve been to often, selective snippets of memories of places from different trips may come to mind. Researchers from several universities, including Columbia University in New York and Emory University in Atlanta, USA, studied individual neurons – called “memory cells” – of 19 patients who had undergone brain surgery for epilepsy treatment.

The patients performed a task designed to assess spatial memory performance. During the task, the subjects were placed on a road using virtual reality (VR) goggles and asked to press a button when they encountered specific objects. In part of the task, the researchers asked participants to walk along a path and mark the location of a distant object. By examining the medial temporal lobe (MTL) and, in particular, the entorhinal cortex, the scientists found that memory-tracking cells were “spatially tuned” to the location and could then retrieve the location information that the person had to recall.

The scientists’ work shows that neurons in the human brain keep track of events we intentionally recall and can change their activity patterns to distinguish between memories. They’re like dots on a Google map that mark where important events in your life happened. This discovery could serve as a potential mechanism for our ability to selectively utilize different experiences from the past and help experts understand how these memories affect the spatial map of the human brain.

In the past, scientists have already tried to understand how this can be done. They found that the cells of the neural network are very important for the operation of spatial memory, as it works similarly to a GPS system. Spatial tuning of neurons is the idea that individual neurons are “activated to represent a location in the environment during navigation.” Previous work has suggested that spatially tuned cells reassign their triggering circuits in different environments, so events that occur in different locations are associated with different spatial maps,” the researchers explain.

Building on this work, the scientists suggested that individual neurons in the MTL, and especially in the entorhinal cortex, will demonstrate a kind of “spatial tuning of neurons” modeled by past experience. The implication is that the human brain creates the truest map of memories.

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Why brain-computer interfaces are flawed

A year ago, Dutch scientists developed a brain-computer interface (BCI) that can translate a patient’s brain activity into words. The researchers detailed it in the journal Nature Communications. Even earlier, a similar brain-computer interface was created by Chinese developers.

These devices effectively recognize words, but work on the basis of electrodes that are implanted in the human brain. In addition, the interfaces focus on the part of the brain that is responsible for the mouth movements made by a person to pronounce a word. That is, in order for the device to recognize a thought, the person has to try to pronounce it with their mouth.

Why brain-computer interfaces are flawed. A brain-computer interface designed to voice thoughts has a number of flaws that make it difficult to use. Photo.
Brain-computer interface, designed to voice thoughts, has a number of shortcomings, which complicates its use

Other ways of reading thoughts, for example, based on electroencephalogram (measuring brain activity with the help of electrodes fixed on the head), were less effective. They only allowed to decipher individual words, but are not able to construct a coherent text.

How the mind-reading device works

Employees of the University of Texas at Austin have developed an interface based on functional magnetic resonance imaging (fMRI). It allows you to monitor the activity of certain areas of the brain during its functioning, and has a high resolution. Sometimes this technology is used to monitor blood flow in the brain. Earlier we told you that with the help of fMRI, during one of the studies scientists tracked the level of stress in people.

In this case, fMRI monitors the part of the brain that is responsible for a person’s imagined speech. The human brain responds to each word in a specific way. So the scientists’ task was to link each word to a specific pattern of brain activity. To do this, the team scanned the brains of three people for 16 hours while they listened to podcasts.

In this way, the team was able to create a specific set of maps of brain activity triggered by different words, phrases or phrase meanings. The authors then trained the artificial intelligence to determine what a person was thinking based on fMRI data. Unlike developments that existed before, this technology does not recognize individual words, but determines the overall meaning of each phrase or sentence.

To construct text based on the fMRI data and processed by the AI, the researchers connected the language model of the GPT-1 neural network, which was the predecessor of ChatGPT. Gradually, they were able to train the AI to recognize words, phrases and sentences. As reported by the authors of the work in the journal Nature Neuroscience, even if the AI developed by them is wrong in some words, it well conveys the essence of the speech that a person hears or mentally pronounce.

In addition, the system has even learned to voice what a person sees in front of him. For example, in one of the experiments, participants watched a video without sound, in which a dragon knocks a man down. In doing so, the system, based on brain activity, voiced the scene as: “He’s knocking me down.” However, participants were not asked to mentally voice what they saw. The short video below shows how accurately the system recognizes thoughts.

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