Thought identification refers to the empirically verified use of technology to, in some sense, read people's minds. Advances in research have made this possible by using human neuroimaging to decode a person's conscious experience based on non-invasive measurements of an individual's brain activity.
Professor of neuropsychology Barbara Sahakian qualifies, "A lot of neuroscientists in the field are very cautious and say we can't talk about reading individuals' minds, and right now that is very true, but we're moving ahead so rapidly, it's not going to be that long before we will be able to tell whether someone's making up a story, or whether someone intended to do a crime with a certain degree of certainty."
Psychologist John Dylan-Haynes experienced breakthroughs in brain imaging research in 2006 by using fMRI. This research included new findings on visual object recognition, tracking dynamic mental processes, lie detecting, and decoding unconscious processing. The combination of these four discoveries revealed such a significant amount of information about an individuals thoughts that Haynes termed it "brain reading."
The fMRI has allowed research to expand by significant amounts because it can track the activity in an individual's brain by measuring the brain's blood flow. It is currently thought to be the best method for measuring brain activity, which is why it has been used in multiple research experiments in order to improve the understanding of how doctors and psychologists can identify thoughts.
The term “thought identification” started being used in 2009 after neuroscientist Marcel Just coined it in a 60 Minutes interview. His reasoning for this term pertains to his overall goal of his research to "to see if they could identify exactly what happens in the brain when people think specific thoughts."
When humans think of an object, such as a screwdriver, many different areas of the brain activate. Marcel Just and his colleague, Tom Mitchell, have used fMRI brain scans to teach a computer to identify the various parts of the brain associated with specific thoughts.
This technology also yielded a discovery: similar thoughts in different human brains are surprisingly similar neurologically. To illustrate this, Just and Mitchell used their computer to predict, based on nothing but fMRI data, which of several images a volunteer was thinking about. The computer was 100% accurate, but so far the machine is only distinguishing between 10 images.
John-Dylan Haynes states that fMRI can also be used to identify recognition in the brain. He provides the example of a criminal being interrogated about whether he recognizes the scene of the crime or murder weapons. Just and Mitchell also claim they are beginning to be able to identify kindness, hypocrisy, and love in the brain. In 2008 IBM applied for a patent on how to extract mental images of human faces from the human brain. It uses a feedback loop based on brain measurements of the fusiform gyrus area in the brain which activates proportionate with degree of facial recognition.
In 2011, a team led by Shinji Nishimoto used only brain recordings to partially reconstruct what volunteers were seeing. The researchers applied a new model, about how moving object information is processed in human brains, while volunteers watched clips from several videos. An algorithm searched through thousands of hours of external YouTube video footage (none of the videos were the same as the ones the volunteers watched) to select the clips that were most similar. The authors have uploaded demos comparing the watched and the computer-estimated videos.
Some researchers in 2008 were able to predict, with 60% accuracy, whether a subject was going to push a button with their left or right hand. This is notable, not just because the accuracy is better than chance, but also because the scientists were able to make these predictions up to 10 seconds before the subject acted - well before the subject felt they had decided. This data is even more striking in light of other research suggesting that the decision to move, and possibly the ability to cancel that movement at the last second, may be the results of unconscious processing.
John Dylan-Haynes has also demonstrated that fMRI can be used to identify whether a volunteer is about to add or subtract two numbers in their head.
Reading thoughts before they are voiced
December 16, 2015, a research conducted by Toshimasa Yamazaki at Kyushu Institute of Technology found using Rock-paper-scissors game a computer is able to determine a choice made by the subjects before they move a hand. An EEG was use to measure activity in the Broca's area to see the words two seconds before the words were uttered.
Brain as input device
Emotiv Systems, an Australian electronics company, has demonstrated a headset that can be trained to recognize a user's thought patterns for different commands. Tan Le demonstrated the headset's ability to manipulate virtual objects on screen, and discussed various future applications for such brain-computer interface devices, from powering wheel chairs to replacing the mouse and keyboard.
Decoding brain activity to reconstruct words
On 31 January 2012 Brian Pasley and colleagues of University of California Berkeley published their paper in PLoS Biology wherein subjects' internal neural processing of auditory information was decoded and reconstructed as sound on computer by gathering and analyzing electrical signals directly from subjects' brains. The research team conducted their studies on the superior temporal gyrus, a region of the brain that is involved in higher order neural processing to make semantic sense from auditory information. The research team used a computer model to analyze various parts of the brain that might be involved in neural firing while processing auditory signals. Using the computational model, scientists were able to identify the brain activity involved in processing auditory information when subjects were presented with recording of individual words. Later, the computer model of auditory information processing was used to reconstruct some of the words back into sound based on the neural processing of the subjects. However the reconstructed sounds were not of good quality and could be recognized only when the audio wave patterns of the reconstructed sound were visually matched with the audio wave patterns of the original sound that was presented to the subjects. However this research marks a direction towards more precise identification of neural activity in cognition.
Recognizing brain waves in security
In 2013 a project led by University of California Berkeley professor John Chuang published findings on the feasibility of brainwave-based computer authentication as a substitute for passwords. Improvements in the use of biometrics for computer authentication has continually improved since the 1980s, but this research team was looking for a method faster and less intrusive than today's retina scans, fingerprinting, and voice recognition. The technology chosen to improve security measures is an electroencephalogram (EEG), or brainwave measurer, to improve passwords into "pass thoughts." Using this method Chuang and his team were able to customize tasks and their authentication thresholds to the point where they were able to reduce error rates under 1%, significantly better than other recent methods. In order to better attract users to this new form of security the team is still researching mental tasks that are enjoyable for the user to perform while having their brainwaves identified. In the future this method could be as cheap, accessible, and straightforward as thought itself.
With brain scanning technology becoming increasingly accurate, experts predict important debates over how and when it should be used. One potential area of application is criminal law. Haynes states that simply refusing to use brain scans on suspects also prevents the wrongly accused from proving their innocence. It has been argued that allowing brain scans in the United States would violate the 5th Amendment's right to not self incriminate. The important question is whether brain imaging is like testimony, or instead like DNA, blood, or semen. Paul Root Wolpe, director of the Center for Ethics at Emory University in Atlanta predicts that this question will be decided by a Supreme Court case.
In other countries outside the United States, thought Identification has already been used in criminal law. In 2008 an Indian woman was convicted of murder after an EEG of her brain allegedly revealed that she was familiar with the circumstances surrounding the poisoning of her ex-fiancé. Some neuroscientists and legal scholars doubt the validity of using thought identification as a whole for anything past research on the nature of deception and the brain.
Experts are unsure of how far thought identification can expand, but Marcel Just believes that in 3–5 years there will be a machine that is able to read complex thoughts such as 'I hate so-and-so'.
Dr. Donald Marks, founder and chief science officer of MMT, is working on playing back thoughts individuals have after they have already been recorded.
Researchers at the University of California Berkeley have already been successful in forming, erasing, and reactivating memories in rats. Dr. Marks says they are working on applying the same techniques to humans. This discovery could be monumental for war veterans who suffer from PTSD.
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