Martha: “It’s not gonna work. I can see myself talking to Fred and he just ignores me. He's an a**hole and doesn’t care about what I have to say to him.”
Martha is a client of mine and her mental strategy is to set herself up for failure. With her mind’s eye she sees himself delivering her presentation to people and especially to Fred, with whom she has a bad relationship, and she always gets what she's set herself up for: Fred does ignore her. In other words, she is following an unconscious strategy that leads to the same negative payback each time.
The field of Neuro-Linguistic Programming (NLP) has long pointed out that the brain creates a so called “MAP” of the reality which is heavily influenced by various filters, such as experience, and that the brain will sometimes inhibit or reinforce certain sensory input we receive from the outside world (Territory). Hence the important supposition that the “Map is Not the Territory”. NLP has also highlighted the relevance of self-talk and mental representations (images with sounds, tastes and smells) we consciously and unconsciously create while setting ourselves up for success or failure like my client John does.
A new landmark study by neuroscientists at the University of Geneva (UNIGE) has uncovered the role of synaptic feedback systems in shaping the brain’s learning processes in the cerebral cortex. The cortex – the brain's outer and largest region – is important for higher cognitive functions such as speech and decision making, complex behaviors, perception, and learning. The cortex is divided into four different lobes, which are each responsible for processing different types of sensory information. Scientists have now figured out how this feedback works as a “gate” or synaptic strengthening by switching particular inhibitory neurons on and off. Upon the arrival of a sensory stimulus, the cortex processes and filters its information before it passes the most relevant aspects on to other brain regions. Some of these brain regions, in turn, send information back to the cortex. These loops, known as "feedback systems," are thought to be essential for the functioning of cortical networks and their adaptation to new sensory information. Essentially, what this means is that brain circuits optimize themselves, the system teaches itself by reading out its own activity.
This study, published in Neuron, is not only an important milestone in the understanding of the mechanisms for perceptual learning, which is the experience-dependent enhancement of our ability to make sense of what we see, hear, feel, taste or smell our way, but may also offer insight into computerized learning systems and artificial intelligence.
Questions like ‘How do brain circuits optimize themselves?’ or ‘How can a system teach itself by reading out its own activity?’ are at the heart of machine learning programs. Indeed, some deep learning specialists try to mimic brain circuits to build artificially intelligent systems. Insights brought to light by the UNIGE team might be relevant for unsupervised learning, a branch of machine learning that occupies itself with circuit models that are able to self-organize and optimize the processing of new information. This is important for the creation of efficient voice or face recognition programs, for example.
So, getting closer to solving one of the greatest challenges of neuroscience – understanding the mechanisms that enable people to learn – can lead to ‘more human’ AI.
I suggested to Martha that she should change her mental strategy and the script she keeps reiterating to herself before it actually happens and create a new one with a positive outcome. After having changed the script in her head and started reflecting on the 'how' ( the process itself), surprisingly (?) her relationship with Fred has significantly improved.
