Summary: Combining artificial intelligence, mathematical modeling, and brain imaging data, researchers shed light on the neural processes that occur when people use mental abstraction.
By using a combination of mathematical modeling, machine learning and brain imaging technology, researchers have discovered what happens in the brain when people use mental abstractions.
In essence, the brain system that normally tracks economic value becomes very active and ‘talks’ to the system that processes visual information. These value signals, much decried as the basis for marketing strategies, actually serve a crucial aspect of our intelligence. Value is used by the brain to select information and create mental abstractions.
The study, published in the journal eLife, could open the way to new advances in basic research, education and rehabilitation, the treatment of psychiatric disorders, as well as for the development of novel algorithms in artificial intelligence.
The international team tested people’s ability to solve decision problems presented on a computer screen, while inside an MR scanner. When participants responded correctly, they were given a small reward. The problems could be solved according to two strategies: an inefficient one based on all the information presented on the screen, and a better one that required mental abstractions.
By analysing the brain data with machine learning, the researchers found that when people used mental abstractions, this coincided with increased activity in the brain area that signals how valuable things are.
In a second experiment the researchers used a novel neurofeedback technique to artificially change, directly in the brain, the value of some of the items used in the decision problems. After the manipulation, participants were more likely to use mental abstractions in those decision problems.
Dr. Aurelio Cortese, Chief Researcher at the Advanced Telecommunications Research Institute International, Kyoto, that led the team, said:
“This study is quite unique in its kind in that a high level, complex function like abstraction was studied with basic visual stimuli and simple decision problems. Yet, this simplicity led us directly to the underlying mechanism, helping resolve a long-standing question in the neuroscience literature: why do we see value signals in the brain literally all the time? Mental abstractions may be the key – we constantly need to think in abstract terms, since our world would be too complex otherwise.”
Dr. Mitsuo Kawato, Director of the Computational Neuroscience Laboratories at ATR, Kyoto, was a co-author on the study, and explained the state-of-the-art neurofeedback manipulation:
“With machine learning and advanced neuroimaging, we can now detect when, and if, a mental representation appears in the brain below the awareness threshold, in real time. When we do so – we give our participants a small reward. With time, that mental representation becomes paired with reward, or in terms of this experiment, with value. This way, we were able to ‘trick’ the brain into using these newly valuable mental representations to construct abstract thoughts.”
Dr. Benedetto De Martino, Professor at University College London, Institute of Cognitive Neuroscience, was the senior author on the study and a leading expert in neuroeconomics:
“The proposal that value – traditionally associated with its hedonic dimension (for example the value of a chocolate bar) – could be crucial for some aspects of our general intelligence is radical. Value may well be an abstraction in its own right.
“This research is part of our broader effort to understand the algorithmic nature of the human mind – and eventually translate this knowledge into new architectures in artificial intelligence, and lead to new treatments for psychiatric illnesses.”
About this neuroscience research news
Source: UCL Contact: Aurelio Cortese – UCL Image: The image is in the public domain
The human brain excels at constructing and using abstractions, such as rules, or concepts.
Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features.
Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence.
We also found that the brain area computing value signals – the ventromedial prefrontal cortex – prioritised and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation.
Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction. Tagging the neural representation of a task feature with rewards evoked abstraction-based decisions.
Together, these findings provide a novel interpretation of value as a goal-dependent, key factor in forging abstract representations.