The brain uses data compression while making decisions.
If you grew up in the 1980s or like playing old video games, you might be familiar with Frogger. The game can be quite difficult. To succeed, you must first make it through a busy traffic flow and then zigzag through moving wooden planks to avoid certain death. How does the brain decide what to pay attention to amid this chaos?
A study published in the scientific journal Nature Neuroscience provides a possible solution: data compression.
“Compressing the representations of the external world is akin to eliminating all irrelevant information and adopting temporary ‘tunnel vision’ of the situation,” said one of the study’s senior authors Christian Machens, head of the Theoretical Neuroscience lab at the Champalimaud Foundation in Portugal.
“The idea that the brain maximizes performance while minimizing cost by using data compression is pervasive in studies of sensory processing. However, it hasn’t really been examined in cognitive functions,” said senior author Joe Paton, Director of the Champalimaud Neuroscience Research Programme. “Using a combination of experimental and computational techniques, we demonstrated that this same principle extends across a much broader range of functions than previously appreciated.”
The researchers employed a timing paradigm in their trials. Mice had to decide whether two tones were separated by a time greater or less than 1.5 seconds in each trial. While the animal was completing the challenge, the researchers simultaneously captured the activity of dopamine neurons in its brain.
“It is well known that dopamine neurons play a key role in learning the value of actions,” Machens explained. “So if the animal wrongly estimated the duration of the interval on a given trial, then the activity of these neurons would produce a ‘prediction error’ that should help improve performance on future trials.”
In order to determine which computational reinforcement learning model best captured both the activity of the neurons and the behavior of the animals, Asma Motiwala, the study’s first author, constructed a number of models. The models varied in how they represented the data that might be relevant for carrying out the task, but they shared certain common principles.
The group found that the data could only be explained by models with a compressed task representation.
“The brain seems to eliminate all irrelevant information. Curiously, it also apparently gets rid of some relevant information, but not enough to take a real hit on how much reward the animal collects overall. It clearly knows how to succeed in this game,” Machens said.
Interestingly, the type of information represented was not only about the variables of the task itself. Instead, it also captured the animal’s own actions.
“Previous research has focused on the features of the environment independently of the individual’s behavior. But we found that only compressed representations that depended on the animal’s actions fully explained the data. Indeed, our study is the first to show that the way representations of the external world are learned, especially taxing ones such as in this task, may interact in unusual ways with how animals choose to act,” Motiwala explained.
According to the authors, this finding has broad implications for Neuroscience as well as for Artificial Intelligence. “While the brain has clearly evolved to process information efficiently, AI algorithms often solve problems by brute force: using lots of data and lots of parameters. Our work provides a set of principles to guide future studies on how internal representations of the world may support intelligent behavior in the context of biology and AI,” Paton concluded.
Reference: “Efficient coding of cognitive variables underlies dopamine response and choice behavior” by Asma Motiwala, Sofia Soares, Bassam V. Atallah, Joseph J. Paton, and Christian K. Machens, 6 June 2022, Nature Neuroscience.
It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461