Feelings from Chemical Gradients
Out of the Sea
The earliest known animals, those comprised of one or only a few cells, lived in the ocean and had receptors on their outside surfaces to detect chemicals in their environments. These receptors could be used to cause the animals to turn toward desirable chemicals and away from undesirable ones.
This was probably the earliest use something like decision-making in animals, despite being purely reflexive at first. This “move into the good and out of the bad” impulse was also probably the earliest use of something like valence, although there would have been no mechanism by which good or bad could be felt.
In slightly more complex organisms, those with simple nervous systems, receptors can send signals to represent whether a chemical gradient in the environment is growing stronger or weaker. Stronger or weaker reception can then be taken to indicate whether the direction of movement is correct. Neurons that detect such changes can adjust their firing rates accordingly. This allows the animal to find the source of something it needs, or avoid danger by sensing whether a poison or threat is becoming fainter, and so further away.
While this description is an over-simplification, the general idea is valid. Animals tend to have “approach/withdraw” reactions that prompt them to move toward what is desirable and away from what is undesirable. For most of evolutionary time, that is probably all that simple animals could use their ability to move to do. The basic mechanism depends only on having receptors to detect changes in the concentrations of desirable and undesirable chemicals, and the ability to follow their gradients by turing toward or away from their sources.
In complex animals, especially those that live on land such as humans, many of these chemical gradients are now inside the body. Indeed, internal gradients are mostly caused by chemicals released by the body’s own organs. These chemicals can then be detected by receptors elsewhere in the body or nervous system. While this is not news, it raises an interesting question. Why is this so. Why would an organ evolve to send chemical signals, only to have those chemicals detected elsewhere in the same body? Why not have the organ simply induce a reflexive action based on what it detects?
Central Control
The answer, it would seem, is that reflexive responses were not sufficient as competition evolved. Over time, as animals became more complex, they did not simply become capable of more complex behaviors. They also needed to combine a wider range of inputs that might independently suggest approach or withdrawal. Sometimes, these inputs would be in conflict, such as when a potential mate and a predator were sensed in the area. Sometimes, the inputs were of enhanced or diminished importance, as when an animal detects a food gradient, but is already digesting a meal, as opposed to its being hungry.
Central decision-making emerged to offer a solution to this problem. It is what allows choices to be made among competing alternatives. Much later, as learning emerged, the results of these choices would support continuing and intensifying behavior in the short term, and reinforcing behavior for the long term.
A central decision system needs inputs. These can come in different forms. Direct signaling is one such, and neurons use this method to transmit electrical states in the form of chemical ions to one another across the synapses between their axon-to-dendrite connections. Adjusting the way that neurons behave with additional chemicals such as neurotransmitters and hormones is another. For reasons that will become clear, brains evolved to use both mechanisms.
Benefits of direct signaling are obvious. They are relatively fast and can have specific meanings. This is why transmitted signals are used in sending sensed data to the brain and for constructing an internal representation of the organism in relation to its environment.
Internal chemical gradients, on the other hand, are slower, disperse more generally, and may not convey a specific meaning. However, using internal chemical gradients also offers several advantages. First, multiple areas can be reached simultaneously. Some of these could act quickly, and others more slowly. Second, following gradients is an old solution, and so systems to detect changes in the concentration of internally produced chemicals could be evolved from proven, existing systems. Finally, chemical gradients have likely always been used to influence behavior because they have dispersal patterns that are geometric or logarithmic, making changes in concentration easy to detect by receptors without the capacity for quantitative measure.
If we fast-forward evolution to the appearance of social animals, we can see how emotions might have emerged from internal gradients. Different chemicals could be released by different organs to indicate different feeling states, with parts of the nervous system having receptors for those chemicals built with the impulse to follow or avoid those feelings by engaging in behaviors that either increased or decreased the release of those chemicals. In short, acting as if they were independent and mobile entities following gradients, despite being statically connected elements in a machine that are seeking higher or lower concentrations indirectly.
To phrase this another way, a chemical receptor in a simple organism that sends a signal that may ultimately result in movement that leads to more of that chemical is not, at the level of the neuron, doing anything materially different than a receptor cell in a complex organism detecting the release of an internal chemical and sending a signal that may ultimately lead to the release of more of that chemical. The same could be said if the receptors were detecting chemicals with a negative valence. All are just signaling detection and adjusting their signals to indicate whether the detection is growing stronger or weaker.
Despite the simplicity of this mechanism, it allows the development of behaviors that let animals be pulled toward bonding, play, mating, and parental behaviors. We can also see how it might generate behaviors consistent with excitement, competition, anger, loss, and fear. This would give the central decision system more to work with than just valence, allowing priorities to be established among competing inputs even as the body’s other systems are shifting blood flow, releasing hormones, and readying responses. Despite this, it would not need to fundamentally alter a basic feature of sensory neuronal operation, that of determining whether it is moving up or down a gradient, and sending a signal that can be used to follow or escape that gradient.
Having evolved to interpret changes in the concentration of gradients may also account for many of the intuitive heuristic approximations animals can make where geometric or logarithmic changes in detected inputs are observed. These may be physical, such as when sound or light volumes are used to estimate distances, but may also help in determining things that are felt emotionally, such as relationships, kinship, opportunities, and risks that would otherwise take rather complicated mathematics to work out. In other words, these gradients may be the roots of intuitive comprehension.
Toward Emotional AI
Animals in nature have neither the time nor the ability for mathematical computations, although they may also develop statistically based heuristics over time for emotional response or environmental signals, but these require experience, and so cannot be the starting point for learning what is punishing or rewarding about the world, including the social environment. However, such heuristics can be trained by a mechanism for comparing the relative strengths of those inputs to experience, providing they can be valenced and compared.
In AI, if we want to imitate this, we must provide an artificial proxy for these motivating gradients. The idea of artificial valence is not new. Punishments and rewards are how many AI systems are trained. In chess playing programs we can see how estimates of board position values might be considered mathematical representations of such valences. Of course, these board position scores are not nuanced, like emotions, but are instead used for reinforcement learning. If we want to a system that can interpret the valence in emotional terms, we will need to go further, providing mechanisms to detect different feeling states and algorithms for combining them.