Kevin Mitchell, Trinity College Dublin
The traditional view of causation in neuroscience involves a “driving” metaphor, in which activation of one set of neurons drives downstream neurons, which drive further-downstream neurons, which eventually drive behavior. This view is inherited from seminal studies on sensorimotor reflex circuits and underlies the interpretation of many optogenetic manipulations. This perspective casts the brain as a complicated stimulus-response machine and identifies the neural activity itself as providing the causal “oomph” in the system.
But these kinds of “production causes”—physical events that drive other physical events—are not the only kinds of causes at play. There are also “dependence causes”—the states and propensities of the system that explain why some neural activity has the effects it does. In the nervous system, these crucially include the configuration of synaptic connections between and within populations of neurons. Collectively, these embody what Peter Tse calls criterial causes—the criteria for a neuron to fire (or not) when it receives various patterns of incoming activity.
Focusing our attention on these criterial causes shifts our view from one in which the downstream neuron is being passively driven by its inputs to one in which it is actively interpreting those inputs. Neurons—both individually and as populations—are selectively tuned to patterns in their inputs, patterns that carry some meaning in the system. These meaningful patterns are often multiply realizable—that is, they can differ in low-level details and still be interpreted in the same way by downstream neurons. The idea that the neural “vehicles” do all the real causal work is thus not accurate, as the low-level details are often arbitrary and incidental. The causal effects of any patterns of neural activity depend in crucial ways on their “content”—the meaning that inheres in the way the patterns are interpreted.
We are already seeing this conceptual paradigm emerging from the empirical literature itself, with new computational tools revealing how the rest of the nervous system makes sense of any given local patterns of activity. What we hope to have provided in our paper is a set of philosophical resources that can ground these efforts within a wider conceptual framework. Under this view, we can see the machine at work and even manipulate its workings, while realizing that it is meaning that drives the mechanism.
That meaning is grounded by the history of the organism (and the evolutionary history of the species). A full explanation of why some behavior occurred thus requires a temporally extended, or “diachronic,” view of causation. But the meanings are not static. As Peter Tse has described, the criteria that any neuron operates under are highly dynamic and can be modulated, in real time, by all sorts of contextual parameters. Whenever beliefs about the state of the world are updated or a new goal is adopted or attention is directed toward something, these criteria can be changed. That is how those “mental” activities are causally implemented in the neural system.
For empirical neuroscientists, this kind of context is often intentionally excluded from our tightly controlled lab experiments. But as we move toward more naturalistic experiments in more complex and dynamic environments, along with the technological and computational tools for more whole-brain recordings and analyses, we should, hopefully, see a more holistic neuroscience emerging, in which the behavior of the organism is not seen as being driven by isolated neural events within it, but the organism is seen instead as an agent using its neural machinery to make sense of the world and actively adapt its behavior accordingly.
Aliya Rumana, University of Texas at El Paso
I thank Henry Potter and Kevin Mitchell for their interesting and generative paper. I agree with them (and with Peter Tse) that modern neuroscience is too fixated on the triggering or driving causes of behavior, but I’m afraid (relieved) that they might overestimate the problem.
Take a structure like a neuron and a function like an action potential. In a case like this, what Tse calls “causal criteria” are the conditions under which a neuron will produce an action potential. This is a kind of “structure-function relationship.” By calling a structure-function relationship a kind of cause—i.e., a “criterial cause”—Tse lumps structure-function relationships together with functions under the umbrella of causation.
Potter and Mitchell offer support for this lumping move. They note that experimental manipulations can target either functions (as in optogenetics, in which lasers are used to drive action potentials) or structures (as in lesioning, in which architecture is changed, i.e., degraded). This is a kind of unity from the perspective of experimental manipulation: Both are possible targets, so both should be understood as causes (given an interventionist picture of causation).
But biological interventions and recordings aren’t the primary means by which we identify structure-function relationships. When we want to discover a neuron’s response criteria, we rarely intervene on its structure (we currently lack the fine-grained control required to do this well). Most of the time, we observe its functional responses across a range of tasks and task conditions. In other words, we generally intervene on the task, not the brain.
From a task perspective, structure-function relationships look very different from functions. Tasks exert a variety of constraints on organisms and the structures inside of them, which must satisfy those constraints to make performance on the task possible for the organism. This explanation comes first, in a sense. Only after we understand how task performance is possible for the organism can we understand how the organism does activities, which convert task performance from a mere possibility into a bona fide actuality.
Thus, structure-function relationships are “potentialities” that explain how task performance is possible for an organism. By comparison, functions are “actualities” that explain how a given task performance is actual for the organism. For this reason, I think it can be misleading to lump structure-function relationships with functions under the umbrella of (interventionist) causality. I talk about this more in a recent paper.
Finally, I am also more optimistic about current experimental practice than Potter and Mitchell are. I think they focus too much on neural interventions and recordings, which are our main tool for investigating triggering causes. This might lead them to overestimate how much priority modern neuroscientific practice gives to triggering causes (even if neuroscientific theory gives them this much priority). Instead, I would encourage us to pay more attention to task interventions, which are our primary tool for investigating structure-function relationships. Once we do so, I think we’ll find that neuroscientific practice gives more attention to them (and relatively less attention to triggering causes) than we might have otherwise feared.
Peter Ulric Tse, Dartmouth College
The Newtonian metaphor of causality as impact does not apply to the domain of neurons. Unlike a billiard ball displaced by the impact of another, a neuron is not driven by the impact of an incoming action potential. Mass or momentum is not what is causal about an action potential. Postsynaptic neurons evaluate the simultaneity of their arriving action potentials. This is an example in which the phase of energy (e.g., timing or shape), rather than the amplitude or frequency of energy, is causal.
Under the Newtonian worldview, cause A impacts recipient B, and B responds in accordance with deterministic laws. But when neuron A causally contributes to neuron B’s firing, B evaluates A’s inputs for satisfaction of informational criteria above some threshold, above which B will fire. For example, imagine a woman politician. If Margaret Thatcher came to mind, that may have been entirely random within the constraints imposed by those criteria. But the result was not entirely random in that it had to be a woman politician. If the universe could be “rewound” to the moment of the criterial setting, Angela Merkel might have come to mind instead.
Physical constraints play a causal role in outcomes. For example, the lay of valleys constrains the possible ways that a river might flow. What evolution created was informational constraints realized in physical constraints. In the brain, these are realized in dynamically switching dendritic weights that evaluate incoming action potentials’ simultaneity. This in turn evaluates the degree to which informational criteria have been met. Neurons operate according to the principle [that when neurons fire coincidentally (i.e., jointly in time), it is not by chance alone (i.e., this event carries information)]. In addition, biological causation has inherent play because criteria can be met in multiple ways.
In addition to criterial causation, another key aspect of causation generally absent from nonliving natural systems is error-correcting negative-feedback loops relative to internal reference signals. Unlike a thermostat, which has no desires or preferences, and whose reference signal is imposed externally, our bodies and minds have innate goal-seeking set points sculpted by natural selection. A cybernetic architecture allows agents to fulfill intentions despite mistakes and setbacks. In William T. Powers’ words, “behavior is the control of input, not output.” Bottom-up input is driven by prior top-down intentions and actions. For example, we move our eyes and bodies to find a restaurant because we are hungry. We do not typically see a restaurant and only then feel hunger. Hunger is an error signal that causes us to try to return to a set point of satiation by moving our bodies toward likely food. And typically there are multiple solutions that will be above threshold.
Under both criterial and cybernetic causation, mental and brain events really can turn out otherwise and yet are not utterly random. Prior neuronally realized informational settings parameterize what subsequent neuronally realized informational states will pass preset physical criteria for firing, which serve as informational filters. These allow only physical causal paths possible at the root-most level that are also informational causal paths to become actualized.
Volitional criterial resetting is closely tied to voluntary attentional manipulation in working memory, more commonly thought of as deliberation or imagination. Those interested in further details are encouraged to read my two 2024 books: “A Neurophilosophy of Libertarian Free Will” and “Free Imagination.”
