Neuroscience is replete with cases that illustrate the fundamental epistemological difficulty of deriving processes from processors. For example, in the case of the roundworm (Caenorhabditis elegans), we know the genome, the cell types, and the connectome—every cell and its connections (). Despite this wealth of knowledge, our understanding of how all this structure maps onto the worm’s behavior remains frustratingly incomplete. Thus, it is readily apparent that it is very hard to infer the mapping between the behavior of a system and its lower-level properties by only looking at the lower-level properties (). When we ask, “How does the brain generate behavior,” we are primarily asking about how putative processing modules are organized so that they combine to generate behavior in a particular task environment. Relying solely on the collection of neural data, with behavior incorporated as an after-thought (and typically over-constrained; Box 1 Figure 1 C), will not lead to meaningful answers. This is a question best answered through precise hypotheses articulated in an a priori conceptual framework, careful task design, and the collection of behavioral data.

Understanding behavior and its component processes at the level of detail necessary to generate meaningful neural level insights will require an emphasis on natural behaviors performed by individuals. Although there are new technologies enabling the blind acquisition of massive behavioral datasets and the application of machine learning algorithms (), they will not lead to the detailed functional analyses of ethological behavior, its developmental trajectory, and its evolutionary basis that are necessary for appropriately constrained implementation-level theories.

Ultimately, the most effective approach may be to simulate the entire natural task environment in order to elicit the full range of adaptive behavioral possibilities. Virtual reality (VR) systems developed for a host of model systems, including rodents, flies, and fish, offer such an approach (). VR systems were originally an answer to the problem of how to mediate the uneasy marriage of the tightly controlled but non-ethological world of laboratory physiology, and the poorly controlled but ethologically relevant world of the behaviors studied by ethologists (). It is critically important to realize, however, that effective use of VR requires a fine-grained quantitative understanding of the behavior under study as it occurs in the unimpeded animal. Only then can the investigator assess, for example, whether the VR system is in fact able to trick the animal into believing it is in the world as it would normally operate within. For example, a VR system has been developed by which we can reliably elicit prey capture behavior in larval zebrafish (which hunt Paramecia) (). Careful behavioral work on the free-swimming animal showed that prey detection is marked by the fish’s eyes verging together to point to the prey (). This eye movement is not seen during any other behaviors. With that knowledge in hand, we can assess the success of a VR system by how often we can see the eyes move in this manner when we display artificial Paramecia on a small screen in front of the animal, which is otherwise fixed in place by being embedded in a block of agar.

In light of the preceding, placing a behaving animal in a situation where it perceives sensory events that are behaviorally relevant, and can act on them in approximately the same way as if they were embedded in the world, can be enormously useful ( Figure 1 B). For example, engaging a subject with the real or simulated presence of another can capture behavioral principles that are common across species. Doing so with marmoset monkeys revealed vocal turn-taking behavior with similar patterns of phase-locking and entrainment as in human communication (). Eschewing the purely big data approach where behavioral data are acquired blindly from large numbers of animals through automation and without regard for the individual (), this organismal-level study led to insights into both developmental () and evolutionary () processes, and, subsequently, to computational principles shared across species (). Similar ethological approaches in other species have led to a number of behaviorally driven investigations of neural level mechanisms: fish (), frogs (), and birds ().

Unfortunately, animals do not raise a checkered flag to indicate when they are about to perform a behavior, or signal when it ends. They are constantly in motion and responding to whatever is around them, which invites the following easy mistake: since an animal is responding to stimuli, and physiological correlates are measurable, one is therefore studying an animal’s behavior (see the “overly constrained behavior” of Figure 1 C). If, following the lead of 20century ethologists, we treat behavior as no less an evolved entity than is, say, the shape of the humerus (), then correctly labeling something as behavior is contingent on the outcome of an investigation into what the animal does to ensure its survival in its native habitat. In this way it would be discovered, for example, that bats navigate through dense forests in total darkness while hunting insects and that rodents beat a hasty retreat when they see a hawk diving towards them, while not responding to similarly sized birds flying straight over. It is therefore a significant confusion to label a coordinated response to a stimulus a “behavior” without first determining the relevance of the response to the animal’s natural life (Tinbergen’s second question, Figure 4 ). Not doing so is to conflate a “stimulus response” with a spatio-temporal pattern that is the product of selection over time. While any perturbation applied to an animal can lead to productive lines of inquiry, whether or not it is founded in anything ethological, the history of many of the most productive moments in neuroscience is a history of having ingeniously abstracted an animal’s Umwelt () in such a way as to admit of controlled, repeatable experiments.

Tinbergen defined behavior as “The total movements made by the intact animal” (). Authors of a recent survey designed to investigate how working scientists define behavior came up with the following attempt at an updated definition, “Behavior is the internally coordinated responses (actions or inactions) of whole living organisms (individuals or groups) to internal and/or external stimuli, excluding responses more easily understood as developmental changes” (). The core of this definition is that behavior is the “internally coordinated responses … to internal and/or external stimuli.” Clearly, however, some stimuli are more important than others in furthering our understanding of animals and their nervous systems.

An analogy from computer science that has both historical and conceptual appeal is the distinction between software and hardware; whereby the software represents “what” the brain (or one of its modules) is doing, and the hardware represents “how” it is doing it. Sternberg has stated it as the “distinction between processors and the processes that they implement” (, p. 158). The core question we address here is whether the processes governing behavior are best inferred from examination of the processors. In a nice irony, the computer science analogy has come full circle with a provocative study that applied numerous neuroscience techniques to a single microprocessor (analogous to a brain) in an attempt to understand how it controls three classic videogames (analogous to behaviors) (). Crucial to the experiment was that the answer was known a priori: the processor’s operations can be drawn as an algorithmic flow chart. The sobering result was that performing interventionist neuroscience on the processor could not explain how the processor worked. We have more to say about this study later.

Advances in technology have allowed the study of neurons, including their component parts and molecular machinery, to an unprecedented degree. This work promises to yield much new information about brain structure and physiology independent of behavior—for example, the biophysics of receptors or details of spatial summation in dendrites. In addition, new methods such as optogenetics allow some causal relationships between brain and behavior to be determined. Here we will argue, however, that detailed examination of brain parts or their selective perturbation is not sufficient to understand how the brain generates behavior ( Figure 1 ). One reason is that we have no prior knowledge of what the relevant level of brain organization is for any given behavior ( Figure 1 A). When this concern is coupled with the brain’s deep degeneracy, it becomes apparent that the causal manipulation approach is not sufficient for gaining a full understanding of the brain’s role in behavior (). The same behavior may result from alternative circuit configurations (), from different circuits altogether or the same circuit may generate different behaviors () ( Figures 1 D and 1E). This concern has been voiced before in a variety of ways (), but we think that it is useful to revisit and reframe the arguments at a time of understandable excitement about ever more effective interventionist approaches in neuroscience.

(E) The same neural activity pattern (small blue circle) can be used in two different behaviors (two magenta circles). The circle with dashed perimeter in (B)–(E) is the subset of all possible neural activity patterns that map onto natural behaviors (from A).

(C) In order to study animal behavior in the lab, the task studied (small white circle) might be so non-ecological it elicits neural responses (small blue circle) that are never used in natural behaviors.

The first step for developing conceptual frameworks that meaningfully relate neural circuits to behavioral predictions is to design hypothesis-based behavioral experiments. Despite this pure behavioral step being of critical importance and highly informative in its own right, it has increasingly been marginalized or at best postponed (). It is disturbingly common for studies to include behavior as simply a hasty add-on in papers that are otherwise crammed full of multiple techniques, types of results, and even species. It is as if every paper needs to be a methodological decathlon in order to be considered important. Behavior must be seen as something that can stand alone as a foundational phenomenon in its own right ( Box 1 ).

In an interesting historical parallel to the argument we make here, the historian of science Lily Kay described how the discipline of molecular biology also arose from placing a premium on technology and its application to simple model systems (). She quotes with concern Monod’s line, “What is true for the bacterium is true for the elephant” (, p. 5). Here we caution similarly against the idea that what is true for the circuit is true for the behavior. Monod’s line has echoed through to the present day with the argument that molecular biology and its techniques should serve as the model for understanding in neuroscience (). We disagree with this totalizing reductionist view but take it as evidence that excessive faith in molecular and cellular biology may be partially to blame for the current dominance of interventionist explanations in neuroscience. We fully acknowledge the crucial role that technology plays in advancing biological knowledge and the value of interventionist approaches, but this tool-driven trend is not sufficient for understanding the brain-behavior relationship. Neural data obtained from new methods cannot substitute for developing new conceptual frameworks that provide the mapping between such neural data and behavior in an algorithmic sense (and not just a correlative or even causal way). Accomplishing this task requires hypotheses and theories based on careful dissection of behavior into its component parts or subroutines (). The behavioral work needs to be as fine-grained as work at the neural level. Otherwise one is imperiled by a granularity mismatch between levels that prevents substantive alignment between different levels of description ().

New technologies have enabled the acquisition of massive and intricate datasets, and the means to analyze them have become concomitantly more complex. This in turn has led to a need for experts in computation and data analysis, with a reduced emphasis on organismal-level thinkers who develop detailed functional analyses of behavior, its developmental trajectory, and its evolutionary basis. Deep and thorny questions like “what would even count as an explanation in this context,” “what is a mechanism for the behavior we are trying to understand,” and “what does it mean to understand the brain” get sidelined. The emphasis in neuroscience has transitioned from these larger scope questions to the development of technologies, model systems, and the approaches needed to analyze the deluge of data they produce. Technique-driven neuroscience could be considered an example of what is known as the substitution bias: “[…] when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution” (, p. 12).

Why We Still Need Behavior

Marr, 1982/2010 Marr D. Vision: A Computational Approach. Bickle, 2015 Bickle J. Marr and reductionism. Figure 2 Marr’s Three Levels of Analysis Show full caption (A) A bird attempts to fly (goal) by flapping its wings (algorithmic realization) whose aerodynamics depend on the features of its feathers (physical implementation). Feathers “have something to do” with flight and flapping, but what level of understanding do we achieve if we dissect the properties of the feathers alone? Bats fly but don’t have feathers, and birds can fly without flapping. (B) The relationship between the three levels is not arbitrary; step 1 comes before step 2: the algorithmic level of understanding is essential to interpret its mechanistic implementation. Step 2: implementation level work feeds back to inform the algorithmic level. (C) An epistemological bias toward manipulation-based view of understanding induced by technology (black filled arrow). Marr took a strong position on the inadequacy of a strictly neurophysiological approach to understanding: “…trying to understand perception by understanding neurons is like trying to understand a bird’s flight by studying only feathers. It just cannot be done” () ( Figure 2 ). Marr’s main intuition was that it is much more difficult to infer from the neural hardware (or implementation; level 3) what algorithm (level 2) the nervous system is employing as compared to getting to it via an analysis of the computational problem (level 1) it is trying to solve. Marr’s main objection to trying to understand the brain by recording from neurons was that this only leads to descriptions rather than explanations. A description of neural activity and connections is not synonymous with knowing what they are doing to cause behavior. Even strong believers in the work done at the level of neurons and molecules (the implementation level) concede Marr’s point (). An analogy that helps get this point across is understanding of the game of chess. Understanding the game does not depend on knowing anything about the material out of which the board or chess pieces are made. Indeed, Marr suggested that the details of the nervous system may not even matter. While it is true that the physical properties of the chess pieces can impinge on application of the rules—for example, if one inadvertently gives a child a chess set for which all the pieces are too heavy for her to pick up. The “therapeutic” solution is lighter chess pieces, but this in no way has changed the analysis or understanding of chess. This chess analogy serves to make an important point: well-designed behavioral experiments in the absence of work at the neural level can be highly informative on their own. Behavioral experiments often are a necessary first step before a subsequent mutually beneficial knowledge loop is set up between implementation and behavioral level work.

di Pellegrino et al., 1992 di Pellegrino G.

Fadiga L.

Fogassi L.

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Rizzolatti G. Understanding motor events: a neurophysiological study. Smit and Hacker, 2014 Smit H.

Hacker P.M. Seven misconceptions about the mereological fallacy: A compilation for the perplexed. Hickok, 2009 Hickok G. Eight problems for the mirror neuron theory of action understanding in monkeys and humans. A more concrete example of the problems that arise if neural data are used to infer a psychological process comes from the debates regarding the behavioral relevance of “mirror neurons.” Mirror neurons, first discovered in the premotor cortex of monkeys, fire whether the monkey itself performs a particular motor goal or observes another individual doing so (). A huge number of variants of these experiments have been done in both humans and monkeys, but they all have the same general approach: show a common neuronal firing (or fMRI or EEG/MEG activation pattern) when a goal is achieved either in the first person or observed in the third person. Interpretation then has the following logic: as neurons can be decoded for intention in the first person, and these same neurons decoded for the same intention in the third person, then activation of the mirror neurons can be interpreted as meaning that the primate has understood the intention of the primate it is watching. The problem with this attempt to posit an algorithm for “understanding” based on neuronal responses is that no independent behavioral experiment is done to show evidence that any kind of understanding is actually occurring, understanding that could then be correlated with the mirror neurons. This is a key error in our view: behavior is used to drive neuronal activity but no either/or behavioral hypothesis is being tested per se. Thus, an interpretation is being mistaken for a result; namely, that the mirror neurons understand the other individual. Additional behavioral evidence that the participant understands the other individual is lacking. This tendency to ascribe psychological properties to single neuron activity that can only be sensibly ascribed to a whole behaving organism is known as the mereological fallacy—a fallacy that we neuroscientists continue to fall for even though we’ve known about it since Aristotle’s De Anima (). Thus, what is needed is a better a priori testable framework for behavioral-level understanding that can lead to more thoughtfully designed neurophysiological experiments. Indeed, to the degree that action understanding has been examined in patients, the evidence does not support a role for the putative mirror neuron mechanism ().

Yuste, 2015 Yuste R. From the neuron doctrine to neural networks. Yuste, 2015 Yuste R. From the neuron doctrine to neural networks. A recent review exemplifies the neuroscience zeitgeist by stating that the time has come to go from considering individual neurons as the functional units of the nervous system to ensembles of neurons (circuits, networks) (). The main argument is that ensembles generate states that would never be appreciated by recording one neuron at a time. The claim is then made that, with the application of new technologies (e.g., two-photon imaging, multielectrode recordings, etc.), identification of these neural states will help us better understand the link between the brain and behavior (although again behavior is at best given backseat status) (). No overt new theory, however, is offered that will bridge ensemble activity and behavior. It is therefore unclear how fundamentally different it really is, conceptually, to move from “neuron” to “neurons.” Modeling and studying the responses of the neural substrate on any scale—large or small—will not by itself lead to insights about how behavior is generated. One reason for this is that the properties of neural tissue may be more diverse than the subset actually exploited for natural behaviors ( Figures 1 A and 1D).

Selverston, 1980 Selverston A.I. Are central pattern generators understandable?. Dennett, 1989 Dennett D.C. The Intentional Stance. Selverston, 1980 Selverston A.I. Are central pattern generators understandable?. Without well-characterized behavior and theories that can act as a constraint on circuit-level inferences, brains and behavior will be like two ships passing in the night. The field has been here before. Concerns with the complete-circuit-description approach were already recognized almost 40 years ago with the publication—along with extensive accompanying commentary—of an article titled “Are Central Pattern Generators Understandable?” (). A plea, similar to those we hear today, was made for ever more detailed characterization of each element in the circuit and specification of the synaptic connectivity between these elements. Many of the commentaries pointed out, however, that increasingly complete descriptions at one level do not serve as a bridge to the next level. For example, Sten Grillner wrote that a central pattern generator may be understood better using the intentional stance, borrowing from the philosopher Daniel Dennett (), which is the view that when an entity is designed for a purpose it is therefore subject to rational rules that can be determined by studying its behavior without necessarily having to analyze all its physical parts (comment by Grillner in).

Reynolds, 1987 Reynolds C.W. Flocks, herds and schools: A distributed behavioral model. The phenomenon at issue here, when making a case for recording from populations of neurons or characterizing whole networks, is emergence—neurons in their aggregate organization cause effects that are not apparent in any single neuron. Following this logic, however, leads to the conclusion that behavior itself is emergent from aggregated neural circuits and therefore should also be studied in its own right. An example of an emergent behavior that can only be understood at the algorithmic level, which in turn can only be determined by studying the emergent behavior itself, is flocking in birds. First one has to observe the behavior and then one can begin to test simple rules that will lead to reproduction of the behavior, in this case best done through simulation. The rules are simple—for example, one of them is “steer to average heading of neighbors” (). Clearly, observing or dissecting an individual bird, or even several birds could never derive such a rule. Substitute flocking with a behavior like reaching, and birds for neurons, and it becomes clear how adopting an overly reductionist approach can hinder understanding.

Bickle, 2015 Bickle J. Marr and reductionism. How has neuroscience dealt with this persistent gap between explanation and description? It has opted to favor interventionist causal versions of explanation. Unfortunately, there are no shortcuts in the trajectory from psychology, cognition, perception, and behavior to neurons and circuits. One might argue that techniques now exist that make it possible to manipulate neural circuits directly, for example, with optogenetics or transcranial magnetic stimulation, so that causal relations—and not just correlations—can be discovered (). The critical point, however, is that causal-mechanistic explanations are qualitatively different from understanding how component modules perform the computations that then combine to produce behavior.

Jonas and Kording, 2017 Jonas E.

Kording K. Could a neuroscientist understand a microprocessor?. Jonas and Kording, 2017 Jonas E.

Kording K. Could a neuroscientist understand a microprocessor?. The distinction between causal claims and understanding via algorithmic or computational processes should be apparent from argument alone. That said, the recent study by Jonas and Kording () we referred to earlier provides an empirical demonstration of the fundamental difference between intervening and recording versus understanding how information flows through processing steps. The study poses the question of whether a neuroscientist could understand a microprocessor. They applied numerous neuroscience techniques to a high-fidelity simulation of a classic video game microprocessor (the “brain”) in an attempt to understand how it controls the initiation of three well-known videogames (which they dubbed as “behaviors”) originally programmed to run on that microprocessor. Crucial to the experiment was the fact that it was performed on an object that is already fully understood: the fundamental fetch-decode-execute structure of a microprocessor can be drawn in a diagram. Understanding the chip using neuroscientific techniques would therefore mean being able to discover this diagram. In the study, (simulated) transistors were lesioned, their tuning determined, local field potentials recorded, and dimensionality reduction performed on activity across all the transistors. The result was that none of these techniques came close to reverse engineering the standard stored-program computer architecture ().

Jonas and Kording, 2017 Jonas E.

Kording K. Could a neuroscientist understand a microprocessor?. A number of noteworthy points emerge from this study that should be further highlighted. The treatment of “behavior” perfectly represents how the neuroscience field typically tends to work with this concept. The behavioral data analyzed consisted of 10 s of spontaneous activity with no player actually playing the game (). This is a fragment of activity, which we refer to as “stimulus-response” ( Box 1 ) to distinguish it from behavior, an adaptive pattern of activity (i.e., one that enhances fitness). As such, this activity is a starting point that is unlikely to result in understanding no matter how advanced the subsequent analysis. But let’s suppose that instead of a fragment, we have a complete activity map for an entire game played by a person. Let’s further suppose that a much better job, using better analysis algorithms, could be done with the data. We suggest this would similarly lead to no meaningful insights into the processor’s functional architecture, since again no behavioral-level hypothesis is being tested; there is no conceptual structure in place. Which of an infinite set of potentially interesting patterns in the data should be selected for further investigation (see Figure 1 C, overly constrained behavior)? The best way to answer this would be to examine the game-play—the behavior—itself. An engineer trying to make a copy of the machine would generate a high-level task analysis of what the microprocessor needs to do in order to play its part of the game. For example, she might study the on-screen positions, shapes, and colors of agents over time, the value of point scores, and the responses to joystick input generated by the player. Then she might ask how is the chip in the machine fulfilling these higher-order needs of game-play. From that starting point, more productive work on the role of specific portions of the chip would be possible.