A Quora conversation led me to recent paper in Neuron that highlights a very important problem with a lot of neuroscience research: there is insufficient attention paid to the careful analysis of behavior. The paper is not quite a call to return to behaviorism, but it is an invitation to consider that the pendulum has swing too far in the opposite direction, towards ‘blind’ searches for neural correlates. The paper is a wonderful big picture critique, so I’d like to just share some excerpts.
“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 (Bargmann, 1998; White et al., 1986). Despite this wealth of knowledge, our understanding of how all this structure maps onto the worm’s behavior remains frustratingly incomplete.”
“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 fromthese 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, weoften answer an easier one instead, usually without noticing the substitution’’ (Kahneman, 2011, p. 12).”
“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.”
Here the authors talk about the importance of emergence as a bridge between neurons and behavior:
“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’’ (Reynolds, 1987). 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.”
Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver, and David Poeppel. “Neuroscience needs behavior: correcting a reductionist Bias.” Neuron 93, no. 3 (2017): 480-490. [Paywalled]
My main criticism of this paper might be that their proposed solution — a separation between analysis of behavior and analysis of neural data, with behavioral analysis ideally happening first — might be too rigid, and also might leave the behavioral analysis somewhat underconstrained. It is definitely important to have a clear behavioral hypothesis if you are running a behavioral study, even if is part of a larger study of neural data. But the actual process of understanding might require a lot more ‘cross-talk’. It may not always be useful to come up with a high-level analysis of behavior in isolation from neural data. There is no guarantee that the high-level analysis will be accurate: there may be multiple high level models that correspond to the same behavioral data. So we might need to add a third circle to their Figure 1: one for the space of behavioral explanations.
