This post is a detailed reply to Sergio Graziosi's useful critique of our Ecological Representation pre-print. As such, it's specific to his particular concerns about our argument, but I'm putting it here so that others can join in the discussion. If you are reading this and are not Sergio, you might want to head over to his blog and read the critique first.
First, thank you very much for your detailed critique of the paper. It is incredibly useful and we are sincerely grateful for the time you've taken to comment on our crazy ideas.
A quick note to start with. I am largely writing this reply reading a little bit of what you’ve written at a time because I want to respond to each point you make and to keep myself honest in evaluating your later proposed solutions to some of the problems you’ve identified in the paper (so I can’t shift my positions on basic issues!). So, apologies if my responses to these points aren’t relevant because of something you say later in your reply.
(Note: things in quotes are text from Sergio's critique)
“if I were formally peer-reviewing your paper I would recommend to reject unless you are willing to show how organisms manage (or may manage) to extract EI from unspecific stimuli.”
I’ll offer an analogy (and then, hopefully, do a lot better, but this might be a useful idea to keep in mind): This relates to the idea of EI being “hidden” by irrelevant data. Think of a lake. There are a lot of independent water molecules jostling around. There is little structure – high entropy. Now, let’s say that a kid throws a rock into the lake. Suddenly, a subset of those molecules is moving in a specific way – structured by the physical event of a rock crashing into them. Would you consider the resulting ripples as being “hidden” in the same way as EI (structure amidst a bunch or irrelevant stuff)? Ecological information variables are like the ripples caused by the rock – structure in an otherwise symmetrical medium. If the ripple isn’t “hidden” then neither is EI. You may very well think the ripple is hidden, but this would be useful for me to know so that I can home in on your concerns!“EI is indeed present outside, but already considering it a representation is at the very least misleading, as it is effectively hidden by the vast amount of potentially irrelevant data.”
Of course, this is a nonsense example because what is “hidden” is relative to an observer. When I ask whether the ripples are hidden, I mean, can the ripple be objectively physically identified. The answer, of course, is yes. The right kind of measurement devise can indeed detect the ripples. The cog psy question is whether this measurement device could, in principle, detect the ripple without having to separate the wheat from the chaff (without having to keep the relevant stuff and disregard the irrelevant)? I’d say that if such a measurement device can exist, then this is consistent with saying that EI in the world is a representation. If such a measurement device can’t exist, if there must be some kind of stimuli extraction, then EI wouldn’t count as a representation, though neural reps caused by EI would.
A point that I don’t think comes through well enough in the paper is that structures in energy arrays only get labelled as informational representations if they play a role in action selection or control in some organism. That is, they have to function as representations (as stand-ins) for something, in order to be representational (this is a pretty classic move and we’ve adopted this perspective to fit in better with traditional representational accounts). The reason we might want to label this thing in the world as a representation (rather than just the resultant neural activity) is related to the first order isomorphism fallacy. This is particularly clear for very basic nervous systems. Essentially, if the representation exists in the world in the form of EI, then the nervous system can get on with the business of coordinating with that information, without needing to copy it over into a representation in neural activity. If there is a reasonably immediate connection between sensors and effectors, then there is no need to invoke neural representations to explain the preservation of informational structure in behavior. For more complex nervous systems (like ours), it is very likely the case that this structure must be preserved neurally to explain information/behavior correlations. Otherwise, the burden is to explain how the right structure is somehow recovered later on in processing (which, as you probably know, gets us in the territory of all of classical cognitive science's biggest problems).
Okay, so that’s a brief justification for why we would like to keep external EI representations (and a better description of when structure in energy counts as a representation). The meat of your critique (and a quite important one) is how we can justify that this structure somehow directly structures neural activity without some kind of pre-processing, at the very least, to separate the structure from irrelevant variation in stimulation.
“Once transduced, what was before unspecific energy or molecules becomes something which can be directly interpreted as a signal (the action potentials traveling through the axons of sensory neurons). Nothing particularly new in this, but this very general and universally accepted picture is apparently hard to reconcile with the vision you are proposing.”
There is an important ecological critique to make of neuroscientific paradigms investigating energy transduction and its consequences for subsequent neural activity. First, this line of research comes directly from an extensional analysis of what is available to perceived. That is, the hypothesis about what there is “out there” to be perceived is related to an analysis of how sets of individuals with certain properties structure energy media. As Turvey et al (1981) show, this analysis leads to the inevitable conclusion that structures in energy media aren’t specific to biologically or psychologically relevant properties. Second, this line of research is almost entirely concerned with how the nervous system detects primitive features (or perceptual primitives) and then builds a coherent representation out of these. This follows on from the belief (justified from the extensional analysis) that psychological properties must necessarily be constructed. Perceptual primitives are basic attributes of physics (e.g., line orientation, stimulus intensity); they are not ecological properties. Therefore, results based on investigation of these (which the ecological position argues are not behaviorally relevant) don’t tell us anything about how the transduction and propagation of EI. Neuroscientists have not been in the business of identifying ecological information variables and measuring how the structure of these variables is preserved or transformed by perceptual receptors and consequent neural activity. So, rather than the positions being difficult to reconcile, I would argue that we mostly haven’t been doing the right experiments to see what happens when EI makes contact with perceptual receptors and nervous systems. I have, however, found several experiments which suggest (to me, at least) that the picture from neuroscience would be a lot more coherent if we went looking for the consequences of EI rather than correlations between perceptual primitives and neural activity. I’ll mention some of these below.
I think there’s also a good argument to make that our nervous systems are built to be systematically shaped (either in evolutionary or individual time scales) by EI, but not by irrelevant stimulation.
Let’s have another analogy (sorry, I can’t help myself). We have a rock floor in a cave. The cave is humid and water condenses on the ceiling and drops to the ground. If the cave ceiling is fairly uniform, then these drops will be uniformly distributed. Although each drop might displace a molecule or two of the cave floor, over time you will have a uniformly worn surface. Now, let’s have a discontinuity in the cave ceiling. Maybe there is a stalactite which funnels all of the condensation from a relatively large area to a single point so that more water hits the cave floor at that point than at any other (please note, I have no idea if this is how caves work). Over time, this will cause a depression in the cave floor relative to any other location. In a crude sense, this is the difference between ecological information and stimulation in terms of nervous system activity. This analogy, I think, points to how ecological information comes to structure neural activity without the need to actively filter out irrelevant stimulation. There is a related issue concerning why a system currently responds to one set of information variables rather than another and I deal with this a bit later. This section deals only with how ecological information is different from stimulation and how this might affect the nervous system.
So, argument 1 - logically, EI wins over stimulation: Ecological information is the only thing stable enough to cause long term systematic changes to nervous system activity (this is EJ Gibson, education of attention logic). These changes are in the direction of increasing sensitivity to EI, as nervous system structures and connectivity are shaped by ongoing exposure to these discontinuities in energy fields. In complex nervous systems like ours, the shaping of neural activity by EI takes place largely during the lifespan of an individual. The point is, no active filter or extraction process needs to be assumed. Let’s let everything in. Over time, only stable spatiotemporal structures like EI will cause systematic changes to nervous system activity.
For behavior to exhibit any stability, it must be linked to stable (and context-appropriate) nervous system activity. If you like, the filter is the shape of the trained (or evolved) nervous system. It’s the cave floor worn unevenly by a discontinuity (i.e., structure) in the location of water dripping from the ceiling. I think this an important point because it does the work you want (namely, selectivity to EI over random stimulation) but achieves it without requiring the type of selection you mention.
Argument 2 - the structure of EI is apparent at the level of perceptual receptors: The structure of retinal flow on the retina preserves the structure of optic flow in the world (Li & Warren 2002). This shows a relationship between the structure of EI and the structure of activity at the site of transduction. Behavior is organized with respect to structure in optic flow. Nervous system activity must somehow carry information about this structure. As argued above, EI variables are the only things available to cause systematic changes to nervous system connectivity, making nervous system activity increasingly efficient at preserving the structure of the relevant EI (see van der Weel and van der Meer 2009).
“To be considered as such, one needs to take as a given (gloss over?) the context and internal state of the perceiving organism: depending on contingent factors, including the task at hand, what counts as EI changes all the time, so I think we’d be better off by accepting that EI is such in virtue of internal factors as defined by the organism itself and, crucially, its own ecological needs.”
“However, in the world out there there is a hell of a lot more structures and dynamics, all of them co-existing in a seemingly chaotic mixture. A priori, all of them may have important ecological implications for a perceiving agent. Importantly, in your own example, what makes the “relative direction” criteria relevant to the subject is determined by something inside the subject (in this case, what the subject is trying to do).”
These points move into the territory where we need to explain why a system is currently coordinating with one set of information variables rather than another. One thing I find useful to think about is that changes in internal state are changes in physical state. The brain at time A is, in many important ways, physically different than the brain at time B. There is nothing mysterious about the fact that EI making contact with brain time A has a different consequence to that of information making contact with brain time B. There is also no mystery about how the system might change so as to begin responding to different variables as the context (or evolution of internal physical state) changes. Even a very simple mechanism of neural habituation provides an example of how such a change might occur.
This, crucially, has no bearing on what counts as EI, which is objectively definable apart from what a given organism is doing at a different time. What changes are the particular variables that are having consequences on action selection or control and this is explained by the current context (what is available) interacting with the current state (the physical state of the system including the entire body, not just brain, and embodying constraints from evolution, learning, and current physiological functioning - are you exhausted, hungry, etc?). So, it is fair to say that a currently present EI variable that is not having consequences on behavior does not represent anything to that system at that time. But, this doesn’t seem to be any different than the fact that a particular mental representation might only be active in some circumstances (depending on current internal state and context) so I don’t think this introduces a particular problem. Nor is it different than the fact that a representation in a formal computational system might not describe the relationship between the physical and abstract level of the computation currently underway (to invoke a more formal sense of representation, a la Horsman et al 2014).
I do think we agree on the idea that something about the notion of EI is organism-dependent. But, I think this is accomplished by the fact that EI variables can specify dispositional properties of the environment, which are inherently defined in terms of effectivities. For example, there is a complementarity necessitated by the dispositional property “throwable.” This property can be effected only by organisms who themselves have certain properties. Nonetheless, the property can be defined without explicit reference to the organism (it is a property, not a relation). The perception of such properties (via specifying EI) IS relational – that is, the organism can organize behavior according to whether that property specifies “throwable by me” – and this successful coordination arises through either evolutionary or learning level constraints.
“Yes, in a sense the EI is out there, it is external, but what makes it “ecological” or, if you prefer, what makes it possible to extract the signal, differentiate it from the irrelevant (not ecological, not relevant for the organism for the current task) is exclusively internal.”
Hopefully, I’ve gone someway to justifying why we want to call the external thing a representation (first order isomorphism fallacy) and limiting the scope in a way I think you’d like (only calling it a representation if it’s used by some system as a stand-in for a property of the environment). There’s another point worth making that is related to this particular way you’ve worded the issue.
Ecological behavioral models are, indeed, very good at predicting and explaining behavior, in large part because they identify the actual structures in energy arrays that are relevant to particular behaviors. But (and I talked about this recently in Poland), these models only work because WE are particular types of physical systems that respond in particular ways to these structures. You can’t just throw EI at any system and get functional behavior (even if it had the right kind of body). What’s required is the right kind of perceptual systems, nervous system, and other bodily systems so that kinematic energy patterns cause reliable changes to nervous system activity that has reliable consequences for other bodily systems (particularly the musculotendon system) such that behavior can be coordinated with respect to the kinematic structures.
I 100% agree that this part of the story is overlooked by basically the entire ecological literature. Our paper is meant to go a small way to begin addressing this by positing that ecological information could structure nervous system activity and that this relationship would be a starting point for talking about how it is that we are the right kind of physical system to respond to EI. I would go further than this though; we (all animals) have evolved into the kind of physical systems we are only because ecological information is (and was) available to act as a stand-in for relevant properties of the world. My bet is that it was the ability of this external structure to function as a stand-in, to designate distal properties, that got the whole animal show running. I encourage you to have a read through some of Fred Keijzer’s stuff on the evolution of nervous systems – it challenges the neuron doctrine premise (that nervous systems are best understood as information processors) and argues that nervous systems are primarily coordination devices. In my view this doesn’t mean that nervous systems don’t also process information, but that’s another issue!
“the information needed is by definition out there, but it actually becomes proper Ecological Information because of how it is internally processed.”
A couple of points. First, by our application of Newell’s definition, only a subset of neural activity is representational. EI could and does (at least in some types of nervous systems) structure behavior without the resulting neural activity preserving the EI structure. Any case of associative learning is a good candidate for this type of example because in these cases the structure of behavior does not correlate with the structure of the information. So, it would be a mistake to think that the only real EI is a neural rep of EI, because this would omit lots of cases where behavior is structured with respect to the information but there is no internal representation.
Second, whatever the result of the “internal processing” the resulting activity is only useful in structuring behavior (in an action control task, e.g.) to the extent that it preserves or systematically transforms the structure of the EI variable. I could see a motivation for you to claim that the information out there only becomes proper information AFTER it is internally processed (though I disagree). But, even if I thought that some kind of active filter process was required to separate out EI from irrelevant stimulation, our hypothesized neural representations owe everything useful about them to the extent to which they preserve behaviorally relevant aspects of the external structure. I guess my problem with the quote above is that it seems to imbue internal processing with powers to make something ambiguous informative and this is precisely what we argue is not the case. Now, it is obviously not the case that there is a direct line through the nervous system that perfectly carries an EI signal. EI makes contact with a number of physical and chemical systems and the structure is changed as a result. The point, though, is that these changes are not in service of enriching ambiguous information, which is the classical cognitive perspective. Furthermore, I think we’d be in a much better position to understand the function of these changes if we adopt an ecological neuroscientific position where we try to understand how the nervous system supports action selection and control in the presence of particular EI variables in a particular task context.
Okay, these are my comments up until the main event in your commentary! I’m looking forward to seeing what’s next…
“The aim is to isolate EI and to discard the rest.”
Depending on how you talk about this process, I may or may not agree. I aimed to show earlier that getting a system to respond selectively to EI doesn’t require an active filter. If you are planning to argue that the effect of repeated exposure to structured EI amidst random stimulation is to shape the nervous system to progressively respond more selectively to EI, then I’m on board.
“The problem is that what counts as EI is both context-dependent and internally defined (depends on the state of the organism).”
This is, I hope to have shown earlier, not true. The physical system in contact with EI (us) is in real and important ways, different at different times and across different contexts. What is available as EI is, at any time, externally and objectively definable, but the consequences of those variables on action will depend on the current state of the system. Again, I see no difference between this and any other representational theory, in that 1) only a subset of representations will structure behavior at any given time and 2) the consequence of a given representation will depend on the rest of the internal state of the system.
“Such a system needs to be dynamically able to identify the correct kinematic projections from the original (outside world) dynamics. At any given time, the set of possible kinematic projections is effectively infinite. How can a system optimally isolate the correct ones when it can’t make many assumptions on what will make them “correct”? [If you wish, I’m merely restating the framing problem.]”
This is where talking more about Bingham’s task specific devices comes in handy. But, it also relates to the point I made above. We are literally different types of physical systems at different times and in different contexts. Some of these systems are built to be responsive to particular EI variables, other systems to others. Mechanisms within these systems change over time according to their own dynamics which changes sensitivity to external information. The environment changes to offer different opportunities to the system such that, if the system is in a state that is potentially responsive to those new variables, it is shaped by this new information. I think the error is in thinking that we need to explain how an identical system somehow responds flexibly and adaptively to a subset of information. The point is that we are not identical systems across time. Mechanisms (e.g., low dimensional assemblies of inherent and incidental task dynamics) that structure behavior at one time may completely cease to exist at another time because task specific devices are softly assembled.
“One solution comes from the prediction-based approach: if you can manage to transform input at time A in such a way that it efficaciously predicts input at time A+1, you are guaranteed that you are keeping as much potential EI as possible, while at the same time you are discarding everything else – you are distilling the potential EI while filtering out all the noise.”
Okay, so my first reading of this tells me I need to do some background work before I can answer properly. In doing this I’ve gone to your information post and I was thrilled to see your criticism of Shannon information as a theory of moving things around and not of information, itself. This has always bugged me! I’ve also gone and read some of the stuff you linked to (re genes and information, e.g. – great stuff).
I also like “information is a structure that makes a difference.” The fact that you think this gives me hope that you’ll like my clarification of when a simple structure in an energy arrays gets to be considered EI. Although, my initial thought on this as a definition of information is that it is potentially trivially broad. For instance, the cave example I gave above introduces a structure (non-uniform ceiling) that makes a difference (to the contour of the floor).
**Cue time spent thinking about your proposed solution…**
Based on the first part of your review, it seems like your main issue with our current explication is that we don’t satisfactorily account for how the system comes to respond to EI when EI is present in a sea of irrelevant stuff. When I read that critique I took it as a challenge to justify how this problem might be solved based on what we know about how nervous systems change over time. In other words, I took it to be a problem that requires a solution grounded in biology.
Perhaps you agree that this would be ideal as well. This seems to be the case as you say you would "recommend to reject unless you are willing to show how organisms manage (or may manage) to extract EI from unspecific stimuli.”In any case, this expectation caused me to scratch my head a bit at your proposed solution since it’s a very abstract description of how such selection might occur. If I understand you correctly, your solution would work because only prediction based on stable structures (EI) would lead to accurate predictions – predictions based on irrelevant stimulation would be inaccurate. I don’t think that this solution succeeds in closing the gap you identify in our original argument. Despite many people using the language of prediction to describe what brains do, and even if we decide to allow that at least some of this language is justified, I don’t follow how we can apply the idea to how real brains use prediction to home in on EI (I’ve explained earlier how I believe they do it, which is more like, EI is the only stable stuff to be perceived so it's the only thing that can cause stable changes to nervous systems).
First, how is the transformation relation established? If it is learned, what is the basis for learning? What drives exploration of the solution space to arrive at the optimal transformation relation? Second, what is the mechanism for evaluating prediction error? This is an important question even if you intend evaluation to be an emergent feature of the system (rather than something requiring an executive). We still need to know how this particular physical system differentially responds on the basis of correct or incorrect prediction. As currently described, I don't think this proposed solution meets your requirement of showing "how organisms manage (or may manage) to extract EI from unspecific stimuli." This is because the solution is not linked to the operation of a particular physical system - sure a system that could implement would solve the problem, but is the human brain this type of system?
I, personally, don’t get a lot of mileage out of using the word “prediction” to talk about what brains do. But, there is certainly something to the idea that an organism operating as a particular kind of task specific device has internal neural dynamics that can unfold for some time even when contact is lost with task-relevant information. This is the kind of thing that supports our ability to keep track of where a moving object is if it is temporarily occluded. If someone wants to call that “prediction” okay, but I think it obscures the real explanation. That said, I think having a formal way to characterize why stable energy structures “make a difference” to our nervous systems, is a useful and important goal. There is way more work to do on this problem than the very cursory sketch I've provided. If you have more to say about how this links with Shannon information, that would be very interesting as well!
Even though I don’t think the solution (if I’ve understood it correctly) exactly solves the problem, thank you very much once again for your thorough critique; it’s been very useful. The paper will be much improved by taking on board this criticism and trying to make an explicit case for how biological systems learn to (or evolve to) respond to EI, but not to unspecific stimuli. I hope we can keep going back and forth on these ideas!
