Modularity & The Argument From Design

Part Of: Cognitive Modularity sequence
See Also: Fodor: Modularity of Mind
Content Summary: 1600 words, 16min read

Introduction

This post represents an argument for a particular thesis, known as massive modularity. This thesis, particularly popular among evolutionary psychologists, states that the mind is rife with mental modules, and that the cognitive life is the interplay between them.

What is a mental module? If you don’t have a clear grasp on what that means, I recommend just glancing my summary of Fodorian modularity. Bear in mind, though, that here the term is used somewhat differently: modules here may be some subset of the listed properties.

The following argument is not my own, it is rather an interpretation of Carruther’s argument, which is presented in this text, under Section 1.3.

Motivators From Biology

Carruthers starts by surveying the biological literature for instances of modularity. And he finds it, by the truckload:

There is a great deal of evidence from across many levels in biology to the effect that complex functional systems are built up out of assemblies of sub-components. This is true for the operations of genes, of cells, of cellular assemblies, of whole organs, of whole organisms, and of multi-organism units like a bee colony. And by extension, we should expect it to be true of cognition also, provided that it is appropriate to think of cognitive systems as biological ones, which have been subject to natural selection.

Amongst other sources, he cites the following research:

  • West-Eberhard, 2003. Developmental Plasticity and Evolution.
  • Seeley, 1995. The Wisdom of the Hive: the social physiology of honey bee colonies.

We thus possess considerable biological reason to believe that:

(3) Natural selection selects for modularity at a variety of different levels.

A Role For Evolvability

It’s one thing to observe natural selection promoting modularity, it is another to understand why it is doing so. To do this, we must appeal to the concept of evolvability.

Biological populations tend to conform themselves to ecological niches. That is, a species tends to adopt a particular survival strategy that exploits a certain subset of the local biosphere. Let me here decorate a concept I like to call niche distance: two species said to be in direct competition are so in virtue of the fact of short niche distance, etc. Thus, we could say that the niche distance between two types of weeds in your backyard is small, and the niche distance between the weed and the bald eagle is large.

The fact that niches change is one of the drivers for biological evolution. For example, as the earth warms in the coming centuries, mammalian species will need to acclimate to a different climate, which entails a changed vegetative response, which entails a need for change in eating patterns, etc. Such niche fluctuations are ubiquitous.

We know that evolution is driven by the engine of mutation. But mutation is simply a stochastic, quantum mechanical phenomenon:  there is no way to “speed it up”. Species typically cannot keep pace with niche fluctuations by directly modulating the rate of mutation. Rather, the genetic infrastructure of species must be able to harness mutations to keep pace with niche fluctuations. To put this concept of evolvability very crudely: natural selection does not only select for number of muscles, but also the ability to grow new ones.

(1) Evolvability is selected to allow for fluctuations within an ecological niche.

This video is a cute exploration of how evolvability may be supported in microorganisms by direct tampering of the genetic replication engine. But for larger organisms, the loci of behavior is trans-cellular. The sheer geometry of size compelled cells to become heterozygous, to constitute interdependent systems. The question of mutation containment, then, becomes central: is it possible for evolution to improve upon one function of an organism, without simultaneously affecting other functions?

Here, finally, is where modularity comes into play. One of the most important features of modularity is encapsulation: the hiding of information within specific containers. Rather than all functions affecting all other functions, computational processes erect walls around themselves, and communicate through them in a controlled fashion. Modular encapsulation is thus seen as a prerequisite for mutation containment:

(2) Modular subsystems are a necessary ingredient for evolvability.

Taken together, premise (1) and (2) support (3) in the following way:

Massive Modularity- Argument From Design- Evolvability

Motivators From Computer Science

In the above section, we were given a nice intuition regarding Premise 2: that modularity affords for mutation containment. But perhaps this intuition can be buffered with evidence from somewhere else entirely:

The basic reason why biological systems are organized hierarchically in modular fashion is a constraint of evolvability. Evolution needs to be able to add new functions without disrupting those that already exist; and it needs to be able to tinker with the operations of a given functional sub-system – either debugging it, or altering its processing in response to changes in external circumstances – without affecting the functionality of the remainder. Human software engineers have hit upon the same problem, and the same solution.

Two of the most widely used languages nowadays are C++ and Java. Languages in this class are often described as ‘object-oriented’. Many programming languages now require a total processing system to treat some of its parts as ‘objects’ which can be queried and informed, but where the processing that takes place within those objects isn’t accessible elsewhere. This enables the code within the ‘objects’ to be altered without having to make alterations in code elsewhere, with all the attendant risks that this would bring; and it likewise allows new ‘objects’ to be added to the system without necessitating wholesale re-writings of code elsewhere. And the resulting architecture is regarded as well nigh inevitable (irrespective of the programming language used) once a certain threshold in the overall degree of complexity of the system gets passed.

Interestingly, since the need for modular organization increases with increasing complexity, we can predict that the human mind will be the most modular amongst animal minds. This is the reverse of the intuition shared by many philosophers and social scientists, who would be prepared to allow that animal minds might be organized along modular lines, while believing that with the appearance of the human mind most of that organization was somehow superseded and swept away.

We extract the following argument from the above appeal to object-oriented programming (OOP):

(4) Software engineering suggests that OOP (modularization) is necessary to manage increasing complexity.
(5) Biological systems are very complex.

These premises buffer our Premise 2.

(2) Modular subsystems are a necessary ingredient for evolvability.

Massive Modularity- Argument From Design- OOP

I particularly enjoyed the originality of this argument. Even though software engineering is notoriously bad at quantifying its practices, its trajectory surely sheds some light on other disciplines. As a computer scientist, this argument made me speculate what other trends, current or future, could be brought to bear on such questions. The interchange between computer science and cognitive neuroscience is broad… with things like neuromorphic computing flowing in one direction, and information theory flowing in the other…

Is Mind Subject To Natural Selection

This phase of the argument is the most philosophical. The question is whether mental processes are subject to the forces of natural selection.

Carruthers begins with a fairly uncontroversial premise:

(6) The central nervous system is subject to natural selection.

So much, so obvious. But the crux of the issue is how to relate mind and brain. Carruthers wants to argue that:

(7) The central nervous system underwrites the mind.

However, this premise falls squarely into a philosophy of mind morass. Carruthers suggests a way forward is to notice that most mainstream approaches (“anyone who is neither an an epiphenomenalist nor an eliminativist about the mind”) support such a premise (see this post for some definitions).

If we find ourselves sympathetic to 7, we are led by the nose to Proposition 8:

(8) Mental processes are subject to natural selection.

Massive Modularity- Argument From Design- Mental Evolution

How Many Minds

While the weight of this argument labors to support the reality of computational modules, we must also spare some words to motivate massive modularity. Carruthers, leveraging Simon, H’s 1962 paper The Architecture of Complexity, points out that the question is one of degrees. Let us try to imagine a modularity thesis that is non-massive:

Moderate Modularity

The x-axis captures number of modules, the y-axis leverages David Marr’s concept of Tri-Level Analysis.  The concave shape of the curve represents the claim that, while the number of neurological functions may be large, the number of computational processes (e.g., belief, desire, motivation) is small.

In contrast, the shape of massive modularity thesis is convex:

Massive Modularity

While Carruthers elsewhere motivates massive modularity by way of task analysis and ethological surveys, he here defends this latter thesis by appealing to the empirically-robust observation that the brain appears to process its algorithms in parallel, and this would be impossible without a relatively plentiful number of processing units. So we have stumbled upon our last premise:

(9) In the mind, massive modularity is computationally superior to moderate modularity.

Putting It All Together

All that remains is to glue together the sub-conclusions of the above arguments. Specifically, take the following propositions:

(3) Natural selection selects for modularity at a variety of different levels.
(8) Mental processes are subject to natural selection.
(9) Within the mind, massive modularity is computationally superior to moderate modularity.

From these, it is clear we have successfully motivated our thesis:

(10) Natural selection selects for massive modularity in the mind

The entire argument, then, is pictured below.

Massive Modularity- Argument From Design- Summary

Concluding Thoughts

While I happen to affirm Premise 8, I feel like Carruthers – and even more so myself – do a poor job at motivating it. This observation is particularly painful because it is arguably the central thesis of evolutionary psychology. Mental note-to-self: revisit that section of the argument.

All told, I find this argument fairly compelling, although I would like to get more clear on several of its distinctions.

Fodor: Modularity Of Mind

Part Of: Cognitive Modularity sequence
Content Summary: 1100 words, 11 min reading time

Let me today review this text, which is widely held to be one of the most influential texts in the cognitive psychology tradition.

Motivations

A milestone within the cognitive psychology tradition. This extended argument for the modularity of input systems reoriented the field back when it was published in 1983, and responses continue to emerge to this day.

Modularity Of Mind is one of those rare books that combine a formidable vocabulary with a concise communicative style. Fodor’s dry humor and deep familiarity with relevant empirical results redeemed the occasionally abstruse discussion. The author’s penchant for polemics was not apparent in this essay. Five sections divide the work:

Part 1: Four Accounts Of Mental Structure

To Fodor, the four competing theories of mental structure are:

  1. Neo-Cartesianism
  2. horizontal faculties
  3. vertical faculties
  4. Associationism

While discussing Neo-Cartesianism, Fodor draws the distinction between innate faculties: propositional vs. architectural. Specifically, there are two kinds of reactions to the tabula rasa. The first is to propose that the mind does not begin life completely undifferentiated; rather, infants come into the world already possessing “cognitive furniture”, such as image rendering engines. The second kind of reaction is to claim that humans are born with a certain set of pre-installed knowledge (e.g., Chomskyan universal grammar).

After the discussion regarding innate faculties, Fodor treats the horizontal/vertical distinction within architectural theories of cognition. Horizontal modular theories are those that would have cognitive furniture be domain-general. Such ideas go back to ancient Greece; a good current exemplar is what modern psychology believes about long-term memory. Vertical modular theories hold cognitive furniture to be domain-specific. Rather than fractionating the mind into perception, memory, and motivational modules, vertical theorists such as Franz Gall (father of phrenology) would insist on different modules for mathematics, music, poetry, etc. Gall would go on to say that there is no such thing as domain-general memory. If there are similarities between musical memory and mathematical memory, that is merely a coincidental similarity across module implementations.

Finally, Associationism (incl. Behaviorism) is treated. Unsurprisingly, given the author’s functionalist credentials, arguments are presented that purport to demonstrate the inadequacy of the movement.

Part 2. A Functional Taxonomy Of Cognitive Mechanisms

Fodor outlines a three-tier mental architecture: transducers, input processing, and central systems. The brain is thought to transduce signals via sensory organs, and feed such raw data to input processing systems. These iteratively raise the level of abstraction, saving intermediate results into states known as interlayers. Finally, the final results of the input systems are presented to the central systems, which are responsible for binding them into coherent beliefs with the help of background knowledge. Interestingly, Fodor holds that language processing is its own sensory system, distinct from acoustic processing, and that this system encapsulates the entire lexicon. Organism output (behavior) was not considered.

Part 3. Input Systems As Modules

The most empirically rich and impactful section. I will briefly sketch each subsection.

  1. Domain specificity. There appear to be separate mechanisms to process distinct stimuli. While several systems may share select resources, they never share information.
  2. Mandatory operation. While human beings can ignore their phenomenological experiences, they cannot consciously repress them.
  3. Hidden interlevels. Introspection cannot unearth the intermediate states of visual stimuli transformation, only the finished product.
  4. Fast processing. Driven by evolutionary pressures, sensory processing is very rapid. For example, many people are able produce a mirrored language stream that trails the original by an astonishing one-quarter of a second.
  5. Informational encapsulation. In principle, input processing can never access the organism’s broader knowledge base. There are few to none feedback loops that inform sensory processing.
  6. Shallow outputs. Input systems do not issue beliefs, but rather non-conceptual (“shallow”) information. Other systems are responsible for subsequent conceptual fixation.
  7. Fixed neural architecture. In contrast with central processes, input systems appear to be localized to specific neural locations (e.g., Wernicke’s Area for language processing).
  8. Idiosyncratic breakdown patterns. Brain damage is associated with selective, severe failures of input processing, not general deficiency introduction.
  9. Shared ontogeny. Cognitive structural maturation occurs in an innately-specified way.

Informational encapsulation is singled out as the most important element of the thesis. This feature explains how an organism protects its raw percepts from contamination from its own biases. Constraining information flow is essential to human beings, and this feature goes a long way in motivating the existence of the others.

During his discussion of shallow outputs, Fodor makes an interesting observation about conceptual fixation. Human concepts are organized hierarchically: “a poodle is a dog is a mammal is a physical organism is a thing”. Central non-modular systems must locate their conclusions at a specific level within this hierarchy. Interestingly, beliefs tend to fixate at a particular level (e.g., “dog” in the above example).

What makes the “dog” level so special? It tends to be: (a) a high-frequency descriptor; (b) learned earliest within development; (c) the least abstract member that is monomorphemically lexicalized; (d) easiest to define without reference to other items in the hierarchy; (e) most informationally dense, in the sense of being the most productive item if one asks for the properties of each item in the hierarchy from most to least abstract; (f) used the most frequently in everyday descriptions; (g) used the most frequently in subvocal descriptions; (h) the most abstract members that give themselves to visual representation. These facts call out for explanation and further research.

Part 4: Central Systems

Fodor perceives little evidence to explicate central processes, so he reverts to analogy. Scientific confirmation is presented as an analogue of psychological belief fixation. An enthusiast of Quinean naturalized epistemology, Fodor is also sympathetic to Quinean holism: that any belief can in principle affect any other. But requiring unconstrained information transfer is a recipe for intractable computation. This is the deep trouble underlying the framing problem of artificial intelligence. According to Fodor, intractability is precisely why academic journals tend to avoid topics of general intelligence.

I found the previous section on input modules to be of greater import. Fodor’s arguments here are empirically impoverished, and his vague notions of networked learning leave much to be desired. If this section characterized the entirety of the text, the reader would be better advised to research modern probabilistic graphical models, and attempts within the AI community to approximate universal induction.

Part 5: Caveats and Conclusions

The essay concludes with a few comments regarding modularity and epistemic boundedness (“are there truths that we are not capable of grasping?”). After reviewing the historical discussion surrounded bounded cognition, Fodor ultimately has little to say on the matter, arguing that this conversation should proceed with little appeal to concepts of modularity. He closes with self-styled gloomy remarks about how our best thinkers have consistently evaluated local phenomena more effectively than global phenomena (c.f., deduction vs. confirmation theory), and that this sociological reality is unlikely to change in the near future.

An incisive, important text that helps to place modern cognitive science debates in sharper focus.