A Manifesto for Integrative Design-oriented Cognitive Science and AI

This is a draft manifesto for integrative design-oriented (IDO) cognitive science and AI.

This manifesto will migrate to its own domain, at which point this web page will link to it.

Preamble

This manifesto may be read as a response to the following problems in cognitive science/AI.

  1. Cognitive science is supposedly the interdisciplinary study of all aspects of the human mind using computational metaphors. For the most part, however, it seems that many, perhaps most, cognitive scientists at best pay lip service to these ambitions. Rarely are affective processes studied under the banner of cognitive science. For instance, at Cognitive Science conferences there are sometimes symposia on emotion but rarely is affect considered in detail outside such symposia. And even when affect is discussed, it tends to be treated as something separate from cognition that influences or may be influenced by cognition. Rarely are cognition and affective processes considered as blended . Motivation, volition and ancillary functions (such as sleep) also similarly tend to be neglected or treated in isolation.
  2. The name “cognitive science” is misleading because although cognitive science is described broadly to include all natural information-processing, it tends to be identified with cognition as dry and separate from other types, aspects or functions of information processing.
  3. “Affective science” has emerged. One might expect motivation in affective science circles to be considered of great interest, yet it tends to be neglected. It is treated at most as a “component” of emotion, or something that is influenced by affect. Ask an affective scientist at an emotion conference, “To what theory of motivation do you subscribe?” and you are likely to get a blank stare.
  4. Much, perhaps most, research in psychology is being conducted without being inscribed in current cognitive science, let alone the IDO approach.
  5. Scientific and engineering research become increasingly specialized and narrow in focus. It is difficult to consider the “big picture” and deal with hard, integrative problems while making measurable progress. Requirements to publish, get one’s work funded, achieve tenure and further promotion, build one’s reputation, administrative loads, teaching requirements and family responsibilities can all get in the way of integrative efforts. Moreover, science has fads and zealots which are not aligned with IDO (e.g., embodied cognition and associationism).
  6. Theoretical cognitive scientists and AI researchers, still relatively few in number, tend to work individually or small groups, and almost never employ a professional software architect. In contrast, development of large commercial software involves dozens or hundreds of developers and a team of software architects. Yet commercial software is infinitely simple compared to the mind/brain.
  7. A large proportion of Artificial Intelligence researchers have bought into (and are peddling) the myth that associationist methods can eventually (and someday soon) be used without recourse to non-associationist methods to explain, model and implement all aspects of human intelligence. The myth is false, as has been argued by Aaron Sloman, Marvin Minsky, and more recently (for instance, in Rebooting AI), by Gary Marcus.
  8. Whether there is a replication crisis in psychology or not, psychology has been held back by lack of concern for integrative theory that can actually explain competence and an extreme tendency to run empirical studies in the absence of such theory. Theoretical terms and psychometric instruments are used willy nilly. (See A problem in theory; Newell’s “You can’t play 20 questions”; Beaudoin et al 2017, Ekkekakis, 2013; etc.)
  9. While research on cognitive architectures is promising, much of it to date tends to ignore affective and motivational requirements. Also it tends to overlook the fact that in some species (e.g., humans) information processing architectures (normally) develop (are not static). Positing architectures, while not merely helpful but necessary for modeling autonomous agency, can lead to problematic simplifications and rigid assumptions. See Pessoa’s arguments in The Cognitive Emotional Brain for embracing complexity; and Sloman’s arguments to recognize multiple discontinuities (rather than simplistic sharp dichotomies) .

Fundamentals of IDO (Integrative design-oriented approach to cognitive science and AI).

To be clarified and slightly expanded.

The fundamentals of an IDO approach are as follows.

  1. The IDO approach recognizes Artificial General Intelligence as the general science of intelligence and competence. Compare Prospects paper. AI provides, or should provide, theoretical and methodological frameworks for modeling the space of possible minds, including those of all present, past, future and possible actual minds.
  2. The IDO approach seeks to understand mental phenomena from the design stance: Specifying environmental niches and requirements for systems; specifying designs to meet those requirements, implementing designs; analyzing and evaluating the extent to which the designs meet the requirements, and that the implementations satisfy the designs and their requirements; repeating this process.
  3. This means the IDO approach seeks to produce information-processing (information processing) theories.
  4. The IDO approach recognizes the necessity of conceptual analysis for understanding mental phenomena, while rejecting (or at least seriously bridling) factor analysis (cf. Osgood and Scherer).
  5. The IDO approach while accepting the relevance of empirical data and phenomena-based methods, rejects both inductivism and unbridled empiricism. (Compare A problem in theory).
  6. The IDO approach seeks to explains competence. It is more concerned with what (actual or possible) systems can do than with predicting what they will actually do in particular circumstances.
  7. The IDO approach is ultimately concerned with explaining and implementing autonomous agency (Beaudoin, 1994).
  8. The IDO approach is truly interdisciplinary: (AI, philosophy, psychology, linguistics, neuroscience, anthropology, etc.).
  9. The IDO approach seeks to explain the integration (interaction and even blending) of major information processing functions: “Cognitive”, motivational, affective, executive, ancillary, etc.
  10. The IDO approach can and should be used not merely to directly understand autonomous agency, but to study any and all types of human capabilities and mechanisms. For instance, this approach can and should be used to understand the human sleep onset control system, or vision.
  11. The IDO approach recognizes the need to explore, develop, implement and assess information processing architectures. The study of information processing architectures is itself a demanding discipline. Therefore IDO research teams should include AI/software architects as consultants or core-members.
  12. The IDO approach while focusing on information processing is multi-scale, bridging genes to competence. (Grant 2003).
  13. The IDO approach recognizes the need to understand the evolution of information-processing and competence. (See the meta-morphogenesis project, as an offshoot of Alan Turing’s final paper; and A problem in theory).
  14. The IDO approach recognizes that associative mechanisms, while necessary, are not sufficient for modeling sophisticated autonomous agents. Kantian mechanisms and as yet unexplored representations and mechanisms will be required.
  15. The IDO approach recognizes that there is not only a single (information processing) architecture that supports all forms of intelligence. For instance, some species support multiple information processing architectures; and in some species some individuals’ information processing architecture develops over time.
  16. The IDO approach is comparative: It analyzes the space of possible niches, requirements, designs, and implementations, relations between them, and tradeoffs.
  17. When developing specific theoretical conjectures that use theoretical terms (e.g., “arousal” or “attention”), IDO research attempts to ground such terms in theories, preferably IDO theories, which make the terms more meaningfully interpretable. This means that IDO theorizing involves provisional commitment to theory.
  18. IDO researchers acknowledge the IDO shortcomings of their own scientific communications. This means they recognize ways in which their own theories fail to meet the IDO objectives. (Humility)

Signatories

Contextual quotes

Some quotes to put this manifesto in perspective. (To be moved to a separate document).

The problem is not that we do not know which theory is correct, but rather that we cannot construct any theory at all which explains the basic facts

(Power, 1979 p. 109)

I think that when we are speculating about very complicated adaptive systems, such as the human brain and social systems, we should especially beware of oversimplification—I call such oversimplification “Ockham’s lobotomy”.

(Good, 1971a p. 375)

Relevant literature

Authorship and revisions

This is intended to become a jointly authored document.

  • 2019-09-22. An earlier version of this manifesto written by Luc Beaudoin was presented at the 2019 World Sleep Congress in Vancouver, BC.
  • 2019-11-17. Luc P. Beaudoin. First draft of the manifesto post 2019-World Sleep Congress.
  • 2019-11-18. Luc P. Beaudoin. Added paragraphs to the pre-amble and to the manifesto (regarding evolution and multi-scales), and a reference.

A blog post introducing this manifesto..

Feedback and signatures

Please send feedback to lucb@cogzest.com, use the comments section below, or use Twitter.

2 thoughts on “A Manifesto for Integrative Design-oriented Cognitive Science and AI”

  1. #1. General comments…

    We made those comments as if you intend to write the manifesto for anyone doing AGI or cognitive science (including psychologists). In particular, we imagined that anyone of those disciplines should be able to understand it and potentially to change its practice to align with IDO.

    ##1. …about the content 

    – As we are familiar with IDO and Sloman taxonomy, we had no problem to understand your points but it is less sure that a naive audience would understand you in details. 
    – Maybe you should add a clear definition of “IDO” and “design stance” somewhere.
    – Give more examples to illustrate and convince people. In our case it was thanks to the examples that we were convinced and that we could understand the potential of the design stance (such as the rich explicit/implicit distinction that comes from AI/computer science).

    – From what we understand, your purpose is the following: (1) to widen the subjects studied by cognitive science (in particular to include emotions, motivations… to the “dry” aspects of cognition), (2) that cognitive scientists adopt the design stance and consequently understand and admit that their research is part of a broader research aiming at understanding the space of possible minds.
    – Maybe these 2 points (if they are indeed your main points) should appear clearly (as an introduction ?) (a) to clarify the document and (b) to help the reader to link each argument to the corresponding point. 
    – Why do you expect people to adopt the design stance is not explicitly mentioned and we think it should be detailed for naive readers as it is the core point of the manifesto.
    – Design stance is a mean to define new relevant concepts and concepts are key issues in cognitive science.
    – Using the design stance, some pitfalls can be avoided such as overfitting and missing some aspects of competences. 
    – Adopting the design stance is acknowledging the difficulty of the endeavour, split it and make space for theory. For us it’s one of the key insights of Minksy and Sloman that changed our way to see cognitive science: we need to give space to the theory to avoid simplifying views (Ockham lobotomy).
    – With the design stance, the mechanisms of intelligence do not compete with each other but inhabit the space of agents capacities. Consequently, some debates (such as associationists methods VS non-associationists) become obsolete.
    – We also think that more emphasis should be done in evaluating the trade-off of the models presented. A lot of work consists in developing some frameworks (Free-energy principle, Reinforcement Learning…) while not focusing on the qualitative differences in terms of competences. In general benchmarking is a key challenge for cognitive computational science.

    ##1.2 …about the structure

    – To help naive readers, it seems to us that few explicit proposals on which you elaborate with examples could be better understood than a list. Or maybe the list should be organised to bring out the main points. For example, the points 1, 2, 3 in the preamble could appear under the same umbrella “Cognitive science should go back to its initial goal, that is explain all aspects of cognition, not only the dry aspects of cognition”.
    – We also think that the manifesto could emphasize that a lot of the IDO approach can be done quite easily without requiring a lot of investment, just by seeing the problem from the design stance.


    ##1.3 …about the tone

    – As the purpose is to change people’s vision of their field, we think that the tone should be encouraging.
    – For that, maybe write the manifesto as the first step toward an ideal for cognitive science can be less pejorative and people will feel less pointed out. 
    – Some points you suggest to change are (we think) acknowledged by researchers but still difficult to implement:
    – Software developer: difficult because of the lack of funding
    – True interdisciplinarity: difficult because of the vast amount of knowledge and methodologies to master. Maybe the point might be to encourage truly interdisciplinary collaboration.

    #2 Comments addressing specific points

    ##2.1 Preamble

    – (6.) *“Theoretical cognitive scientists and AI researchers, still relatively few in number, tend to work individually or in small groups”*
    – This point made us think to a more general problem existing in research, the fact that a lab is made of a PI, his postdoc and phD students (maybe this is a scheme specific to France, we are not really sure for other countries). This is problematic as to elaborate on a complex problem it is necessary to have people expert in different domains. This is why people do collaborations with other labs but these collaborations are constrained by spatio temporal limits. To have labs with several mature researchers and engineers interacting seems more promising.
    – (9.) *“While research on cognitive architectures is promising, much of it to date tends to ignore affective and motivational requirements.”* 
    – We agree, but we see a significant trend toward integrating motivation in cognitive architectures with the work of J. Bach on MicroPsi, R. Sun on Clarion, eBICA from the BICA community led by A. Samsonovich…


    ##2.2 Fundamentals of IDO

    – (1.) The name Artificial General Intelligence (AGI) is quite blurry. Especially because it is also a specific community with their own vision.
    – (4.) Maybe a detailed explanation of the limitations of a factor analysis could help understand the relevance of the IDO approach.
    – (5.) Same as 4.
    – (8.) *“The IDO approach is truly interdisciplinary”* 
    – Maybe to explain the consequences of IDO approach on interdisciplinary can help. For example, IDO can provide coherent definitions across domains, allowing researchers to speak the same (computational) language. It might also be interesting to point the advantage of it.

  2. Thank you for these comments! We had discussed over email, but I didn’t see the email from WordPress that you had submitted the comments. I will make some changes to the draft.

    cheers, Luc.

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