This is a draft manifesto for an integrative design-oriented (IDO) approach to understanding humans as autonomous agents (“cognitive science”, “AI”, “psychology”, etc.)
This manifesto will migrate to its own domain, at which point this web page will link to it.
This manifesto may be read as a response to the following problems in cognitive science/AI/psychology.
- 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.
- 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.
- “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.
- Much, perhaps most, research in psychology is being conducted without being inscribed in current cognitive science, let alone the approach.
- 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).
- 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.
- 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.
- 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.)
- 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.
- 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.
- 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.
- This means the IDO approach seeks to produce information-processing (information processing) theories.
- 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).
- 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).
- 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.
- The IDO approach is ultimately concerned with explaining and implementing autonomous agency (Beaudoin, 1994).
- The IDO approach is truly interdisciplinary: (AI, philosophy, psychology, linguistics, neuroscience, anthropology, etc.).
- The IDO approach seeks to explain the integration (interaction and even blending) of major information processing functions: “Cognitive”, motivational, affective, executive, ancillary, etc.
- 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.
- 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.
- The IDO approach while focusing on information processing is multi-scale, bridging genes to competence. (Grant 2003).
- 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).
- 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.
- 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.
- The IDO approach is comparative: It analyzes the space of possible niches, requirements, designs, and implementations, relations between them, and tradeoffs.
- 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.
- 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)
The opening quotations of my 1994 Ph.D. thesis are still relevant
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)
Some relevant literature
- The Journal of General Artificial Intelligence..
- Bach, J. (2008). Seven principles of synthetic intelligence. Frontiers in Artificial Intelligence and Applications.
Beaudoin, L. P. (1994). Goal processing in autonomous agents. (Ph.D. thesis). University of Birmingham, England.
- Beaudoin, L. P. (2014) Cognitive Productivity: Using Knowledge to Become Profoundly Effective. That book used the expression “broad cognitive science” for what we now call IDO approach.
- Beaudoin, L. P. (2017). Perturbance: Unifying Research on Emotion, Intrusive Mentation and Other Psychological Phenomena with AI.
- Beaudoin, L. P., Hyniewska, S., & Hudlicka, E. (2017). Perturbance: Unifying Research on Emotion, Intrusive Mentation and Other Psychological Phenomena with AI. Paper presented at the Symposium on Computational Modelling of Emotion: Theory and Application at AISB-2017. Paper available from http://summit.sfu.ca/item/16776.
- Beaudoin, L. P., Lemyre, Pudlo, M. & Bastien, C. (2019). Towards an integrative design-oriented theory of sleep-onset and insomnolence from which a new cognitive treatment for insomnolence (serial diverse kinesthetic imagining, a form of cognitive shuffling) is proposed.
- Boden, M. A. (2006). Mind as machine: A history of cognitive science (2 volumes).
- Ekkekakis, P. (2013). The Measurement of Affect, Mood, and Emotion. Cambridge: Cambridge University Press. http://doi.org/10.1017/CBO9780511820724
- Grant, S. (2003). Systems biology in neuroscience: Bridging genes to cognition. Current Opinion in Neurobiology, 13(5), 577–582. doi:10.1016/j.conb.2003.09.016
- Hudlicka, E. (2017) Computational Modeling of Cognition–Emotion Interactions: Theoretical and Practical Relevance for Behavioral Healthcare
- Marcus, G., & Davis, E. (2019). Rebooting AI. Pantheon.
- McCarthy, J. (2008). The well-designed child. Artificial Intelligence, 172(18), 2003–2014. http://doi.org/10.1016/j.artint.2008.10.001
- Minsky, M. L. (1986). The society of mind. New York, NY: Simon & Schuster.
- Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 1–9. http://doi.org/10.1038/s41562-018-0522-1
- Newell, A. (1973). You can’t play 20 questions with nature and win.
- Pessoa, L. (2013). The cognitive-emotional brain. MIT Press.
- Rooij, Iris v. , Lack of theory building and testing impedes progress in the factor and network literature | PsyArXiv Preprints
- Rooij, Iris v. Theory before the test: How to build high-verisimilitude explanatory theories in psychological science | PsyArXiv Preprints
- Sloman, A. (1978). The computer revolution in philosophy: Philosophy, science and models of mind. Harvester Press.
- Sloman, A. (1984) The structure of the space of possible minds
- Sloman, A. (1989/2017) On designing a visual system
- Sloman, A. (1993a). Prospects for AI as the general science of intelligence. In A. Sloman, D. Hogg, G. Humphreys, D. Partridge, & A. Ramsay (Eds.). Prospects for Artificial Intelligence, (pp. 1–10). Amsterdam, Netherlands: IOS Press.
- Sloman, A. (2008). The well-designed young mathematician. Artificial Intelligence, 172(18), 2015–2034.
- Sloman, A. (2014). Meta-Morphogenesis-Overview.
- Sloman, A. (2018) Evolved Compositionality
- Sloman, A. (2019) The Meta-Configured Genome (2019 Version).
Authorship and revisions
This is intended to lead to a jointly authored document.
- 2021-07-16. Merged two bibliographical sections, minor edits.
- 2021-01-13. Title and reference and minor changes.
- 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.
Feedback and signatures
Thanks to Alice Dauphin and Guillaume Pourcel for feedback on this document. 2021-07-16: Some of their feedback still needs to be incorporated in this document