From unified to specific theories of cognition

Frank van der Velde


Abstract: This article discusses the unity of cognitive science that seemed to emerge in the 1950s, based on the computational view of cognition. This unity would entail that there is a single set of mechanisms (i.e. algorithms) for all cognitive behavior, in particular at the level of productive human cognition as exemplified in language and reasoning. In turn, this would imply that theories in psychology, and cognitive science in general, would consist of algorithms based on symbol manipulation as found in digital computing. However, a number of developments in recent decades cast doubt on this unity of cognitive science. Also, there are fundamental problems with the claim that cognitive theories are just algorithms. This article discusses some of these problems and suggests that, instead of unified theories of cognition, specific mechanisms for cognitive behavior in specific cognitive domains could be needed, with architectures that are tailor-made for specific forms of implementation. A sketch of such an architecture for language is presented, based on modifiable connection paths in small-world like network structures.

Keywords: Connection Paths; Control of Activation; Small-world Networks; Symbol Manipulation; Unity of Cognition


Dalle teoria unificate della cognizione a quelle specifiche

Riassunto: Questo articolo discute l’unità della scienza cognitiva che sembrava emergere negli Anni ’50 e che era basata su una concezione computazionale della cognizione. Questa unità prevedeva l’esistenza di un singolo insieme di meccanismi (algoritmi) per tutti i comportamenti cognitivi, in particolare al livello della cognizione umana produttiva come, per esempio, linguaggio e ragionamento. A sua volta ciò implicava che le teorie psicologiche e, più in generale della scienza cognitiva, prevedessero algoritmi basati sulla manipolazione di simboli come nella computazione digitale. E, tuttavia, diversi sviluppi degli ultimi decenni hanno messo in dubbio questa unità della scienza cognitiva. Affermare che le teorie cognitive sarebbero solo algoritmi presenta problemi di fondo. Questo articolo discute alcuni di questi problemi, suggerendo che, invece di teorie della cognizione unificata, si potrebbe aver bisogno di meccanismi specifici per il comportamento cognitivo in specifici domini cognitivi, con architetture ritagliate per specifiche forme di implementazione. Questo articolo presenta uno schizzo di una simile architettura per il linguaggio, basata su vie di connessione modificabili in piccoli mondi come le strutture di reti.

Parole chiave: Vie di connessione; Controllo dell’attivazione; Reti di piccoli mondi; Manipolazione di simboli; Unità della cognizione

Parole chiave

Connection Paths; Control of Activation; Small-world Networks; Symbol Manipulation; Unity of Cognition

Full Text


Riferimenti bibliografici

AMSEL, A., RASHOTTE, M.E. (eds.) (1984). Mechanisms of adaptive behavior: Clark L. Hull’s theoretical papers, with commentary, Columbia University Press, New York.

BLOOM, P. (2000). How children learn the meaning of words, MIT Press, Cambridge (MA).

BROWN, T.B., MANN, B., RYDER, N., SUBBIAH, M., KA-PLAN, I., DHARIWAL, P., NEELAKANTAN, A., SHYAM, P., SASTRY, G., ASKELL, A., AGARWAL, S., HERBERT-VOSS, A., KRUEGER, G., HENIGHAN, T., CHILD, R., RAMESH, A., ZIEGLER, D.M., WU, J., WINTER, C., HESSE, C., CHEN, M., SIGLER, E., LITWIN, M., GRAY, S., CHESS, B., CLARK, J., BERNER, C., MCCANDLISH, S., RADFORD, A., SUTSKEVER, I., AMODEI, D. (2020). Language models are few-shot learners. In: «ArXiv», arXiv:2005.14165v4 - doi: 10.48550/arXiv.2005.14165, last revision: 22 July 2020.

BROWNING, J., LE CUN, Y. (2022). What AI can tell us about intelligence. In: «Noema», available at URL:

CHOMSKY, N. (1957). Syntactic structures, Mouton, The Hague.

FODOR, J.A., PYLYSHYN, Z.W. (1988). Connectionism and cognitive architecture: A critical analysis. In: S. PINKER, J. MEHLER (eds.), Connections and symbols, MIT Press, Cambridge (MA), pp. 73-193.

HANSON, N.R. (1958). Patterns of discovery: An inquiry into the conceptual foundations of science, Cambridge University Press, Cambridge.

HAUSER, M.D., CHOMSKY, N., FITCH, W.T. (2002). The faculty of language: What is it, who has it, and how did it evolve?. In: «Science», vol. CCXCVIII, n. 5598, pp. 1569-1579.

HEBB, D.O. (1949). The organisation of behavior: A neuropsychological theory, Wiley, New York.

HOTHERSALL, D. (2004). History of psychology, McGraw-Hill, Boston, 4th edition.

HUTH, A.G., DE HEER, W.A., GRIFFITHS, T.L., THEUNISSEN, F.E., GALLANT, J.L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. In: «Nature», vol. DXXXII, n. 7600, pp. 453-458.

JACKSON, E.A. (1991). Perspectives of nonlinear dynamics, 2 voll., Cambridge University Press, Cambridge.

JAMES, W. (1950). The principles of psychology (1890), 2 voll., Dover Publications.

KANDEL, E.R., SCHWARTZ, J.H., JESSELL, T.M., SIEGELBAUM, S.A., HUDSPETH, A.J. (eds.) (2013). Principles of neural science, McGraw-Hill, New York, 5th edition.

KÖHLER, W. (1965). On the insight of apes (1917). In: R.J. HERRNSTEIN, E.G. BORING (eds.), A source book in the history of psychology, Harvard University Press, Cambridge (MA), pp. 569-578.

KRIETE, T., NOELLE, D.C., COHEN, J.D., O’REILLY, R.C. (2013). Indirection and symbol-like processing in the prefrontal cortex. In: «Proceedings of the Academy of Sciences of the United States of America», vol. CX, n. 41, pp. 16390-16395.

KUHN, T.S. (1970). The structure of scientific revolutions (1962), University of Chicago Press, Chicago, 2nd edition.

RALPH, M.A.L., JEFFERIES, E., PATTERSON, K., ROGERS, T.T. (2017). The neural and computational bases of semantic cognition. In: «Nature Reviews Neuroscience», vol. XVIII, n. 1, pp. 42–55.

MARCUS, G. (2020). The next decade in AI: Four steps towards robust Artificial Intelligence. In: «ArXiv», arXiv:2002.06177 – doi: 10.48550/arXiv.2002.06177 - last revision 2020, February 19th.

MARCUS, G., DAVIS, E. (2020). GPT-3, Bloviator: O-penAI’s language generator has no idea what it’s talking about. In: «MIT Technological Review», published: 22 August 2020, available at URL:

NEWELL, A. (1990). Unified theories of cognition, Harvard University Press, Cambridge (MA).

O’REILLY, R.C., RUDY, J.W. (2001). Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. In: «Psychological Review», vol. CVIII, n. 2, pp. 311-345.

PAVLOV, I.P. (1965) On conditioned reflexes (1904). In: R.J. HERRNSTEIN, E.G. BORING (eds.), A source book in the history of psychology, Harvard University Press, Cambridge (MA), pp. 564-569.

PINKER, S. (1994). The language instinct, Penguin, London.

PYLYSHYN, Z.W. (1984). Computation and cognition: Toward a foundation for cognitive science, MIT Press, Cambridge (MA).

ROGERS, H. (1988). Theory of recursive functions and effective computability, MIT Press, Cambridge (MA).

ROSENBLATT, F. (1958). The perceptron: A probabilistic model of information storage and organization in the brain. In: «Psychological Review», vol. LXV, n. 6, pp. 386-408.

SHANAHAN, M. (2010). Embodiment and the inner life, Oxford University Press, Oxford.

SILVER, D., SCHRITTWIESER, J., SIMONYAN, K., ANTONOGLOU, I., HUANG, A., GUEZ, A., HUBERT, T., BAKER, L., LAI, M., BOLTON, A., CHEN, Y., LILLICRAP, T., HUI, F., SIFRE, L., VAN DER DRIESSCHE, G., GRAEPEL, T, HASSABIS, D. (2017). Mastering the game of Go without human knowledge. In: «Nature», vol. DL, n. 7676, pp. 354-359.

SKINNER, B.F. (1957). Verbal behavior, Appleton-Century-Crofts, New York.

THORNE, B.M., HENLEY, T.B. (2001). Connections in the history and systems of psychology, Houghton Mifflin, Boston.

TURING, A.M. (1937). On computable numbers, with an application to the Entscheidungsproblem. In: «Proceedings of the London Mathematical Society», vol. XLII, S2, n. 1, pp. 230-265.

VAN DER VELDE, F. (in press). The neural blackboard theory of neurosymbolic processing: Logistics of access, connection paths and intrinsic structures. In: P. HITZLER, K. SARKER, A. EBERHART (eds.), Compendium of neurosymbolic artificial intelligence, IOS Press, Amsterdam.

VAN DER VELDE, F. (2022). Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processing. In: «ArXiv», – doi: 10.48550/arXiv.2210.10543.

VAN DER VELDE, F. (2015). Computation and dissipative dynamical systems in neural networks for classification. In: «Pattern Recognition Letters», vol. LXIV, pp. 44-52.

VAN DER VELDE, F., DE KAMPS, M. (2006). Neural black-board architectures of combinatorial structures in cognition. In: «Behavioral and Brain Sciences», vol. XXIX, n. 1, pp. 37-70.

VAN DER VELDE, F., FORTH, J., NAZARETH, D.S., WIGGINS, G.A. (2017). Linking neural and symbolic representation and processing of conceptual structures. In: «Frontiers in Psychology», vol. VIII, Art. Nr. 1297 - doi: 10.3389/fpsyg.2017.01297.

WATSON, J.B. (1913). Psychology as the behaviorist views it. In: «Psychological Review», vol. XX, n. 2, pp. 158-177.

WATSON, J.B. (1924). Behaviorism, The People’s Institute Publishing Co., New York.

WATTS, D.J., STROGATZ, S.H. (1998). Collective dynamics of “small-world” networks. In: «Nature», vol. CCCXCIII, n. 6684, pp. 440-442.


Copyright (c) 2023 Frank van der Velde

URLdella licenza:

Rivista internazionale di Filosofia e Psicologia - ISSN: 2039-4667 (print) - E-ISSN: 2239-2629 (online)

Registrazione al Tribunale di Milano n. 634 del 26-11-2010 - Direttore Responsabile: Aurelia Delfino

Web provider Aruba spa - Loc. Palazzetto, 4 - 52011 Bibbiena (AR) - P.IVA 01573850516 - C.F. e R.I./AR 04552920482

Licenza Creative Commons
Dove non diversamente specificato, i contenuti di Rivista Internazionale di Filosofia e Psicologia sono distribuiti con Licenza Creative Commons Attribuzione 4.0 Internazionale.