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(Mis)computation in Computational Psychiatry

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Neural Mechanisms

Part of the book series: Studies in Brain and Mind ((SIBM,volume 17))

Abstract

An adequate explication of miscomputation should do justice to relevant practices in the computational sciences. While philosophers of computation have neglected scientific practices outside computer science, here I focus on computational psychiatry. I argue that computational psychiatrists use a concept of miscomputation in their explanations, and that this concept should be explicated as interest-relative and perspectival, although non-arbitrary, relatively clear-cut, experimentally evaluable, and instrumentally useful. To the extent my argument is convincing, we should reconsider the general adequacy of the mechanistic view of computation for illuminating relevant methodological and explanatory practices in the computational sciences.

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Notes

  1. 1.

    This task requires participants to learn from probabilistic feedback, where the structure of the task can change so that what used to be positive outcomes (i.e., a positive reward) are now negative outcomes (i.e., a punishment, or negative reward), and what used to be negative are now positive outcomes.

  2. 2.

    Specifically, the best fitting model for healthy controls and some schizophrenia patients was a Hidden Markov Model. According to this model, participants built and updated a representation of the structure of the task, based on the past history of choices and resulting rewards. Their belief about the current state of the task would be used to make a choice. Instead, the best fitting model for the other schizophrenia patients was a Rescorla-Wagner model. According to this model, participants did not build a representation of the structure of the task. For each trial, participants would choose an option based on its expected value. After a trial, the expected value of only the chosen option would be updated on the basis of a prediction error (Schlagenhauf etal. 2014, 172–3).

  3. 3.

    If an essential component of a computing system is missing, altered or broken, then the system may not compute anymore. If a system does not compute at all, then it cannot miscompute.

  4. 4.

    There’s no consensus among proponents of the mechanistic view about how we should individuate what a computing system actually computes at a time. For example, unlike Piccinini (2015), Tucker (2018, 8) argues that a system’s computational structure is individuated without any reference to factors external to the system; what the system is actually computing at a time is determined by the actual inputs to the system at that time, in addition to its computational structure.

  5. 5.

    In Sect. 18.2, I referred to Huys et al. (2015), who distinguished three classes of “failure modes” that computational modelling highlights in mental illnesses. One failure mode, viz. performing the right computations to solve the wrong problem, arises when the system M returns o 2, while computing a function g(i), which differs altogether from the f(i) it ought to compute. In this case, o 2 may be the right output to solve the wrong problem, g(i).

  6. 6.

    Writes Turing: “We may call [… these two types of errors] ‘errors of functioning’ and ‘errors of conclusion’. Errors of functioning are due to some mechanical or electrical fault which causes the machine to behave otherwise than it was designed to do. In philosophical discussions one likes to ignore the possibility of such errors; one is therefore discussing ‘abstract machines’. These abstract machines are mathematical fictions rather than physical objects. By definition they are incapable of errors of functioning. In this sense we can truly say that ‘machines can never make mistakes’. Errors of conclusion can only arise when some meaning is attached to the output signals from the machine. […] When a false proposition is typed we say that the machine has committed an error of conclusion. There is clearly no reason at all for saying that a machine cannot make this kind of mistake.” (Turing 1950, 449).

  7. 7.

    By ‘degrees of freedom’, I mean one of two things: either certain formal syntactic differences, or certain concrete physical differences between inputs and outputs and states of a system along some dimension of variation (e.g., voltage levels, rate of activation, or timing of activation).

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Acknowledgements

I am grateful to Andreas Heinz, J. Brendan Ritchie, Corey J. Maley, Dimitri Coelho Mollo, Joe Dewhurst, Nir Fresco, and an anonymous reviewer for their generous comments on previous versions of this paper. This work was supported by the Alexander von Humboldt Foundation through a Humboldt Research Fellowship for Experienced Researchers at the Department of Psychiatry and Psychotherapy, at the Charité University Clinic in Berlin.

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Colombo, M. (2021). (Mis)computation in Computational Psychiatry. In: Calzavarini, F., Viola, M. (eds) Neural Mechanisms. Studies in Brain and Mind, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-54092-0_18

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