• wednesday, 14 march 2018—12:15

    Computational models of decision making applied to spontaneous action initiation and volition

    Aaron Schurger

    How does the brain decide when to act, or whether to act in
    the first place, when decisions are not dictated by immediate external
    imperatives? The mechanisms that govern so-called “self-initiated”
    decisions-to-act remain poorly understood. One promising avenue for
    shedding light on self-initiated action is the use of formal
    computational models. Integration-to-bound models, which have long
    been used in research on cued decision making, have recently taken
    center stage in the study of self-initiated action.
    Integration-to-bound models at a minimum involve temporal integration
    over an input signal (the “evidence” or “imperative”) plus Gaussian
    white noise. In the context of self-initiated action, the “noise” in
    the model plays a more prominent role because the imperative to move
    is often weak or absent. At the same time, neural data show that noise
    in the brain is not white, but rather is long-term correlated, with a
    spectral density of the form 1/f^ß (with ß close to 1.5) across a wide
    range of spatial scales. I will discuss ongoing work using
    computational models to account for self-initiated action and
    “volition” (the conscious feeling of an urge to initiate movement)
    while staying true to the spectral properties of noise that are
    demonstrably present in real neural data. The results expose new
    perspectives for models of the RP and for accumulator models in
    general by suggesting that the spectral properties of the stochastic
    input to the accumulator should be allowed to vary, in keeping with
    the nature of real biological neural noise.

    external seminar