Deliberation in the Motor System: Reflex Gains Track Evolving Evidence Leading to a Decision

Selen, Luc P. J.; Shadlen, Michael N.; Wolpert, Daniel M.

doi:10.1523/jneurosci.5273-11.2012

Cited by 190 publications

(186 citation statements)

References 61 publications

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“…To make accurate decisions about motion direction, subjects typically need around several hundred milliseconds, depending on the strength of the motion stimulus. Consistent with previous findings, Selen et al (2012) report that subject performance improved as a function of both increased motion coherence and viewing time.…”

supporting

confidence: 91%

“…A recent paper by Selen et al (2012), published in The Journal of Neuroscience, builds upon this emerging body of work. This study provides novel behavioral evidence for decision-related signals in the human motor system and advances the current state of the field in two major ways.…”

mentioning

confidence: 98%

“…Confirming the presence of similar effects in normal, untrained human subjects is therefore a critical step forward. Second, in moving from invasive recording and microstimulation paradigms involving monkeys Shadlen, 2000, 2007) to human studies, Selen et al (2012) were pressed to find a noninvasive method of obtaining a behavioral readout of evolving decision signals. Their recruitment of the elbow stretch reflex in this experiment thus represents an important achievement that potentially opens doors to other analogous, noninvasive methods for probing decision variables in humans.…”

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confidence: 99%

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Evidence for a Decision Variable in the Human Motor System

Kubanek¹,

Kaplan²

2012

Journal of Neuroscience

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supporting

confidence: 91%

mentioning

confidence: 98%

mentioning

confidence: 99%

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Evidence for a Decision Variable in the Human Motor System

Kubanek¹,

Kaplan²

2012

Journal of Neuroscience

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“…First, we needed a multistep decision-making design. Indeed, in standard (single-step) experimental designs using, for example, a constant coherence percentage of a random dot motion display (9,10,14) or singlestep reward values (15), serial stage/HEI and HLCA/PEI models are not easily distinguishable (6). Second, in a multistep decisionmaking design, we needed mean-max conflict trials.…”

Section: Discussionmentioning

confidence: 99%

Continuous track paths reveal additive evidence integration in multistep decision making

Calderon

Dewulf

Gevers

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Multistep decision making pervades daily life, but its underlying mechanisms remain obscure. We distinguish four prominent models of multistep decision making, namely serial stage, hierarchical evidence integration, hierarchical leaky competing accumulation (HLCA), and probabilistic evidence integration (PEI). To empirically disentangle these models, we design a two-step reward-based decision paradigm and implement it in a reaching task experiment. In a first step, participants choose between two potential upcoming choices, each associated with two rewards. In a second step, participants choose between the two rewards selected in the first step. Strikingly, as predicted by the HLCA and PEI models, the first-step decision dynamics were initially biased toward the choice representing the highest sum/mean before being redirected toward the choice representing the maximal reward (i.e., initial dip). Only HLCA and PEI predicted this initial dip, suggesting that first-step decision dynamics depend on additive integration of competing second-step choices. Our data suggest that potential future outcomes are progressively unraveled during multistep decision making.multistep decision making | computational modeling | reaching task I magine leaving your house in search of food in the neighborhood. Outside, you must first decide to go left or right. Going left subsequently affords a second left-right choice between Thai and Italian food, whereas going right affords another left-right choice between Mexican and Lebanese food. This illustrates a typical twostep tree path decision-making scenario (i.e., four potential tree paths; see Fig. 1A). Such two-step decisions have been conceptualized within the framework of model-based reinforcement learning (1, 2), and recent work has focused on which brain areas underpin reward representation in multistep decision making (3-5). However, the computations underlying multistep decision making are still debated. To address this issue, we distinguish four computational models. We derive and contrast empirical predictions from the four models and test them. In the following paragraph we explain the common ideas and distinguishing features of the four models.In each model, each tree path is associated with an evidence (E) accumulator (e.g., in Fig. 1A, there are four tree paths; we will use Fig. 1A and Supporting Information, Appendix A: Computational Models, Fig. S1, to illustrate the four models). The two leftmost E accumulators (i.e., leading to 3 and 9 in Fig. 1A) are taken as inputs to a left motor evidence (ME) accumulator. The two rightmost E accumulators project to a right ME accumulator. All models reach decisions by gradually updating their E and/or ME accumulator values at each iteration depending on the rewards associated with each tree path. Models can be conceptually distinguished based on three features. The first featuremapping-defines how E accumulators map to ME accumulators; in particular, this feature distinguishes models where only the maximally active E accumulator pro...

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“…It is conceivable that once activity is in this "optimal subspace," the motor system is abruptly drawn into the local attractor, with little chance of an override, consistent with a relatively late commitment to move (12). This view is supported by recent evidence showing that action preparation can also be read out from muscle tonus (13), suggesting that during movement preparation cortical, spinal, and peripheral systems are all interacting in triggering the upcoming movement. In addition, recent physiological findings in muscle spindles show that the activation of these receptor cells in the belly of a muscle does not correlate with current, but rather with upcoming muscle length (14), indicating not only feedback to the motor cortices about planned actions, but also constant input from the cortical motor system to the muscle spindles.…”

mentioning

confidence: 91%