Increasing resolution in the mechanisms of resolve

Abstract

Ainslie offers an encompassing and compelling account of willpower, although his big-picture view comes occasionally at the cost of low resolution. We comment on ambiguity in the metacognitive and prospective mechanisms of resolve implicated in recursive self-prediction. We hope to show both the necessity and promise of specifying testable cognitive mechanisms of willpower.

Full text

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Commentary on Ainslie, G., (2021) “Willpower With and Without Effort”:
Increasing resolution in the mechanisms of resolve
Adam Bulley1* & Daniel L. Schacter2
1 Department of Psychology, Harvard University, Cambridge, MA 02138, USA; The
University of Sydney, School of Psychology and Brain and Mind Centre, NSW 2050,
Australia. Email: adam_bulley@fas.harvard.edu; Web: http://adambulley.org/
2 Department of Psychology, Harvard University, Cambridge, MA 02138, USA. Email:
dls@wjh.harvard.edu; Web: https://scholar.harvard.edu/schacterlab/home
*Corresponding author
Abstract: 53 words
Main Text: 997 words
References: 301 words
Entire Text: 1482 words

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Abstract
Ainslie offers an encompassing and compelling account of willpower, though his big-picture
view comes occasionally at the cost of low resolution. We comment on ambiguity in the
metacognitive and prospective mechanisms of resolve implicated in recursive self-prediction.
We hope to show both the necessity and promise of specifying testable cognitive mechanisms
of willpower.
Main text
While Ainslie frames resolve in terms of game-theoretic intertemporal bargaining, he leaves
the cognitive and neural instantiation of resolve at times underspecified. In part, this is because
the empirical evidence is wanting – as he acknowledges – but it is also because, by design,
game-theoretical accounts remain agnostic about underlying mechanisms. In a prisoner’s
dilemma, the rules of the game and its payoff matrix are similar whether the agents involved
happen to be bacteria or bankers. Nonetheless, we think there are costs associated with low
resolution in the proximate mechanisms of resolve, as well as promising routes forward if
proposals concerning the nature of these mechanisms can be sharpened up. We attempt to
demonstrate these points of constructive clarification in the context of the metacognitive and
prospective mechanisms implicated in “recursive self-prediction” that Ainslie argues forms the
basis of resolve.
As a starting point, we take it as a given that humans don’t consistently think through their
intertemporal trade-offs with the kind of game-theoretic bargaining logic that observers can
attribute to them. Ainslie acknowledges that the intertemporal bargaining of resolve could
indeed happen below the level of self-awareness, or without any explicit representation at all.
In fact, he suggests that the recursive self-prediction underpinning resolve might operate
through “explicit self-enforcing contracts”, via “vague awareness”, perhaps “displaced away
from any explicit self-knowledge”, or even as purely “implicit contracts”. It is therefore unclear
how much “self” we should expect to find in “self-prediction.”
One cost of this low specificity in the metacognitive mechanisms of resolve is that it leaves
Ainslie’s model resistant to disconfirmation in the face of new evidence. For instance, any
failures to find recursive self-prediction in the implementation of resolve could be explained
away by shuttling the relevant level of explanation around inside the mind of the resolver.
Suppose that, upon a careful experimental investigation, we find that participants report
resolving to delay their gratification for a later payoff simply because they foresee the long-
term benefits of doing so, absent any anticipation of their own future behavior. In such a case,
the enforcement mechanism that maintains an intention against lapses could be the anticipated
negative costs of the smaller, sooner reward option. For instance, to answer Ainslie’s question,
“Why not eat this piece of chocolate – it will barely show?” a non-self-predictive resolver might
answer, “because I foresee even the small damage of a single piece as sufficiently costly,
however tempting”. Under Ainslie’s view, could we not explain away this finding by arguing
that the underlying logical structure of the participant’s decision-making is nonetheless one of

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game-theoretical self-predictive bargaining, even if the participants themselves are not aware
of it and would opt to explain their own decision-making differently?
The “prediction” portion of “self-prediction” is likewise somewhat ambiguous. Ainslie argues
that because resolve is “a matter of framing and monitoring choices”, it “might not be
accompanied by measurable brain activity any more than other semantic content is” [our
emphasis]. Elsewhere, though, Ainslie suggests instead that “scenarios created in episodic
memory might also serve this function [of formulating and monitoring the intertemporal
bargains that form resolve].”
These alternatives lead to various questions that could be productively reformulated as testable
hypotheses. Does one need to actually imagine oneself failing in the future to adhere to a “no
alcohol on weeknights” rule in order to implement the resolve to put down the Shiraz, as an
episodic simulation account would entail? Is it enough to simply “know”, in semantic terms,
that one is more likely to fail in the future if one fails now? Situating resolve amidst existing
frameworks of prospective cognition and deliberation could carve out a space for empirical
steps forwards (see Bulley & Schacter, 2020; Szpunar et al., 2014).
For instance, we might test the evidence accumulation process by which people generate
whatever predictions are central to resolve. Ainslie describes the act of reneging on a rule as if
it constitutes a piece of empirical evidence that people use to anticipate their own future
behaviors. But how so? One possibility is that episodic memories of reneging serve as raw
material in the constructive episodic simulation of one’s behavior in facing future willpower
challenges. Convergent lines of evidence support the proposal that episodic future simulation
operates via the recombination of episodic details from memory (Schacter et al., 2007;
Suddendorf & Corballis, 2007), with a common core network of brain activity supporting
remembering the past and imagining the future (Benoit & Schacter, 2015). Accordingly, if
Ainslie’s “recursive self-prediction” is a constructive process that samples episodic memories
to inform anticipated behaviors, we should hypothesize that resolve will be associated with
activity in this core network, similar to when participants directly retrieve episodic memories
of willpower failures.
Research on prospection may also help to accommodate the idea that both semantic and
episodic processes are sufficient for resolve in different contexts. The development of “good
habits” that Ainslie equates to the successful operation of resolve may involve shifting
contributions along a gradient of semantic and episodic processes (Irish & Vatansever, 2020;
Szpunar et al., 2014). For instance, episodic simulation might be required to get resolve “off
the ground”, but after repeated (successful) instances, resolve could be eventually implemented
in entirely semantic terms (for a similar suggestion about external precommitment see Bulley
& Schacter, 2020). In this case, we should hypothesize that people with hippocampal damage
who have deficits in the ability to imagine the future (Schacter et al., 2017) would be less
capable of initiating intertemporal resolve in Ainslie’s terms – but perhaps less impaired when
it comes to maintaining “good habits” once these have been established (see Bakkour et al.,
2019; Kwan et al., 2012; Palombo et al., 2015).

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In the foregoing, we have pointed out some costs associated with ambiguities in Ainslie’s
otherwise encompassing big-picture account of willpower. We have provided some examples
where pinning down specific mechanisms leads to testable predictions, focusing on the nature
of the metacognitive and prospective mechanisms involved in recursive self-prediction where
increased clarity would be perhaps most instructive.
Conflicts of interest: None
Funding statement: AB is supported by an Australian National Health and Medical Research
Council CJ Martin Biomedical Fellowship APP1162811 (GNT1162811). DLS is supported
by by National Institute of Mental Health grant R01 MH060941 and National Institute on
Aging grant R01 AG008441.
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