Liu, Shuze
@liushuze.bsky.social
95 followers 140 following 29 posts
PhD student @ Harvard || computational cognitive science, human decision making and reasoning
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Liu, Shuze @liushuze.bsky.social · Aug 19
Overall, by jointly studying constraints on action sets and policy complexity, we provide a general picture of how humans adjust the two together. The results reveal lossy problem simplification beyond typical modeling assumptions, which may be an integral part of naturalistic human decision-making!
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Liu, Shuze @liushuze.bsky.social · Aug 19
In a large-action-space contextual bandit experiment, we found that humans exploit the above interplay, enlarging action sets alongside policy complexity to mitigate suboptimality. Under time limits, they remain near-optimal for their chosen action set, indicating spontaneous problem simplification.
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Liu, Shuze @liushuze.bsky.social · Aug 19
Beyond explaining past data, our framework prescribes a complex interaction. Enlarging the action set size uncaps policy complexity and enables greater reward. It also mitigates the increase in suboptimality following policy complexity increments, boosting the reward-efficiency of cognitive effort!
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Liu, Shuze @liushuze.bsky.social · Aug 19
Using rate-distortion theory, we assess suboptimalities incurred by smaller action consideration set sizes at various levels of policy state-dependence. We rationalize empirical signatures of human option generation as adaptations to joint limitations on action set size and policy complexity.
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Liu, Shuze @liushuze.bsky.social · Aug 19
In real-life decisions, vast action spaces often preclude our exhaustive consideration. Furthermore, cognitive constraints limit the state-dependence of policies that map world states to the actions considered. We build a resource-rational framework unifying both ecologically relevant constraints!
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Liu, Shuze @liushuze.bsky.social · Aug 19
@gershbrain.bsky.social and I have a new paper in PLOS Comp Bio!

We study how two cognitive constraints—action consideration set size & policy complexity—interact in context-dependent decision making, and how humans exploit their synergy to reduce behavioral suboptimality.

osf.io/preprints/ps...
OSF
osf.io
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Liu, Shuze @liushuze.bsky.social · Jul 28
Finally, a huge thanks to my mentors @gershbrain.bsky.social and Bilal Bari for their support, insight, and encouragement!

For those interested, we also have an earlier JEP:G paper, which inspired the mental cost modeling in our CogSci submission:
gershmanlab.com/pubs/Liu25.pdf
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Liu, Shuze @liushuze.bsky.social · Jul 27
We found that humans meta-reason their policy complexity according to both time and mental costs, exhibiting consistently supralinear mental cost functions across tasks. This overturns common assumptions, and supports the construct validity of info-theoretic measures as a domain-general mental cost!
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Liu, Shuze @liushuze.bsky.social · Jul 27
To address this literature gap, we designed a series of contextual bandit experiments---addressing speed-accuracy tradeoffs, working memory set size manipulations, and reward magnitudes---to stress-test the mutual information formulation of mental costs and look for consistent relationships.
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Liu, Shuze @liushuze.bsky.social · Jul 27
While rational inattention & policy compression have formulated mental costs via mutual information, there remains disagreement on whether it induces a capacity limit or a linear cost. Time costs further confound the picture, as they also incentivize low policy complexity to reduce decision time.
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Liu, Shuze @liushuze.bsky.social · Jul 27
It is well known that context sensitivity incurs mental costs. However, it is unclear which domain-general cognitive resources underlie mental costs, and whether existing cost formulations have construct validity--which likely requires them to scale robustly with the assumed resource substrate.
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Liu, Shuze @liushuze.bsky.social · Jul 27
Looking forward to sharing our work at #cogsci2025! We aim at getting one step closer to a domain-general formulation of mental costs via policy compression.

Come see my presentation at "Talks 35: Reasoning". It is scheduled at 16:44 PST, August 1 at Nob Hill C!

gershmanlab.com/pubs/LiuGers...
gershmanlab.com
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Reposted by Liu, Shuze
weijima.bsky.social
Wei Ji Ma @weijima.bsky.social · Jun 10
This is an unusual one: the project started in 2009, with Trevor Holland collecting data in an auditory-visual integration paradigm with many conditions within subjects. But modeling it remained challenging over the years until Luigi Acerbi and Shuze Liu took it on. osf.io/preprints/ps...
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Liu, Shuze @liushuze.bsky.social · Jun 11
Overall, we stress-test the Bayesian account of multisensory perception by systematically traversing its full modeling space. Human behavior remains well-explained, but only under specific, often overlooked assumptions. A richer picture emerges when we let data guide our modeling assumptions!
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Liu, Shuze @liushuze.bsky.social · Jun 11
Beyond core inference, other complex perceptual factors play a role too:
1) Sensory noise increases in multisensory trials, likely due to divided attention;
2) Auditory observations are stretched according to the visual range, suggesting spontaneous cross-modal recalibration in humans.
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Liu, Shuze @liushuze.bsky.social · Jun 11
We found that key model choices drastically affect fits and better explain the human central tendency:
1) Human priors are non-Gaussian;
2) Sensory noise is heteroskedastic, dipping centrally and plateauing peripherally;
3) Both model averaging (optimal) and probability matching fit behavior well.
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Liu, Shuze @liushuze.bsky.social · Jun 11
We explore modeling choices in Bayesian cue integration—priors, sensory noise functions, and causal inference strategies—using a data-driven, semiparametric approach. With promising candidates identified, we enumerate them in a combinatorial model space and test them via model comparison.
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Liu, Shuze @liushuze.bsky.social · Jun 11
@weijima.bsky.social, @lacerbi.bsky.social, Trevor, and I have a new preprint on Bayesian models of multisensory perception!

We systematically inspect the underexplored degrees of freedom in Bayesian models, squeezing out their best capability in capturing human behavior.

osf.io/preprints/ps...
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Liu, Shuze @liushuze.bsky.social · Apr 12
Overall, our study:
1) Connects seemingly disparate cognitive measures: state-dependence v. RT, goal-directed v. habitual behavior;
2) Prescribes task-general insight via normative principles;
3) Highlights the utility of incorporating multiple resource formulations in resource-rational studies!
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Liu, Shuze @liushuze.bsky.social · Apr 12
Across three experiments, humans adaptively adjusted policy complexity in the predicted directions (though with a leftward bias, which we model via memory costs in our CogSci paper). LBA modeling revealed that policy-compression-style perseveration had manifested strongly in participant behavior.
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Liu, Shuze @liushuze.bsky.social · Apr 12
Given policy complexity-RT relations, we can derive policy complexity levels that maximize reward over time. This generates predictions across various task manipulations, including ITIs, reward regularities, and set sizes (reward magnitudes forthcoming in CogSci 2025), which we test in this paper.
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Liu, Shuze @liushuze.bsky.social · Apr 12
Policy compression applies rate-distortion theory to action selection, specifying the attainable reward at every policy state-dependence/complexity level. It prescribes a linear relationship b/w policy complexity and RTs, and rationalizes action perserveration as optimal usage of limited resources.
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Liu, Shuze @liushuze.bsky.social · Apr 12
Tailoring actions to states taxes cognitive resources. Two prominent resource formulations are time and memory, studied in speed-accuracy tradeoffs and set-size effects. We unify them under policy compression, prescribing how humans should adaptively adjust the state-dependence of their policies.
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Liu, Shuze @liushuze.bsky.social · Apr 12
@lucylai.bsky.social, @gershbrain.bsky.social, Bilal Bari, and I have a new paper out in JEP:G!

We study the time and memory costs of policy compression—a resource-rational framework for decision making, focusing on how state-dependent our policies ought to be.

psycnet.apa.org/record/2026-...
APA PsycNet
psycnet.apa.org
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Liu, Shuze @liushuze.bsky.social · Mar 23
By offering an initial characterization of how people select and switch among assets, we aim to lay the groundwork for understanding human decisions in temporally extended contexts, where uncertain future potential—as opposed to immediate payoff—plays a central role in our current reasoning. (6/6)
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