Liu, Shuze
@liushuze.bsky.social
PhD student @ Harvard || computational cognitive science, human decision making and reasoning
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!
August 19, 2025 at 4:04 AM
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!
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.
August 19, 2025 at 4:03 AM
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.
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!
August 19, 2025 at 4:02 AM
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!
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.
August 19, 2025 at 4:01 AM
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.
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!
August 19, 2025 at 3:58 AM
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!
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
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
July 28, 2025 at 12:13 AM
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
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
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!
July 27, 2025 at 7:41 PM
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!
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.
July 27, 2025 at 7:36 PM
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.
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.
July 27, 2025 at 7:34 PM
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.
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.
July 27, 2025 at 7:31 PM
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.
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!
June 11, 2025 at 4:00 PM
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!
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.
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.
June 11, 2025 at 3:56 PM
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.
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.
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.
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.
June 11, 2025 at 3:54 PM
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.
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.
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.
June 11, 2025 at 3:48 PM
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.
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!
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!
April 12, 2025 at 7:05 PM
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!
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!
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.
April 12, 2025 at 7:04 PM
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.
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.
April 12, 2025 at 7:04 PM
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.
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.
April 12, 2025 at 7:03 PM
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.
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.
April 12, 2025 at 7:02 PM
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.
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)
March 23, 2025 at 1:05 AM
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)