Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

Abstract

Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

Computational Evidence for Hierarchically-Structured Reinforcement Learning in Humans

“…Bayesian-RL hybrids have combined Bayesian inference at the abstract level with RL at the lower level (17; 28). Lastly, resource-rationality is an approach that combines principles of rationality and cognitive constrains (40), and would explain our results through the lens of effort allocation.…”

Origin of perseveration in the trade-off between reward and complexity

“…The idea that many aspects of cognition can be explained in terms of resource-bounded rationality has gained wide currency (Gershman et al, 2015;Lieder and Griffiths, 2019). The precise nature of the resource constraints and their implications is a matter of ongoing research.…”

Efficient sampling and noisy decisions

“…This is essential, as it helps to establish frameworks that allow comparing behavior not only across different tasks and goals, but also across different levels of description, for instance, from single cell operation to observed behavior(24). We argue that this approach is fundamental to provide benchmarks for human performance that can lead to the discovery of alternative heuristics(48) that could also be suboptimal under resource-constrained frameworks – as we demonstrate here for DbS – but that might be in turn the optimal strategy to implement if one considers costs of optimal adaptation(25, 49). We conclude that the understanding of brain function under a principled research agenda, which takes into account decision mechanisms that are biologically feasible, will be essential to accelerate the elucidation of the mechanisms underlying human cognition.…”

“…So far, we have proposed a computational problem and elaborated an algorithmic solution to our resource-constrained framework. However, it would be desirable to propose an implementational representation that also solves our resource-constrained problem(24, 25). Here we show that an advantage of our proposed coding scheme is that it allows a minimalistic and parsimonious implementational architecture.…”