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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.
When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serious tractability problems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.
The “resource-rational” approach is ambitious and worthwhile. A shortcoming of the proposed approach is that it fails to constrain what counts as a constraint. As a result, constraints used in different cognitive domains often have nothing in common. We describe an alternative framework that satisfies many of the desiderata of the resource-rational approach, but in a more disciplined manner.
We agree with the authors regarding the utility of viewing cognition as resulting from an optimal use of limited resources. Here, we advocate for extending this approach to the study of cognitive development, which we feel provides particularly powerful insight into the debate between bounded optimality and true sub-optimality, precisely because young children have limited computational and cognitive resources.
Lieder and Griffiths introduce resource-rational analysis as a methodological device for the empirical study of the mind. But they also suggest resource-rationality serves as a normative standard to reassess the limits and scope of human rationality. Although the methodological status of resource-rational analysis is convincing, its normative status is not.
History can help refine the resource-rational model by uncovering how cultural and cognitive forces act together to shape decision-making. Specifically, history reveals how the meanings of key terms like “problem” and “solution” shift over time. Studying choices in their cultural contexts illuminates how changing perceptions of the decision-making process affect how choices are made on the ground.
The project of justifying all the limits and failings of human cognition as inevitable consequences of strategies that are actually “optimal” relative to the limits on computational resources available may have some value, but it is far from a complete explanation. It is inconsistent with both common observation and a large body of experimentation, and it is of limited use in explaining human cognition.
A major constraint in resource-rational analysis is cognitive resources. Yet, uncovering the nature of individual components of the human mind has progressed slowly, because even the simplest behavior is a function of most (if not all) of the mind. Accelerating our understanding of the mind's structure requires more efforts in developing cognitive architectures.
Resource-rational approaches offer much promise for understanding human cognition, especially if they can reach beyond the confines of individual minds. Language allows people to transcend individual resource limitations by augmenting computation and enabling distributed cognition. Interactive language use, an environment where social rational agents routinely deal with resource constraints together, offers a natural laboratory to test resource-rationality in the wild.
We review evidence that the resource-rationality principle generalizes to human movement control. Optimization of the use of limited neurocomputational resources is described by the inclusion of the “neurocomputational cost” of sensory information processing and decision making in the optimality criterion of movement control. A resulting tendency to decrease this cost can account for various phenomena observed during goal-directed movements.
We argue that Lieder and Griffiths’ method for analyzing rational process models cannot capture an important constraint on resource allocation, which is competition between different processes for shared resources (Klein 2018, Biology and Philosophy33:36). We suggest that holistic interactions between processes on at least three different timescales – episodic, developmental, and evolutionary – must be taken into account by a complete resource-bounded explanation.
Lieder and Griffith's account of resource-rationality relies heavily on a notion of teleology. In this commentary, I criticize their teleocentric view as being incompatible with evolutionary theory, in which they aim to ground their analysis. As such, to save their view, I argue that they must jettison the notion of teleology, and their teleologically laden conclusions.
Although augmenting rational models with cognitive constraints is long overdue, the emotional system – our innately evaluative “affective” constraints – is missing from the model. Factoring in the informational nature of emotional perception, its explicit self-regulatory functional logic, and the predictable pitfalls of its hardwired behavioral responses (including a maladaptive form of “identity management”) can offer dramatic enhancements.
We agree that combining rational analysis with cognitive bounds, what we previously introduced as Cognitively Bounded Rational Analysis, is a promising and under-used methodology in psychology. We further situate the framework in the literature, and highlight the important issue of a theory of subjective utility, which is not addressed sufficiently clearly in the framework or related previous work.
Resource rationality holds great promise as a unifying principle across theories in neuroscience, cognitive science, and economics. The target article clearly lays out this potential for unification. However, resource-rational models are more diverse and less easily unified than might appear from the target article. Here, we explore some of that diversity.
Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena – belief polarization and the size-weight illusion – that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information compared to classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics or on assumptions of the computational resources of the mind.
Lieder and Griffiths present the computational framework “resource-rational analysis” to address the reverse-engineering problem in cognition. Here we discuss how developmental psychology affords a unique and critical opportunity to employ this framework, but which is overlooked in this piece. We describe how developmental change provides an avenue for ongoing work as well as inspiration for expansion of the resource-rational approach.
Lieder and Griffiths advocate for resource-rational analysis as a methodological device employed by the experimenter. However, at times this methodological device appears to morph into the substantive claim that humans are actually resource-rational. Such morphing is problematic; the methodological approach used by the experimenter and claims about the nature of human behavior ought to be kept completely separate.
Leider and Griffiths clarify the basis for unification between mechanism-driven and solution-driven disciplines and methodologies in cognitive science. But, two outstanding issues arise for their model of resource-rationality: human brains co-process information with their environments, rather than merely adapt to them; and this is expressed in methodological differences between disciplines that complicate Leider and Griffiths’ proposed structural unification.