Pushing the Bounds of Bounded Optimality and Rationality

Musslick, Sebastian; Masís, Javier

doi:10.1111/cogs.13259

Cited by 6 publications

(1 citation statement)

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“…Our ability to flexibly adjust control processes lies at the core of goal-directed behavior. Given the importance of such behavior, the question of why there are costs to adjusting cognitive control state which support it, remains a puzzle within cognitive science (Musslick & Masís, 2023). By focusing on adjustments of continuous control states, rather than changes in discrete task-sets, we show how costs of adjusting control can arise from the dynamics of cognitive control.…”

Section: Discussionmentioning

confidence: 99%

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

Grahek,

Leng,

Musslick

et al. 2023

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The ability to switch between goals is the cornerstone of human cognition and behavior. Cognitive control allows for rapid adjustments of cognition in accordance with new goals, but control adjustments come at a cost. This cost is traditionally studied in situations that demand changes to one's task, without necessitating other changes in the control state. Goal flexibility, however, often entails maintaining the same task while adjusting the amount and type of control being allocated to that task. For instance, different stages of a given task might require us to process information more or less efficiently (e.g., by varying levels of attention) and/or respond more or less cautiously (e.g., by varying response thresholds). Across four experiments, we show that such within-task control adjustments incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task (the color-word Stroop) under varying performance goals (e.g., to be fast or to be accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model predicts and our empirical findings confirm that people will under-shoot their target control state more (i.e., exhibit larger adjustment costs) when (a) goals switch rather than remain fixed over a block (Study 1); (b) target control states are more distant from one another (Study 2); (c) less time is given to adjust to the new goal (Study 3); and (d) when anticipating having to switch goals more frequently (Study 4). Our findings demonstrate that there is a cost to adjusting control to meet one's goal - even in the absence of a task change - and show that this cost can emerge directly from the dynamics of control adjustment. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior.

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Section: Discussionmentioning

confidence: 99%

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

Grahek,

Leng,

Musslick

et al. 2023

Preprint

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An Information-Theoretic Approach to Reward Rate Optimization in the Tradeoff Between Controlled and Automatic Processing in Neural Network Architectures

Petri,

Musslick,

Cohen

2023

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This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more simultaneous tasks, and its relationship to automaticity. We begin by formalizing two forms of cost associated with a given level of performance: an intensity cost that quantifies how much information must be added to the input to achieve the desired response for a given task, that we treat as the contribution of control; and an interaction cost that quantifies the degree to which performance is degraded as a result of interference between processes responsible for performing two or more tasks, that we treat as inversely related to automaticity. We develop a formal expression of the relationship between these two costs, and use this to derive the optimal control policy for a desired level of performance. We use that, in turn, to quantify the tradeoff between control and automaticity, and suggest how this can be used as a normative framework for understanding how people adjudicate between the benefits of control and automaticity.

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Advancing a theoretical framework for exploring heuristics and biases within HR decision-making contexts

Hunt,

Wang,

Alizadeh

et al. 2024

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PurposeThis paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.Design/methodology/approachThis paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.FindingsThe authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.Practical implicationsDecades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.Originality/valueHeuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

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Pushing the Bounds of Bounded Optimality and Rationality

Cited by 6 publications

References 70 publications

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

An Information-Theoretic Approach to Reward Rate Optimization in the Tradeoff Between Controlled and Automatic Processing in Neural Network Architectures

Advancing a theoretical framework for exploring heuristics and biases within HR decision-making contexts

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