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

Abstract A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.g., levels of attention, response thresholds). However, adjusting our control to meet new goals comes at a cost: we are better at achieving a goal in isolation than when transitioning between goals. The source of thesecontrol adjustment costsremains poorly understood, and the bulk of our understanding of such costs comes from settings in which participants transition between discrete task sets, rather than performance goals. Across four experiments, we show that adjustments in continuous control states incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task 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 accounts for our empirical findings that people 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 characterize the costs of adjusting control to meet changing goals, and show that these costs can emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior..

Medienart:	Preprint

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	bioRxiv.org - (2024) vom: 16. Apr. Zur Gesamtaufnahme - year:2024

Sprache:	Englisch

Beteiligte Personen:	Grahek, Ivan [VerfasserIn] Leng, Xiamin [VerfasserIn] Musslick, Sebastian [VerfasserIn] Shenhav, Amitai [VerfasserIn]

Links:	Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.1101/2023.08.22.554296

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	XBI040615669