Variability and harshness shape flexible strategy-use in support of the constrained flexibility framework

Abstract

Human cognition is incredibly flexible, allowing us to thrive within diverse environments. However, humans also tend to stick to familiar strategies, even when there are better solutions available. How do we exhibit flexibility in some contexts, yet inflexibility in others? The constrained flexibility framework (CFF) proposes that cognitive flexibility is shaped by variability, predictability, and harshness within decision-making environments. The CFF asserts that high elective switching (switching away from a working strategy) is maladaptive in stable or predictably variable environments, but adaptive in unpredictable environments, so long as harshness is low. Here we provide evidence for the CFF using a decision-making task completed across two studies with a total of 299 English-speaking adults. In line with the CFF, we found that elective switching was suppressed by harshness, using both within- and between-subjects harshness manipulations. Our results highlight the need to study how cognitive flexibility adapts to diverse contexts.

Keywords: Adaptive cognition; Cognitive flexibility; Constrained flexibility framework; Decision-making.

Figure 1

The impacts of variability, predictability, and harshness on responsive and elective switching, as described by the CFF. Responsive switching occurs when strategy efficacy is low, or failing. Elective switching is sampling alternatives despite strategy efficacy being high. a) As variability increases from stable to variable, responsive switching occurs more frequently, in response to strategy failures. However, responsive switches should occur whenever failure occurs or is imminent, regardless of whether b) the change in efficacy is predictable or c) the consequences of failure are high. d) Elective switching is also increasingly beneficial with increasing variability because the current strategy’s efficacy might be surpassed by an alternative. e) Yet, in predictably variable environments, elective switching is less valuable because after an initial sampling period, the best alternatives are already identified (although preemptive switching from effective strategies to soon-to-be effective alternatives is possible). f) Under conditions of high harshness, elective switching is suppressed to minimize the risk of failure.

Figure 2

Task design for the Bandit Jars task. a) On each trial, participants selected any of the four jars and collected the water it yielded by selecting the puddle. Puddle-size corresponded to the amount of water that was added to the tube. b) Each time the tube was filled, a rain shower was produced, putting out one of the fires. Fires regenerated or spread after every three trials, with rain showers resetting the count. The amount of water each jar produced was predetermined by an underlying reward schedule which was either c) stable (i.e., jar yields were consistent relative to one another) or d) variable (jar yields fluctuated drastically) over the course of the 75-trial blocks. e) In the Harsh condition, the tube was cracked and leaked after every selection, making it more difficult to fill the tube.

Figure 3

The likelihood of switching jars (y-axis) after finding reward values from 0–100 (x-axis) in Study 1: No Added Incentive—Not Harsh, No Added Incentive – Harsh and Study 2: Monetary Incentive—Not Harsh and Monetary Incentive—Harsh conditions in A) Stable and B) Variable environments. Recall that responsive switching is switching after finding low value rewards, while elective switching is switching that occurs after finding high value rewards.

Figure 4

Studies 1 and 2 Reinforcement Learning Model results. Posterior probability distributions in Stable and Variable reward environments for participants’ learning rate (A-B) and elective exploration parameters (C-D) in Study 1: No Added Incentive—Not Harsh, No Added Incentive—Harsh and Study 2: Monetary Incentive—Not Harsh and Monetary Incentive—Harsh conditions.