Instruction effects on randomness in sequence generation

Guseva, Maja; Bogler, Carsten; Allefeld, Carsten; Haynes, John‐Dylan

doi:10.3389/fpsyg.2023.1113654

Cited by 3 publications

(14 citation statements)

References 113 publications

(166 reference statements)

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“…In a recent study we confirmed that randomization performance depends on the precise instructions (Guseva et al, 2023). In that experiment, the requirement to choose randomly was explicitly stated in some instruction conditions ("choose randomly") while being more indirectly implied in others.…”

Section: Task Diversity In Rsg Studiessupporting

confidence: 63%

“…As in our previous behavioral study (Guseva et al, 2023), we studied the neural effects of different randomization task instructions in a between-subjects design to avoid carry-overs between tasks. Each participant received one of the three task instructions: Explicit Randomness (ER), Free Choice (FC) and Mental Coin Toss (MC).…”

Section: Methodsmentioning

confidence: 99%

“…Human randomization behavior has been commonly studied using random sequence generation (RSG) tasks (Daniels et al, 2003; Guseva et al, 2023; Heuer et al, 2010; Jahanshahi et al, 2000; Naefgen & Janczyk, 2018; Nickerson, 2002; Peters et al, 2007; Schneider et al, 2004). In such tasks participants make consecutive choices between different choice option sets in a seemingly random way (see Nickerson, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Participants were required to make repeated binary choices between heads and tails of a coin while we acquired functional magnetic resonance imaging data from their brain activity. Importantly, we used three task variations that elicit differences in randomization behavior based on a previous study (Guseva et al, 2023). The Explicit Randomness (ER) task group received the instruction to “ make random choices ”, the Free Choice group (FC) task group to “ choose freely between the two sides ”, and the Mental Coin Toss (MC) task group to “ simulate the output of a fair coin toss ”.…”

Section: Introductionmentioning

confidence: 99%

“…The Explicit Randomness (ER) task group received the instruction to “ make random choices ”, the Free Choice group (FC) task group to “ choose freely between the two sides ”, and the Mental Coin Toss (MC) task group to “ simulate the output of a fair coin toss ”. These tasks were chosen based on previous behavioral work (Guseva et al, 2023) and because they present conceptually different ways to elicit random behavior. In line with that previous work, we expected significant differences in randomization measures between MC sequences compared to ER and FC and no differences between ER and FC.…”

Section: Introductionmentioning

confidence: 99%

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Neural Correlates of Different Randomization Tasks

Guseva,

Bogler,

Allefeld

et al. 2024

Preprint

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In some cases, when we are making decisions, the available choices can appear to be equivalent. When this happens, our choices appear not to be constrained by external factors and instead we can believe to be selecting "randomly". Furthermore, randomness is sometimes even explicitly required by task conditions such as in random sequence generation (RSG) tasks. This is a challenging task that involves the coordination of multiple cognitive processes, which can include the inhibition of habitual choice patterns and monitoring of the running choice sequence. It has been shown that random choices are strongly influenced by the way they are instructed. This raises the question whether the brain mechanisms underlying random selection also differ between different task instructions. To assess this, we measured brain activity while participants were engaging in three different variations of a sequence generation task: Based on previous work, participants were instructed to either (1) "generate a random sequence of choices", (2) "simulate a fair coin toss", or (3) "choose freely". Our results reveal a consistent frontoparietal activation pattern that is shared across all tasks. Specifically, increased activity was observed in bilateral inferior and right middle frontal gyrus, left pre-supplementary motor area, bilateral inferior parietal lobules and portions of anterior insular cortex in both hemispheres. Activity in the mental coin toss condition was higher in right dorsolateral prefrontal cortex, left (pre-) supplementary motor area, a portion of right inferior frontal gyrus, bilateral superior parietal lobules and bilateral anterior insula. Additionally, our multivariate analysis revealed a distinct region in the right frontal pole to be predictive of the outcome of choices, but only when randomness was explicitly instructed. These results emphasize that different randomization tasks involve both shared and unique neural mechanisms. Thus, even seemingly similar randomization behavior can be produced by different neural pathways.

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Section: Task Diversity In Rsg Studiessupporting

confidence: 63%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Correlates of Different Randomization Tasks

Guseva,

Bogler,

Allefeld

et al. 2024

Preprint

Self Cite

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Explaining the flaws in human random generation as local sampling with momentum

Castillo,

León-Villagrá,

Chater

et al. 2024

PLoS Comput Biol

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In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people’s sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.

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Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Colas,

O’Doherty,

Grafton

2024

PLoS Comput Biol

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Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants—even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

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Instruction effects on randomness in sequence generation

Cited by 3 publications

References 113 publications

Neural Correlates of Different Randomization Tasks

Neural Correlates of Different Randomization Tasks

Explaining the flaws in human random generation as local sampling with momentum

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

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