Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
Faculty of Psychology, University of Basel, Basel, Switzerland.
Psychon Bull Rev. 2024 Feb;31(1):32-48. doi: 10.3758/s13423-023-02284-4. Epub 2023 Jul 19.
According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.
根据简单决策的现有理论，决策是通过不断地采样和积累感知证据来启动的，直到达到阈值。许多模型，如扩散决策模型，都假设了一个噪声积累过程，数学上描述为具有高斯分布噪声的随机 Wiener 过程。最近，在 Lévy Flights (LF) 模型中提出了一种替代决策的方法，在该模型中，积累噪声的特征是具有重尾幂律分布，由一个参数 [Formula: see text] 控制。LF 模型在证据积累中产生突然的大“跳跃”，这是标准 Wiener 扩散模型所没有的，一些人认为这可以更好地拟合数据。然而，目前还不清楚证据积累中的跳跃是否具有任何实际的心理意义。在这里，我们研究了 Voss 等人的假说（《心理科学通报与评论》，26(3)，813-832，2019），即跳跃可能反映了人们在做决策时依赖的证据来源的突然变化。我们推断，如果跳跃是心理上真实的，我们应该观察到随着人们对任务的熟练程度的提高（即随着经验的积累，人们采用稳定的决策策略），跳跃会系统地减少。我们使用一个五层深度推理神经网络来拟合来自 Evans 和 Brown 的研究（《心理科学通报与评论》，24(2)，597-606，2017）的行为数据，为五个版本的 LF 模型进行参数估计。分析表明，随着实践的进行，跳跃会系统地减少，以至于 LF 模型随着时间的推移更接近标准 Wiener 模型。这种趋势不能归因于其他参数变化的来源，这排除了与其他模型参数进行权衡的可能性。我们的分析表明，LF 模型中的跳跃可能是在任务执行早期，相对缺乏经验的观察者表现出的策略不稳定性。我们得出结论，进一步研究证据积累中的跳跃是否具有潜在的心理解释是值得的。
