Jumping to Conclusion? A Lévy Flight Model of Decision Making [Dataset]
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The diffusion model is one of the most prominent response time models in cognitive psychology. The model describes evidence accumulation as a stochastic process that runs between two boundaries until a threshold is hit, and a decision is made. The model assumes that information accumulation follows a Wiener diffusion process with normally distributed noise. However, the model's assumption of Gaussian noise might not be the optimal description of decision making. We argue that Lévy flights, incorporating more heavy-tailed, non-Gaussian noise, might provide a more accurate description of actual decision processes. In contrast to diffusion processes, Lévy flights are characterized by larger jumps in the decision process. To further examine this proposal, we compare the fit of the basic diffusion model and the full diffusion model (includin' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["decision making","diffusion model","Lévy flights"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The diffusion model is one of the most prominent response time models in cognitive psychology. The model describes evidence accumulation as a stochastic process that runs between two boundaries until a threshold is hit, and a decision is made. The model assumes that information accumulation follows a Wiener diffusion process with normally distributed noise. However, the model's assumption of Gaussian noise might not be the optimal description of decision making. We argue that Lévy flights, incorporating more heavy-tailed, non-Gaussian noise, might provide a more accurate description of actual decision processes. In contrast to diffusion processes, Lévy flights are characterized by larger jumps in the decision process. To further examine this proposal, we compare the fit of the basic diffusion model and the full diffusion model (includin' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The diffusion model is one of the most prominent response time models in cognitive psychology. The model describes evidence accumulation as a stochastic process that runs between two boundaries until a threshold is hit, and a decision is made. The model assumes that information accumulation follows a Wiener diffusion process with normally distributed noise. However, the model's assumption of Gaussian noise might not be the optimal description of decision making. We argue that Lévy flights, incorporating more heavy-tailed, non-Gaussian noise, might provide a more accurate description of actual decision processes. In contrast to diffusion processes, Lévy flights are characterized by larger jumps in the decision process. To further examine this proposal, we compare the fit of the basic diffusion model and the full diffusion model (includin
decision making; diffusion model; Lévy flights