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Attention as a source of variability in decision-making: Accounting for overall-value effects with diffusion models
توجه به عنوان منبع تغییرپذیری در تصمیم گیری: حسابداری اثرات ارزش کلی با مدل های انتشار-2021 Research has demonstrated that value-based decisions depend on not only the relative value difference
between options, but also their overall value. In particular, response times tend to decrease as the
overall (summed) value of the options increase. Standard sequential sampling models such as the
diffusion model can account for this fact by assuming that decision thresholds or noise vary with
overall value. Alternatively, gaze-based models that incorporate eye-tracking data can accommodate
this overall-value effect directly as a consequence of the multiplicative relationship between gaze and
option value. We compare the fit of non-gaze diffusion models to data simulated with a multiplicativegaze model. The results show how parameters related to decision thresholds or noise will vary as a
function of overall value, even when there is no such variability in the data generating process. In
empirical data, we find similar patterns where decision thresholds, noise, or non-decision time seem to
vary with overall value. Our results reveal that specific patterns in standard diffusion model parameters
can arise from a latent process of gaze-dependent evidence accumulation.
keywords: توجه | انتشار رانش | ارزش کلی | زمان پاسخ | ردیابی چشم | تصمیم گیری | Attention | Drift diffusion | Overall value | Response time | Eye tracking | Decision making |
مقاله انگلیسی |
2 |
Mutual benefits: Combining reinforcement learning with sequential sampling models
مزایای متقابل: تلفیق یادگیری تقویتی با مدلهای نمونه برداری متوالی-2020 Reinforcement learning models of error-driven learning and sequential-sampling models of decision making have
provided significant insight into the neural basis of a variety of cognitive processes. Until recently, model-based
cognitive neuroscience research using both frameworks has evolved separately and independently. Recent efforts
have illustrated the complementary nature of both modelling traditions and showed how they can be integrated
into a unified theoretical framework, explaining trial-by-trial dependencies in choice behavior as well as
response time distributions. Here, we review a theoretical background of integrating the two classes of models,
and review recent empirical efforts towards this goal. We furthermore argue that the integration of both
modelling traditions provides mutual benefits for both fields, and highlight promises of this approach for
cognitive modelling and model-based cognitive neuroscience. Keywords: Sequential sampling models | Reinforcement learning | Instrumental learning | Decision-making |
مقاله انگلیسی |