1 / 45

In the Drift Diffusion Model, the height of the threshold controls the speed–accuracy trade-off

胡元. In the Drift Diffusion Model, the height of the threshold controls the speed–accuracy trade-off. Drift Diffusion Model. What if the participant not explicitly told to emphasize speed or accuracy? . Response correct → reward (score) → delay → next trial

bevis
Télécharger la présentation

In the Drift Diffusion Model, the height of the threshold controls the speed–accuracy trade-off

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 胡元

  2. In the Drift Diffusion Model, the height of the threshold controls the speed–accuracy trade-off

  3. Drift Diffusion Model

  4. What if the participant not explicitly told to emphasize speed or accuracy?

  5. Responsecorrect→ reward (score)→ delay→ next trial Response incorrect→ delay + delay penalty→ next trial Gold, J. I., & Shadlen, M. N. (2002). Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36(2), 299-308.

  6. Reward rate (RR):averagenumber of rewards per unit of time

  7. Optimal threshold: maximize RR Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two- alternative forced choice tasks. Psychological Review, 113, 700–765.

  8. 1. Optimal threshold is higher for longer D: lower ER and longer RT in blocks of trials with longer D Prediction

  9. 2. Optimal threshold depends only on D and Dp Prediction

  10. 3. The values of decision thresholds should match the optimal values that maximize RR. Prediction

  11. 4. The RT, plotted as a function of ER, should follow a particular relationship defined by the optimal performance curve Prediction

  12. Normalized Threshold

  13. Prediction z ~D z(D + Dp ) → z(D + Dp + T0 ) z = z0 DT(ER)

  14. Prediction z ~D z(D + Dp ) → z(D + Dp + T0 ) z = z0 DT(ER)

  15. Optimal performance curves:

  16. Maximization of RR and accuracy: weighted sum of RR and accuracy q: the weight accorded to accuracy

  17. Maximization of RR and accuracy: weighted sum of RR and accuracy : 錯誤的損失 q: the weight accorded to accuracy

  18. Modified RR Participant receives a unit of reward for a correct choice andloses q units of reward for an error

  19. Optimal performance curves:

  20. Exp. 1: moving dot paradigmExp. 2: asterisk detection task

  21. Stimuli (until response) Responsecorrect→ reward (score)→ D→ next trial Response incorrect→ D + Dp→ next trial

  22. (1) D = 0.5 sec(2) D = 1 sec(3) D = 2 sec(4) D =0.5 sec, Dp=1.5 sec Delay condition: blocked design

  23. Exp. 1: 5 blocks, each lasting 7 minsExp. 2: 10 blocked, each lasting 4 mins

  24. Result of Exp. 1

  25. Behavioral data ER:Delay F(3)=5.84*** RT: Delay F(3)=2.02, n.s.

  26. Prediction: z ~ D confirmed r = -.53*** r = .29*

  27. Prediction z ~D z(D + Dp ) → z(D + Dp + T0 ) z = z0 DT(ER)

  28. Prediction: z(D + Dp) (3) D = 2 sec(4) D = 0.5 sec, Dp=1.5 sec ER: t(19)=2.23* RT: t(19)=2.49* Not confirmed in behavioral data

  29. Quantile probability plots

  30. Accuracy increases, the RT also increases

  31. Difference RT pattern in correct and wrong trials

  32. Strong correlation between pure/extend DDM → pure DDM as a tool to investigateSAT

  33. Decision threshold r1*** r1**

  34. Suboptimal threshold (Participants set their threshold higher)

  35. Result of Exp. 2

  36. Behavioral data ER:Difficulty F(1)=103***Delay F(3)=3.79**Interaction F(3)=0.67, n.s. RT: Difficulty F(1)=870***Delay F(3)=9.04***Interaction F(3)=.52, n.s.

  37. Prediction: z ~ D confirmed r = -.32*** r = .31***

  38. Prediction: z(D + Dp) (3) D = 2 sec(4) D=0.5 sec, Dp=1.5 sec ER: t(119)=.43, n.s. RT: t(119)=2.88** Not confirmed in behavioral data

  39. Suboptimal threshold (Participants set their threshold higher)

  40. Theory matching error

  41. Theory matching error Gender comparison: t(58)=2.13*

  42. Performance of participants with high reward scores (above median)

  43. Prediction: z(D+Dp) confirmed n.s. n.s.

  44. Prediction: z=z0confirmed in mostof condition

More Related