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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

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

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  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

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