Article Figures & Data
Figures
- Figure 1.
Experimental design. A, A photograph of a tree shrew in the home cage. B, A schematic of the training procedure. C, The contrast discrimination task. The animal needs to choose the side that has a higher contrast gabor and report the choice by licking the corresponding port. D, Learning curve of individual animals. The y-axis is the response accuracy for the easiest condition on each day. Day 1 refers to the first day of training with two-sided gabor stimulus. Dashed gray line, 75% accuracy. Most animals reached this level by day 2 and all by day 7.
- Figure 2.
Tree shrews show different behaviors under two training schemes. A, A fixed delay of 4 s (solid line) was used in training one group of animals. The dashed line shows the theoretical reward rate under this fixed delay. B, Psychometric curve of animals from this training scheme. Contrast difference: right contrast (R) – left contrast (L). Gray dashed line, Individual animals. Black solid line, Average across animals. C, Response time (RT) as a function of contrast difference. Dashed line, Individual animals. Solid line, Average across animals. The shaded area is 95% confidence interval. D, RT density histogram from a representative animal. Correct and incorrect trials are separately plotted. E, An exponential decay delay scheme (solid line) was applied in another group. The dashed line shows the theoretical reward rate under this scheme. F–H, Same as C–E but for the second group. Figures 2-1 and 2-2 show the RT distributions of individual animals from the fixed-delay group and exponential-delay group respectively.
- Figure 3.
Modeling results suggest that evidence accumulation combined with a timing mechanism better fits tree shrew decision-making behavior. A, B, Racing diffusion model (RDM; A) and timed racing diffusion model (TRDM; B). Blue trace, The evidence accumulator for left choice. Yellow trace, The evidence accumulator for right choice. Gray trace, The time accumulator. The two evidence accumulation processes race against each other. In these schematics, the accumulator for right stimuli (yellow) reaches the threshold first, resulting in a rightward choice. C, Observed (histograms) and simulated (lines) RT distribution for the representative animal from the fixed-delay group. Top, RDM simulation. Bottom, TRDM simulation. D, Observed and simulated RT distribution for the representative animal from the exponential-delay group. Top, RDM simulation. Bottom, TRDM simulation. E, Estimated log Bayes factor comparing the two models’ performance. Positive values favor TRDM, while negative values favor RDM. Gray dots represent the animals from the fixed-delay training, and green dots represent the exponential-delay group. The upper and lower edges of the gray shaded area represent the lower limit for “very strong” evidence [ln(BF) = 5].
- Figure 4.
Model simulation of the psychometric curves and associated response time, and the posterior of the timer-related parameters. A, TRDM simulation for the fixed-delay group. Left, Observed (black) and simulated (red) psychometric curves for individual animals (dotted lines) and the group average (solid lines). The simulations were done with the best fitting parameters of the TRDM. Right, Observed (dots, solid lines, and dotted lines) and simulated RT function (“x”). Dotted lines, Individual animals. Solid lines, Group average. B, RDM simulation for the fixed-delay group. C, TRDM simulation for the exponential-delay group. D, RDM simulation for the exponential-delay group. E, Percentage of timer-induced choice calculated from the TRDM-simulated data for each animal. F, The posterior distribution of the time accumulator mean drift rate (ρt) for individual animals from the TRDM fitting. The dot in each distribution indicates the mean value. G, Same as F, but for the drift rate variability of the time accumulator (ηt). Figure 4-1 shows the decomposed simulation data of TRDM for one example animal.
Tables
Parameter Description Prior ω Bias IL(0, 1.4) t0,c Nondecision time of choice IL(0, 1.4) Drift rate coefficients of choice LN(1.56, 1.5) Mean drift rate of timer LN(1.56, 1.5) Within-trial variability LN(1.56, 1.5) γ * Mixture between random
and evidence-based
timer-induced decisionIL(–1, 1.0) IL inverse logit distribution.
LN log normal distribution.
↵* parameters only exist in TRDM.
The best fitting parameters of the two models for each animal are shown in Extended Data Tables 1-2 and 1-3. We also tested the relationship between RT and contrast difference using nonmodel statistics described in Extended Data Table 1-1.
Extended Data Table 1-1
Statistical table. Download Table 1-1, DOC file.
Extended Data Table 1-2
TRDM best fitting parameters of each animal. Download Table 1-2, DOC file.
Extended Data Table 1-3
RDM best fitting parameters of each animal. Download Table 1-3, DOC file.
Extended Data 1
Code for analysis and modeling. fit_rdm.py fit_trdm.py single_animal_preprocessing.ipynb waldrace.py Download Extended Data 1, ZIP file.
Extended Data Figure 2-1
Response time distributions of the individual animals from the fixed-delay group. Download Figure 2-1, TIF file.
Extended Data Figure 2-2
Response time distributions of the individual animals from the exponential-delay group. Download Figure 2-2, TIF file.
Extended Data Figure 4-1
Decomposition of an example animal’s simulated RT distribution by the TRDM. A, The simulated RTs for one example animal (TS085) from the first group are divided into four groups: evidence accumulator generated RT for correct (blue) and incorrect (pink) responses, and time accumulator generated RT for correct (green) and incorrect (yellow) choices. Compared with the observed data (B), the plots show that the TRDM interprets the first peak (fast RT) in the RT distribution as generated by the time accumulator. C, Simulated psychometric curves generated by the evidence accumulators and the time accumulator. D, Evidence accumulator simulated RT as a function of contrast difference. Download Figure 4-1, TIF file.
In this issue
