Fig 1.
(A) Temporal presentation of events during a trial. Each trial starts with all the tokens in the central circle. After the participant moves the cursor to inside the central circle, the tokens start jumping successively to the other two (target) circles. In the all-stay trials the tokens remain visible after jumping, while in the all-away trials they disappear soon after they have jumped. The participant has to guess which of the two target circles will contain more tokens at the end of the trial. (B) Success probability profiles for specific trial types. Top panel, success probability for easy (black) and ambiguous (gray) trials. Bottom panel, success probability for bias-against (black) and bias-for (gray) trials.
Fig 2.
Behavior of subjects during easy and ambiguous trials.
(A) Left panel, individual mean decision times (DTs) observed during easy and ambiguous trials for all-stay (gray) and all-away (black) conditions. Inset panel shows a histogram with the difference in DTs between trial types within each condition. The DTs for easy and ambiguous trials were significantly different in both conditions (n = 15; paired-samples t-test, ** p < 0.001; all-stay, p = 0.000003, t = 7.48; all-away, p = 0.0007, t = 4.32). Right panel, success probability (SP) at decision time for easy and ambiguous trials. Inset panel shows a histogram with the difference in SPs for the two trial types within each condition. The difference is significant for both conditions (n = 15; paired-samples t-test, ** p < 10−8; all-stay, p = 0.4 × 10−12, t = 25.58; all-away, p = 0.2 × 10−9, t = 16.38). Error bars indicate SEM. (B) DTs (left panels) and SPs (right panels) of a representative subject, whose mean DT and SP values are indicated by arrows in (A). The subject clearly shows the same behavioral effect as was observed for the group: faster DT and higher SP at decision time for easy trials than for ambiguous trials in both all-stay and all-away conditions (Kolmogorov–Smirnov test, ** p < 0.01; neasy = 52, nambiguous = 37, all-stay: DTs, p = 0.00005, D = 0.4; SPs, p = 0, D = 0.89; neasy = 28, nambiguous = 22, all-away: DTs, p = 0.002, D = 0.35; p = 0, D = 0.97).
Fig 3.
Behavior of subjects during bias-against and bias-for trials.
(A) Left panel, DTs observed during bias-against and bias-for trials in all-stay (gray) and all-away (black) conditions. Inset panel shows a histogram with the difference in DTs between the two trial types within each condition. The difference in DTs between bias-against and bias-for trials is significant in the all-away condition but not in the all-stay condition (n = 15; paired-samples t-test, ** p < 0.01; p = 0.001, t = 3.96). A Bayes Factor of 16.929 (> 3) in the all-away condition and of 0.249 (< 1/3) in the all-stay condition confirm the results. Right panel, SPs at decision time for bias-against and bias-for trials. Inset panel shows a histogram with the difference between SPs for the two trial types within each condition. The difference is significant only in the all-away condition (n = 15; paired-samples t-test, * p < 0.05; p = 0.01, t = 2.78). A Bayes Factor of 4.063 (> 3) in the all-away condition and of 0.277 (< 1/3) in the all-stay condition support the result. Error bars indicate SEM. (B) DTs and SPs for the same subject as in Fig 2B, whose mean DT and SP values are indicated with arrows in (A). The subject exhibits the same behavioral effect as was observed for the group: the DT and SP only differed significantly between bias-against and bias-for trials in the all-away condition (nbias-against = 33, nbias-for = 24; Kolmogorov–Smirnov test, ** p < 0.001; DTs, p = 0.00002, D = 0.62; SPs, p = 0.0001, D = 0.57).
Fig 4.
Computational framework and model simulations for correct and error trials.
(A) Schematic diagram of the complete network that simulates the observed experimental results. Visual evidence is either provided directly to the decision-making module or stored in a working memory that then provides the information (eleaky) to the decision-making module. The information (eleaky or e) is used by the EAM or the UGM to make a choice. (B) Distributions of DTs in correct and error trials in the all-stay condition when all individual trials are pooled together for real data and simulated EAM and UGM. (C) Same conventions as in (B) for the all-away condition. In this case, simulations were performed with and without leakage in the sensory evidence.
Table 1.
Mean and SEM across subjects of the best fitting parameters for the EAM and the UGM in the all-stay and all-away conditions with and without leakage in the sensory evidence.
Fig 5.
Model simulations for different trial types.
(A) Distributions of DTs for ambiguous, easy, bias-for, and bias-against trials in real and simulated data in the all-stay condition. Inset panel, Cumulative distributions of SPs for each trial type. Differences in DTs and SPs in ambiguous and easy trials are fitted correctly by the EAM and the UGM (paired-samples t-test, ** p < 0.01). Lack of difference in DTs and SPs in bias-for and bias-against trials is only fitted by the UGM (paired-samples t-test, ** p < 0.01, ns: not significant). Dashed lines indicate the mean of the distributions. Green rectangle shows the results that are consistent with the real data. (B) Same conventions as in (A) for the all-away condition with simulations performed without and with leakage in the sensory evidence. Differences between DTs and SPs in easy and ambiguous trials are fitted correctly by the EAM and the UGM without and with sensory leakage (paired-samples t-test, ** p < 0.01). Longer DTs and higher SPs in bias-for than in bias-against trials are only correctly fitted by the UGM when sensory evidence leaks away (paired-samples t-test, * p < 0.05, ** p < 0.01, ns: not significant).