Texture Discrimination Task (TDT) and Serial Dependence Effect (SDE)

(A) TDT Trial Sequence: Observers identified the orientation (vertical or horizontal) of a target composed of three diagonal lines embedded within a background of horizontal lines while simultaneously performing a forced-choice letter discrimination task (T vs L) to maintain fixation. The 10 ms target frame was followed by a 100 ms patterned mask, with the stimulus onset asynchrony (SOA) between target and mask (10 to 300 ms) randomized across trials. (B) SDE via history sequence: Correct identifications (‘Hits’) increased by approximately 20% when the current target orientation matched the preceding three orientations (e.g., ‘1’ preceded by ‘111’) and decreased by about 20% with mismatching orientations (e.g., ‘1’ preceded by ‘222’), relative to average performance (dotted light blue line). ‘1’ and ‘2’ represent the two possible orientations, either vertical and horizontal, or vice versa. The fixation task (red) showed much smaller biases, likely due to its higher overall performance. (C) SDE via Linear Mixed Effects (LME) Weights (W-bias, %): Influence of 1–10 back trials on current report. Summing the W-bias values (%) from the 1st, 2nd, and 3rd prior trials corresponds to the ±20% bias for 1–3 back trials shown in panel B. Panels B and C include data from the 1loc condition (N=14) pooled across all training days, including only trials with low-visibility current targets (SOA < SOAthreshold + 20 ms). Blue represents the texture discrimination task, and red indicates the letter discrimination (fixation control) task.

Serial dependence effects across trial history

(A) Target visibility: SDEs (W-bias) were strongest when current targets had low visibility (high uncertainty) and prior targets had high visibility. Blue lines show target (T) histories and red lines show key (K) histories, with solid vs. dotted lines indicating prior high-vs. low-visibility trials. Visibility had a much weaker effect on key histories compared to target histories, with key-driven SDEs remaining high even when prior targets were invisible. The yellow line shows target history with high visibility in both history and current trials, demonstrating that SDEs are strongly reduced when current targets are highly visible (certainty) (N = 50). (B) Location specificity: SDEs were larger when history originated from the same location as the current target compared to a diagonally opposite location (2loc condition, N = 14). Error bars represent the standard error of the mean. The horizontal dashed line indicates zero bias.

Effect of reaction time on SDEs.

(A) SDEs (W-bias) as a function of N-back trial, calculated separately for the fastest (first 25%) and slowest (last 25%) RT quartiles, defined per day within each observer. SDEs were then computed on the corresponding fast and slow trials and averaged across observers. Biases were stronger for fast RTs, particularly at recent lags. (B) Paired comparisons of recent and distant SDEs for fast vs. slow RTs. Recent SDEs were significantly higher for fast RTs (left panel), whereas distant SDEs did not differ between RT conditions (right panel). Gray bars indicate group means; dots and connecting lines represent individual observers (N = 50).

Dynamics of SDE across days and locations

(A) SDEs and TDT thresholds across days: Serial dependence (SDE-all, red) remained strong and consistent throughout the eight training days, despite large improvements in TDT thresholds (blue). A small reduction in SDE was observed across days and reached significance only between Days 7–8. The target location was changed after Day 4. (B) SDEs across locations: Correlation of SDE-all between the first (Days 1–4) and second (Days 5–8) trained locations across observers (N = 50). The strong correlation indicates that the magnitude of serial dependence is a stable observer-specific trait, consistent across retinotopic locations.

Within-session dynamics of SDE

(A) SDEs (W-bias) as a function of N-back trial, computed separately for the first (blue) and last (red) third of each session. Biases showed an overall decrease across the session, while the 1-back bias (W1) increased slightly. (B) Recent and distant SDE components. Recent SDEs remained stable across the session (left panel), whereas distant SDEs showed a significant reduction (right panel). This pattern is consistent with sensory adaptation developing over the course of the session, selectively attenuating serial dependence from more temporally distant trials (N = 50).

SDEs across experimental conditions and trial history

(A) Mean SDEs (W-bias) as a function of trial history (N-back) for the three experimental conditions: dummy (blue, N = 22), 1loc (red, N = 14), and 2loc (yellow, N = 14). In the 1loc condition, biases decayed more rapidly, while in the 2loc and dummy conditions they persisted further back in trial history. (B) SDEs across individual observers (N = 50), shown separately for recent lags (1–3 back; left panel) and distant lags (4–6 back; right panel). Each dot represents one observer; gray bars indicate group means. Recent SDEs were consistent across conditions, whereas distant SDEs were significantly stronger in the 2loc and dummy conditions compared to the 1loc condition (***p ≤ 0.001, **p ≤ 0.01).

Relationship between SDE and learning transfer

(A) Group-level comparison of learning transfer (blue), SDE-distant (solid red), and SDE-recent (dotted red) across the three experimental conditions (dummy, 1loc, 2loc). Transfer and SDE values are plotted on separate axes, with SDE measures normalized by subtracting the mean of the 1-loc condition. Conditions showing greater learning generalization (dummy, 2loc) also exhibited stronger SDE-distant effects. In contrast, SDE-recent was relatively constant across conditions, suggesting that generalization was primarily linked to distant serial dependence. (B) Across observers, learning transfer correlated positively with SDE-distant (r = 0.37, p < 0.01, N = 50), indicating that stronger distant serial dependence predicted greater generalization. (C) SDE-distant values were normalized to the 1-back effect to estimate the temporal decay constant of SDE, reflecting how long biases persisted across trials. Observers with longer decay constants showed greater learning transfer (r = 0.50, p < 0.001, N = 48; two outliers >10 SD excluded), indicating that extended temporal integration supports generalization. (D) No significant correlation was found between SDE-recent and learning transfer ( = – 0.22, p = 0.12, N = 50), suggesting that recent serial dependence does not predict generalization. Learning transfer was defined as the change in TDT threshold between Day 4 (final day at the first location) and Day 5 (initial day at the second location), with negative values indicating performance loss.