Trans-saccadic memory consists of keeping track of objects’ locations and features across saccades; pre-saccadic information is remembered and compared with post-saccadic information. It has been shown to have limited resources and involve attention with respect to the selection of objects and features. In support, a previous study showed that recognition of distinct post-saccadic objects in the visual scene is impaired when pre-saccadic objects are relevant and thus already encoded in memory (Poth, Herwig, Schneider, 2015). Here, we investigated the inverse (i.e. how the memory of pre-saccadic objects is affected by abrupt but irrelevant changes in the post-saccadic visual scene). We also modulated the amount of attention to the relevant pre-saccadic object by having participants either make a saccade to it or elsewhere and observed that pre-saccadic attentional facilitation affected how much post-saccadic changes disrupted trans-saccadic memory of pre-saccadic objects.

Participants identified a flashed symbol (d, b, p, or q, among distracters), at one of six placeholders (figures “8”) arranged in circle around fixation while planning a saccade to one of them. They reported the identity of the symbol after the saccade. We changed the post-saccadic scene in Experiment one by removing the entire scene, only the placeholder where the pre-saccadic symbol was presented, or all other placeholders except this one. We observed reduced identification performance when only the saccade-target placeholder disappeared after the saccade. In Experiment two, we changed one placeholder location (inward/outward shift or rotation re. saccade vector) after the saccade and observed that identification performance decreased with increased shift/rotation of the saccade-target placeholder. We conclude that pre-saccadic memory is disrupted by abrupt attention-capturing post-saccadic changes of visual scene, particularly when these changes involve the object prioritized by being the goal of a saccade. These findings support the notion that limited trans-saccadic memory resources are disrupted when object correspondence at saccadic goal is broken through removal or location change.

*t*-tests to compare performance. ANOVA degrees of freedom reported were Greenhouse-Geiser corrected if Mauchly's test of sphericity was significant. We estimated the extent to which performance differences could be explained by our manipulations with effect sizes, reporting partial eta squared (η

^{2}

_{p}) seconds for ANOVAs (Lakens, 2013). We also reported Bayes factors for all ANOVAs and

*t*-tests performed. We used JASP 0.14.1 for statistical analysis (JASP Team, 2020). We used default priors from JASP for all analyses and report BF

_{10}values for the best model compared to the null hypothesis.

*p*< 0.001, η

^{2}

_{p}= 0.941). A Bayesian paired samples

*t*-test strongly favored the alternative hypothesis of a difference between the two positions compared to the null hypothesis (BF

_{10}= 19706.9). In addition, while performance was significantly different from chance (25%) in the valid position (76.6% correct,

*t*(9) = 19.5,

*p*< 0.001), it was not for the invalid positions (31.3%,

*t*(9) = 2.1,

*p*= 0.064). These findings are very similar to our and others’ findings demonstrating that attention is shifted to the goal of the saccade and not elsewhere when planning a saccade (Castet et al., 2006; Deubel & Schneider, 1996; Hoffman & Subramaniam, 1995; Khan et al., 2015; Mikula et al., 2018).

*p*< 0.001, η

^{2}

_{p}= 0.451) and position (F(1,9) = 165,

*p*< 0.001, η

^{2}

_{p}= 0.948). There was also a significant interaction effect (F(3,27) = 4.8,

*p*= 0.008, η

^{2}

_{p}= 0.347). A two-way Bayesian repeated measures ANOVA strongly favored the alternative hypothesis with the model with condition and position as factors with no interaction (BF

_{10}= 1.628e + 29, best model) compared to the null model. This model was slightly better than the model that included the interaction effect (BF

_{10}= 1.316e + 29).

*t*-tests confirmed significantly lower performance for the OneOff condition (M = 62.2%, SD = 14.2%) compared to all other conditions (baseline - M = 76.6%, SD = 8.3%,

*t*(9) = 4.4,

*p*= 0.002, AllOff – M = 79.7%, SD = 14,

*t*(9) = 5.7,

*p*< 0.001, OneOn - M = 76.8%, SD = 11.4%,

*t*(9) = 4.3,

*p*= 0.002). Performance for the AllOff and the OneOn condition were not different from the baseline condition (AllOff – t(9) = 0.777,

*p*= 0.457, OneOn - t(9) = 0.046,

*p*= 0.964). For Bayesian paired samples

*t*-tests, there was strong evidence in favor of the alternative hypothesis of a difference between the OneOff compared to all other conditions (baseline – BF

_{10}= 26.9, AllOff – BF

_{10}= 111.06, OneOn – BF

_{10}= 22.9). In contrast, there was moderate evidence in favor of the null hypothesis when comparing between the other conditions (Alloff versus baseline – BF

_{10}= 0.397, OneOn versus baseline – BF

_{10}= 0.309).

*p*= 0.108; AllOff versus baseline - t(9) = 0.098,

*p*= 0.924; OneOn versus baseline - t(9) = 0.844,

*p*= 0.421; OneOff versus AllOff - t(9) = 1.571,

*p*= 0.151; OneOff versus OneOn - t(9) = 3.161,

*p*= 0.012; AllOff versus OneOn - t(9) = 0.975,

*p*= 0.355; Holm-Bonferroni corrected). For Bayesian paired samples

*t*-tests, there was moderate support in favor of the alternative hypothesis of a difference between the OneOff and OneOn conditions (BF

_{10}= 5.528), while all other comparisons showed evidence that supported the null hypothesis or showed equal evidence for either hypothesis (Alloff versus baseline – BF

_{10}= 0.31, OneOff versus baseline – BF

_{10}= 0.997, OneOn versus baseline – BF

_{10}= 0.415, AllOff versus OneOn - BF

_{10}= 0.456).

*p*= 0.422) or position (F(1,9) = 4.123,

*p*= 0.073 nor a significant interaction effect (F(2,18) = 1.771,

*p*= 0.198). A Bayesian repeated-measures ANOVA revealed weak to moderate support for the null hypothesis (best model = condition factor only, BF

_{10}= 0.524 compared to null hypothesis). Thus, we conclude that differences across conditions in the timing of the placeholder change cannot explain the results.

*p*= 0.297) or position - F(1,9) = 5.075,

*p*= 0.051 nor a significant interaction effect - F(3,27) = 0.119,

*p*= 0.948). A Bayesian repeated-measures ANOVA revealed equal support for any model and the null hypothesis (best model = condition factor only, BF

_{10}= 1.4 compared to null hypothesis).

*t*-tests were conducted for all statistical analyses. ANOVA degrees of freedom reported were Greenhouse-Geiser corrected if Mauchly's test of sphericity was significant. We estimated the extent to which performance differences could be explained by our manipulations with effect sizes, reporting partial eta squared (η

^{2}

_{p}) seconds for ANOVAs (Lakens, 2013). We also reported Bayes factors for all ANOVAs and

*t*-tests performed in the same manner as Experiment one.

*p*= 0.213 (range: 30.2% to 36.2%, 3 significantly different from chance of 25% (−1° - t(18) = 3.152,

*p*= 0.006; 0 degrees - t(18) = 3.202,

*p*= 0.005; +3 degrees - t(18) = 3.083,

*p*= 0.006) and four not significantly different from chance (−3 degrees - t(18) = 1.518,

*p*= 0.146; −2 degrees - t(18) = 2.01,

*p*= 0.06; +1 degrees - t(18) = 2.484,

*p*= 0.023; +1 degrees - t(18) = 2.671,

*p*= 0.016, Holm-Bonferroni corrected). The Bayesian repeated measures ANOVA strongly favored the null hypothesis (position – BF

_{10}= 0.2).

*p*< 0.001, η

_{p}

^{2}= 0.2. For Bayesian analyses, the alternative hypothesis with a model with position as a factor was also strongly favored against the null hypothesis (BF

_{10}= 1.945e + 21).

*t*-tests to test for differences from the baseline (0 degrees) condition; they revealed differences between the highest performance observed for 0 degrees shift (baseline; M = 74.1%, SD = 15.9) and performance observed for almost every other shift (−3 degrees inward, M = 64.4%, SD = 19.3%, t(18) = 2.735,

*p*= 0.014; +1 degree outward, M = 66.3%, SD = 19.4%, t(18) = 2.852,

*p*= 0.011; +2 degrees outward, M = 64.4%, SD = 17.9%, t(18) = 2.959,

*p*= 0.008; +3 degrees outward, M = 62.4%, SD = 19.5%: t(18) = 3.241,

*p*= 0.005), except for the −2 degrees (t(18) = 1.865,

*p*= 0.079) and −1 degree inward locations (t(18) = 0.446,

*p*= 0.661). Bayesian paired samples

*t*-tests revealed moderate support for the alternative hypothesis of a difference from the baseline location for the −3 degrees inward (BF

_{10}= 4.03), the +1 degrees outward (BF

_{10}= 4.96), the +2 degrees outward (BF

_{10}= 6.02), and the +3 degrees outward locations (BF

_{10}= 10.11). There was equal support for the alternative hypothesis of a difference between the −2 degrees inward location and the null hypothesis (BF

_{10}= 1) and support in favor of the null hypothesis for the −1 degree inward location (BF

_{10}= 0.26).

*p*= 0.504, range = 31% to 36.4%, 2 significantly different from chance of 25% (0 degrees, t(11) = 5.407,

*p*< 0.001; +10 degrees, t(11) = 3.407,

*p*= 0.006), three not significantly different from chance (−20 degrees, t(11) = 1.956,

*p*= 0.076; −10 degrees, t(11) = 1.823,

*p*= 0.096; +20 degrees, t(11) = 2.777,

*p*= 0.018, Holm-Bonferroni corrected). Consistent with this, the Bayesian repeated-measures ANOVA strongly supported the null hypothesis (BF

_{10}= 0.19).

*p*< 0.001, η

_{p}

^{2}= 0.41). For Bayesian analyses, the alternative model was also strongly favored against the null hypothesis (BF

_{10}= 232).

*t*-tests to test for differences from the baseline (0 degrees) condition; these tests revealed significantly decreased performances in all rotations, −20 degrees (M = 61%, SD = 18.8%; t(11) = 4.324,

*p*= 0.001), −10 degrees (M = 64.9%, SD = 16.1%; t(11) = 3.15,

*p*= 0.009), +10 degrees (M = 66.6%, SD = 15.8%; t(11) = 2.833,

*p*= 0.016) and +20 degrees (M = 64.6%, SD = 16.1; t(11) = 4.083,

*p*= 0.002), compared to no rotation baseline (M = 77%, SD = 9%).

*t*-tests revealed moderate to strong support for the alternative hypothesis of a difference from the baseline condition for all rotations, the −20 degrees rotation (BF

_{10}= 34), the −10 degrees rotation (BF

_{10}= 6.31), the +10 degrees rotation (BF

_{10}= 4), and the +20 degrees rotation (BF

_{10}= 24).

*p*< 0.001). A Bayesian one sample

*t*-test strongly favored the alternative hypothesis that mean amplitude was different from the placeholder location (BF

_{10}= 1119). This is consistent with many studies which show that participants tend to undershoot targets particularly for centrifugal saccades (Gillen, Weiler, & Heath, 2013; Irving, Steinbach, Lillakas, Babu & Hutchings, 2006; Nuthmann, Vitu, Engbert, & Kliegl, 2016). First, we investigated participants’ mean amplitudes to determine whether they undershot the pre-saccadic placeholder location, consistent with good performance at the no shift, −1 degree and −2 degrees placeholder shifts. We tested whether there was a correlation between the shifts at which a participant had their best performance and their mean saccade amplitude. We did not find a significant correlation (r(19) = 0.016,

*p*= 0.947). It should be noted that most participants (9 of them) had their best performance at the no shift baseline location and that the range of mean saccade amplitudes across participants was small. We also tested within each participant whether performance was different when their saccade amplitudes were smaller compared to bigger. We performed a median split on each participant's saccade amplitudes and then calculated performance at each DS shift separately for the trials with smaller amplitudes (M across participants = 4.88 degrees versus bigger amplitudes (M across participants = 5.88 degrees). We then compared performance using a repeated measures ANOVA with median group (smaller versus bigger saccades amplitudes) and shift (all 7 locations) as factors. We found no significant main effect of the group (F(1,18) = 2.493,

*p*= 0.132) nor a significant interaction effect (F(6,108) = 1.4,

*p*= 0.21) as would be expected if there was a difference in performance depending on saccade amplitude. A Bayesian repeated-measures ANOVA revealed strong support for the null hypothesis as opposed to the alternative hypothesis with the model of the Group factor (BF

_{10}= 0.00007).

*p*= 0.248; outward shift - t(17) = 0.79,

*p*= 0.442; narrow distribution group SD = 0.57 degrees, 13.72% inward decrease in performance and 5.3% outward; wide distribution group SD = 0.7 degrees, 14.4% inward and 8.6% outward). A Bayesian independent samples

*t*-test weakly favored the null hypothesis (BF

_{10}= 0.406).

*p*= 0.9). A Bayesian one sample

*t*-test strongly favored the null hypothesis of no difference (BF

_{10}= 0.28). We did not find a significant correlation (r(12) = -0.062,

*p*= 0.848) between the rotation location of best performance and mean individual saccade direction; the best identification performance was at the no rotation baseline location for eight of our 12 participants and the range of mean saccade directions across participants was small. We also tested within each participant whether the pattern of performance across the different rotations was different when their saccade directions were more clockwise compared to more counter-clockwise, using a median split analysis (M across participants more clockwise = −0.57 degrees, M across participants = 0.56 degrees more counter-clockwise) but found no significant effects (Group main effect – F(1,11) = 0.193,

*p*= 0.669; interaction effect – F(4,44) = 2.032,

*p*= 0.067). Consistent with this, the Bayesian repeated measures ANOVA strongly favored the null hypothesis above the alternative one with the Group factor (BF

_{10}= 0.198). Finally, we found that participants with wider distributions in direction (SD = 5.73 degrees) did not show differences in decreases in performance from baseline to the outward-most rotations compared to those with narrower distributions (SD = 4.32 degrees, inward - t(10) = 0.619,

*p*= 0.55, outward - t(10) = 0.443,

*p*= 0.67). Consistent with this result, Bayesian independent samples

*t*-tests strongly favored the null hypothesis (inward – BF

_{10}= 0.528, outward – BF

_{10}= 0.498).

*p*= 0.132) or the rotation condition (rotation factor – F(4,44) = 0.496,

*p*= 0.738). Bayesian repeated measures ANOVAs strongly supported the null hypotheses in both cases (shift factor – BF

_{10}= 0.324, rotation factor – BF

_{10}= 0.123).

*before*saccade offset for the parallel shift condition and 11.75 ms (SD across participants = 4.32 ms, average participant SD =8.84 ms)

*before*saccade offset for the rotation condition.

*p*= 0.082, shift location – F(6,108) = 1.535,

*p*= 0.174, interaction – F(6,108) = 1,

*p*= 0.425) or the rotation condition (position – F(1,11) = 0.109,

*p*= 0.747, rotation – F(4,44) = 1.481,

*p*= 0.224, interaction – F(4,44) = 1.418,

*p*= 0.244). Bayesian repeated measures ANOVAs weakly favored the alternative hypothesis for the shift condition (best model, position factor only, BF

_{10}= 2.026) and strongly favored the null hypothesis for the rotation condition (best model, position factor only, BF

_{10}= 0.351).

*exclusively*the saccade goal object more salient and disruptive of trans-saccadic memory, likely because the correspondence of the most attended object between the pre- and post-saccadic views relative to a constant scene was broken.

*PLoS Computational Biology,*15(2), e1006563. [CrossRef]

*Science,*321(5890), 851–854. [CrossRef]

*Vision Research,*15(6), 719–722. [CrossRef]

*Journal of Vision,*6(3), 196–212. [CrossRef]

*Trends in Cognitive Sciences,*14(4), 147–153. [CrossRef]

*Nature Reviews Neuroscience,*3(3), 201–215. [CrossRef]

*Neuropsychologia,*49(6), 1401–1406. [CrossRef]

*Psychological Research,*72(6), 630–640. [CrossRef]

*Vision Research,*38(20), 3147–3159. [CrossRef]

*Vision Research,*36(12), 1827–1837. [CrossRef]

*Vision Research,*36(7), 985–996. [CrossRef]

*Psychological Research,*69(1–2), 67–76.

*Behavior Research Methods,*39(2), 175–191. [CrossRef]

*Journal of Vision,*10(13), 1–17. [CrossRef]

*Experimental Brain Research,*11, 2939–2956.

*Journal of Vision,*15(16), 8. [CrossRef]

*Experimental Brain Research,*230(2), 165–174. [CrossRef]

*Journal of Vision,*8(14), 1–13. [CrossRef]

*Journal of Neuroscience,*33(7), 2927–2933. [CrossRef]

*Perception,*20(3), 393–402. [CrossRef]

*Perception & Psychophysics,*57(6), 787–795.

*Attention, Perception, and Psychophysics,*81(6), 1822–1835.

*Investigative Ophthalmology and Visual Science,*47(6), 2478–2484.

*Journal of Experimental Psychology: Learning, Memory, and Cognition,*18(2), 307–317.

*Current Directions in Psychological Science,*5(3), 94–100.

*Attention and performance XVI: Information integration in perception and communication*(pp. 125–155). Cambridge, MA: MIT Press.

*Frontiers in Systems Neuroscience,*9, 161.

*Journal of Vision,*18(1), 6.

*Current Opinion in Psychology,*29, 126–134.

*Journal of Vision,*18(3), 10–10.

*European Journal of Neuroscience,*41(12), 1624–1634.

*Journal of Neuroscience,*30(16), 5481–5488.

*Journal of Vision,*21(5), 24.

*Vision Research,*35(13), 1897–1916.

*Frontiers in Psychology,*4, 863.

*Current Biology*, 26(12), 1564–1570. [PubMed]

*Nature,*390(6657), 279–284.

*Proceedings of the National Academy of Sciences of the United States of America*, 111(2), 291–299. [PubMed]

*Nature Neuroscience,*1(2), 144–149.

*Philosophical Transactions of the Royal Society B: Biological Sciences*(Vol. 366, Issue 1564, pp. 516–527). Royal Society.

*Vision Research,*10, 1249–1255.

*Philosophical Transactions of the Royal Society B: Biological Sciences,*366(1564), 468–475.

*Journal of Experimental Psychology: Human Perception and Performance,*47(5), 635–647.

*Journal of Vision,*18(11), 1–16.

*Nature,*422(6927), 76–80.

*PLoS One,*11(9), e0162449.

*Journal of Vision*, 17(13), 2. [PubMed]

*Frontiers in Systems Neuroscience,*9(DEC), 176.

*Journal of Vision,*16(11), 1.

*Acta Psychologica,*90, 27–37.

*Experimental Brain Research,*180(4), 609–628.

*Perception,*44(8–9), 900–919.

*Journal of Neuroscience,*32(40), 13744–13752.

*Nature Neuroscience,*14(2), 252–258.

*Studies in Visual Information Processing*(Vol. 6, Issue C, pp. 317–324). Amsterdam City, Amsterdam: North-Holland Publishing Company.

*Journal of Vision,*18(7), 1–17.

*Cortex,*140, 179–198.

*Vision Research,*142, 1–10.

*Vision Research,*153, 70–81.

*Journal of Neurophysiology,*116(4), 1592–1602.

*Journal of Vision,*12(11), 18.

*Journal of Vision,*21(2), 1–14.

*Philosophical Transactions of the Royal Society B: Biological Sciences,*366(1564), 596–610.

*Trends in Neurosciences*(Vol. 32, Issue 11, pp. 583–590). New York, NY: Elsevier Current Trends.

*Spatial Vision,*16(3–4), 255–275.

*Journal of Experimental Psychology: General,*147(12), 1827–1850.

*Journal of Vision,*20(9), 1–12.

*Journal of Vision,*8(14), 1–16.

*Brain, Behavior and Evolution,*33(2–3), 90–94.

*Journal of Neuroscience,*26(16), 4188–4197.

*Attention, Perception, and Psychophysics,*77(5), 1500–1506.

*Journal of Vision,*14(2), 1–9.

*Vision Research,*85, 26–35.

*Frontiers in Human Neuroscience,*10, 568.

*Journal of Vision,*15(16), 1.

*Vision Research,*48(20), 2070–2089.