Abstract
Recent studies have provided conflicting accounts regarding where in the human brain visual short-term memory (VSTM) content is stored, with strong univariate fMRI responses being reported in superior intraparietal sulcus (IPS), but robust multivariate decoding being reported in occipital cortex. Given the continuous influx of information in everyday vision, VSTM storage under distraction is often required. We found that neither distractor presence nor predictability during the memory delay affected behavioral performance. Similarly, superior IPS exhibited consistent decoding of VSTM content across all distractor manipulations and had multivariate responses that closely tracked behavioral VSTM performance. However, occipital decoding of VSTM content was substantially modulated by distractor presence and predictability. Furthermore, we found no effect of target–distractor similarity on VSTM behavioral performance, further challenging the role of sensory regions in VSTM storage. Overall, consistent with previous univariate findings, our results indicate that superior IPS, but not occipital cortex, has a central role in VSTM storage.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Pasternak, T. & Greenlee, M.W. Working memory in primate sensory systems. Nat. Rev. Neurosci. 6, 97–107 (2005).
Todd, J.J. & Marois, R. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428, 751–754 (2004).
Xu, Y. Distinctive neural mechanisms supporting visual object individuation and identification. J. Cogn. Neurosci. 21, 511–518 (2009).
Xu, Y. & Chun, M.M. Dissociable neural mechanisms supporting visual short-term memory for objects. Nature 440, 91–95 (2006).
Jeong, S.K. & Xu, Y. Neural representation of targets and distractors during object individuation and identification. J. Cogn. Neurosci. 25, 117–126 (2013).
Xu, Y. The neural fate of task-irrelevant features in object-based processing. J. Neurosci. 30, 14020–14028 (2010).
Todd, J.J. & Marois, R. Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cogn. Affect. Behav. Neurosci. 5, 144–155 (2005).
Beck, D.M., Muggleton, N., Walsh, V. & Lavie, N. Right parietal cortex plays a critical role in change blindness. Cereb. Cortex 16, 712–717 (2006).
Tseng, P. et al. Posterior parietal cortex mediates encoding and maintenance processes in change blindness. Neuropsychologia 48, 1063–1070 (2010).
Ester, E.F., Sprague, T.C. & Serences, J.T. Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron 87, 893–905 (2015).
Emrich, S.M., Riggall, A.C., LaRocque, J.J. & Postle, B.R. Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. J. Neurosci. 33, 6516–6523 (2013).
Ester, E.F., Serences, J.T. & Awh, E. Spatially global representations in human primary visual cortex during working memory maintenance. J. Neurosci. 29, 15258–15265 (2009).
Harrison, S.A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632–635 (2009).
Lee, S.H., Kravitz, D.J. & Baker, C.I. Goal-dependent dissociation of visual and prefrontal cortices during working memory. Nat. Neurosci. 16, 997–999 (2013).
Linden, D.E., Oosterhof, N.N., Klein, C. & Downing, P.E. Mapping brain activation and information during category-specific visual working memory. J. Neurophysiol. 107, 628–639 (2012).
Riggall, A.C. & Postle, B.R. The relationship between working memory storage and elevated activity as measured with functional magnetic resonance imaging. J. Neurosci. 32, 12990–12998 (2012).
Serences, J.T., Ester, E.F., Vogel, E.K. & Awh, E. Stimulus-specific delay activity in human primary visual cortex. Psychol. Sci. 20, 207–214 (2009).
Sneve, M.H., Alnaes, D., Endestad, T., Greenlee, M.W. & Magnussen, S. Visual short-term memory: activity supporting encoding and maintenance in retinotopic visual cortex. Neuroimage 63, 166–178 (2012).
Sprague, T.C., Ester, E.F. & Serences, J.T. Reconstructions of information in visual spatial working memory degrade with memory load. Curr. Biol. 24, 2174–2180 (2014).
Hou, Y. & Liu, T. Neural correlates of object-based attentional selection in human cortex. Neuropsychologia 50, 2916–2925 (2012).
Liu, T., Hospadaruk, L., Zhu, D.C. & Gardner, J.L. Feature-specific attentional priority signals in human cortex. J. Neurosci. 31, 4484–4495 (2011).
Xu, Y. & Jeong, S.K. The contribution of human superior intraparietal sulcus to visual short-term memory and perception. in Mechanisms of Sensory Working Memory: Attention and Perfomance XXV (eds. Jolicoeur, P., Lefebrve, C. & Martinez-Trujillo, J.) 33 (Academic Press, 2015).
Christophel, T.B., Hebart, M.N. & Haynes, J.-D. Decoding the contents of visual short-term memory from human visual and parietal cortex. J. Neurosci. 32, 12983–12989 (2012).
Nelissen, N., Stokes, M., Nobre, A.C. & Rushworth, M.F. Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection. J. Neurosci. 33, 16443–16458 (2013).
Jacob, S.N. & Nieder, A. Complementary roles for primate frontal and parietal cortex in guarding working memory from distractor stimuli. Neuron 83, 226–237 (2014).
Suzuki, M. & Gottlieb, J. Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nat. Neurosci. 16, 98–104 (2013).
Qi, X.L., Elworthy, A.C., Lambert, B.C. & Constantinidis, C. Representation of remembered stimuli and task information in the monkey dorsolateral prefrontal and posterior parietal cortex. J. Neurophysiol. 113, 44–57 (2015).
Albers, A.M., Kok, P., Toni, I., Dijkerman, H.C. & de Lange, F.P. Shared representations for working memory and mental imagery in early visual cortex. Curr. Biol. 23, 1427–1431 (2013).
Kriegeskorte, N. et al. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60, 1126–1141 (2008).
Kriegeskorte, N. & Kievit, R.A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17, 401–412 (2013).
Vogel, E.K., Woodman, G.F. & Luck, S.J. The time course of consolidation in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 32, 1436–1451 (2006).
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J.C. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nat. Neurosci. 17, 1255–1262 (2014).
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).
Kosslyn, S.M., Ganis, G. & Thompson, W.L. Neural foundations of imagery. Nat. Rev. Neurosci. 2, 635–642 (2001).
Slotnick, S.D., Thompson, W.L. & Kosslyn, S.M. Visual mental imagery induces retinotopically organized activation of early visual areas. Cereb. Cortex 15, 1570–1583 (2005).
Keogh, R. & Pearson, J. Mental imagery and visual working memory. PloS One 6, e29221 (2011).
Wojciulik, E. & Kanwisher, N. The generality of parietal involvement in visual attention. Neuron 23, 747–764 (1999).
Yantis, S. et al. Transient neural activity in human parietal cortex during spatial attention shifts. Nat. Neurosci. 5, 995–1002 (2002).
Behrmann, M., Geng, J.J. & Shomstein, S. Parietal cortex and attention. Curr. Opin. Neurobiol. 14, 212–217 (2004).
Colby, C.L. & Goldberg, M.E. Space and attention in parietal cortex. Annu. Rev. Neurosci. 22, 319–349 (1999).
Culham, J.C. & Kanwisher, N.G. Neuroimaging of cognitive functions in human parietal cortex. Curr. Opin. Neurobiol. 11, 157–163 (2001).
DeYoe, E.A. et al. Mapping striate and extrastriate visual areas in human cerebral cortex. Proc. Natl. Acad. Sci. USA 93, 2382–2386 (1996).
Engel, S.A. et al. fMRI of human visual cortex. Nature 369, 6481 (1994).
Sereno, M.I. et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268, 889–893 (1995).
Swisher, J.D., Halko, M.A., Merabet, L.B., McMains, S.A. & Somers, D.C. Visual topography of human intraparietal sulcus. J. Neurosci. 27, 5326–5337 (2007).
Bressler, D.W. & Silver, M.A. Spatial attention improves reliability of fMRI retinotopic mapping signals in occipital and parietal cortex. Neuroimage 53, 526–533 (2010).
Xu, Y. & Chun, M.M. Selecting and perceiving multiple visual objects. Trends Cogn. Sci. 13, 167–174 (2009).
Straw, A.D. Vision egg: an open-source library for realtime visual stimulus generation. Front. Neuroinform. 2, 4 (2008).
Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
Dale, A.M., Fischl, B. & Sereno, M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Fischl, B., Liu, A. & Dale, A.M. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70–80 (2001).
Fischl, B., Sereno, M.I. & Dale, A.M. Cortical surface-based analysis. II. Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).
Reuter, M., Rosas, H.D. & Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 53, 1181–1196 (2010).
Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
Fischl, B. et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage 23 (suppl. 1), S69–S84 (2004).
Cowan, N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–114, discussion 114–185 (2001).
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).
Haynes, J.-D. & Rees, G. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat. Neurosci. 8, 686–691 (2005).
Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685 (2005).
Acknowledgements
We would like to thank J. Swisher for his retinotopy code and members of the Harvard Vision Lab for their valuable comments on this study. This research was supported by US National Institutes of Health (NIH) grant 1R01EY022355 to Y.X. and NIH grant F32-EY022874 to K.C.B.
Author information
Authors and Affiliations
Contributions
K.C.B. and Y.X. designed the experiments. K.C.B. conducted the experiments and analyzed the data. K.C.B. and Y.X. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Univariate fMRI responses for V1-V4 (a) and superior IPS (b) in Experiment 1 (with predictable distractors) and Experiment 3 (with unpredictable distractors)
The same ten participants took part in both experiments. In both experiments, V1-V4 showed a mostly bottom-up based response, showing strong activation whenever stimuli were present. Superior IPS was driven by both bottom-up and top-down input, showing activation during both stimulus presentation and the delay. In Experiment 1, activity in V1-V4 decreased following the encoding period in trials without distractors (t(9) = 5.3, p = 0.005), but increased in trials with distractors (t(9) = 9.1, p = 0.0001), with a significant difference between the two trial types during the delay period (t(9) = 9.8, p < 0.0001). In superior IPS, while there was significantly more activation during the delay period for trials with distractors than without (t(9) = 2.9, p = 0.02), activity for both trial types decreased following the encoding period (trials without distractors: t(9) = 8.2, p = 0.0001; trials with distractors: t(9) = 3.4, p = 0.008). In a 2 (ROI) x 2 (trial type) x 2 (time period: encoding vs. delay) ANOVA, these differences resulted in a significant main effect of time period (F(1,9) = 7.2, p = 0.03), ROI (F(1,9) = 38.4, p = 0.0002), and trial type (F(1,9) = 40.5, p = 0.0001), and significant interactions between all three factors (F(1,9) = 88.1, p < 0.0001). Thus, response amplitudes in occipital cortex track the encoding of incoming visual information, regardless of whether or not it is task relevant. In superior IPS, however, while we do see increased activation for trials with distractors, relative to trials without distractors, this activity is lower than the task relevant information presented at encoding, suggesting that response amplitude in superior IPS is significantly modulated by the task relevance of the information presented. In Experiment 3, activity in V1-V4 decreased following the encoding period in trials without distractors (t(9) = 6.6, p = 0.0001) and increased in trials with distractors (t(9) = 7.6, p = 0.0001), similar to what was seen in Experiment 1. The difference in activation between the two trial types during the delay period was significant (t(9) = 15.4, p < 0.0001). Delay period activity for each trial type did not differ between Experiments 1 and 3 (trials without distractors: p = 0.3; trials with distractors: p = 0.8), suggesting that univariate response amplitude within this region was not affected by the predictability of distractor presence. In superior IPS, as in Experiment 1, we saw a decrease in activation following the encoding period for trials with (t(9) = 8.6, p = 0.0001) and without distractors (t(9) = 12.1, p = 0.0001). However, unlike in Experiment 1, there was no difference in delay period activity between the two trial types (p = 0.65). Comparisons between Experiments 1 and 3 revealed that, delay period activity in Experiment 3 was significantly higher than in Experiment 1 in trials without distractors (t(9) = 3.1, p = 0.01), but trending towards significantly lower than delay period activity in Experiment 1 in trials with distractors (t(9) = 2.1, p = 0.07). Previously, it has been suggested that univariate activity in this region reflects the amount of information stored. Here the presence and predictability of distractors appears to affect response amplitudes even though the amount of information stored in VSTM remains constant. Specifically, activity seems to be the highest when distractors are most likely to appear, and decreases as the likelihood of distraction decreases. This suggests that superior IPS not only maintains VSTM information across the delay, but that it may be able to dynamically modulate its activation to protect this information from distraction, either by increasing suppression or by strengthening and enhancing the stored information. Error bars indicate s.e.m. No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary Figure 2 Univariate fMRI responses for Experiment 2.
Eight of the participants from Experiment 1 completed this experiment. Similar to what was seen during the delay period of Experiment 1, V1-V4 showed stronger activation for trials with distractors than those without distractors (t(7) = 6.0, p = 0.0006). When the weak grating was presented alone (trials without distractors), we saw little to no univariate response in this region (p = 0.4), similar to the delay period response for trials without distractor in Experiment 1. This suggests that the memory and perceptual representations were similar in strength across the two experiments. Error bars indicate s.e.m. No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary Figure 3 MVPA decoding accuracy for the average VSTM delay period in topographic IPS regions (V3A-IPS4) for when distractors were predictable (Experiment 1) and unpredictable (Experiment 3).
The same ten participants took part in both experiments. While all regions showed significant, above chance decoding of VSTM contents in at least one of the experiments/trial types, none showed consistent decoding irrespective of distractor presence or predictability. Above chance decoding was seen in trials without distractors in Experiment 1 in areas V3A (t(9) = 3.9, p = 0.003), V3B (t(9) = 4.2, p = 0.002), IPS0 (t(9) = 3.5, p = 0.007), IPS3 (t(9) = 2.9, p = 0.02), IPS4 ((t(9) = 2.4, p = 0.04), with a trend towards above chance decoding in IPS2 (t(9) = 2.1, p = 0.06), and in Experiment 3 in areas IPS0 (t(9) = 3.5, p = 0.007), IPS1 (t(9) = 2.4, p = 0.04), IPS2 (t(9) = 6.2, p = 0.0001), and IPS4 (t(9) = 2.3, p = 0.05), with a trend towards above chance decoding in V3A (t(8) = 2.3, p = 0.05). Above chance decoding was seen in trials with distractors in Experiment 1 in all topographic regions (V3A: (t(9) = 3.1, p = 0.01), V3B: (t(9) = 2.6, p = 0.03), IPS0: (t(9) = 4.1, p = 0.003), IPS1: (t(9) = 3.6, p = 0.006), IPS3: (t(9) = 2.5, p = 0.04), IPS4: (t(9) = 2.4, p = 0.04)), except for IPS2 where it was trending (t(9) = 2.2, p = 0.05), but only in IPS1 (t(9) = 2.4, p = 0.04), and IPS2 (t(9) = 3.0, p = 0.01) in Experiment 3. Significant or trending towards significant differences between trial types were seen in IPS1 in Experiment 1 (t(9) = 2.1, p = 0.07) and in IPS2 in Experiment 3 (t(9) = 2.5, p = 0.03). Decoding accuracy was not improved by combining IPS1-4 into one larger ROI. In this combined IPS1-4 ROI, above chance decoding was seen in trials without distractors for both Experiment 1 (t(9) = 2.7, p = 0.03) and Experiment 3 (t(9) = 3.7, p = 0.005), but not in trials with distractors in either experiment (Exp. 1: p = 0.17; Exp. 3: p = 0.13). Additionally, decoding accuracy was higher in trials without distractors than with in Experiment 3 (t(9) = 2.4, p = 0.04), but not in Experiment 1 (p = 0.51). Error bars indicate s.e.m. †p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001; ns non-significant; No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary Figure 4 MVPA decoding accuracy for the average VSTM delay period in IPL and SPL for when distractors were predictable (Experiment 1) and unpredictable (Experiment 3).
The same ten participants took part in both experiments. Both IPL and SPL showed significant decoding of VSTM information in trials without distractors in both experiments, but neither showed consistent decoding of VSTM information across both experiments and trial types. Above chance VSTM decoding was seen in trials without distractors in IPL and SPL in both Experiments 1 and 3 (IPL, Exp. 1: t(9) = 3.0, p = 0.01; IPL, Exp. 3: t(9) = 2.3, p = 0.05; SPL, Exp. 1: t(9) = 4.4, p = 0.002; SPL, Exp. 3: t(9) = 4.2, p = 0.002). However, in trials with distractors, above chance decoding was only seen in Experiment 3 and only in SPL (t(9) = 3.4, p = 0.008). A trend towards a significant difference between trial types was also seen in SPL in Experiment 3 (t(9) = 2.2, p = 0.06). Across the two experiments, there was a main effect of distractor presence in SPL (F(1,9) = 7.1, p = 0.03) and a trend towards the same in IPL (F(1,9) = 4.4, p = 0.07), but no effect of distractor predictability (IPL: p = 0.93; SPL: p = 0.52) or an interaction between the two factors (IPL: p = 0.75; SPL: p = 0.97) in either region. Error bars indicate s.e.m. †p < 0.10; * p < 0.05; ns non-significant; No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary Figure 5 Univariate fMRI responses from each of the topographic IPS regions (V3A-IPS0) for both when distractors were predictable (Experiment 1) and unpredictable (Experiment 3).
The same ten participants took part in both experiments. Topographic regions show a clear progression from primarily bottom up based univariate activity in V3A/B to activity that reflected a mixture of bottom up and top down activity in higher topographic regions. In both Experiments 1 and 3, topographic regions showed a clear progression from primarily bottom up based univariate activity in V3A/B to activity that reflected a mixture of bottom up and top down activity in higher topographic regions. In trials without distractors, in both experiments, all topographic regions showed significantly decreased activity following the encoding period (Exp. 1: V3A: t(9) = 5.8, p = 0.0002; V3B: t(9) = 6.3, p = 0.0001; IPS0: t(9) = 6.7, p < 0.0001; IPS1: t(9) = 5.0, p = 0.0007; IPS2: t(9) = 3.9, p = 0.003; IPS3: t(9) = 5.1, p = 0.0006; IPS4: t(9) = 5.5, p = 0.0004; Exp. 3: V3A: t(9) = 7.8, p < 0.0001; V3B: t(9) = 10.2, p < 0.0001; IPS0: t(9) = 9.7, p < 0.0001; IPS1: t(9) = 6.2, p = 0.0002; IPS2: t(9) = 5.7, p = 0.0002; IPS3: t(9) = 6.6, p = 0.0001; IPS4: t(9) = 9.7, p < 0.0001). In trials with distractors, in both experiments, activity increased following the encoding period in V3A (Exp. 1: t(9) = 5.2, p = 0.0006; Exp. 3: t(9) = 4.5, p = 0.001) and V3B (Exp. 1: t(9) = 6.9, p < 0.0001; Exp. 3: t(9) = 4.8, p = 0.001) decreased in IPS1 (,Exp. 1: t(9) = 3.2, p = 0.01; Exp. 3: t(9) = 5.7, p = 0.0002), IPS2 (Exp. 1: t(9) = 4.8 p = 0.001; Exp. 3: t(9) = 6.2, p = 0.0002), IPS3 (Exp. 1: t(9) = 3.9, p = 0.003; Exp. 3: t(9) = 4.6, p = 0.001), and IPS4 (Exp. 1: t(9) = 4.5, p = 0.002; Exp. 3: t(9) = 7.7, p < 0.0001), and showed a trend towards decreasing in IPS0 (Exp. 1: t(9) = 2.9, p = 0.08; Exp. 3: t(9) = 2.1, p = 0.07). In V3A-IPS0, there was significantly more activation in the delay period for trials with distractors than without in both Experiments 1 (V3A: t(9) = 7.1, p < 0.0001; V3B: t(9) = 7.8, p < 0.0001; IPS0: t(9) = 5.4, p = 0.0004) and 3 (V3A: t(9) = 11.3, p < 0.0001; V3B: t(9) = 9.4, p < 0.0001; IPS0: t(9) = 6.4, p = 0.0001). IPS2 showed no difference in activation between the trial types for both experiments (Exp. 1: p = 0.74; Exp. 3: p = 0.29). In IPS1, there was no significant difference between trials with distractors and those without in Experiment 1 (p = 0.26), but a trend towards a significant difference between trial types in Experiment 3 (t(9) = 2.0, p = 0.08). In IPS3-4, there were no significant differences between trial types in Experiment 1 (IPS3: p = 0.13; IPS4: p = 0.4), but significantly higher activation for trials with distractors than those without in Experiment 3 (IPS3: t(9) = 3.6, p = 0.006; IPS4: t(9) = 3.5, p = 0.007). Error bars indicate s.e.m. No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary Figure 6 Univariate fMRI responses from anatomically defined IPL and SPL regions for both when distractors were predictable (Experiment 1) and unpredictable (Experiment 3).
The same ten participants took part in both experiments. Both regions showed activity similar to that of superior IPS, showing a mixture of top-down and bottom-up processing. In IPL and SPL, we found a significant decrease in activity following encoding in both trials with and without distractors in both experiments (Exp. 1, trials without distractors: IPL: t(9) = 8.6, p < 0.0001; SPL: t(9) = 6.3, p = 0.0001; trials with distractors: IPL: t(9) = 3.6, p = 0.006; SPL: t(9) = 4.1, p = 0.003; Exp. 3, trials without distractors: IPL: t(9) = 10.0, p < 0.0001; SPL: t(9) = 8.8, p < 0.0001; trials with distractors: IPL: t(9) = 3.1, p = 0.01; SPL: t(9) = 6.2, p = 0.0002). Activity in IPL was significantly higher during the delay period for trials with distractors than trials without in both Experiments 1 (t(9) = 4.0, p = 0.003) and 3 (t(9) = 5.0, p = 0.0007), but activity between trial types in SPL only differed in Experiment 3 (t(9) = 3.1, p = 0.01). Error bars indicate s.e.m. No distractors, trials without distractors; Distractors, trials with distractors.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–6 (PDF 620 kb)
Rights and permissions
About this article
Cite this article
Bettencourt, K., Xu, Y. Decoding the content of visual short-term memory under distraction in occipital and parietal areas. Nat Neurosci 19, 150–157 (2016). https://doi.org/10.1038/nn.4174
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nn.4174
This article is cited by
-
The effect of visual working memory load on attentional bias in social anxiety
Current Psychology (2024)
-
Spectrotemporal content of human auditory working memory represented in functional connectivity patterns
Communications Biology (2023)
-
Neural correlates of visual and tactile path integration and their task related modulation
Scientific Reports (2023)
-
Neural Mechanisms of the Maintenance and Manipulation of Gustatory Working Memory in Orbitofrontal Cortex
Cognitive Computation (2023)
-
Dynamic Networks with Multi-scale Temporal Structure
Sankhya A (2022)