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Series:   Elements in Perception

Competition and Control during Working Memory

Published online by Cambridge University Press:  13 October 2020

Anastasia Kiyonaga  and
Mark D'Esposito
Anastasia Kiyonaga
Affiliation:
University of California, San Diego
Mark D'Esposito
Affiliation:
University of California, Berkeley

Summary

Working memory and perceptual attention are related functions, engaging many similar mechanisms and brain regions. As a consequence, behavioral and neural measures often reveal competition between working memory and attention demands. Yet there remains widespread debate about how working memory operates, and whether it truly shares processes and representations with attention. This Element will examine local-level representational properties to illuminate the storage format of working memory content, as well as systems-level and brain network communication properties to illuminate the attentional processes that control working memory. The Element will integrate both cognitive and neuroscientific accounts, describing shared substrates for working memory and perceptual attention, in a multi-level network architecture that provides robustness to disruptions and allows flexible attentional control in line with goals.
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Keywords

attentioncognitive controlsensory recruitmentvisual short-term memoryworking memory
Type
Element
Information
Series: Elements in Perception
Online ISBN: 9781108581073
Publisher: Cambridge University Press
Print publication: 12 November 2020

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References

Albouy, P., Weiss, A., Baillet, S., & Zatorre, R. J. (2017). Selective entrainment of theta oscillations in the dorsal stream causally enhances auditory working memory performance. Neuron, 94(1), 193–206.e5. https://doi.org/10.1016/j.neuron.2017.03.015CrossRefGoogle ScholarPubMed
Arnemann, K. L., Chen, A. J.-W., Novakovic-Agopian, T., Gratton, C., Nomura, E. M., & D’Esposito, M. (2015). Functional brain network modularity predicts response to cognitive training after brain injury. Neurology, 84(15), 1568–1574. https://doi.org/10.1212/WNL.0000000000001476Google Scholar
Awh, E., & Jonides, J. (2001). Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Sciences, 5(3), 119–126.Google Scholar
Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839.CrossRefGoogle ScholarPubMed
Baddeley, A. D., & Hitch, G. J. (2018). The phonological loop as a buffer store: An update. Cortex. https://doi.org/10.1016/j.cortex.2018.05.015Google Scholar
Badre, D., & D’Esposito, M. (2007). Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19(12), 2082–2099. https://doi.org/10.1162/jocn.2007.19.12.2082Google Scholar
Badre, D., & D’esposito, M. (2009). Is the rostro-caudal axis of the frontal lobe hierarchical?. Nature Reviews Neuroscience, 10(9), 659–669.CrossRefGoogle Scholar
Badre, D., Hoffman, J., Cooney, J. W., & D’Esposito, M. (2009). Hierarchical cognitive control deficits following damage to the human frontal lobe. Nature Neuroscience, 12(4), 515–522. https://doi.org/10.1038/nn.2277CrossRefGoogle Scholar
Badre, D., & Nee, D. E. (2018). Frontal cortex and the hierarchical control of behavior. Trends in Cognitive Sciences, 22(2), 170–188. https://doi.org/10.1016/j.tics.2017.11.005Google Scholar
Bae, G.-Y., & Luck, S. J. (2018). Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials. Journal of Neuroscience, 38(2), 409–422. https://doi.org/10.1523/JNEUROSCI.2860–17.2017CrossRefGoogle ScholarPubMed
Baniqued, P. L., Gallen, C. L., Voss, M. W., Burzynska, A. Z., Wong, C. N., Cooke, G. E., … D’Esposito, M. (2018). Brain network modularity predicts exercise-related executive function gains in older adults. Frontiers in Aging Neuroscience, 9. https://doi.org/10.3389/fnagi.2017.00426CrossRefGoogle ScholarPubMed
Barbosa, J., Stein, H., Martinez, R. L., Galan-Gadea, A., Li, S., Dalmau, J., … & Compte, A. (2020). Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory. Nature Neuroscience, 23, 1016–1024.CrossRefGoogle ScholarPubMed
Barrouillet, P., Portrat, S., & Camos, V. (2011). On the law relating processing to storage in working memory. Psychological Review, 118(2), 175.CrossRefGoogle ScholarPubMed
Behrmann, M., & Plaut, D. C. (2013). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in Cognitive Sciences, 17(5), 210–219. https://doi.org/10.1016/j.tics.2013.03.007CrossRefGoogle Scholar
Bergmann, J., Genç, E., Kohler, A., Singer, W., & Pearson, J. (2016). Neural anatomy of primary visual cortex limits visual working memory. Cerebral Cortex, 26(1), 43–50. https://doi.org/10.1093/cercor/bhu168CrossRefGoogle ScholarPubMed
Bertolero, M. A., Yeo, B. T., & D’Esposito, M. (2015). The modular and integrative functional architecture of the human brain. Proceedings of the National Academy of Sciences, 112(49), E6798–E6807.CrossRefGoogle Scholar
Bertolero, M. A., Yeo, B. T., & D’Esposito, M. (2017). The diverse club. Nature Communications, 8(1), 1–11.Google Scholar
Bettencourt, K. C., & Xu, Y. (2016). Decoding the content of visual short-term memory under distraction in occipital and parietal areas. Nature Neuroscience, 19(1), 150–157. https://doi.org/10.1038/nn.4174CrossRefGoogle ScholarPubMed
Bliss, D. P., & D’Esposito, M. (2017). Synaptic augmentation in a cortical circuit model reproduces serial dependence in visual working memory. PLOS ONE, 12(12), e0188927. https://doi.org/10.1371/journal.pone.0188927Google Scholar
Bliss, D. P., Sun, J. J., & D’Esposito, M. (2017). Serial dependence is absent at the time of perception but increases in visual working memory. Scientific Reports, 7. https://doi.org/10.1038/s41598-017–15199-7CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575Google Scholar
Buschman, T. J., & Miller, E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315(5820), 1860–1862. https://doi.org/10.1126/science.1138071CrossRefGoogle ScholarPubMed
Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929. https://doi.org/10.1126/science.1099745CrossRefGoogle ScholarPubMed
Carlisle, N. B., & Woodman, G. F. (2011). Automatic and strategic effects in the guidance of attention by working memory representations. Acta Psychologica, 137(2), 217–225.CrossRefGoogle ScholarPubMed
Chen, K., Ye, Y., Xie, J., Xia, T., & Mo, L. (2017). Working memory operates over the same representations as attention. PLOS ONE, 12(6), e0179382.CrossRefGoogle ScholarPubMed
Choi, E. Y., Drayna, G. K., & Badre, D. (2018). Evidence for a functional hierarchy of association networks. Journal of Cognitive Neuroscience, 30(5), 722–736.Google Scholar
Christophel, T. B., Hebart, M. N., & Haynes, J. D. (2012). Decoding the contents of visual short-term memory from human visual and parietal cortex. Journal of Neuroscience, 32(38), 12983–12989.CrossRefGoogle Scholar
Christophel, T. B., Iamshchinina, P., Yan, C., Allefeld, C., & Haynes, J.-D. (2018). Cortical specialization for attended versus unattended working memory. Nature Neuroscience, 21, 494–496. https://doi.org/10.1038/s41593-018–0094-4CrossRefGoogle ScholarPubMed
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J.-D. (2017). The distributed nature of working memory. Trends in Cognitive Sciences, 21(2), 111–124. https://doi.org/10.1016/j.tics.2016.12.007Google Scholar
Chun, M. M. (2011). Visual working memory as visual attention sustained internally over time. Neuropsychologia, 49(6), 1407–1409. https://doi.org/10.1016/j.neuropsychologia.2011.01.029CrossRefGoogle ScholarPubMed
Chun, M. M., & Johnson, M. K. (2011). Memory: Enduring traces of perceptual and reflective attention. Neuron, 72(4), 520–535. https://doi.org/10.1016/j.neuron.2011.10.026Google Scholar
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332–361. https://doi.org/10.1037/0033-295X.97.3.332Google Scholar
Cohen, J. R., & D’Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience, 36(48), 12083–12094. https://doi.org/10.1523/JNEUROSCI.2965–15.2016Google Scholar
Cohen, J. R., Gallen, C. L., Jacobs, E. G., Lee, T. G., & D’Esposito, M. (2014). Quantifying the reconfiguration of intrinsic networks during working memory. PLOS ONE, 9(9), e106636. https://doi.org/10.1371/journal.pone.0106636Google Scholar
Constantinidis, C., & Klingberg, T. (2016). The neuroscience of working memory capacity and training. Nature Reviews Neuroscience, 17(7), 438–449. https://doi.org/10.1038/nrn.2016.43CrossRefGoogle ScholarPubMed
Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 415–423. https://doi.org/10.1016/S1364-6613(03)00197–9Google Scholar
Cutzu, F., & Tsotsos, J. K. (2003). The selective tuning model of attention: Psychophysical evidence for a suppressive annulus around an attended item. Vision Research, 43(2), 205–219. https://doi.org/10.1016/S0042-6989(02)00491–1Google Scholar
David, S. V., Hayden, B. Y., Mazer, J. A., & Gallant, J. L. (2008). Attention to stimulus features shifts spectral tuning of V4 neurons during natural vision. Neuron, 59(3), 509–521. https://doi.org/10.1016/j.neuron.2008.07.001Google Scholar
Derrfuss, J., Ekman, M., Hanke, M., Tittgemeyer, M., & Fiebach, C. J. (2017). Distractor-resistant short-term memory is supported by transient changes in neural stimulus representations. Journal of Cognitive Neuroscience, 29(9), 1547–1565. https://doi.org/10.1162/jocn_a_01141Google Scholar
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193–222. https://doi.org/10.1146/annurev.ne.18.030195.001205Google Scholar
D’Esposito, M. (2007). From cognitive to neural models of working memory. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 761–772.Google Scholar
D’Esposito, M., Aguirre, G. K., Zarahn, E., Ballard, D., Shin, R. K., & Lease, J. (1998). Functional MRI studies of spatial and nonspatial working memory. Cognitive Brain Research, 7(1), 1–13. https://doi.org/10.1016/S0926-6410(98)00004–4Google Scholar
D’Esposito, M., Ballard, D., Aguirre, G. K., & Zarahn, E. (1998). Human prefrontal cortex is not specific for working memory: A functional MRI study. NeuroImage, 8(3), 274–282. https://doi.org/10.1006/nimg.1998.0364Google Scholar
D’Esposito, M., Cooney, J. W., Gazzaley, A., Gibbs, S. E. B., & Postle, B. R. (2006). Is the prefrontal cortex necessary for delay task performance? Evidence from lesion and FMRI data. Journal of the International Neuropsychological Society: JINS, 12(2), 248–260. https://doi.org/10.1017/S1355617706060322CrossRefGoogle ScholarPubMed
D’Esposito, M., & Grossman, M. (1996). The physiological basis of executive function and working memory. The Neuroscientist, 2(6), 345–352. https://doi.org/10.1177/107385849600200612CrossRefGoogle Scholar
D’Esposito, M., & Postle, B. R. (1999). The dependence of span and delayed-response performance on prefrontal cortex. Neuropsychologia, 37(11), 1303–1315. https://doi.org/10.1016/S0028-3932(99)00021–4Google Scholar
D’Esposito, M., & Postle, B. R.(2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66(1), 115–142. https://doi.org/10.1146/annurev-psych-010814–015031Google Scholar
D’Esposito, M., Postle, B. R., Ballard, D., & Lease, J. (1999). Maintenance versus manipulation of information held in working memory: An event-related fMRI study. Brain and Cognition, 41(1), 66–86. https://doi.org/10.1006/brcg.1999.1096CrossRefGoogle ScholarPubMed
D’Esposito, M., Postle, B. R., Jonides, J., & Smith, E. E. (1999). The neural substrate and temporal dynamics of interference effects in working memory as revealed by event-related functional MRI. Proceedings of the National Academy of Sciences, 96(13), 7514–7519. https://doi.org/10.1073/pnas.96.13.7514Google Scholar
de Vries, I. E., Slagter, H. A., & Olivers, C. N. (2020). Oscillatory control over representational states in working memory. Trends in Cognitive Sciences, 24(2), 150–162.Google Scholar
Dowd, E. W., Kiyonaga, A., Beck, J. M., & Egner, T. (2015). Quality and accessibility of visual working memory during cognitive control of attentional guidance: A Bayesian model comparison approach. Visual Cognition, 23(3), 337–356. https://doi.org/10.1080/13506285.2014.1003631Google Scholar
Dowd, E. W., Kiyonaga, A., Egner, T., & Mitroff, S. R. (2015). Attentional guidance by working memory differs by paradigm: An individual-differences approach. Attention, Perception, & Psychophysics, 77(3), 704–712. https://doi.org/10.3758/s13414-015–0847-zCrossRefGoogle ScholarPubMed
Druzgal, T. J., & D’Esposito, M. (2001). Activity in fusiform face area modulated as a function of working memory. Cognitive Brain Research, 10(3), 355–364. https://doi.org/10.1016/S0926-6410(00)00056–2Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in cognitive sciences, 14(4), 172–179.Google Scholar
Emrich, S. M., & Ferber, S. (2012). Competition increases binding errors in visual working memory. Journal of Vision, 12(4), 12–12. https://doi.org/10.1167/12.4.12Google Scholar
Eriksson, J., Vogel, E. K., Lansner, A., Bergström, F., & Nyberg, L. (2015). Neurocognitive architecture of working memory. Neuron, 88(1), 33–46. https://doi.org/10.1016/j.neuron.2015.09.020Google Scholar
Ester, E. F., Anderson, D. E., Serences, J. T., & Awh, E. (2013). A neural measure of precision in visual working memory. Journal of Cognitive Neuroscience, 25(5), 754–761. https://doi.org/10.1162/jocn_a_00357CrossRefGoogle ScholarPubMed
Ester, E. F., Sprague, T. C., & Serences, J. T. (2015). Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron, 87(4), 893–905.CrossRefGoogle Scholar
Feldmann-Wüstefeld, T., Vogel, E. K., & Awh, E. (2018). Contralateral delay activity indexes working memory storage, not the current focus of spatial attention. Journal of Cognitive Neuroscience, 30(8), 1185–1196.Google Scholar
Feredoes, E., Heinen, K., Weiskopf, N., Ruff, C., & Driver, J. (2011). Causal evidence for frontal involvement in memory target maintenance by posterior brain areas during distracter interference of visual working memory. Proceedings of the National Academy of Sciences, 108(42), 17510–17515. https://doi.org/10.1073/pnas.1106439108Google Scholar
Fiebach, C. J., Rissman, J., & D’Esposito, M. (2006). Modulation of inferotemporal cortex activation during verbal working memory maintenance. Neuron, 51(2), 251–261. https://doi.org/10.1016/j.neuron.2006.06.007Google Scholar
Fischer, J., & Whitney, D. (2014). Serial dependence in visual perception. Nature Neuroscience, 17(5), 738–743.Google Scholar
Franconeri, S. L., Alvarez, G. A., & Cavanagh, P. (2013). Flexible cognitive resources: Competitive content maps for attention and memory. Trends in Cognitive Sciences, 17(3), 134–141. https://doi.org/10.1016/j.tics.2013.01.010Google Scholar
Franconeri, S. L., Jonathan, S. V., & Scimeca, J. M. (2010). Tracking multiple objects is limited only by object spacing, not by speed, time, or capacity. Psychological Science, 21(7), 920–925. https://doi.org/10.1177/0956797610373935CrossRefGoogle ScholarPubMed
Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291(5502), 312–316. https://doi.org/10.1126/science.291.5502.312Google Scholar
Friedman-Hill, S. R., Robertson, L. C., Desimone, R., & Ungerleider, L. G. (2003). Posterior parietal cortex and the filtering of distractors. Proceedings of the National Academy of Sciences, 100(7), 4263–4268. https://doi.org/10.1073/pnas.0730772100Google Scholar
Fries, P. (2015). Rhythms for cognition: communication through coherence. Neuron, 88(1), 220–235. https://doi.org/10.1016/j.neuron.2015.09.034Google Scholar
Gallen, C. L., Baniqued, P. L., Chapman, S. B., Aslan, S., Keebler, M., Didehbani, N., & D’Esposito, M. (2016). Modular brain network organization predicts response to cognitive training in older adults. PLOS ONE, 11(12), e0169015. https://doi.org/10.1371/journal.pone.0169015Google Scholar
Gallen, C. L., & D’Esposito, M. (2019). Brain modularity: A biomarker of intervention-related plasticity. Trends in Cognitive Sciences, 23, 293–304. https://doi.org/10.1016/j.tics.2019.01.014CrossRefGoogle ScholarPubMed
Gallen, C. L., Turner, G. R., Adnan, A., & D’Esposito, M. (2016). Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging, 44, 42–52. https://doi.org/10.1016/j.neurobiolaging.2016.04.003Google Scholar
Gayet, S., Guggenmos, M., Christophel, T. B., Haynes, J.-D., Paffen, C. L. E., der Stigchel, S. V., & Sterzer, P. (2017). Visual working memory enhances the neural response to matching visual input. Journal of Neuroscience, 37(28), 6638–6647. https://doi.org/10.1523/JNEUROSCI.3418–16.2017Google Scholar
Gayet, S., Paffen, C. L. E., & der Stigchel, S. V. (2018). Visual working memory storage recruits sensory processing areas. Trends in Cognitive Sciences, 22(3), 189–190. https://doi.org/10.1016/j.tics.2017.09.011Google Scholar
Gazzaley, A., & Nobre, A. C. (2012). Top-down modulation: Bridging selective attention and working memory. Trends in Cognitive Sciences, 16(2), 129–135. https://doi.org/10.1016/j.tics.2011.11.014Google Scholar
Gazzaley, A., Rissman, J., Cooney, J., Rutman, A., Seibert, T., Clapp, W., & D’Esposito, M. (2007). Functional interactions between prefrontal and visual association cortex contribute to top-down modulation of visual processing. Cerebral Cortex, 17(suppl_1), i125–i135. https://doi.org/10.1093/cercor/bhm113Google Scholar
Gazzaley, A., Rissman, J., & D’Esposito, M. (2004). Functional connectivity during working memory maintenance. Cognitive, Affective, & Behavioral Neuroscience, 4(4), 580–599. https://doi.org/10.3758/CABN.4.4.580Google Scholar
Gratton, C., Nomura, E. M., Pérez, F., & D’Esposito, M. (2012). Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. Journal of Cognitive Neuroscience, 24(6), 1275–1285. https://doi.org/10.1162/jocn_a_00222Google Scholar
Griffin, I. C., & Nobre, A. C. (2003). Orienting attention to locations in internal representations. Journal of Cognitive Neuroscience, 15(8), 1176–1194. https://doi.org/10.1162/089892903322598139CrossRefGoogle ScholarPubMed
Hakim, N., Adam, K. C., Gunseli, E., Awh, E., & Vogel, E. K. (2019). Dissecting the neural focus of attention reveals distinct processes for spatial attention and object-based storage in visual working memory. Psychological Science, 30(4), 526–540.Google Scholar
Hakim, N., Feldmann-Wüstefeld, T., Awh, E., & Vogel, E. K. (2020). Perturbing neural representations of working memory with task-irrelevant interruption. Journal of Cognitive Neuroscience, 32(3), 558–569.Google Scholar
Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458(7238), 632–635. https://doi.org/10.1038/nature07832Google Scholar
Harrison, W. J., & Bays, P. M. (2018). Visual working memory is independent of the cortical spacing between memoranda. Journal of Neuroscience, 38(12), 3116–3123. https://doi.org/10.1523/JNEUROSCI.2645–17.2017Google Scholar
Hillyard, S. A., Vogel, E. K., & Luck, S. J. (1998). Sensory gain control (amplification) as a mechanism of selective attention: Electrophysiological and neuroimaging evidence. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 353(1373), 1257–1270. https://doi.org/10.1098/rstb.1998.0281CrossRefGoogle ScholarPubMed
Hollingworth, A., Matsukura, M., & Luck, S. J. (2013). Visual working memory modulates low-level saccade target selection: Evidence from rapidly generated saccades in the global effect paradigm. Journal of Vision, 13(13), 4–4. https://doi.org/10.1167/13.13.4Google Scholar
Hollingworth, A., & Maxcey-Richard, A. M. (2012). Selective maintenance in visual working memory does not require sustained visual attention. Journal of Experimental Psychology. Human Perception and Performance, 39(4), 1047–1058. https://doi.org/10.1037/a0030238Google Scholar
Hopf, J.-M., Boehler, C. N., Luck, S. J., Tsotsos, J. K., Heinze, H.-J., & Schoenfeld, M. A. (2006). Direct neurophysiological evidence for spatial suppression surrounding the focus of attention in vision. Proceedings of the National Academy of Sciences of the United States of America, 103(4), 1053–1058. https://doi.org/10.1073/pnas.0507746103CrossRefGoogle ScholarPubMed
Hwang, K., Shine, J. M., & D’Esposito, M. (2018). Frontoparietal activity interacts with task-evoked changes in functional connectivity. Cerebral Cortex, 29(2) 802–813. https://doi.org/10.1093/cercor/bhy011Google Scholar
Jacob, S. N., & Nieder, A. (2014). Complementary roles for primate frontal and parietal cortex in guarding working memory from distractor stimuli. Neuron, 83(1), 226–237. https://doi.org/10.1016/j.neuron.2014.05.009Google Scholar
Jacobsen, C. F. (1935). Functions of frontal association area in primates. Archives of Neurology & Psychiatry, 33(3), 558–569. https://doi.org/10.1001/archneurpsyc.1935.02250150108009CrossRefGoogle Scholar
Jha, A. P., & Kiyonaga, A. (2010). Working-memory-triggered dynamic adjustments in cognitive control. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36(4), 1036–1042. https://doi.org/10.1037/a0019337Google Scholar
John-Saaltink, E. S., Kok, P., Lau, H. C., & Lange, F. P. de. (2016). Serial dependence in perceptual decisions is reflected in activity patterns in primary visual cortex. Journal of Neuroscience, 36(23), 6186–6192. https://doi.org/10.1523/JNEUROSCI.4390–15.2016Google Scholar
Johnson, M. R., Higgins, J. A., Norman, K. A., Sederberg, P. B., Smith, T. A., & Johnson, M. K. (2013). Foraging for thought: An inhibition-of-return-like effect resulting from directing attention within working memory. Psychological Science, 24(7), 1104–1112. https://doi.org/10.1177/0956797612466414Google Scholar
Jonides, J., & Nee, D. E. (2006). Brain mechanisms of proactive interference in working memory. Neuroscience, 139(1), 181–193. https://doi.org/10.1016/j.neuroscience.2005.06.042CrossRefGoogle ScholarPubMed
Kim, S. Y., Kim, M. S., & Chun, M. M. (2005). Concurrent working memory load can reduce distraction. Proceedings of the National Academy of Sciences of the United States of America, 102(45), 16524.Google Scholar
Kiyonaga, A., Dowd, E. W., & Egner, T. (2017). Neural representation of working memory content is modulated by visual attentional demand. Journal of Cognitive Neuroscience, 29(12), 2011–2024. https://doi.org/10.1162/jocn_a_01174Google Scholar
Kiyonaga, A., & Egner, T. (2013). Working memory as internal attention: Toward an integrative account of internal and external selection processes. Psychonomic Bulletin & Review, 20(2), 228–242. https://doi.org/10.3758/s13423-012–0359-yGoogle Scholar
Kiyonaga, A., & Egner, T. (2014a). Resource-sharing between internal maintenance and external selection modulates attentional capture by working memory content. Frontiers in Human Neuroscience, 8, 670. https://doi.org/10.3389/fnhum.2014.00670Google Scholar
Kiyonaga, A., & Egner, T.(2014b). The working memory Stroop effect when internal representations clash with external stimuli. Psychological Science, 25(8), 1619–1629. https://doi.org/10.1177/0956797614536739CrossRefGoogle ScholarPubMed
Kiyonaga, A., & Egner, T.(2016). Center-surround inhibition in working memory. Current Biology, 26(1), 64–68. https://doi.org/10.1016/j.cub.2015.11.013Google Scholar
Kiyonaga, A., Egner, T., & Soto, D. (2012). Cognitive control over working memory biases of selection. Psychonomic Bulletin & Review, 19(4), 639–646. https://doi.org/10.3758/s13423-012–0253-7Google Scholar
Kiyonaga, A., Korb, F. M., Lucas, J., Soto, D., & Egner, T. (2014). Dissociable causal roles for left and right parietal cortex in controlling attentional biases from the contents of working memory. NeuroImage, 100, 200–205. https://doi.org/10.1016/j.neuroimage.2014.06.019Google Scholar
Kiyonaga, A., Scimeca, J. M., Bliss, D. P., & Whitney, D. (2017). Serial dependence across perception, attention, and memory. Trends in Cognitive Sciences, 21(7), 493–497. https://doi.org/10.1016/j.tics.2017.04.011CrossRefGoogle ScholarPubMed
Knight, R. T., Richard Staines, W., Swick, D., & Chao, L. L. (1999). Prefrontal cortex regulates inhibition and excitation in distributed neural networks. Acta Psychologica, 101(2–3), 159–178. https://doi.org/10.1016/S0001-6918(99)00004–9Google Scholar
Lara, A. H., & Wallis, J. D. (2015). The role of prefrontal cortex in working memory: A mini review. Frontiers in Systems Neuroscience, 173. https://doi.org/10.3389/fnsys.2015.00173Google Scholar
LaRocque, J. J., Lewis-Peacock, J. A., & Postle, B. R. (2014). Multiple neural states of representation in short-term memory? It’s a matter of attention. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00005Google Scholar
Leavitt, M. L., Mendoza-Halliday, D., & Martinez-Trujillo, J. C. (2017). Sustained activity encoding working memories: Not fully distributed. Trends in Neurosciences, 40(6), 328–346. https://doi.org/10.1016/j.tins.2017.04.004Google Scholar
Lee, T. G., & D’Esposito, M. (2012). The dynamic nature of top-down signals originating from prefrontal cortex: A combined fMRI–TMS study. The Journal of Neuroscience, 32(44), 15458–15466. https://doi.org/10.1523/JNEUROSCI.0627–12.2012Google Scholar
Lepsien, J., Griffin, I. C., Devlin, J. T., & Nobre, A. C. (2005). Directing spatial attention in mental representations: Interactions between attentional orienting and working-memory load. NeuroImage, 26(3), 733–743. https://doi.org/10.1016/j.neuroimage.2005.02.026Google Scholar
Lepsien, J., & Nobre, A. C. (2006). Cognitive control of attention in the human brain: Insights from orienting attention to mental representations. Brain Research, 1105(1), 20–31. https://doi.org/10.1016/j.brainres.2006.03.033Google Scholar
Lepsien, J., & Nobre, A. C.(2007). Attentional modulation of object representations in working memory. Cerebral Cortex, 17(9), 2072–2083. https://doi.org/10.1093/cercor/bhl116CrossRefGoogle ScholarPubMed
Lewis-Peacock, J. A., Drysdale, A. T., & Postle, B. R. (2014). Neural evidence for the flexible control of mental representations. Cerebral Cortex, 25(10), 3303–3313. https://doi.org/10.1093/cercor/bhu130Google Scholar
Lorenc, E. S., Lee, T. G., Chen, A. J.-W., & D’Esposito, M. (2015). The effect of disruption of prefrontal cortical function with transcranial magnetic stimulation on visual working memory. Frontiers in Systems Neuroscience, 9. https://doi.org/10.3389/fnsys.2015.00169Google Scholar
Lorenc, E. S., Sreenivasan, K. K., Nee, D. E., Vandenbroucke, A. R. E., & D’Esposito, M. (2018). Flexible coding of visual working memory representations during distraction. Journal of Neuroscience, 38(23), 5267–5276. https://doi.org/10.1523/JNEUROSCI.3061–17.2018Google Scholar
Lorenc, E. S., Vandenbroucke, A. R., Nee, D. E., de Lange, F. P., & D’Esposito, M. (2020). Dissociable neural mechanisms underlie currently-relevant, future-relevant, and discarded working memory representations. Scientific Reports, 10(1), 1–17.Google Scholar
MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163–203. https://doi.org/10.1037/0033–2909.109.2.163Google Scholar
Magen, H., Emmanouil, T.-A., McMains, S. A., Kastner, S., & Treisman, A. (2009). Attentional demands predict short-term memory load response in posterior parietal cortex. Neuropsychologia, 47(8–9), 1790–1798. https://doi.org/10.1016/j.neuropsychologia.2009.02.015Google Scholar
Magnussen, S., & Greenlee, M. W. (1992). Retention and disruption of motion information in visual short-term memory. Journal of Experimental Psychology. Learning, Memory, and Cognition, 18(1), 151–156.Google Scholar
Magnussen, S., Greenlee, M. W., Asplund, R., & Dyrnes, S. (1991). Stimulus-specific mechanisms of visual short-term memory. Vision Research, 31(7), 1213–1219. https://doi.org/10.1016/0042–6989(91)90046–8CrossRefGoogle ScholarPubMed
Mallett, R., & Lewis‐Peacock, J. A. (2018). Behavioral decoding of working memory items inside and outside the focus of attention. Annals of the New York Academy of Sciences, 1424(1), 256–267. https://doi.org/10.1111/nyas.13647Google Scholar
Mallett, R., Mummaneni, A., & Lewis-Peacock, J. A. (2020). Distraction biases working memory for faces. Psychonomic Bulletin & Review, 1–7.Google Scholar
Malmo, R. B. (1942). Interference factors in delayed response in monkeys after removal of frontal lobes. Journal of Neurophysiology, 5(4), 295–308.Google Scholar
McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11(1), 103–107. https://doi.org/10.1038/nn2024Google Scholar
Mendoza-Halliday, D., & Martinez-Trujillo, J. C. (2017). Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex. Nature Communications, 8, 15471. https://doi.org/10.1038/ncomms15471Google Scholar
Merrikhi, Y., Clark, K., Albarran, E., Parsa, M., Zirnsak, M., Moore, T., & Noudoost, B. (2017). Spatial working memory alters the efficacy of input to visual cortex. Nature Communications, 8, 15041. https://doi.org/10.1038/ncomms15041CrossRefGoogle ScholarPubMed
Miller, B. T., & D’Esposito, M. (2005). Searching for “the top” in top-down control. Neuron, 48(4), 535–538. https://doi.org/10.1016/j.neuron.2005.11.002Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167–202.CrossRefGoogle Scholar
Miller, J. A., Kiyonaga, A., Ivry, R. B., & D’Esposito, M. (in press). Prioritized working memory content biases ongoing action. Journal of Experimental Psychology: Human Perception and Performance. http://dx.doi.org/10.1037/xhp0000868Google Scholar
Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science, 229(4715), 782–784.Google Scholar
Myers, N. E., Stokes, M. G., & Nobre, A. C. (2017). Prioritizing information during working memory: Beyond sustained internal attention. Trends in Cognitive Sciences, 21(6), 449–461. https://doi.org/10.1016/j.tics.2017.03.010Google Scholar
Nassi, J. J., Lomber, S. G., & Born, R. T. (2013). Corticocortical feedback contributes to surround suppression in V1 of the alert primate. Journal of Neuroscience, 33(19), 8504–8517. https://doi.org/10.1523/JNEUROSCI.5124–12.2013Google Scholar
Nee, D. E., & D’Esposito, M. (2016). The hierarchical organization of the lateral prefrontal cortex. ELife, 5. https://doi.org/10.7554/eLife.12112Google Scholar
Nee, D. E., & Jonides, J. (2008). Neural correlates of access to short-term memory. Proceedings of the National Academy of Sciences, 105(37), 14228–14233. https://doi.org/10.1073/pnas.0802081105Google Scholar
Nee, D. E., & Jonides, J.(2009). Common and distinct neural correlates of perceptual and memorial selection. NeuroImage, 45(3), 963–975. https://doi.org/10.1016/j.neuroimage.2009.01.005Google Scholar
Nelissen, N., Stokes, M., Nobre, A. C., & Rushworth, M. F. S. (2013). Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection. The Journal of Neuroscience, 33(42), 16443–16458. https://doi.org/10.1523/JNEUROSCI.2625–13.2013Google Scholar
Nemes, V. A., Parry, N. R. A., Whitaker, D., & McKeefry, D. J. (2012). The retention and disruption of color information in human short-term visual memory. Journal of Vision, 12(1), 26–26. https://doi.org/10.1167/12.1.26Google Scholar
Nobre, A. C., Coull, J. T., Maquet, P., Frith, C. D., Vandenberghe, R., & Mesulam, M. M. (2004). Orienting attention to locations in perceptual versus mental representations. Journal of Cognitive Neuroscience, 16(3), 363–373. https://doi.org/10.1162/089892904322926700Google Scholar
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. https://doi.org/10.1016/j.tics.2006.07.005Google Scholar
Oberauer, K., & Lin, H.-Y. (2017). An interference model of visual working memory. Psychological Review, 124(1), 21–59. https://doi.org/10.1037/rev0000044Google Scholar
O’Craven, K. M., Downing, P. E., & Kanwisher, N. (1999). fMRI evidence for objects as the units of attentional selection. Nature, 401(6753), 584–587. https://doi.org/10.1038/44134Google Scholar
Olivers, C. N. L., Peters, J., Houtkamp, R., & Roelfsema, P. R. (2011). Different states in visual working memory: When it guides attention and when it does not. Trends in Cognitive Sciences, 15(7). https://doi.org/10.1016/j.tics.2011.05.004Google Scholar
Oztekin, I., & Badre, D. (2011). Distributed patterns of brain activity that lead to forgetting. Frontiers in Human Neuroscience, 5. https://doi.org/10.3389/fnhum.2011.00086Google Scholar
Pan, Y., Han, Y., & Zuo, W. (2019). The color-word Stroop effect driven by working memory maintenance. Attention, Perception, & Psychophysics, 81(8), 2722–2731.Google Scholar
Papadimitriou, C., White, R. L., & Snyder, L. H. (2016). Ghosts in the machine II: Neural correlates of memory interference from the previous trial. Cerebral Cortex, 27(4), 2513–2527. https://doi.org/10.1093/cercor/bhw106Google Scholar
Pasternak, T., & Greenlee, M. W. (2005). Working memory in primate sensory systems. Nature Reviews Neuroscience, 6(2), 97–107. https://doi.org/10.1038/nrn1603Google Scholar
Pelli, D. G., & Tillman, K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11(10), 1129–1135. https://doi.org/10.1038/nn.2187Google Scholar
Pertzov, Y., Manohar, S., & Husain, M. (2017). rapid forgetting results from competition over time between items in visual working memory. Journal of Experimental Psychology. Learning, Memory, and Cognition, 43(4), 528–536. https://doi.org/10.1037/xlm0000328Google Scholar
Pessoa, L. (2012). Beyond brain regions: Network perspective of cognition–emotion interactions. Behavioral and Brain Sciences, 35(3), 158–159. https://doi.org/10.1017/S0140525X11001567Google Scholar
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32(1), 3–25. https://doi.org/10.1080/00335558008248231Google Scholar
Posner, M. I., & Snyder, C. R. R. (1975). Facilitation and inhibition in the processing of signals. Attention and Performance 5, 669–682.Google Scholar
Postle, B. R. (2005). Delay-period activity in the prefrontal cortex: One function is sensory gating. Journal of Cognitive Neuroscience, 17(11), 1679–1690. https://doi.org/10.1162/089892905774589208Google Scholar
Postle, B. R.(2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139(1), 23–38. https://doi.org/10.1016/j.neuroscience.2005.06.005Google Scholar
Postle, B. R.(2015). Activation and Information in Working Memory Research. In The Wiley Handbook on the Cognitive Neuroscience of Memory (pp. 21–43). Wiley-Blackwell. https://doi.org/10.1002/9781118332634.ch2Google Scholar
Postle, B. R., Berger, J. S., & D’Esposito, M. (1999). Functional neuroanatomical double dissociation of mnemonic and executive control processes contributing to working memory performance. Proceedings of the National Academy of Sciences, 96(22), 12959–12964. https://doi.org/10.1073/pnas.96.22.12959Google Scholar
Postle, B. R., & D’Esposito, M. (1999). “What” – then – “where” in visual working memory: An event-related fMRI study. Journal of Cognitive Neuroscience, 11(6), 585–597. https://doi.org/10.1162/089892999563652Google Scholar
Postle, B. R., Druzgal, T. J., & D’Esposito, M. (2003). Seeking the neural substrates of visual working memory storage. Cortex, 39(4), 927–946. https://doi.org/10.1016/S0010-9452(08)70871–2Google Scholar
Power, J. D., & Petersen, S. E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 23(2), 223–228. https://doi.org/10.1016/j.conb.2012.12.009Google Scholar
Rademaker, R. L., Bloem, I. M., De Weerd, P., & Sack, A. T. (2015). The impact of interference on short-term memory for visual orientation. Journal of Experimental Psychology. Human Perception and Performance, 41(6), 1650–1665. https://doi.org/10.1037/xhp0000110Google Scholar
Rademaker, R. L., Chunharas, C., & Serences, J. T. (2019). Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience, 22(8), 1336–1344.Google Scholar
Ranganath, C., Johnson, M. K., & D’Esposito, M. (2000). Left anterior prefrontal activation increases with demands to recall specific perceptual information. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 20(22), RC108.Google Scholar
Ranganath, C., DeGutis, J., & D’Esposito, M. (2004). Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. Brain Research. Cognitive Brain Research, 20(1), 37–45. https://doi.org/10.1016/j.cogbrainres.2003.11.017Google Scholar
Reddy, L., Kanwisher, N. G., & VanRullen, R. (2009). Attention and biased competition in multi-voxel object representations. Proceedings of the National Academy of Sciences, 106(50), 21447–21452. https://doi.org/10.1073/pnas.0907330106Google Scholar
Reynolds, J. H., & Chelazzi, L. (2004). Attentional modulation of visual processing. Annual Review of Neuroscience, 27(1), 611–647. https://doi.org/10.1146/annurev.neuro.26.041002.131039Google Scholar
Riddle, J., Hwang, K., Cellier, D., Dhanani, S., & D’Esposito, M. (2019). Causal evidence for the role of neuronal oscillations in top–down and bottom–up attention. Journal of Cognitive Neuroscience, 31(5),768–779. https://doi.org/10.1162/jocn_a_01376Google Scholar
Riggall, A. C., & Postle, B. R. (2012). The relationship between working memory storage and elevated activity as measured with functional magnetic resonance imaging. The Journal of Neuroscience, 32(38), 12990–12998. https://doi.org/10.1523/JNEUROSCI.1892–12.2012Google Scholar
Rigotti, M., Barak, O., Warden, M. R., Wang, X. J., Daw, N. D., Miller, E. K., & Fusi, S. (2013). The importance of mixed selectivity in complex cognitive tasks. Nature, 497(7451), 585–590.Google Scholar
Riley, M. R., & Constantinidis, C. (2016). Role of prefrontal persistent activity in working memory. Frontiers in systems neuroscience, 9, 181.Google Scholar
Rissman, J., Gazzaley, A., & D’Esposito, M. (2008). Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. Cerebral Cortex, 18(7), 1618–1629. https://doi.org/10.1093/cercor/bhm195Google Scholar
Rissman, J., Gazzaley, A., & D’Esposito, M.(2009). The effect of non-visual working memory load on top-down modulation of visual processing. Neuropsychologia, 47(7), 1637–1646. https://doi.org/10.1016/j.neuropsychologia.2009.01.036Google Scholar
Rose, N. S., LaRocque, J. J., Riggall, A. C., Gosseries, O., Starrett, M. J., Meyering, E. E., & Postle, B. R. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science, 354(6316), 1136–1139. https://doi.org/10.1126/science.aah7011Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003Google Scholar
Saad, E., & Silvanto, J. (2013). how visual short-term memory maintenance modulates subsequent visual aftereffects. Psychological Science, 24(5), 803–808. https://doi.org/10.1177/0956797612462140CrossRefGoogle ScholarPubMed
Sadaghiani, S., Poline, J.-B., Kleinschmidt, A., & D’Esposito, M. (2015). Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences of the United States of America, 112(27), 8463–8468. https://doi.org/10.1073/pnas.1420687112Google Scholar
Sauseng, P., Klimesch, W., Heise, K. F., Gruber, W. R., Holz, E., Karim, A. A., … Hummel, F. C. (2009). brain oscillatory substrates of visual short-term memory capacity. Current Biology, 19(21), 1846–1852. https://doi.org/10.1016/j.cub.2009.08.062Google Scholar
Scimeca, J. M., Kiyonaga, A., & D’Esposito, M. (2018). Reaffirming the sensory recruitment account of working memory. Trends in Cognitive Sciences, 22(3), 190–192. https://doi.org/10.1016/j.tics.2017.12.007Google Scholar
Serences, J. T. (2016). Neural mechanisms of information storage in visual short-term memory. Vision Research, 128, 53–67. https://doi.org/10.1016/j.visres.2016.09.010Google Scholar
Serences, J. T., Ester, E. F., Vogel, E. K., & Awh, E. (2009). Stimulus-specific delay activity in human primary visual cortex. Psychological Science, 20(2), 207–214. https://doi.org/10.1111/j.1467–9280.2009.02276.xGoogle Scholar
Sneve, M. H., Magnussen, S., Alnæs, D., Endestad, T., & D’Esposito, M. (2013). Top–down modulation from inferior frontal junction to FEFs and intraparietal sulcus during short-term memory for visual features. Journal of Cognitive Neuroscience, 25(11), 1944–1956. https://doi.org/10.1162/jocn_a_00426Google Scholar
Sneve, M. H., Sreenivasan, K. K., Alnæs, D., Endestad, T., & Magnussen, S. (2015). Short-term retention of visual information: Evidence in support of feature-based attention as an underlying mechanism. Neuropsychologia, 66, 1–9. https://doi.org/10.1016/j.neuropsychologia.2014.11.004Google Scholar
Soto, D., Greene, C. M., Kiyonaga, A., Rosenthal, C. R., & Egner, T. (2012). A parieto-medial temporal pathway for the strategic control over working memory biases in human visual attention. The Journal of Neuroscience, 32(49), 17563–17571. https://doi.org/10.1523/JNEUROSCI.2647–12.2012Google Scholar
Soto, D., Hodsoll, J., Rotshtein, P., & Humphreys, G. W. (2008). Automatic guidance of attention from working memory. Trends in Cognitive Sciences, 12(9),342–348.Google Scholar
Soto, D., Wriglesworth, A., Bahrami-Balani, A., & Humphreys, G. W. (2010). Working memory enhances visual perception: Evidence from signal detection analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(2), 441.Google Scholar
Sprague, T. C., Adam, K. C., Foster, J. J., Rahmati, M., Sutterer, D. W., & Vo, V. A. (2018). Inverted encoding models assay population-level stimulus representations, not single-unit neural tuning. Eneuro, 5(3).Google Scholar
Sprague, T. C., Ester, E. F., & Serences, J. T. (2016). Restoring latent visual working memory representations in human cortex. Neuron, 91(3), 694–707. https://doi.org/10.1016/j.neuron.2016.07.006Google Scholar
Sprague, T. C., Saproo, S., & Serences, J. T. (2015). Visual attention mitigates information loss in small- and large-scale neural codes. Trends in Cognitive Sciences, 19(4), 215–226. https://doi.org/10.1016/j.tics.2015.02.005Google Scholar
Sreenivasan, K. K., Curtis, C. E., & D’Esposito, M. (2014). Revisiting the role of persistent neural activity during working memory. Trends in Cognitive Sciences, 18(2), 82–89. https://doi.org/10.1016/j.tics.2013.12.001Google Scholar
Sreenivasan, K. K., & D’Esposito, M. (2019). The what, where and how of delay activity. Nature Reviews Neuroscience, 20, 466–481. https://doi.org/10.1038/s41583-019–0176-7Google Scholar
Sreenivasan, K. K., Gratton, C., Vytlacil, J., & D’Esposito, M. (2014). Evidence for working memory storage operations in perceptual cortex. Cognitive, Affective, & Behavioral Neuroscience, 14(1), 117–128. https://doi.org/10.3758/s13415-013–0246-7Google Scholar
Sreenivasan, K. K., & Jha, A. P. (2007). Selective attention supports working memory maintenance by modulating perceptual processing of distractors. Journal of Cognitive Neuroscience, 19(1), 32–41. https://doi.org/10.1162/jocn.2007.19.1.32Google Scholar
Sreenivasan, K. K., Vytlacil, J., & D’Esposito, M. (2014). Distributed and dynamic storage of working memory stimulus information in extrastriate cortex. Journal of Cognitive Neuroscience, 26(5), 1141–1153. https://doi.org/10.1162/jocn_a_00556Google Scholar
Stokes, M. G. (2015). “Activity-silent” working memory in prefrontal cortex: A dynamic coding framework. Trends in Cognitive Sciences, 19(7), 394–405. https://doi.org/10.1016/j.tics.2015.05.004Google Scholar
Störmer, V. S., & Alvarez, G. A. (2014). Feature-based attention elicits surround suppression in feature space. Current Biology, 24(17), 1985–1988. https://doi.org/10.1016/j.cub.2014.07.030Google Scholar
Suzuki, M., & Gottlieb, J. (2013). Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nature Neuroscience, 16(1), 98–104. https://doi.org/10.1038/nn.3282Google Scholar
Teng, C., & Kravitz, D. J. (2019). Visual working memory directly alters perception. Nature Human Behaviour, 3(8), 827–836.Google Scholar
Theeuwes, J., Kramer, A. F., & Irwin, D. E. (2011). Attention on our mind: The role of spatial attention in visual working memory. Acta Psychologica, 137(2), 248–251.Google Scholar
Thompson-Schill, S. L., D’Esposito, M., Aguirre, G. K., & Farah, M. J. (1997). Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation. Proceedings of the National Academy of Sciences, 94(26), 14792–14797.Google Scholar
Thompson-Schill, S. L., D’Esposito, M., & Kan, I. P. (1999). Effects of repetition and competition on activity in left prefrontal cortex during word generation. Neuron, 23(3), 513–522. https://doi.org/10.1016/S0896-6273(00)80804–1Google Scholar
Thompson-Schill, S. L., Jonides, J., Marshuetz, C., Smith, E. E., D’Esposito, M., Kan, I. P., … Swick, D. (2002). Effects of frontal lobe damage on interference effects in working memory. Cognitive, Affective, & Behavioral Neuroscience, 2(2), 109–120. https://doi.org/10.3758/CABN.2.2.109Google Scholar
Tsotsos, J. K., Culhane, S. M., Kei Wai, W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995). Modeling visual attention via selective tuning. Artificial Intelligence, 78(1–2), 507–545. https://doi.org/10.1016/0004–3702(95)00025–9Google Scholar
Umemoto, A., Drew, T., Ester, E. F., & Awh, E. (2010). A bilateral advantage for storage in visual working memory. Cognition, 117(1), 69–79. https://doi.org/10.1016/j.cognition.2010.07.001Google Scholar
van Kerkoerle, T., Self, M. W., & Roelfsema, P. R. (2017). Layer-specificity in the effects of attention and working memory on activity in primary visual cortex. Nature Communications, 8, 13804. https://doi.org/10.1038/ncomms13804Google Scholar
van Moorselaar, D., Theeuwes, J., & Olivers, C. N. L. (2014). In competition for the attentional template: Can multiple items within visual working memory guide attention? Journal of Experimental Psychology: Human Perception and Performance, 40(4), 1450–1464. https://doi.org/10.1037/a0036229Google Scholar
Voytek, B., Kayser, A. S., Badre, D., Fegen, D., Chang, E. F., Crone, N. E., … D’Esposito, M. (2015). Oscillatory dynamics coordinating human frontal networks in support of goal maintenance. Nature Neuroscience, 18(9), 1318–1324. https://doi.org/10.1038/nn.4071Google Scholar
Watanabe, K., & Funahashi, S. (2015). Primate models of interference control. Current Opinion in Behavioral Sciences, 1, 9–16. https://doi.org/10.1016/j.cobeha.2014.07.004Google Scholar
Wolff, M. J., Jochim, J., Akyürek, E. G., & Stokes, M. G. (2017). Dynamic hidden states underlying working-memory-guided behavior. Nature Neuroscience, 20(6), 864–871. https://doi.org/10.1038/nn.4546Google Scholar
Woodman, G. F., & Luck, S. J. (2007). Do the contents of visual working memory automatically influence attentional selection during visual search? Journal of Experimental Psychology: Human Perception and Performance, 33(2), 363.Google Scholar
Woodman, G. F., & Luck, S. J.(2010). Why is information displaced from visual working memory during visual search? Visual Cognition, 18(2), 275–295. https://doi.org/10.1080/13506280902734326Google Scholar
Wutz, A., Loonis, R., Roy, J. E., Donoghue, J. A., & Miller, E. K. (2018). Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron, 97(3), 716–726. https://doi.org/10.1016/j.neuron.2018.01.009Google Scholar
Xu, Y. (2017). Reevaluating the sensory account of visual working memory storage. Trends in Cognitive Sciences, 21(10), 794–815. https://doi.org/10.1016/j.tics.2017.06.013Google Scholar
Yoon, J. H., Curtis, C. E., & D’Esposito, M. (2006). Differential effects of distraction during working memory on delay-period activity in the prefrontal cortex and the visual association cortex. NeuroImage, 29(4), 1117–1126. https://doi.org/10.1016/j.neuroimage.2005.08.024Google Scholar
Yu, Q., Teng, C., & Postle, B. R. (2020). Different states of priority recruit different neural representations in visual working memory. PLOS Biology, 18(6), e3000769.Google Scholar
Yue, Q., Martin, R. C., Hamilton, A. C., & Rose, N. S. (2019). Non-perceptual regions in the left inferior parietal lobe support phonological short-term memory: Evidence for a buffer account? Cerebral Cortex, 29(4), 1398–1413. https://doi.org/10.1093/cercor/bhy037Google Scholar

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