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What brain regions are activated when a dream is remembered?

What brain regions are activated when a dream is remembered?


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Some people remember dreams, others don't. The same person can wake up with dream recall one day and without on other days. I know that the association between REM sleep and dreaming was initially established by awakening test subjects during REM and asking them what they remember.

My question is about the dream recall phenomenon - imagine a person is woken up from a dream while his brain is being scanned. What is the difference in brain region activations shortly before awakening between the case where a dream is recalled and a dream is not recalled? In both cases polysomnographic readings would conclude that REM is experienced and probability of dreaming is high.

I'm asking this question in relation to this question about working memory of the memory recall patient HM. As I heard about his case, I could not help but think that his "living in the present" seems very similar to the experience of dreaming. While dreaming, one does not have a clear memory of the past events, or any desire to even access this memory. I'm trying to understand: can the difference in dream recall for the same test subject be localized to some inhibition of hippocampus or another brain structure, similar to what HM had surgically removed?

Dreams are prone to be "episodic", where a continuous dream of say 20 minutes may contain several very distinct scenes (2-5 minutes each) that have no relation to each other. I'm interested if such dream changes involve the same mechanism that HM experienced when he was distracted.


Your memory of a dream would be an autobiographical memory, which is a memory system that is based on a combination of episodic memories and semantic memories. Autobiographical memories are memories of events that have happened to you, and thus are often retrieved from a first-person perspective. These are not the same thing as episodic memories, however, as episodic memories are contained in a separate memory system.

Your autobiographical memory knowledge base is distributed through neural networks in the frontal, temporal, and occipital lobes. The frontal lobes and anterior temporal lobes represent the more abstract or conceptual parts of the memory, and the occipital lobes/posterior temporal lobes represent the more sensory and perceptual details of specific events. These are predominantly represented in the right cortex. [1] Initially, a memory is formed in the left prefrontal region. Over time, this memory is 'moved' and held at a higher level in the posterior region.

However, there are more widespread activation patterns throughout the brain, and almost every region of the brain has been represented in at least one instance of autobiographic memory retrieval. [2] This lends itself to the belief that certain regions are used for certain types of retrieval, such as re-living experiences associated with the memory versus retrieving factual information from the memory.

Dreams themselves are not episodic memories. During REM sleep, there are specific brain regions that appear to be affected; in particular, there is increased activity in the formation of the hippocampus and decreased activity in prefrontal regions. There appears to be no temporal lobe activity, which implies that 'dream memories' are not true episodic memories, but instead representative of episodic fragments (at best). One study [3] even found that the majority of dreams (>80%) were comprised of primarily autobiographical memories. This suggests that dreams themselves are comprised of fragmented and selective personal experiences, perhaps to aid in the assimilation of memories into the autobiographical memory schema.


Sources used:

[1] Conway, M.A., Pleydell-Pearce, C.W., & Whitecross, S.E. (2001). The neuroanatomy of autobiographical memory: a slow cortical potential study of autobiographical memory retrieval, Journal of Memory and Language. 45, 493-524.

[2] Svoboda, E.; McKinnon, M.C.; Levine, B. (2006). "The functional neuroanatomy of autobiographical memory: a meta-analysis". Neuropsychologia 44 (12): 2189-2208.

[3] Malinowski, J. E. and Horton, C. L. (2014), Memory sources of dreams: the incorporation of autobiographical rather than episodic experiences. Journal of Sleep Research, 23: 441-447.


References

Addis DR, Wong AT, Schacter DL: Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 2007, 45 (7): 1363-1377. 10.1016/j.neuropsychologia.2006.10.016.

Schacter DL, Slotnick SD: The cognitive neuroscience of memory distortion. Neuron. 2004, 44 (1): 149-160. 10.1016/j.neuron.2004.08.017.

Schacter DL: The seven sins of memory: how the mind forgets and remembers. 2001, Houghton Mifflin, Boston

Squire LR: Memory and brain systems: 1969–2009. J Neurosci. 2009, 29 (41): 12711-12716. 10.1523/JNEUROSCI.3575-09.2009.

Squire LR: Memory systems of the brain: a brief history and current perspective. Neurobiol Learn Mem. 2004, 82 (3): 171-177. 10.1016/j.nlm.2004.06.005.

Henke K: A model for memory systems based on processing modes rather than consciousness. Nat Rev Neurosci. 2010, 11 (7): 523-532. 10.1038/nrn2850.

Tulving E: Episodic and Semantic Memory. Organization of memory. Edited by: Tulving E, Donaldson W. 1972, Academic, New York, 381-402.

Tulving E: Episodic memory: from mind to brain. Annu Rev Psychol. 2002, 53: 1-25. 10.1146/annurev.psych.53.100901.135114.

Shimamura AP, Squire LR: A neuropsychological study of fact memory and source amnesia. J Exp Psychol Learn Mem Cogn. 1987, 13 (3): 464-473.

Wheeler MA, Stuss DT, Tulving E: Toward a theory of episodic memory: the frontal lobes and autonoetic consciousness. Psychol Bull. 1997, 121 (3): 331-354.

Mitchell KJ, Johnson MK: Source monitoring 15 years later: what have we learned from fMRI about the neural mechanisms of source memory?. Psychol Bull. 2009, 135 (4): 638-677.

Straube B, Green A, Chatterjee A, Kircher T: Encoding social interactions: the neural correlates of true and false memories. J Cogn Neurosci. 2011, 23 (2): 306-324. 10.1162/jocn.2010.21505.

Gallo DA: False memories and fantastic beliefs: 15 years of the DRM illusion. Mem Cognit. 2010, 38 (7): 833-848. 10.3758/MC.38.7.833.

Jacoby LL, Wahlheim CN, Rhodes MG, Daniels KA, Rogers CS: Learning to diminish the effects of proactive interference: reducing false memory for young and older adults. Mem Cognit. 2010, 38 (6): 820-829. 10.3758/MC.38.6.820.

Long DL, Prat C, Johns C, Morris P, Jonathan E: The importance of knowledge in vivid text memory: an individual-differences investigation of recollection and familiarity. Psychon Bull Rev. 2008, 15 (3): 604-609. 10.3758/PBR.15.3.604.

Brainerd CJ, Reyna VF, Aydin C: Remembering in contradictory minds: disjunction fallacies in episodic memory. J Exp Psychol Learn Mem Cogn. 2010, 36 (3): 711-735.

Koo M, Oishi S: False memory and the associative network of happiness. Pers Soc Psychol Bull. 2009, 35 (2): 212-220. 10.1177/0146167208327191.

El Sharkawy J, Groth K, Vetter C, Beraldi A, Fast K: False memories of emotional and neutral words. Behav Neurol. 2008, 19 (1–2): 7-11.

Laney C, Loftus EF: Emotional content of true and false memories. Memory. 2008, 16 (5): 500-516. 10.1080/09658210802065939.

Smeets T, Otgaar H, Candel I, Wolf OT: True or false? Memory is differentially affected by stress-induced cortisol elevations and sympathetic activity at consolidation and retrieval. Psychoneuroendocrinology. 2008, 33 (10): 1378-1386. 10.1016/j.psyneuen.2008.07.009.

Baym CL, Gonsalves BD: Comparison of neural activity that leads to true memories, false memories, and forgetting: An fMRI study of the misinformation effect. Cogn Affect Behav Neurosci. 2010, 10 (3): 339-348. 10.3758/CABN.10.3.339.

Bhatt R, Laws KR, McKenna PJ: False memory in schizophrenia patients with and without delusions. Psychiatry Res. 2010, 178 (2): 260-265. 10.1016/j.psychres.2009.02.006.

Klumpp H, Amir N, Garfinkel SN: False memory and obsessive-compulsive symptoms. Depress Anxiety. 2009, 26 (5): 396-402. 10.1002/da.20526.

Moritz S, Woodward TS: Metacognitive control over false memories: a key determinant of delusional thinking. Curr Psychiatry Rep. 2006, 8 (3): 184-190. 10.1007/s11920-006-0022-2.

Brainerd CJ, Reyna VF, Ceci SJ: Developmental reversals in false memory: a review of data and theory. Psychol Bull. 2008, 134 (3): 343-382.

Loftus EF: Planting misinformation in the human mind: a 30-year investigation of the malleability of memory. Learn Mem. 2005, 12 (4): 361-366. 10.1101/lm.94705.

Loftus E: Our changeable memories: legal and practical implications. Nat Rev Neurosci. 2003, 4 (3): 231-234. 10.1038/nrn1054.

Gonsalves B, Reber PJ, Gitelman DR, Parrish TB, Mesulam MM, Paller KA: Neural evidence that vivid imagining can lead to false remembering. Psychol Sci. 2004, 15 (10): 655-660. 10.1111/j.0956-7976.2004.00736.x.

Gonsalves B, Paller KA: Mistaken memories: remembering events that never happened. Neuroscientist. 2002, 8 (5): 391-395. 10.1177/107385802236964.

Gonsalves B, Paller KA: Neural events that underlie remembering something that never happened. Nat Neurosci. 2000, 3 (12): 1316-1321. 10.1038/81851.

Ratcliff R, McKoon G: Retrieving information from memory: spreading-activation theories versus compound-cue theories. Psychol Rev. 1994, 101 (1): 177-184. discussion 185–177

Joordens S, Besner D: Priming effects that span an intervening unrelated word: implications for models of memory representation and retrieval. J Exp Psychol Learn Mem Cogn. 1992, 18 (3): 483-491.

Balota DA, Duchek JM: Spreading activation in episodic memory: further evidence for age independence. Q J Exp Psychol A. 1989, 41 (4): 849-876. 10.1080/14640748908402396.

Dell GS: A spreading-activation theory of retrieval in sentence production. Psychol Rev. 1986, 93 (3): 283-321.

Kim H, Cabeza R: Differential contributions of prefrontal, medial temporal, and sensory-perceptual regions to true and false memory formation. Cereb Cortex. 2007, 17 (9): 2143-2150.

Johnson MK, Hashtroudi S, Lindsay DS: Source Monitoring. Psychol Bull. 1993, 114 (1): 3-28.

Johnson MK, Raye CL: Reality monitoring. Psychol Rev. 1981, 88 (1): 67-85.

Cabeza R, Lennartson ER: False memory across languages: implicit associative response vs fuzzy trace views. Memory. 2005, 13 (1): 1-5. 10.1080/09658210344000161.

Brainerd CJ, Reyna VF: Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. Adv Child Dev Behav. 2001, 28: 41-100.

Reyna VF, Brainerd CJ: Fuzzy-trace theory and false memory: new frontiers. J Exp Child Psychol. 1998, 71 (2): 194-209. 10.1006/jecp.1998.2472.

Brainerd CJ, Reyna VF, Howe ML, Kingma J: The development of forgetting and reminiscence. Monogr Soc Res Child Dev. 1990, 55 (3–4): 1-93. discussion 94–109

Deese J: On the prediction of occurrence of particular verbal intrusions in immediate recall. J Exp Psychol. 1959, 58: 5-

Roediger HL, McDermott KB: Creating false memories: Remembering words not presented in lists. J Exp Psychol Learn Mem Cogn. 1995, 21: 11-

McKoon G, Ratcliff R: Spreading activation versus compound cue accounts of priming: mediated priming revisited. J Exp Psychol Learn Mem Cogn. 1992, 18 (6): 1155-1172.

Anderson JR: Retrieval of information from long-term memory. Science. 1983, 220 (4592): 25-30. 10.1126/science.6828877.

Collins AM, Loftus EF: A spreading-activation theory of semantic processing. . 1975, 82: -.

van Kesteren MT, Rijpkema M, Ruiter DJ, Fernández G: Retrieval of associative information congruent with prior knowledge is related to increased medial prefrontal activity and connectivity. J Neurosci. 2010, 30 (47): 15888-15894. 10.1523/JNEUROSCI.2674-10.2010.

van Kesteren MT, Fernández G, Norris DG, Hermans EJ: Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proc Natl Acad Sci U S A. 2010, 107 (16): 7550-7555. 10.1073/pnas.0914892107.

Frankland PW, Bontempi B: The organization of recent and remote memories. Nat Rev Neurosci. 2005, 6 (2): 119-130.

Northoff G, Qin P, Feinberg TE: Brain imaging of the self–conceptual, anatomical and methodological issues. Conscious Cogn. 2011, 20 (1): 52-63. 10.1016/j.concog.2010.09.011.

Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H, Panksepp J: Self-referential processing in our brain–a meta-analysis of imaging studies on the self. Neuroimage. 2006, 31 (1): 440-457. 10.1016/j.neuroimage.2005.12.002.

Benoit RG, Gilbert SJ, Volle E, Burgess PW: When I think about me and simulate you: medial rostral prefrontal cortex and self-referential processes. Neuroimage. 2010, 50 (3): 1340-1349. 10.1016/j.neuroimage.2009.12.091.

Kircher TT, Brammer M, Bullmore E, Simmons A, Bartels M, David AS: The neural correlates of intentional and incidental self processing. Neuropsychologia. 2002, 40 (6): 683-692. 10.1016/S0028-3932(01)00138-5.

Kircher TT, Senior C, Phillips ML, Benson PJ, Bullmore ET, Brammer M, Simmons A, Williams SC, Bartels M, David AS: Towards a functional neuroanatomy of self processing: effects of faces and words. Brain Res Cogn Brain Res. 2000, 10 (1–2): 133-144.

Northoff G, Bermpohl F: Cortical midline structures and the self. Trends Cogn Sci. 2004, 8 (3): 102-107. 10.1016/j.tics.2004.01.004.

Zaragoza MS, Mitchell KJ, Payment K, Drivdahl S: False Memories for Suggestions: The Impact of Conceptual Elaboration. J Mem Lang. 2011, 64 (1): 18-31. 10.1016/j.jml.2010.09.004.

Wright DB, Loftus EF: How misinformation alters memories. J Exp Child Psychol. 1998, 71 (2): 155-164. 10.1006/jecp.1998.2467.

Payne JD, Schacter DL, Propper RE, Huang LW, Wamsley EJ, Tucker MA, Walker MP, Stickgold R: The role of sleep in false memory formation. Neurobiol Learn Mem. 2009, 92 (3): 327-334. 10.1016/j.nlm.2009.03.007.

Fenn KM, Gallo DA, Margoliash D, Roediger HL, Nusbaum HC: Reduced false memory after sleep. Learn Mem. 2009, 16 (9): 509-513. 10.1101/lm.1500808.

Diekelmann S, Born J, Wagner U: Sleep enhances false memories depending on general memory performance. Behav Brain Res. 2010, 208 (2): 425-429. 10.1016/j.bbr.2009.12.021.

Diekelmann S, Landolt HP, Lahl O, Born J, Wagner U: Sleep loss produces false memories. PLoS One. 2008, 3 (10): e3512-10.1371/journal.pone.0003512.

Darsaud A, Dehon H, Lahl O, Sterpenich V, Boly M, Dang-Vu T, Desseilles M, Gais S, Matarazzo L, Peters F: Does sleep promote false memories?. J Cogn Neurosci. 2011, 23 (1): 26-40. 10.1162/jocn.2010.21448.

Stickgold R: Sleep-dependent memory consolidation. Nature. 2005, 437 (7063): 1272-1278. 10.1038/nature04286.

Diekelmann S, Born J: The memory function of sleep. Nat Rev Neurosci. 2010, 11 (2): 114-126.

Stickgold R, Walker MP: Memory consolidation and reconsolidation: what is the role of sleep?. Trends Neurosci. 2005, 28 (8): 408-415. 10.1016/j.tins.2005.06.004.

Tamminen J, Payne JD, Stickgold R, Wamsley EJ, Gaskell MG: Sleep spindle activity is associated with the integration of new memories and existing knowledge. J Neurosci. 2010, 30 (43): 14356-14360. 10.1523/JNEUROSCI.3028-10.2010.

Ellenbogen JM, Payne JD, Stickgold R: The role of sleep in declarative memory consolidation: passive, permissive, active or none?. Curr Opin Neurobiol. 2006, 16 (6): 716-722. 10.1016/j.conb.2006.10.006.

Ellenbogen JM, Hulbert JC, Stickgold R, Dinges DF, Thompson-Schill SL: Interfering with theories of sleep and memory: sleep, declarative memory, and associative interference. Curr Biol. 2006, 16 (13): 1290-1294. 10.1016/j.cub.2006.05.024.

Diekelmann S, Born J: One memory, two ways to consolidate?. Nat Neurosci. 2007, 10 (9): 1085-1086. 10.1038/nn0907-1085.

Fenn KM, Nusbaum HC, Margoliash D: Consolidation during sleep of perceptual learning of spoken language. Nature. 2003, 425 (6958): 614-616. 10.1038/nature01951.

Wagner U, Gais S, Haider H, Verleger R, Born J: Sleep inspires insight. Nature. 2004, 427 (6972): 352-355. 10.1038/nature02223.

Kuriyama K, Stickgold R, Walker MP: Sleep-dependent learning and motor-skill complexity. Learn Mem. 2004, 11 (6): 705-713. 10.1101/lm.76304.

Drosopoulos S, Schulze C, Fischer S, Born J: Sleep’s function in the spontaneous recovery and consolidation of memories. J Exp Psychol Gen. 2007, 136 (2): 169-183.

Loftus EF, Palmer JC: Reconstruction of auto-mobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behaviour. 1974, 13: 5-

Ecker UK, Lewandowsky S, Swire B, Chang D: Correcting false information in memory: manipulating the strength of misinformation encoding and its retraction. Psychon Bull Rev. 2011, 18 (3): 570-578. 10.3758/s13423-011-0065-1.

Zhu B, Chen C, Loftus EF, Lin C, He Q, Li H, Xue G, Lu Z, Dong Q: Individual differences in false memory from misinformation: cognitive factors. Memory. 2010, 18 (5): 543-555. 10.1080/09658211.2010.487051.

Loftus EF: Searching for the neurobiology of the misinformation effect. Learn Mem. 2005, 12 (1): 1-2. 10.1101/lm.90805.

Okado Y, Stark CE: Neural activity during encoding predicts false memories created by misinformation. Learn Mem. 2005, 12 (1): 3-11. 10.1101/lm.87605.

Loftus EF, Hoffman HG: Misinformation and memory: the creation of new memories. J Exp Psychol Gen. 1989, 118 (1): 100-104.

Stark CE, Okado Y, Loftus EF: Imaging the reconstruction of true and false memories using sensory reactivation and the misinformation paradigms. Learn Mem. 2010, 17 (10): 485-488. 10.1101/lm.1845710.

Slotnick SD, Schacter DL: The nature of memory related activity in early visual areas. Neuropsychologia. 2006, 44 (14): 2874-2886. 10.1016/j.neuropsychologia.2006.06.021.

Slotnick SD, Schacter DL: A sensory signature that distinguishes true from false memories. Nat Neurosci. 2004, 7 (6): 664-672. 10.1038/nn1252.

Slotnick SD: Visual memory and visual perception recruit common neural substrates. Behav Cogn Neurosci Rev. 2004, 3 (4): 207-221. 10.1177/1534582304274070.

Watson JM, McDermott KB, Balota DA: Attempting to avoid false memories in the Deese/Roediger-McDermott paradigm: assessing the combined influence of practice and warnings in young and old adults. Mem Cognit. 2004, 32 (1): 135-141. 10.3758/BF03195826.

Sederberg PB, Schulze-Bonhage A, Madsen JR, Bromfield EB, Litt B, Brandt A, Kahana MJ: Gamma oscillations distinguish true from false memories. Psychol Sci. 2007, 18 (11): 927-932. 10.1111/j.1467-9280.2007.02003.x.

Seamon JG, Luo CR, Shulman EP, Toner SK, Caglar S: False memories are hard to inhibit: differential effects of directed forgetting on accurate and false recall in the DRM procedure. Memory. 2002, 10 (4): 225-237. 10.1080/09658210143000344.

Weinstein Y, Shanks DR: Rapid induction of false memory for pictures. Memory. 2010, 18 (5): 533-542. 10.1080/09658211.2010.483232.

Turner MS, Cipolotti L, Shallice T: Spontaneous confabulation, temporal context confusion and reality monitoring: a study of three patients with anterior communicating artery aneurysms. J Int Neuropsychol Soc. 2010, 16 (6): 984-994. 10.1017/S1355617710001104.

Schacter DL, Guerin SA, St Jacques PL: Memory distortion: an adaptive perspective. Trends Cogn Sci. 2011, 15 (10): 467-474. 10.1016/j.tics.2011.08.004.

Deese J: On the prediction of occurrence of particular verbal intrusions in immediate recall. Journal of Experimental Psychology. 1959, 58: 5-

Roediger HL, Watson JM, McDermott KB, Gallo DA: Factors that determine false recall: a multiple regression analysis. Psychon Bull Rev. 2001, 8 (3): 385-407. 10.3758/BF03196177.

McDonough IM, Gallo DA: Autobiographical elaboration reduces memory distortion: cognitive operations and the distinctiveness heuristic. J Exp Psychol Learn Mem Cogn. 2008, 34 (6): 1430-1445.

Harrison Y, Horne JA: The impact of sleep deprivation on decision making: a review. J Exp Psychol Appl. 2000, 6 (3): 236-249.

Schacter DL, Reiman E, Curran T, Yun LS, Bandy D, McDermott KB, Roediger HL: Neuroanatomical correlates of veridical and illusory recognition memory: evidence from positron emission tomography. Neuron. 1996, 17 (2): 267-274. 10.1016/S0896-6273(00)80158-0.

Cabeza R, Rao SM, Wagner AD, Mayer AR, Schacter DL: Can medial temporal lobe regions distinguish true from false? An event-related functional MRI study of veridical and illusory recognition memory. Proc Natl Acad Sci U S A. 2001, 98 (8): 4805-4810. 10.1073/pnas.081082698.

Schacter DL: Illusory memories: a cognitive neuroscience analysis. Proc Natl Acad Sci U S A. 1996, 93 (24): 13527-13533. 10.1073/pnas.93.24.13527.

Curran T, Schacter DL, Norman KA, Galluccio L: False recognition after a right frontal lobe infarction: Memory for general and specific information. Neuropsychologia. 1997, 35 (7): 1035-1049. 10.1016/S0028-3932(97)00029-8.

Ciaramelli E, Ghetti S, Frattarelli M, Làdavas E: When true memory availability promotes false memory: evidence from confabulating patients. Neuropsychologia. 2006, 44 (10): 1866-1877. 10.1016/j.neuropsychologia.2006.02.008.

Schacter DL, Norman KA, Koutstaal W: The cognitive neuroscience of constructive memory. Annu Rev Psychol. 1998, 49: 289-318. 10.1146/annurev.psych.49.1.289.

Abe N, Okuda J, Suzuki M, Sasaki H, Matsuda T, Mori E, Tsukada M, Fujii T: Neural correlates of true memory, false memory, and deception. Cereb Cortex. 2008, 18 (12): 2811-2819. 10.1093/cercor/bhn037.

Dennis NA, Kim H, Cabeza R: Age-related differences in brain activity during true and false memory retrieval. J Cogn Neurosci. 2008, 20 (8): 1390-1402. 10.1162/jocn.2008.20096.

Garoff-Eaton RJ, Slotnick SD, Schacter DL: Not all false memories are created equal: the neural basis of false recognition. Cereb Cortex. 2006, 16 (11): 1645-1652.

Addis DR, Schacter DL: Constructive episodic simulation: temporal distance and detail of past and future events modulate hippocampal engagement. Hippocampus. 2008, 18 (2): 227-237. 10.1002/hipo.20405.

Schacter DL, Addis DR: On the nature of medial temporal lobe contributions to the constructive simulation of future events. Philos Trans R Soc Lond B Biol Sci. 2009, 364 (1521): 1245-1253. 10.1098/rstb.2008.0308.

Schacter DL, Addis DR, Buckner RL: Episodic simulation of future events: concepts, data, and applications. Ann N Y Acad Sci. 2008, 1124: 39-60. 10.1196/annals.1440.001.

Schacter DL, Addis DR: Constructive memory: the ghosts of past and future. Nature. 2007, 445 (7123): 27-10.1038/445027a.

Schacter DL, Addis DR: The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philos Trans R Soc Lond B Biol Sci. 2007, 362 (1481): 773-786. 10.1098/rstb.2007.2087.

Schacter DL, Buckner RL, Koutstaal W, Dale AM, Rosen BR: Late onset of anterior prefrontal activity during true and false recognition: an event-related fMRI study. Neuroimage. 1997, 6 (4): 259-269. 10.1006/nimg.1997.0305.

Giovanello KS, Kensinger EA, Wong AT, Schacter DL: Age-related neural changes during memory conjunction errors. J Cogn Neurosci. 2010, 22 (7): 1348-1361. 10.1162/jocn.2009.21274.

Chen JC, Li W, Westerberg CE, Tzeng OJ: Test-item sequence affects false memory formation: an event-related potential study. Neurosci Lett. 2008, 431 (1): 51-56. 10.1016/j.neulet.2007.11.020.

Dennis NA, Kim H, Cabeza R: Effects of aging on true and false memory formation: an fMRI study. Neuropsychologia. 2007, 45 (14): 3157-3166. 10.1016/j.neuropsychologia.2007.07.003.

Hanczakowski M, Mazzoni G: Both differences in encoding processes and monitoring at retrieval reduce false alarms when distinctive information is studied. Memory. 2011, 19 (3): 280-289. 10.1080/09658211.2011.558514.

Garoff-Eaton RJ, Kensinger EA, Schacter DL: The neural correlates of conceptual and perceptual false recognition. Learn Mem. 2007, 14 (10): 684-692. 10.1101/lm.695707.

Dodson CS, Hege AC: Speeded retrieval abolishes the false-memory suppression effect: evidence for the distinctiveness heuristic. Psychon Bull Rev. 2005, 12 (4): 726-731. 10.3758/BF03196764.

Arndt J, Reder LM: The effect of distinctive visual information on false recognition. J Mem Lang. 2003, 48 (1): 1-15. 10.1016/S0749-596X(02)00518-1.

Straube B, Green A, Bromberger B, Kircher T: The differentiation of iconic and metaphoric gestures: Common and unique integration processes. Hum Brain Mapp. 2011, 32 (4): 520-533. 10.1002/hbm.21041.

Green A, Straube B, Weis S, Jansen A, Willmes K, Konrad K, Kircher T: Neural integration of iconic and unrelated coverbal gestures: a functional MRI study. Hum Brain Mapp. 2009, 30 (10): 3309-3324. 10.1002/hbm.20753.

Kircher T, Straube B, Leube D, Weis S, Sachs O, Willmes K, Konrad K, Green A: Neural interaction of speech and gesture: differential activations of metaphoric co-verbal gestures. Neuropsychologia. 2009, 47 (1): 169-179. 10.1016/j.neuropsychologia.2008.08.009.

Straube B, Green A, Jansen A, Chatterjee A, Kircher T: Social cues, mentalizing and the neural processing of speech accompanied by gestures. Neuropsychologia. 2010, 48 (2): 382-393. 10.1016/j.neuropsychologia.2009.09.025.

Straube B, Green A, Weis S, Chatterjee A, Kircher T: Memory effects of speech and gesture binding: cortical and hippocampal activation in relation to subsequent memory performance. J Cogn Neurosci. 2009, 21 (4): 821-836. 10.1162/jocn.2009.21053.

Boggio PS, Fregni F, Valasek C, Ellwood S, Chi R, Gallate J, Pascual-Leone A, Snyder A: Temporal lobe cortical electrical stimulation during the encoding and retrieval phase reduces false memories. PLoS One. 2009, 4 (3): e4959-10.1371/journal.pone.0004959.

Straube B, Wolk D, Chatterjee A: The role of the right parietal lobe in the perception of causality: a tDCS study. Exp Brain Res. 2011, 215 (3–4): 315-325.

Fazio LK, Marsh EJ: Older, not younger, children learn more false facts from stories. Cognition. 2008, 106 (2): 1081-1089. 10.1016/j.cognition.2007.04.012.

Lövdén M, Wahlin A: The sensory-cognition association in adulthood: Different magnitudes for processing speed, inhibition, episodic memory, and false memory?. Scand J Psychol. 2005, 46 (3): 253-262. 10.1111/j.1467-9450.2005.00455.x.

Mammarella N, Altamura M, Padalino FA, Petito A, Fairfield B, Bellomo A: False memories in schizophrenia? An imagination inflation study. Psychiatry Res. 2010, 179 (3): 267-273. 10.1016/j.psychres.2009.05.005.


Nightmares and the Brain

This definition came from the popular reference text, An Universal Etymological English Dictionary, first published by Nathan Bailey in 1721 and reprinted through 1802. Although that definition doesn’t surface often today, nightmares are still considered to be frightening dreams that result in feelings of terror, fear, distress, or anxiety.

Despite our colloquial use of the term, for example, “my commute was a nightmare,” for an estimated 3 to 7 percent of the U.S. population, nightmares can be a real problem. Although adults can suffer from nightmares, they are more typical in children, especially those between the ages of 3 and 6. “We think that some of this may be evolutionary,” says Deirdre Barrett, PhD, an HMS assistant clinical professor of psychology at Cambridge Health Alliance and editor of “Trauma and Dreams,” published by Harvard University Press in 2001. “Children are smaller and are vulnerable to many more threats than adults. Nightmares may partially reflect this vulnerability.”

Dreams are understood to be recent autobiographical episodes that become woven with past memories to create a new memory that can be referenced later, but nightmares are simply dreams that cause a strong but unpleasant emotional response. Dreams are part of the brain’s default network—a system of interconnected regions, which includes the thalamus, medial prefrontal cortex, and posterior cingulate cortex—that remains active during comparatively quiet periods.

REM sleep is one example of a quiet period. It is a stage of sleep that is characterized by rapid eye movement, irregular heartbeat, and increased rates of respiration. REM sleep is discontinuous, chunked into four or five periods that together make up about 20 percent of our slumber. It is during these REM episodes that brain structures in the default network exert influence, and it is during REM sleep that vividly recalled dreams occur most often.

Nightmares tend to happen during the period of sleep when REM intervals lengthen these usually occur halfway through slumber. As we prepare to awaken, memories begin to integrate and consolidate. We dream as we emerge from REM sleep. Because we tend to dream on the sleep-wake cusp, images imagined while dreaming, including the vivid, often terrifying images produced during nightmares, are remembered.


Brain scans unlock the secrets of how we dream and could even reveal what we're dreaming of

A neuroscience study has upended what we know about dreaming. It identifies a “hot zone” in the brain that indicates when dreams are occurring, and describes how the signals in the brain can even predict what a person is dreaming about.

Typically, in dream studies, a person is identified as dreaming when they are in rapid-eye-movement (REM) sleep. In the brain, this is indicated by high-frequency electrical activity. We do know, however, that dreaming also occurs during non-REM sleep when there is low-frequency activity but the nuts and bolts behind this capability have not been well understood, until now.

A study from the Wisconsin Institute of Sleep and Consciousness (WISC), published in Nature, shows that when dreaming was reported in both REM and non-REM sleep, a decrease in low-frequency activity occurred in the posterior cortical region, an area at the back of the brain associated with spatial reasoning and attention. The neuroscientists say they were able to correctly predict whether a volunteer was dreaming 92 per cent of the time, simply by monitoring activity in this “hot zone”. “[The region] may constitute a core correlate of conscious experiences in sleep,” they write.

Picking up further on this “hot zone”, the team was also able to begin breaking down the contents of a dream by monitoring which regions were activated.

“We’ve been able to identify the brain areas that correspond to specific dream contents (like faces, spatial setting, movement and speech) during well-established sleep,” said co-author Francesca Siclari in a statement.

Forty-six subjects at the WISC lab had their sleep monitored using an EEG net worn on the head, covered in 256 electrodes. The volunteers were woken periodically, then asked whether or not they had been dreaming. They first looked specifically at REM and non-REM sleep, noting that volunteers reported they had dreamt when the “hot zone” was activated, regardless of what state of sleep they were in.

In a second experiment, the volunteers reported the content of their dreams, based on key themes the neuroscientists could identify in the posterior cortex: the aforementioned faces, spatial setting, movement and speech. If a volunteer reported hearing speech in their dream, it would correlate with the region of the brain responsible for language and understanding if they dreamt about people, the region responsible for facial recognition was ignited. This means, says Siclari, that we probably use the same areas of the brain during dreaming, as we do when awake, explaining the sense of reality a dream often portrays for an individual.


Does the weirdness of dreams help keep the brain flexible?

In the early months of 2020, as millions of people around the world went into isolation due to the burgeoning COVID-19 pandemic, many reported an increase in the vividness and frequency of their dreams.

The hashtag #pandemicdreams began to trend on Twitter as users shared their bizarre dreams.

According to Erik Hoel, Ph.D., a research assistant professor of neuroscience at Tufts University, in Medford, MA, the tedium of our lives under lockdown may have provoked our brains to dose themselves with bursts of random nighttime “noise.”

Dr. Hoel believes that the nervous systems of all kinds of animals, from nematode worms to humans, risk becoming “overfitted” to the information acquired during waking hours.

This means that while animals, including humans, may become very good at performing specific tasks, they fail to generalize what they have learned to other tasks.

To resolve this issue, Dr. Hoel reasons, dreams evolved in higher animals to inject flexibility into their brains’ models of the world.

Psychologists have found that if a person’s tasks during the day are narrow and repetitive, such as playing the game Tetris, they are more likely to have dreams related to these tasks.

This could explain why the unexciting and repetitive experience of life under lockdown has provoked a burst of dreaming in so many people. “Of course, it’s hypothetical, but it does provide an explanation,” Dr. Hoel told Medical News Today.

Dr. Hoel studies machine learning algorithms called deep neural networks, which can be trained to perform tasks such as translating text and recognizing particular features in pictures.

In a paper in the journal Patterns, he writes that all deep neural networks run up against the same problem: They “overfit” to the particular datasets that modelers use to train them.

This means that the networks fail to generalize what they have learned to novel data.

Modelers often use “noise injections” to solve the problem of overfitting. These are random or corrupted datasets that restore flexibility to the network’s operations.

In his paper, Dr. Hoel argues that after a day’s learning experiences, the brain faces a similar problem of overfitting, which it solves in much the same way.

He speculates that dreams are “corrupted sensory inputs” — which the brain concocts from random, or “stochastic,” brain activity — that evolved to increase the generalizability of its internal models of reality.

“It is the very strangeness of dreams in their divergence from waking experience that gives them their biological function,” he writes. “Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately,” he adds.

Dr. Hoel calls his idea the “overfitted brain” hypothesis.

To find out whether his hypothesis is correct, the researcher says, it should be possible for psychologists to design behavioral tests that differentiate between the ability to memorize new things and the ability to generalize that knowledge to other tasks.

They would use repetitive training tasks to induce overfitting in participants then measure the effects of sleep deprivation on their ability to remember and generalize.

Dreams may be so beneficial for efficient brain function, Dr. Hoel speculates, that humans have found ways to dream while awake.

Contrary to the prominent idea among psychologists that art forms such as novels, painting, and music are evolutionary “cheesecake,” pleasurable but with no value for survival, Dr. Hoel believes that they prevent our brains from overfitting.

“The [overfitted brain hypothesis] suggests [that] fictions, and perhaps the arts in general, may actually have a deeper underlying cognitive utility, in the form of improving generalization and preventing overfitting, by acting as artificial dreams.”

“As a novelist, myself,” Dr. Hoel observes, “It is nice to think that fictions, which are in a sense artificial dreams, may have cognitive utility by keeping us from fitting to the daily quotidian events of our lives too well,” he told MNT.

The idea that the brain becomes overfitted to its experiences during waking hours and that dreams help build the process of generalizing knowledge has deep roots in machine learning.

In 1995, computer scientists proposed the idea of a “wake-sleep algorithm” that can learn without human supervision by alternating between waking and sleeping phases.

Nearly two decades later, in 2014, the neuroscientist Prof. Karl Friston, from University College London, in the United Kingdom, and co-authors built on this concept by developing a theory that dreams are the brain’s way of minimizing the complexity of its models.

Prof. Friston views the brain as a machine for generating predictions about the world that make our every perception, thought, and action possible. According to his free energy principle , we dream in order to streamline or simplify the brain’s predictive models.

“This was recently extended to periods of waking reflection and an account of ‘aha!’ moments, when the simplicity of things becomes apparent,” he told MNT in an email. “We have even used complexity minimization to critique deep learning!”


Seeing a ghost

Credit: AFP Contributor via Getty Images

Perhaps more distressing than becoming a ghost is seeing one. Sleep paralysis is arguably most infamous for the sinister shadowy "bedroom-intruder" that sometimes attacks the sleeper. The "creature" is usually lurking in the distant dark, slowly approaching in on its victim.

From here, all kinds of ominous things can happen, as far as the imagination can stretch. Commonly, the intruder chokes and suffocates the person by crushing his chest or pressing on his neck. And occasionally, the creature brutally rapes the paralyzed sleeper. The figure often appears simply as a dark shadow, similar to the human size and shape. But, it can also include detailed features, say, a scary demonic face with animal characteristics, like sharp teeth and cat eyes.

This figure goes by different names around the world. My colleague Devon Hinton of Harvard Medical School and I found that in Egypt, the creature is thought to be a Jinn (an "evil genie") a spirit-like entity that may hunt down, terrorize, and even kill its victims. In another study, we've discovered that among some Italians, it is believed to be a malevolent witch or a terrifying human-like cat, known locally as the Pandafeche. Some space alien abduction cases also fit the sleep paralysis scenario: the person is laying in his bed paralyzed suddenly the alien appears and begins to experiment on the sleeper's sexual organs, collecting eggs and semen.


Reduced cognition due to stress

Just shy of one year into the pandemic, a national survey of Canadians suggested that more than half of all respondents — 56 per cent — said they were feeling increased stress or anxiety as a result of COVID-19. Among those aged 18-34, it was even higher, at 63 per cent.

You don't have to be lonely or depressed — you're just living through a pandemic. Or as Dr. Roger McIntyre describes it, "daily, unpredictable, malignant stress."

McIntyre, a professor of psychiatry and pharmacology at the University of Toronto's Temerty Faculty of Medicine, has recently co-authored a review on cognitive impairment in patients with COVID-19, which found prevalence of delirium and markers of inflammation.

For the rest of us, living in a world changed by the disease, McIntyre says our cognitive issues come from stress.

He describes two kinds of stress: one which is short and predictable and has an end point, and another which is long in duration, unpredictable and seems interminable.

That second one sound familiar? Yup. Pandemic.

Unpredictability upon layers of unpredictability, as McIntyre put it.


Declarative Memory

8 Amazing Advancements That Will Fulfill Your Cyborg Fantasies

Declarative memory is an umbrella term for episodic memory, which is memory of events, and semantic memory, which is memory of rules and fact-based information. Declarative memory is processed and stored in different areas of the brain's limbic and cortical systems.

A study conducted by neurobiologists Emily Malin and James McGaugh at the Center for the Neurobiology of Learning and Memory at University of California, Irvine, showed that a single memory is processed in three separate areas of the brain. The hippocampus, a part of the limbic system located in the basal medial part of the temporal lobe, is responsible for processing memory for context. The anterior cingulate cortex, a part of the cerebral cortex connected with the prefrontal cortex, is involved in retaining unpleasant memories. Finally, the amygdala, an almond-shaped subcortical region in the medial temporal lobe, binds memories together and initiates the storage of both contextual and unpleasant information.

The significance of the amygdala in memory storage is a newer finding. Psychology professor Michael Gabriel from the University of Illinois Beckman Institute for Advanced Science and Technology and Amy Poremba of the National Institute of Mental Health in Bethesda simultaneously tracked neuron activity in several regions of the brains of rabbits whose amygdala had been temporarily disabled. Unlike the rabbits with unaltered brains, rabbits with a disabled amygdala were unable to learn to distinguish tones that lead to a mild shock from those that did not. The study shows that the amygdala sorts experiences worth storing from those that are not on the basis of emotional significance.

  • Declarative memory is an umbrella term for episodic memory, which is memory of events, and semantic memory, which is memory of rules and fact-based information.
  • Finally, the amygdala, an almond-shaped subcortical region in the medial temporal lobe, binds memories together and initiates the storage of both contextual and unpleasant information.

Dreams could be critical to consolidating memories, argue two sleep researchers

In When Brains Dream, sleep scientists Antonio Zadra and Robert Stickgold detail the latest research that seeks to understand what occurs in our brains when we dream, and they present theories about what purposes dreaming may serve. The book takes the reader from humanity’s early religious understanding of dreams, through our initial attempts to study the psychology of dreaming, to current experiments on the neurophysiology of the sleeping brain, providing relatable and often humorous anecdotal evidence from the authors’ own lives and work along the way.

Humans have contemplated the purpose of dreams throughout recorded history. Ancient texts including the Sumerian Iškar Zaqı-qu and Artemidorus’s Oneiro­critica reveal a prevalent belief that dreams convey important and often supernatural messages, necessitating skilled interpretation. Even Aristotle weighed in on the nature and use of dreams, although he concluded that they were likely just the result of our organs shifting during sleep.

As the world became more secularized in the 19th century, psychologists began to focus on applying their efforts to understanding the sleeping brain. Early research explored the link between waking experiences and dream content, examined the abstract nature of symbols in dreams, and even began applying statistical principles to quantify data gathered in dream journals. By the time Freud published his seminal treatise on dreams in 1899, lesser-known scientists had already gathered evidence that remains remarkably relevant to sleep research today, including the observation that vivid dreams occur most frequently during early morning sleep and that physiological changes occur as sleep progresses

Since the discovery of rapid eye movement (REM) sleep in 1950, however, most studies have approached sleep from a biological perspective. We now know that sleep appears to play a crucial role in clearing waste from the brain, that it regulates hormone levels, and that it helps boost the immune system. There is also a large body of evidence linking sleep to learning and memory.

But are dreams necessary to these functions? Or are they a meaningless consequence of random neural firing? In the latter half of the book, the authors present the ambitious theory that dreams themselves, not just the dream sleep state, play a critical role in the consolidation of memories—a theory they refer to as network exploration to understand possibilities (NEXTUP).

On the surface, the idea that the infrequently remembered and often bizarre narratives we construct during sleep could contribute to memory consolidation seems unlikely. But Zadra and Stickgold argue that the disjointed nature of dreams is actually key to their role in memory processing. They maintain that dreams, rather than merely repeating the events of the day to cement them into long-term storage, allow our brains to freely explore memories that have been filed away over time, extracting information and developing a narrative based on associations.

According to the NEXTUP theory, dreams primarily feature weakly associated themes and memories, an idea supported by previous work in the Stickgold lab, which found that weakly associated word pairs are preferentially activated during REM sleep (1). Coactivation of weakly associated items could underlie the strange and often unexpected twists that unfold during dreaming.

But why do dreams take on a narrative structure at all? The authors suggest that the narrative allows the dreamer to explore and evaluate possible scenarios, providing a mechanism by which a verdict can be rendered. Dreams that elicit strong emotions, they argue, may cue the brain about the association’s potential utility, which may in turn lead to a strengthening of that association.

The NEXTUP theory appears to be supported by animal studies. Rats trained on a maze later appear to dream about maze routes they had never taken (2). REM sleep also appears to be a crucial time for zebra finches to “write” their songs (3). The brain is highly plastic during REM sleep, allowing the erasure of erroneous and weak connections as well as the establishment of new ones (4). These lines of evidence suggest that dreams may allow the brain to explore potential results that would be dangerous or not possible during wakefulness.

References and Notes:
1. R. Stickgold, L. Scott, C. Rittenhouse, J. A. Hobson,
J. Cogn. Neurosci. 11, 182 (1999).
2. A. S. Gupta, M. A. A. van der Meer, D. S. Touretzky,
A. D. Redish, Neuron 65, 695 (2010).
3. A. S. Dave, D. Margoliash, Science 290, 812 (2000).
4. G. R. Poe, J. Neurosci. 37, 464 (2017).

About the author

Frazer is at the UCLA Interdepartmental Program in Neuroscience. Poe is at the Department of Integrative Biology and Physiology, and the Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA 90095, USA.


In the brain, ‘dislike’ and ‘dehumanization’ are not the same thing

During the past week, the news has brought us difficult images and sounds: Migrant and refugee children huddled in steel cages. Children and parents wailing as they are torn apart by American agents. Detention buses filled with infant car seats.

The majority of Americans oppose the policy of separating families at the border, but a substantial percentage have no problem with it. “How is that possible?” many wonder. “These are human beings.”

Researchers who study dehumanization, however, know that not all people see it that way. It is very common for people around the world to look at entire groups—for example, Muslims, Native populations, Roma, Africans, or Mexican immigrants—as not fully human.

Conventional wisdom has long assumed that talking about people in dehumanizing terms, as “dogs” or “pigs” or “pests,” was simply an extreme expression of dislike for them. But according to new research published in the Journal of Experimental Psychology, dehumanization and dislike are processed by two completely separate brain regions, which suggests that they may be two different psychological processes.

For example, many people would say that children and puppies don’t have a fully realized human mind, but that they are still lovable. On the other hand, it is possible to dislike an arrogant colleague while still believing that he or she is fully human.

“When people are dehumanizing others, they are mobilizing different brain regions than when they are registering their dislike,” explains co-lead author Emile Bruneau, director of the Peace and Conflict Neuroscience Lab at the University of Pennsylvania’s Annenberg School for Communication and lead scientist of the Beyond Conflict Innovation Lab. “Brain regions sensitive to dehumanizing other groups were not sensitive to dislike. And brain regions activated when registering dislike for those same groups were not activated when thinking about how human those groups are.”

In the experiment conducted by Bruneau and colleagues, the researchers used functional magnetic resonance imaging (fMRI) to observe participants’ brain activity as they rated how they felt about 10 different groups. They ranged from high-status groups like Americans, Europeans, and surgeons to lower-status groups like Muslims, Roma, and the homeless, and also included animals like puppies and rats.

“Dislike” was measured on a feeling thermometer scale, which asks people to rate how “cold” or “warm” they feel toward the target group, and dehumanization was measured by asking participants to place the target group where they thought it belonged on the popular “Ascent of Man” scale depicting stages of evolution. Previous research from Bruneau and co-lead author Nour Kteily of Northwestern University found that while researchers had long been measuring dehumanization implicitly, believing that few would openly admit they felt other people weren’t fully human, in fact many people have no problem blatantly saying so.

In any real-life situation with high levels of dehumanization, the stakes are high, as it is a strong predictor of aggressive outcomes such as support for torture, reluctance to provide aid to violence victims, support for armed conflict, and support for hostile policies. But knowing that dislike and dehumanization are two separate factors can help understand and address people’s viewpoints.

The belief that the American government is justified in separating migrant or refugee children from their parents, Bruneau explains, isn’t necessarily values-driven or infused with hatred. It can be a cold, rational evaluation: “These children are just less human and less deserving of moral concern.” Removal of children from families has a long tradition, and the impetus is often not anchored in dislike or hatred. In fact, some people justify these removals as paternalistic care.

“High dehumanization and low prejudice is the perfect profile of paternalism,” Bruneau explains. “Some Americans may feel we’re doing good in taking these ‘poor’ immigrant children away from their ‘lawless’ parents.”

“The whole reason I study dehumanization is that I’m interested in intervening to reduce intergroup hostility,” he adds. “Understanding there’s a fundamental difference between dehumanization and dislike is academically interesting, but more importantly, it may prove practically useful.”

Many interventions to try and reduce intergroup conflict—between groups like Israelis and Palestinians, blacks and whites in South Africa, or Muslim refugees and Westerners—focus on getting people to like each other more. That, Bruneau says, is very difficult.

It may be easier to get people to see each other as human, which is, after all, an objective truth. At the very least, knowing that dehumanization and dislike are independent roads to intergroup hostility can increase the number of avenues to peace and reconciliation.

Emile Bruneau is director of the Peace and Conflict Neuroscience Lab at the University of Pennsylvania’s Annenberg School for Communication. He co-authored the paper with Nour Kteily of Northwestern University, Nir Jacoby of the Massachusetts Institute of Technology and Columbia University, and Rebecca Saxe of the Massachusetts Institute of Technology. Funding came from the DRAPER Research Laboratories and a fellowship from Beyond Conflict.


Reduced cognition due to stress

Just shy of one year into the pandemic, a national survey of Canadians suggested that more than half of all respondents — 56 per cent — said they were feeling increased stress or anxiety as a result of COVID-19. Among those aged 18-34, it was even higher, at 63 per cent.

You don't have to be lonely or depressed — you're just living through a pandemic. Or as Dr. Roger McIntyre describes it, "daily, unpredictable, malignant stress."

McIntyre, a professor of psychiatry and pharmacology at the University of Toronto's Temerty Faculty of Medicine, has recently co-authored a review on cognitive impairment in patients with COVID-19, which found prevalence of delirium and markers of inflammation.

For the rest of us, living in a world changed by the disease, McIntyre says our cognitive issues come from stress.

He describes two kinds of stress: one which is short and predictable and has an end point, and another which is long in duration, unpredictable and seems interminable.

That second one sound familiar? Yup. Pandemic.

Unpredictability upon layers of unpredictability, as McIntyre put it.


Declarative Memory

8 Amazing Advancements That Will Fulfill Your Cyborg Fantasies

Declarative memory is an umbrella term for episodic memory, which is memory of events, and semantic memory, which is memory of rules and fact-based information. Declarative memory is processed and stored in different areas of the brain's limbic and cortical systems.

A study conducted by neurobiologists Emily Malin and James McGaugh at the Center for the Neurobiology of Learning and Memory at University of California, Irvine, showed that a single memory is processed in three separate areas of the brain. The hippocampus, a part of the limbic system located in the basal medial part of the temporal lobe, is responsible for processing memory for context. The anterior cingulate cortex, a part of the cerebral cortex connected with the prefrontal cortex, is involved in retaining unpleasant memories. Finally, the amygdala, an almond-shaped subcortical region in the medial temporal lobe, binds memories together and initiates the storage of both contextual and unpleasant information.

The significance of the amygdala in memory storage is a newer finding. Psychology professor Michael Gabriel from the University of Illinois Beckman Institute for Advanced Science and Technology and Amy Poremba of the National Institute of Mental Health in Bethesda simultaneously tracked neuron activity in several regions of the brains of rabbits whose amygdala had been temporarily disabled. Unlike the rabbits with unaltered brains, rabbits with a disabled amygdala were unable to learn to distinguish tones that lead to a mild shock from those that did not. The study shows that the amygdala sorts experiences worth storing from those that are not on the basis of emotional significance.

  • Declarative memory is an umbrella term for episodic memory, which is memory of events, and semantic memory, which is memory of rules and fact-based information.
  • Finally, the amygdala, an almond-shaped subcortical region in the medial temporal lobe, binds memories together and initiates the storage of both contextual and unpleasant information.

Does the weirdness of dreams help keep the brain flexible?

In the early months of 2020, as millions of people around the world went into isolation due to the burgeoning COVID-19 pandemic, many reported an increase in the vividness and frequency of their dreams.

The hashtag #pandemicdreams began to trend on Twitter as users shared their bizarre dreams.

According to Erik Hoel, Ph.D., a research assistant professor of neuroscience at Tufts University, in Medford, MA, the tedium of our lives under lockdown may have provoked our brains to dose themselves with bursts of random nighttime “noise.”

Dr. Hoel believes that the nervous systems of all kinds of animals, from nematode worms to humans, risk becoming “overfitted” to the information acquired during waking hours.

This means that while animals, including humans, may become very good at performing specific tasks, they fail to generalize what they have learned to other tasks.

To resolve this issue, Dr. Hoel reasons, dreams evolved in higher animals to inject flexibility into their brains’ models of the world.

Psychologists have found that if a person’s tasks during the day are narrow and repetitive, such as playing the game Tetris, they are more likely to have dreams related to these tasks.

This could explain why the unexciting and repetitive experience of life under lockdown has provoked a burst of dreaming in so many people. “Of course, it’s hypothetical, but it does provide an explanation,” Dr. Hoel told Medical News Today.

Dr. Hoel studies machine learning algorithms called deep neural networks, which can be trained to perform tasks such as translating text and recognizing particular features in pictures.

In a paper in the journal Patterns, he writes that all deep neural networks run up against the same problem: They “overfit” to the particular datasets that modelers use to train them.

This means that the networks fail to generalize what they have learned to novel data.

Modelers often use “noise injections” to solve the problem of overfitting. These are random or corrupted datasets that restore flexibility to the network’s operations.

In his paper, Dr. Hoel argues that after a day’s learning experiences, the brain faces a similar problem of overfitting, which it solves in much the same way.

He speculates that dreams are “corrupted sensory inputs” — which the brain concocts from random, or “stochastic,” brain activity — that evolved to increase the generalizability of its internal models of reality.

“It is the very strangeness of dreams in their divergence from waking experience that gives them their biological function,” he writes. “Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately,” he adds.

Dr. Hoel calls his idea the “overfitted brain” hypothesis.

To find out whether his hypothesis is correct, the researcher says, it should be possible for psychologists to design behavioral tests that differentiate between the ability to memorize new things and the ability to generalize that knowledge to other tasks.

They would use repetitive training tasks to induce overfitting in participants then measure the effects of sleep deprivation on their ability to remember and generalize.

Dreams may be so beneficial for efficient brain function, Dr. Hoel speculates, that humans have found ways to dream while awake.

Contrary to the prominent idea among psychologists that art forms such as novels, painting, and music are evolutionary “cheesecake,” pleasurable but with no value for survival, Dr. Hoel believes that they prevent our brains from overfitting.

“The [overfitted brain hypothesis] suggests [that] fictions, and perhaps the arts in general, may actually have a deeper underlying cognitive utility, in the form of improving generalization and preventing overfitting, by acting as artificial dreams.”

“As a novelist, myself,” Dr. Hoel observes, “It is nice to think that fictions, which are in a sense artificial dreams, may have cognitive utility by keeping us from fitting to the daily quotidian events of our lives too well,” he told MNT.

The idea that the brain becomes overfitted to its experiences during waking hours and that dreams help build the process of generalizing knowledge has deep roots in machine learning.

In 1995, computer scientists proposed the idea of a “wake-sleep algorithm” that can learn without human supervision by alternating between waking and sleeping phases.

Nearly two decades later, in 2014, the neuroscientist Prof. Karl Friston, from University College London, in the United Kingdom, and co-authors built on this concept by developing a theory that dreams are the brain’s way of minimizing the complexity of its models.

Prof. Friston views the brain as a machine for generating predictions about the world that make our every perception, thought, and action possible. According to his free energy principle , we dream in order to streamline or simplify the brain’s predictive models.

“This was recently extended to periods of waking reflection and an account of ‘aha!’ moments, when the simplicity of things becomes apparent,” he told MNT in an email. “We have even used complexity minimization to critique deep learning!”


Brain scans unlock the secrets of how we dream and could even reveal what we're dreaming of

A neuroscience study has upended what we know about dreaming. It identifies a “hot zone” in the brain that indicates when dreams are occurring, and describes how the signals in the brain can even predict what a person is dreaming about.

Typically, in dream studies, a person is identified as dreaming when they are in rapid-eye-movement (REM) sleep. In the brain, this is indicated by high-frequency electrical activity. We do know, however, that dreaming also occurs during non-REM sleep when there is low-frequency activity but the nuts and bolts behind this capability have not been well understood, until now.

A study from the Wisconsin Institute of Sleep and Consciousness (WISC), published in Nature, shows that when dreaming was reported in both REM and non-REM sleep, a decrease in low-frequency activity occurred in the posterior cortical region, an area at the back of the brain associated with spatial reasoning and attention. The neuroscientists say they were able to correctly predict whether a volunteer was dreaming 92 per cent of the time, simply by monitoring activity in this “hot zone”. “[The region] may constitute a core correlate of conscious experiences in sleep,” they write.

Picking up further on this “hot zone”, the team was also able to begin breaking down the contents of a dream by monitoring which regions were activated.

“We’ve been able to identify the brain areas that correspond to specific dream contents (like faces, spatial setting, movement and speech) during well-established sleep,” said co-author Francesca Siclari in a statement.

Forty-six subjects at the WISC lab had their sleep monitored using an EEG net worn on the head, covered in 256 electrodes. The volunteers were woken periodically, then asked whether or not they had been dreaming. They first looked specifically at REM and non-REM sleep, noting that volunteers reported they had dreamt when the “hot zone” was activated, regardless of what state of sleep they were in.

In a second experiment, the volunteers reported the content of their dreams, based on key themes the neuroscientists could identify in the posterior cortex: the aforementioned faces, spatial setting, movement and speech. If a volunteer reported hearing speech in their dream, it would correlate with the region of the brain responsible for language and understanding if they dreamt about people, the region responsible for facial recognition was ignited. This means, says Siclari, that we probably use the same areas of the brain during dreaming, as we do when awake, explaining the sense of reality a dream often portrays for an individual.


Dreams could be critical to consolidating memories, argue two sleep researchers

In When Brains Dream, sleep scientists Antonio Zadra and Robert Stickgold detail the latest research that seeks to understand what occurs in our brains when we dream, and they present theories about what purposes dreaming may serve. The book takes the reader from humanity’s early religious understanding of dreams, through our initial attempts to study the psychology of dreaming, to current experiments on the neurophysiology of the sleeping brain, providing relatable and often humorous anecdotal evidence from the authors’ own lives and work along the way.

Humans have contemplated the purpose of dreams throughout recorded history. Ancient texts including the Sumerian Iškar Zaqı-qu and Artemidorus’s Oneiro­critica reveal a prevalent belief that dreams convey important and often supernatural messages, necessitating skilled interpretation. Even Aristotle weighed in on the nature and use of dreams, although he concluded that they were likely just the result of our organs shifting during sleep.

As the world became more secularized in the 19th century, psychologists began to focus on applying their efforts to understanding the sleeping brain. Early research explored the link between waking experiences and dream content, examined the abstract nature of symbols in dreams, and even began applying statistical principles to quantify data gathered in dream journals. By the time Freud published his seminal treatise on dreams in 1899, lesser-known scientists had already gathered evidence that remains remarkably relevant to sleep research today, including the observation that vivid dreams occur most frequently during early morning sleep and that physiological changes occur as sleep progresses

Since the discovery of rapid eye movement (REM) sleep in 1950, however, most studies have approached sleep from a biological perspective. We now know that sleep appears to play a crucial role in clearing waste from the brain, that it regulates hormone levels, and that it helps boost the immune system. There is also a large body of evidence linking sleep to learning and memory.

But are dreams necessary to these functions? Or are they a meaningless consequence of random neural firing? In the latter half of the book, the authors present the ambitious theory that dreams themselves, not just the dream sleep state, play a critical role in the consolidation of memories—a theory they refer to as network exploration to understand possibilities (NEXTUP).

On the surface, the idea that the infrequently remembered and often bizarre narratives we construct during sleep could contribute to memory consolidation seems unlikely. But Zadra and Stickgold argue that the disjointed nature of dreams is actually key to their role in memory processing. They maintain that dreams, rather than merely repeating the events of the day to cement them into long-term storage, allow our brains to freely explore memories that have been filed away over time, extracting information and developing a narrative based on associations.

According to the NEXTUP theory, dreams primarily feature weakly associated themes and memories, an idea supported by previous work in the Stickgold lab, which found that weakly associated word pairs are preferentially activated during REM sleep (1). Coactivation of weakly associated items could underlie the strange and often unexpected twists that unfold during dreaming.

But why do dreams take on a narrative structure at all? The authors suggest that the narrative allows the dreamer to explore and evaluate possible scenarios, providing a mechanism by which a verdict can be rendered. Dreams that elicit strong emotions, they argue, may cue the brain about the association’s potential utility, which may in turn lead to a strengthening of that association.

The NEXTUP theory appears to be supported by animal studies. Rats trained on a maze later appear to dream about maze routes they had never taken (2). REM sleep also appears to be a crucial time for zebra finches to “write” their songs (3). The brain is highly plastic during REM sleep, allowing the erasure of erroneous and weak connections as well as the establishment of new ones (4). These lines of evidence suggest that dreams may allow the brain to explore potential results that would be dangerous or not possible during wakefulness.

References and Notes:
1. R. Stickgold, L. Scott, C. Rittenhouse, J. A. Hobson,
J. Cogn. Neurosci. 11, 182 (1999).
2. A. S. Gupta, M. A. A. van der Meer, D. S. Touretzky,
A. D. Redish, Neuron 65, 695 (2010).
3. A. S. Dave, D. Margoliash, Science 290, 812 (2000).
4. G. R. Poe, J. Neurosci. 37, 464 (2017).

About the author

Frazer is at the UCLA Interdepartmental Program in Neuroscience. Poe is at the Department of Integrative Biology and Physiology, and the Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA 90095, USA.


In the brain, ‘dislike’ and ‘dehumanization’ are not the same thing

During the past week, the news has brought us difficult images and sounds: Migrant and refugee children huddled in steel cages. Children and parents wailing as they are torn apart by American agents. Detention buses filled with infant car seats.

The majority of Americans oppose the policy of separating families at the border, but a substantial percentage have no problem with it. “How is that possible?” many wonder. “These are human beings.”

Researchers who study dehumanization, however, know that not all people see it that way. It is very common for people around the world to look at entire groups—for example, Muslims, Native populations, Roma, Africans, or Mexican immigrants—as not fully human.

Conventional wisdom has long assumed that talking about people in dehumanizing terms, as “dogs” or “pigs” or “pests,” was simply an extreme expression of dislike for them. But according to new research published in the Journal of Experimental Psychology, dehumanization and dislike are processed by two completely separate brain regions, which suggests that they may be two different psychological processes.

For example, many people would say that children and puppies don’t have a fully realized human mind, but that they are still lovable. On the other hand, it is possible to dislike an arrogant colleague while still believing that he or she is fully human.

“When people are dehumanizing others, they are mobilizing different brain regions than when they are registering their dislike,” explains co-lead author Emile Bruneau, director of the Peace and Conflict Neuroscience Lab at the University of Pennsylvania’s Annenberg School for Communication and lead scientist of the Beyond Conflict Innovation Lab. “Brain regions sensitive to dehumanizing other groups were not sensitive to dislike. And brain regions activated when registering dislike for those same groups were not activated when thinking about how human those groups are.”

In the experiment conducted by Bruneau and colleagues, the researchers used functional magnetic resonance imaging (fMRI) to observe participants’ brain activity as they rated how they felt about 10 different groups. They ranged from high-status groups like Americans, Europeans, and surgeons to lower-status groups like Muslims, Roma, and the homeless, and also included animals like puppies and rats.

“Dislike” was measured on a feeling thermometer scale, which asks people to rate how “cold” or “warm” they feel toward the target group, and dehumanization was measured by asking participants to place the target group where they thought it belonged on the popular “Ascent of Man” scale depicting stages of evolution. Previous research from Bruneau and co-lead author Nour Kteily of Northwestern University found that while researchers had long been measuring dehumanization implicitly, believing that few would openly admit they felt other people weren’t fully human, in fact many people have no problem blatantly saying so.

In any real-life situation with high levels of dehumanization, the stakes are high, as it is a strong predictor of aggressive outcomes such as support for torture, reluctance to provide aid to violence victims, support for armed conflict, and support for hostile policies. But knowing that dislike and dehumanization are two separate factors can help understand and address people’s viewpoints.

The belief that the American government is justified in separating migrant or refugee children from their parents, Bruneau explains, isn’t necessarily values-driven or infused with hatred. It can be a cold, rational evaluation: “These children are just less human and less deserving of moral concern.” Removal of children from families has a long tradition, and the impetus is often not anchored in dislike or hatred. In fact, some people justify these removals as paternalistic care.

“High dehumanization and low prejudice is the perfect profile of paternalism,” Bruneau explains. “Some Americans may feel we’re doing good in taking these ‘poor’ immigrant children away from their ‘lawless’ parents.”

“The whole reason I study dehumanization is that I’m interested in intervening to reduce intergroup hostility,” he adds. “Understanding there’s a fundamental difference between dehumanization and dislike is academically interesting, but more importantly, it may prove practically useful.”

Many interventions to try and reduce intergroup conflict—between groups like Israelis and Palestinians, blacks and whites in South Africa, or Muslim refugees and Westerners—focus on getting people to like each other more. That, Bruneau says, is very difficult.

It may be easier to get people to see each other as human, which is, after all, an objective truth. At the very least, knowing that dehumanization and dislike are independent roads to intergroup hostility can increase the number of avenues to peace and reconciliation.

Emile Bruneau is director of the Peace and Conflict Neuroscience Lab at the University of Pennsylvania’s Annenberg School for Communication. He co-authored the paper with Nour Kteily of Northwestern University, Nir Jacoby of the Massachusetts Institute of Technology and Columbia University, and Rebecca Saxe of the Massachusetts Institute of Technology. Funding came from the DRAPER Research Laboratories and a fellowship from Beyond Conflict.


References

Addis DR, Wong AT, Schacter DL: Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 2007, 45 (7): 1363-1377. 10.1016/j.neuropsychologia.2006.10.016.

Schacter DL, Slotnick SD: The cognitive neuroscience of memory distortion. Neuron. 2004, 44 (1): 149-160. 10.1016/j.neuron.2004.08.017.

Schacter DL: The seven sins of memory: how the mind forgets and remembers. 2001, Houghton Mifflin, Boston

Squire LR: Memory and brain systems: 1969–2009. J Neurosci. 2009, 29 (41): 12711-12716. 10.1523/JNEUROSCI.3575-09.2009.

Squire LR: Memory systems of the brain: a brief history and current perspective. Neurobiol Learn Mem. 2004, 82 (3): 171-177. 10.1016/j.nlm.2004.06.005.

Henke K: A model for memory systems based on processing modes rather than consciousness. Nat Rev Neurosci. 2010, 11 (7): 523-532. 10.1038/nrn2850.

Tulving E: Episodic and Semantic Memory. Organization of memory. Edited by: Tulving E, Donaldson W. 1972, Academic, New York, 381-402.

Tulving E: Episodic memory: from mind to brain. Annu Rev Psychol. 2002, 53: 1-25. 10.1146/annurev.psych.53.100901.135114.

Shimamura AP, Squire LR: A neuropsychological study of fact memory and source amnesia. J Exp Psychol Learn Mem Cogn. 1987, 13 (3): 464-473.

Wheeler MA, Stuss DT, Tulving E: Toward a theory of episodic memory: the frontal lobes and autonoetic consciousness. Psychol Bull. 1997, 121 (3): 331-354.

Mitchell KJ, Johnson MK: Source monitoring 15 years later: what have we learned from fMRI about the neural mechanisms of source memory?. Psychol Bull. 2009, 135 (4): 638-677.

Straube B, Green A, Chatterjee A, Kircher T: Encoding social interactions: the neural correlates of true and false memories. J Cogn Neurosci. 2011, 23 (2): 306-324. 10.1162/jocn.2010.21505.

Gallo DA: False memories and fantastic beliefs: 15 years of the DRM illusion. Mem Cognit. 2010, 38 (7): 833-848. 10.3758/MC.38.7.833.

Jacoby LL, Wahlheim CN, Rhodes MG, Daniels KA, Rogers CS: Learning to diminish the effects of proactive interference: reducing false memory for young and older adults. Mem Cognit. 2010, 38 (6): 820-829. 10.3758/MC.38.6.820.

Long DL, Prat C, Johns C, Morris P, Jonathan E: The importance of knowledge in vivid text memory: an individual-differences investigation of recollection and familiarity. Psychon Bull Rev. 2008, 15 (3): 604-609. 10.3758/PBR.15.3.604.

Brainerd CJ, Reyna VF, Aydin C: Remembering in contradictory minds: disjunction fallacies in episodic memory. J Exp Psychol Learn Mem Cogn. 2010, 36 (3): 711-735.

Koo M, Oishi S: False memory and the associative network of happiness. Pers Soc Psychol Bull. 2009, 35 (2): 212-220. 10.1177/0146167208327191.

El Sharkawy J, Groth K, Vetter C, Beraldi A, Fast K: False memories of emotional and neutral words. Behav Neurol. 2008, 19 (1–2): 7-11.

Laney C, Loftus EF: Emotional content of true and false memories. Memory. 2008, 16 (5): 500-516. 10.1080/09658210802065939.

Smeets T, Otgaar H, Candel I, Wolf OT: True or false? Memory is differentially affected by stress-induced cortisol elevations and sympathetic activity at consolidation and retrieval. Psychoneuroendocrinology. 2008, 33 (10): 1378-1386. 10.1016/j.psyneuen.2008.07.009.

Baym CL, Gonsalves BD: Comparison of neural activity that leads to true memories, false memories, and forgetting: An fMRI study of the misinformation effect. Cogn Affect Behav Neurosci. 2010, 10 (3): 339-348. 10.3758/CABN.10.3.339.

Bhatt R, Laws KR, McKenna PJ: False memory in schizophrenia patients with and without delusions. Psychiatry Res. 2010, 178 (2): 260-265. 10.1016/j.psychres.2009.02.006.

Klumpp H, Amir N, Garfinkel SN: False memory and obsessive-compulsive symptoms. Depress Anxiety. 2009, 26 (5): 396-402. 10.1002/da.20526.

Moritz S, Woodward TS: Metacognitive control over false memories: a key determinant of delusional thinking. Curr Psychiatry Rep. 2006, 8 (3): 184-190. 10.1007/s11920-006-0022-2.

Brainerd CJ, Reyna VF, Ceci SJ: Developmental reversals in false memory: a review of data and theory. Psychol Bull. 2008, 134 (3): 343-382.

Loftus EF: Planting misinformation in the human mind: a 30-year investigation of the malleability of memory. Learn Mem. 2005, 12 (4): 361-366. 10.1101/lm.94705.

Loftus E: Our changeable memories: legal and practical implications. Nat Rev Neurosci. 2003, 4 (3): 231-234. 10.1038/nrn1054.

Gonsalves B, Reber PJ, Gitelman DR, Parrish TB, Mesulam MM, Paller KA: Neural evidence that vivid imagining can lead to false remembering. Psychol Sci. 2004, 15 (10): 655-660. 10.1111/j.0956-7976.2004.00736.x.

Gonsalves B, Paller KA: Mistaken memories: remembering events that never happened. Neuroscientist. 2002, 8 (5): 391-395. 10.1177/107385802236964.

Gonsalves B, Paller KA: Neural events that underlie remembering something that never happened. Nat Neurosci. 2000, 3 (12): 1316-1321. 10.1038/81851.

Ratcliff R, McKoon G: Retrieving information from memory: spreading-activation theories versus compound-cue theories. Psychol Rev. 1994, 101 (1): 177-184. discussion 185–177

Joordens S, Besner D: Priming effects that span an intervening unrelated word: implications for models of memory representation and retrieval. J Exp Psychol Learn Mem Cogn. 1992, 18 (3): 483-491.

Balota DA, Duchek JM: Spreading activation in episodic memory: further evidence for age independence. Q J Exp Psychol A. 1989, 41 (4): 849-876. 10.1080/14640748908402396.

Dell GS: A spreading-activation theory of retrieval in sentence production. Psychol Rev. 1986, 93 (3): 283-321.

Kim H, Cabeza R: Differential contributions of prefrontal, medial temporal, and sensory-perceptual regions to true and false memory formation. Cereb Cortex. 2007, 17 (9): 2143-2150.

Johnson MK, Hashtroudi S, Lindsay DS: Source Monitoring. Psychol Bull. 1993, 114 (1): 3-28.

Johnson MK, Raye CL: Reality monitoring. Psychol Rev. 1981, 88 (1): 67-85.

Cabeza R, Lennartson ER: False memory across languages: implicit associative response vs fuzzy trace views. Memory. 2005, 13 (1): 1-5. 10.1080/09658210344000161.

Brainerd CJ, Reyna VF: Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. Adv Child Dev Behav. 2001, 28: 41-100.

Reyna VF, Brainerd CJ: Fuzzy-trace theory and false memory: new frontiers. J Exp Child Psychol. 1998, 71 (2): 194-209. 10.1006/jecp.1998.2472.

Brainerd CJ, Reyna VF, Howe ML, Kingma J: The development of forgetting and reminiscence. Monogr Soc Res Child Dev. 1990, 55 (3–4): 1-93. discussion 94–109

Deese J: On the prediction of occurrence of particular verbal intrusions in immediate recall. J Exp Psychol. 1959, 58: 5-

Roediger HL, McDermott KB: Creating false memories: Remembering words not presented in lists. J Exp Psychol Learn Mem Cogn. 1995, 21: 11-

McKoon G, Ratcliff R: Spreading activation versus compound cue accounts of priming: mediated priming revisited. J Exp Psychol Learn Mem Cogn. 1992, 18 (6): 1155-1172.

Anderson JR: Retrieval of information from long-term memory. Science. 1983, 220 (4592): 25-30. 10.1126/science.6828877.

Collins AM, Loftus EF: A spreading-activation theory of semantic processing. . 1975, 82: -.

van Kesteren MT, Rijpkema M, Ruiter DJ, Fernández G: Retrieval of associative information congruent with prior knowledge is related to increased medial prefrontal activity and connectivity. J Neurosci. 2010, 30 (47): 15888-15894. 10.1523/JNEUROSCI.2674-10.2010.

van Kesteren MT, Fernández G, Norris DG, Hermans EJ: Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proc Natl Acad Sci U S A. 2010, 107 (16): 7550-7555. 10.1073/pnas.0914892107.

Frankland PW, Bontempi B: The organization of recent and remote memories. Nat Rev Neurosci. 2005, 6 (2): 119-130.

Northoff G, Qin P, Feinberg TE: Brain imaging of the self–conceptual, anatomical and methodological issues. Conscious Cogn. 2011, 20 (1): 52-63. 10.1016/j.concog.2010.09.011.

Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H, Panksepp J: Self-referential processing in our brain–a meta-analysis of imaging studies on the self. Neuroimage. 2006, 31 (1): 440-457. 10.1016/j.neuroimage.2005.12.002.

Benoit RG, Gilbert SJ, Volle E, Burgess PW: When I think about me and simulate you: medial rostral prefrontal cortex and self-referential processes. Neuroimage. 2010, 50 (3): 1340-1349. 10.1016/j.neuroimage.2009.12.091.

Kircher TT, Brammer M, Bullmore E, Simmons A, Bartels M, David AS: The neural correlates of intentional and incidental self processing. Neuropsychologia. 2002, 40 (6): 683-692. 10.1016/S0028-3932(01)00138-5.

Kircher TT, Senior C, Phillips ML, Benson PJ, Bullmore ET, Brammer M, Simmons A, Williams SC, Bartels M, David AS: Towards a functional neuroanatomy of self processing: effects of faces and words. Brain Res Cogn Brain Res. 2000, 10 (1–2): 133-144.

Northoff G, Bermpohl F: Cortical midline structures and the self. Trends Cogn Sci. 2004, 8 (3): 102-107. 10.1016/j.tics.2004.01.004.

Zaragoza MS, Mitchell KJ, Payment K, Drivdahl S: False Memories for Suggestions: The Impact of Conceptual Elaboration. J Mem Lang. 2011, 64 (1): 18-31. 10.1016/j.jml.2010.09.004.

Wright DB, Loftus EF: How misinformation alters memories. J Exp Child Psychol. 1998, 71 (2): 155-164. 10.1006/jecp.1998.2467.

Payne JD, Schacter DL, Propper RE, Huang LW, Wamsley EJ, Tucker MA, Walker MP, Stickgold R: The role of sleep in false memory formation. Neurobiol Learn Mem. 2009, 92 (3): 327-334. 10.1016/j.nlm.2009.03.007.

Fenn KM, Gallo DA, Margoliash D, Roediger HL, Nusbaum HC: Reduced false memory after sleep. Learn Mem. 2009, 16 (9): 509-513. 10.1101/lm.1500808.

Diekelmann S, Born J, Wagner U: Sleep enhances false memories depending on general memory performance. Behav Brain Res. 2010, 208 (2): 425-429. 10.1016/j.bbr.2009.12.021.

Diekelmann S, Landolt HP, Lahl O, Born J, Wagner U: Sleep loss produces false memories. PLoS One. 2008, 3 (10): e3512-10.1371/journal.pone.0003512.

Darsaud A, Dehon H, Lahl O, Sterpenich V, Boly M, Dang-Vu T, Desseilles M, Gais S, Matarazzo L, Peters F: Does sleep promote false memories?. J Cogn Neurosci. 2011, 23 (1): 26-40. 10.1162/jocn.2010.21448.

Stickgold R: Sleep-dependent memory consolidation. Nature. 2005, 437 (7063): 1272-1278. 10.1038/nature04286.

Diekelmann S, Born J: The memory function of sleep. Nat Rev Neurosci. 2010, 11 (2): 114-126.

Stickgold R, Walker MP: Memory consolidation and reconsolidation: what is the role of sleep?. Trends Neurosci. 2005, 28 (8): 408-415. 10.1016/j.tins.2005.06.004.

Tamminen J, Payne JD, Stickgold R, Wamsley EJ, Gaskell MG: Sleep spindle activity is associated with the integration of new memories and existing knowledge. J Neurosci. 2010, 30 (43): 14356-14360. 10.1523/JNEUROSCI.3028-10.2010.

Ellenbogen JM, Payne JD, Stickgold R: The role of sleep in declarative memory consolidation: passive, permissive, active or none?. Curr Opin Neurobiol. 2006, 16 (6): 716-722. 10.1016/j.conb.2006.10.006.

Ellenbogen JM, Hulbert JC, Stickgold R, Dinges DF, Thompson-Schill SL: Interfering with theories of sleep and memory: sleep, declarative memory, and associative interference. Curr Biol. 2006, 16 (13): 1290-1294. 10.1016/j.cub.2006.05.024.

Diekelmann S, Born J: One memory, two ways to consolidate?. Nat Neurosci. 2007, 10 (9): 1085-1086. 10.1038/nn0907-1085.

Fenn KM, Nusbaum HC, Margoliash D: Consolidation during sleep of perceptual learning of spoken language. Nature. 2003, 425 (6958): 614-616. 10.1038/nature01951.

Wagner U, Gais S, Haider H, Verleger R, Born J: Sleep inspires insight. Nature. 2004, 427 (6972): 352-355. 10.1038/nature02223.

Kuriyama K, Stickgold R, Walker MP: Sleep-dependent learning and motor-skill complexity. Learn Mem. 2004, 11 (6): 705-713. 10.1101/lm.76304.

Drosopoulos S, Schulze C, Fischer S, Born J: Sleep’s function in the spontaneous recovery and consolidation of memories. J Exp Psychol Gen. 2007, 136 (2): 169-183.

Loftus EF, Palmer JC: Reconstruction of auto-mobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behaviour. 1974, 13: 5-

Ecker UK, Lewandowsky S, Swire B, Chang D: Correcting false information in memory: manipulating the strength of misinformation encoding and its retraction. Psychon Bull Rev. 2011, 18 (3): 570-578. 10.3758/s13423-011-0065-1.

Zhu B, Chen C, Loftus EF, Lin C, He Q, Li H, Xue G, Lu Z, Dong Q: Individual differences in false memory from misinformation: cognitive factors. Memory. 2010, 18 (5): 543-555. 10.1080/09658211.2010.487051.

Loftus EF: Searching for the neurobiology of the misinformation effect. Learn Mem. 2005, 12 (1): 1-2. 10.1101/lm.90805.

Okado Y, Stark CE: Neural activity during encoding predicts false memories created by misinformation. Learn Mem. 2005, 12 (1): 3-11. 10.1101/lm.87605.

Loftus EF, Hoffman HG: Misinformation and memory: the creation of new memories. J Exp Psychol Gen. 1989, 118 (1): 100-104.

Stark CE, Okado Y, Loftus EF: Imaging the reconstruction of true and false memories using sensory reactivation and the misinformation paradigms. Learn Mem. 2010, 17 (10): 485-488. 10.1101/lm.1845710.

Slotnick SD, Schacter DL: The nature of memory related activity in early visual areas. Neuropsychologia. 2006, 44 (14): 2874-2886. 10.1016/j.neuropsychologia.2006.06.021.

Slotnick SD, Schacter DL: A sensory signature that distinguishes true from false memories. Nat Neurosci. 2004, 7 (6): 664-672. 10.1038/nn1252.

Slotnick SD: Visual memory and visual perception recruit common neural substrates. Behav Cogn Neurosci Rev. 2004, 3 (4): 207-221. 10.1177/1534582304274070.

Watson JM, McDermott KB, Balota DA: Attempting to avoid false memories in the Deese/Roediger-McDermott paradigm: assessing the combined influence of practice and warnings in young and old adults. Mem Cognit. 2004, 32 (1): 135-141. 10.3758/BF03195826.

Sederberg PB, Schulze-Bonhage A, Madsen JR, Bromfield EB, Litt B, Brandt A, Kahana MJ: Gamma oscillations distinguish true from false memories. Psychol Sci. 2007, 18 (11): 927-932. 10.1111/j.1467-9280.2007.02003.x.

Seamon JG, Luo CR, Shulman EP, Toner SK, Caglar S: False memories are hard to inhibit: differential effects of directed forgetting on accurate and false recall in the DRM procedure. Memory. 2002, 10 (4): 225-237. 10.1080/09658210143000344.

Weinstein Y, Shanks DR: Rapid induction of false memory for pictures. Memory. 2010, 18 (5): 533-542. 10.1080/09658211.2010.483232.

Turner MS, Cipolotti L, Shallice T: Spontaneous confabulation, temporal context confusion and reality monitoring: a study of three patients with anterior communicating artery aneurysms. J Int Neuropsychol Soc. 2010, 16 (6): 984-994. 10.1017/S1355617710001104.

Schacter DL, Guerin SA, St Jacques PL: Memory distortion: an adaptive perspective. Trends Cogn Sci. 2011, 15 (10): 467-474. 10.1016/j.tics.2011.08.004.

Deese J: On the prediction of occurrence of particular verbal intrusions in immediate recall. Journal of Experimental Psychology. 1959, 58: 5-

Roediger HL, Watson JM, McDermott KB, Gallo DA: Factors that determine false recall: a multiple regression analysis. Psychon Bull Rev. 2001, 8 (3): 385-407. 10.3758/BF03196177.

McDonough IM, Gallo DA: Autobiographical elaboration reduces memory distortion: cognitive operations and the distinctiveness heuristic. J Exp Psychol Learn Mem Cogn. 2008, 34 (6): 1430-1445.

Harrison Y, Horne JA: The impact of sleep deprivation on decision making: a review. J Exp Psychol Appl. 2000, 6 (3): 236-249.

Schacter DL, Reiman E, Curran T, Yun LS, Bandy D, McDermott KB, Roediger HL: Neuroanatomical correlates of veridical and illusory recognition memory: evidence from positron emission tomography. Neuron. 1996, 17 (2): 267-274. 10.1016/S0896-6273(00)80158-0.

Cabeza R, Rao SM, Wagner AD, Mayer AR, Schacter DL: Can medial temporal lobe regions distinguish true from false? An event-related functional MRI study of veridical and illusory recognition memory. Proc Natl Acad Sci U S A. 2001, 98 (8): 4805-4810. 10.1073/pnas.081082698.

Schacter DL: Illusory memories: a cognitive neuroscience analysis. Proc Natl Acad Sci U S A. 1996, 93 (24): 13527-13533. 10.1073/pnas.93.24.13527.

Curran T, Schacter DL, Norman KA, Galluccio L: False recognition after a right frontal lobe infarction: Memory for general and specific information. Neuropsychologia. 1997, 35 (7): 1035-1049. 10.1016/S0028-3932(97)00029-8.

Ciaramelli E, Ghetti S, Frattarelli M, Làdavas E: When true memory availability promotes false memory: evidence from confabulating patients. Neuropsychologia. 2006, 44 (10): 1866-1877. 10.1016/j.neuropsychologia.2006.02.008.

Schacter DL, Norman KA, Koutstaal W: The cognitive neuroscience of constructive memory. Annu Rev Psychol. 1998, 49: 289-318. 10.1146/annurev.psych.49.1.289.

Abe N, Okuda J, Suzuki M, Sasaki H, Matsuda T, Mori E, Tsukada M, Fujii T: Neural correlates of true memory, false memory, and deception. Cereb Cortex. 2008, 18 (12): 2811-2819. 10.1093/cercor/bhn037.

Dennis NA, Kim H, Cabeza R: Age-related differences in brain activity during true and false memory retrieval. J Cogn Neurosci. 2008, 20 (8): 1390-1402. 10.1162/jocn.2008.20096.

Garoff-Eaton RJ, Slotnick SD, Schacter DL: Not all false memories are created equal: the neural basis of false recognition. Cereb Cortex. 2006, 16 (11): 1645-1652.

Addis DR, Schacter DL: Constructive episodic simulation: temporal distance and detail of past and future events modulate hippocampal engagement. Hippocampus. 2008, 18 (2): 227-237. 10.1002/hipo.20405.

Schacter DL, Addis DR: On the nature of medial temporal lobe contributions to the constructive simulation of future events. Philos Trans R Soc Lond B Biol Sci. 2009, 364 (1521): 1245-1253. 10.1098/rstb.2008.0308.

Schacter DL, Addis DR, Buckner RL: Episodic simulation of future events: concepts, data, and applications. Ann N Y Acad Sci. 2008, 1124: 39-60. 10.1196/annals.1440.001.

Schacter DL, Addis DR: Constructive memory: the ghosts of past and future. Nature. 2007, 445 (7123): 27-10.1038/445027a.

Schacter DL, Addis DR: The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philos Trans R Soc Lond B Biol Sci. 2007, 362 (1481): 773-786. 10.1098/rstb.2007.2087.

Schacter DL, Buckner RL, Koutstaal W, Dale AM, Rosen BR: Late onset of anterior prefrontal activity during true and false recognition: an event-related fMRI study. Neuroimage. 1997, 6 (4): 259-269. 10.1006/nimg.1997.0305.

Giovanello KS, Kensinger EA, Wong AT, Schacter DL: Age-related neural changes during memory conjunction errors. J Cogn Neurosci. 2010, 22 (7): 1348-1361. 10.1162/jocn.2009.21274.

Chen JC, Li W, Westerberg CE, Tzeng OJ: Test-item sequence affects false memory formation: an event-related potential study. Neurosci Lett. 2008, 431 (1): 51-56. 10.1016/j.neulet.2007.11.020.

Dennis NA, Kim H, Cabeza R: Effects of aging on true and false memory formation: an fMRI study. Neuropsychologia. 2007, 45 (14): 3157-3166. 10.1016/j.neuropsychologia.2007.07.003.

Hanczakowski M, Mazzoni G: Both differences in encoding processes and monitoring at retrieval reduce false alarms when distinctive information is studied. Memory. 2011, 19 (3): 280-289. 10.1080/09658211.2011.558514.

Garoff-Eaton RJ, Kensinger EA, Schacter DL: The neural correlates of conceptual and perceptual false recognition. Learn Mem. 2007, 14 (10): 684-692. 10.1101/lm.695707.

Dodson CS, Hege AC: Speeded retrieval abolishes the false-memory suppression effect: evidence for the distinctiveness heuristic. Psychon Bull Rev. 2005, 12 (4): 726-731. 10.3758/BF03196764.

Arndt J, Reder LM: The effect of distinctive visual information on false recognition. J Mem Lang. 2003, 48 (1): 1-15. 10.1016/S0749-596X(02)00518-1.

Straube B, Green A, Bromberger B, Kircher T: The differentiation of iconic and metaphoric gestures: Common and unique integration processes. Hum Brain Mapp. 2011, 32 (4): 520-533. 10.1002/hbm.21041.

Green A, Straube B, Weis S, Jansen A, Willmes K, Konrad K, Kircher T: Neural integration of iconic and unrelated coverbal gestures: a functional MRI study. Hum Brain Mapp. 2009, 30 (10): 3309-3324. 10.1002/hbm.20753.

Kircher T, Straube B, Leube D, Weis S, Sachs O, Willmes K, Konrad K, Green A: Neural interaction of speech and gesture: differential activations of metaphoric co-verbal gestures. Neuropsychologia. 2009, 47 (1): 169-179. 10.1016/j.neuropsychologia.2008.08.009.

Straube B, Green A, Jansen A, Chatterjee A, Kircher T: Social cues, mentalizing and the neural processing of speech accompanied by gestures. Neuropsychologia. 2010, 48 (2): 382-393. 10.1016/j.neuropsychologia.2009.09.025.

Straube B, Green A, Weis S, Chatterjee A, Kircher T: Memory effects of speech and gesture binding: cortical and hippocampal activation in relation to subsequent memory performance. J Cogn Neurosci. 2009, 21 (4): 821-836. 10.1162/jocn.2009.21053.

Boggio PS, Fregni F, Valasek C, Ellwood S, Chi R, Gallate J, Pascual-Leone A, Snyder A: Temporal lobe cortical electrical stimulation during the encoding and retrieval phase reduces false memories. PLoS One. 2009, 4 (3): e4959-10.1371/journal.pone.0004959.

Straube B, Wolk D, Chatterjee A: The role of the right parietal lobe in the perception of causality: a tDCS study. Exp Brain Res. 2011, 215 (3–4): 315-325.

Fazio LK, Marsh EJ: Older, not younger, children learn more false facts from stories. Cognition. 2008, 106 (2): 1081-1089. 10.1016/j.cognition.2007.04.012.

Lövdén M, Wahlin A: The sensory-cognition association in adulthood: Different magnitudes for processing speed, inhibition, episodic memory, and false memory?. Scand J Psychol. 2005, 46 (3): 253-262. 10.1111/j.1467-9450.2005.00455.x.

Mammarella N, Altamura M, Padalino FA, Petito A, Fairfield B, Bellomo A: False memories in schizophrenia? An imagination inflation study. Psychiatry Res. 2010, 179 (3): 267-273. 10.1016/j.psychres.2009.05.005.


Nightmares and the Brain

This definition came from the popular reference text, An Universal Etymological English Dictionary, first published by Nathan Bailey in 1721 and reprinted through 1802. Although that definition doesn’t surface often today, nightmares are still considered to be frightening dreams that result in feelings of terror, fear, distress, or anxiety.

Despite our colloquial use of the term, for example, “my commute was a nightmare,” for an estimated 3 to 7 percent of the U.S. population, nightmares can be a real problem. Although adults can suffer from nightmares, they are more typical in children, especially those between the ages of 3 and 6. “We think that some of this may be evolutionary,” says Deirdre Barrett, PhD, an HMS assistant clinical professor of psychology at Cambridge Health Alliance and editor of “Trauma and Dreams,” published by Harvard University Press in 2001. “Children are smaller and are vulnerable to many more threats than adults. Nightmares may partially reflect this vulnerability.”

Dreams are understood to be recent autobiographical episodes that become woven with past memories to create a new memory that can be referenced later, but nightmares are simply dreams that cause a strong but unpleasant emotional response. Dreams are part of the brain’s default network—a system of interconnected regions, which includes the thalamus, medial prefrontal cortex, and posterior cingulate cortex—that remains active during comparatively quiet periods.

REM sleep is one example of a quiet period. It is a stage of sleep that is characterized by rapid eye movement, irregular heartbeat, and increased rates of respiration. REM sleep is discontinuous, chunked into four or five periods that together make up about 20 percent of our slumber. It is during these REM episodes that brain structures in the default network exert influence, and it is during REM sleep that vividly recalled dreams occur most often.

Nightmares tend to happen during the period of sleep when REM intervals lengthen these usually occur halfway through slumber. As we prepare to awaken, memories begin to integrate and consolidate. We dream as we emerge from REM sleep. Because we tend to dream on the sleep-wake cusp, images imagined while dreaming, including the vivid, often terrifying images produced during nightmares, are remembered.


Seeing a ghost

Credit: AFP Contributor via Getty Images

Perhaps more distressing than becoming a ghost is seeing one. Sleep paralysis is arguably most infamous for the sinister shadowy "bedroom-intruder" that sometimes attacks the sleeper. The "creature" is usually lurking in the distant dark, slowly approaching in on its victim.

From here, all kinds of ominous things can happen, as far as the imagination can stretch. Commonly, the intruder chokes and suffocates the person by crushing his chest or pressing on his neck. And occasionally, the creature brutally rapes the paralyzed sleeper. The figure often appears simply as a dark shadow, similar to the human size and shape. But, it can also include detailed features, say, a scary demonic face with animal characteristics, like sharp teeth and cat eyes.

This figure goes by different names around the world. My colleague Devon Hinton of Harvard Medical School and I found that in Egypt, the creature is thought to be a Jinn (an "evil genie") a spirit-like entity that may hunt down, terrorize, and even kill its victims. In another study, we've discovered that among some Italians, it is believed to be a malevolent witch or a terrifying human-like cat, known locally as the Pandafeche. Some space alien abduction cases also fit the sleep paralysis scenario: the person is laying in his bed paralyzed suddenly the alien appears and begins to experiment on the sleeper's sexual organs, collecting eggs and semen.



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