Permanent Present Tense

by Suzanne Corkin

  22. McGauch, “Memory—A Century of Consolidation”; Memory consolidation theorists speculated that long-term declarative memory, Henry’s nemesis, depends on such close interaction and coordination between the workings of the hippocampus and processes in the cortex. Henry’s intact cerebral cortex could not do the job by itself. In 2012, research cintnues to focus on how the hippocampal system interacts with cortical circuits to consolidate and store memories. Because consolidation takes place gradually, it is reasonable to suppose that multiple mechanisms in the hippocampus and cortex are recruited along the way. See D. Marr, “Simple Memory: A Theory for Archicortex,” Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences 262 (1971): 23–81; L. R. Squire et al., “The Medial Temporal Region and Memory Consolidation: A New Hypothesis,” in Memory Consolidation: Psychobiology of Cognition, eds, H. Weingartner et al. (Hillsdale, NJ: Lawrence Erlbaum Associates, 1984), 185–210; and J. L. McClelland et al., “Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights from the Successes and Failures of Connectionist Models of Learning and Memory,” Psychological Review 102 (1995): 419–57.

  23. S. Ramón y Cajal, “La Fine Structure des Centres Nerveux,” Proceedings of the Royal Society of London 55 (1894): 444–68; D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory (New York: John Wiley & Sons, 1949).

  24. T. Lømo, “Frequency Potentiation of Excitatory Synaptic Activity in the Dentate Areas of the Hippocampal Formation,” Acta Physiologica Scandinavica 68 (1966): 128; T.V.P. Bliss and T. Lømo, Long-Lasting Potentiation of Synaptic Transmission in the Dentate Area of the Anaesthetized Rabbit Following Stimulation of the Perforant Path,” Journal of Physiology 232 (1973): 331–56; R. M. Douglas and G. Goddard, “Long-Term Potentiation of the Perforant Path-Granule Cell Synapse in the Rat Hippocampus,” Brain Research 86 (1975): 205–15.

  25. S. J. Martin et al., “Synaptic Plasticity and Memory: An Evaluation of the Hypothesis,” Annual Review of Neuroscience 23 (2000): 649–711; T. Bliss et al., “Synaptic Plasticity in the Hippocampus,” in The Hippocampus Book, eds, P. Anderson et al. (New York: Oxford University Press, 2007), 343–474.

  26. Ibid.

  27. Next, these scientists asked whether this learning deficit applied to all kinds of learning or was specific to spatial learning. He trained rats on a simple visual-discrimination task where they were allowed to choose between two platforms based on how they looked—a grey one that floated and provided escape, and a black-and-white striped one that sank. This task did not require spatial learning. Rats who received the drug to block LTP performed the visual-discrimination task normally, indicating that the hippocampus was not necessary for this task. The sharp contrast between the dramatic deficit in spatial (declarative) learning and the intact discrimination (nondeclarative) learning is reminiscent of Henry’s postoperative inability to find his way to the bathroom in the hospital alongside his facility in learning new motor skills. See R. G. Morris et al., “Selective Impairment of Learning and Blockade of Long-Term Potentiation by an N-Methyl-D-Aspartate Receptor Antagonist, Ap5,” Nature 319 (1986): 774–6.

  28. J. Z. Tsien, et al., “Subregion-and Cell Type-Restricted Gene Knockout in Mouse Brain,” Cell 87 (1996): 1317–26; T. J. McHugh, et al., “Impaired Hippocampal Representation of Space in CA1-Specific NMDAR1 Knockout Mice,” Cell 87 (1996): 1339–49; A. Rotenberg, et al., “Mice Expressing Activated CaMKII Lack Low Frequency LTP and Do Not Form Stable Place Cells in the CA1 Region of the Hippocampus,” Cell 87 (1996): 1351–61.

  29. T.V.P. Bliss and S. F. Cooke, “Long-Term Potentiation and Long-Term Depression: A Clinical Perspective,” Clinics 66 (2011): 3–17.

  30. J. O’Keefe and J. Dostrovsky, “The Hippocampus as a Spatial Map: Preliminary Evidence from Unit Activity in the Freely-Moving Rat,” Brain Research 34 (1971): 171–5.

  31. Y. L. Qin et al. “Memory Reprocessing in Corticocortical and Hippocampocortical Neuronal Ensembles,” Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences 352 (1997): 1525–33.

  32. J. D. Payne, Learning, Memory, and Sleep in Humans,” Sleep Medicine Clinics 6 (2011): 145–56.

  33. K. Louie and M. A. Wilson, “Temporally Structured Replay of Awake Hippocampal Ensemble Activity During Rapid Eye Movement Sleep,” Neuron 29 (2001): 145–56.

  34. Ibid.

  35. A. K. Lee and M. A. Wilson, “Memory of Sequential Experience in the Hippocampus During Slow Wave Sleep,” Neuron 36 (2002): 1183–94. Memory replay in awake rats also advances our understanding of consolidation. In 2006, Wilson and colleagues discovered that after a rat ran a novel track and then stopped to take time out to groom, whisk its whiskers, or just stand still, the memories of locations in the maze formed in its hippocampus were played back in reverse order—the place cells associated with the end of the track fired first, and those related to the beginning fired last. This backward instant replay suggests that the rat stopped to literally think back in time, contemplating, assimilating, and consolidating what it had just experienced. As two neuroscientists at Rutgers University showed in 2007, awake rats can also replay sequences forward—in the same order in which they were experienced. The puzzle is: What are these rats thinking about, and why do they engage in replay? If this is not full-blown thought, then it is at least a gigantic leap in that direction. See D. J. Foster and M. A. Wilson, “Reverse Replay of Behavioural Sequences in Hippocampal Place Cells During the Awake State,” Nature 440 (2006): 680–3

  36. Ibid.; and K. Diba and G. Buzsaki, “Forward and Reverse Hippocampal Place-Cell Sequences During Ripples,” Nature Neuroscience 10m (2007): 1241–2.

  37. D. Ji and M.A. Wilson, Coordinated Memory Replay in the Visual Cortex and Hippocampus During Sleep,” Nature Neuroscience 10 (2007): 100–7.

  38. E. Tulving and D. M. Thomson, “Encoding Specificity and Retrieval Processes in Episodic Memory,” Psychological Review 80 (1973): 352–73.

  39. H. Schmolck, et al., “Memory Distortions Develop over Time: Recollections of the O.J. Simpson Trial Verdict after 15 and 32 Months,” Psychological Science 11 (2000): 39–45.

  40. J. Przybyslawski and S. J. Sara, “Reconsolidation of Memory after Its Reactivation,” Behavioural Brain Research 84(1997): 241–6.

  41. Ibid.

  42. O. Hardt et al., “A Bridge over Troubled Water: Reconsolidation as a Link between Cognitive and Neuroscientific Memory Research Traditions,” Annual Review of Psychology 61 (2010): 141–67; See also D. Schiller et al., “Preventing the Return of Fear in Humans Using Reconsolidation Update Mechanisms,” Nature 463 (2010): 49–53.

  43. J. T. Wixted, “The Psychology and Neuroscience of Forgetting,” Annual Review of Psychology 55 (2004): 235–69.

  44. D. M. Freed et al., “Forgetting in H.M.: A Second Look,” Neuropsychologia 25 (1987): 461–71.

  45. Freed, “Forgetting in H.M.”; D. M. Freed and S. Corkin, “Rate of Forgetting in H.M.: 6-Month Recognition,” Behavioral Neuroscience 102 (1988): 823–7.

  46. R. C. Atkinson and J. F. Juola, “Search and Decision Processes in Recognition Memory,” in Contemporary Developments in Mathematical Psychology: Learning, Memory, and Thinking, eds, D. H. Krantz (San Francisco, CA: W. H. Freeman, 1974), 242–93; G. Mandler, “Recognizing: The Judgement of Previous Occurrence,” Psychological Review 87 (1980): 252–71; L. L. Jacoby, “A Process Dissociation Framework: Separating Automatic from Intentional Uses of Memory,” Journal of Memory and Language 30 (1991): 513–41.

  47. J. P. Aggleton and M. W. Brown, “Episodic Memory, Amnesia, and the Hippocampal-Anterior Thalamic Axis,” Behavioral and Brain Science 22 (1999): 425–44.

  48. Freed, “Forgetting in H.M.”; Freed and Corkin, “Rate of Forgetting in H.M.”; and Aggleton and brown, “Episodic Memory.”

  49. C. Ranganath et al., “Dissociable Correlates of Recollection and Familiarity within the Medial Temporal Lobes,” Neuropsychologia 42 (203): 2–13.

  50. I
bid.

  51. Ibid.

  52. B. Bowles et al., “Impaired Familiarity with Preserved Recollection after Anterior Temporal-Lobe Resection That Spares the Hippocampus,” Proceedings of the National Academy of Sciences 104 (2007): 16382–7; M. W. Brown et al., “Recognition Memory: Material, Processes, and Substrates: Hippocampus 20 (2010): 1228–44. In 2011, cognitive neuroscientists at New York University proposed a different view about the organization of recognition memory in medial temporal-lobe areas. Their functional MRI results in healthy research participants suggested that the perirhinal cortex was specialized for imagining individual objects, whereas the parahippocampal cortex was specialized for imagining scenes. See B. P. Staresina et al., “Perirhinal and Parahippocamal Cortices Differentially Contribute to Later Recollection of Object- and Scene-Related Event Details,” Journal of Neuroscience 31 (2011): 8739–47.

  Chapter Eight: Memory without Remembering I

  1. A. S. Reber, “Implicit Learning of Artificial Grammars,” Journal of Verbal Learning and Verbal Behavior 6 (1967): 855–63; L. R. Squire and S. Zola-Morgan, “Memory: Brain Systems and Behavior,” Trends in Neuroscience 11 (1988): 170–75; K. S. Giovanello and M. Verfaellie, “Memory Systems of the Brain: A Cognitive Neuropsychological Analysis,” Seminars in Speech and Language 22 (2001): 107–16.

  2. S. Nicolas, “Experiments on Implicit Memory in a Korsakoff Patient by Claparède (1907),” Cognitive Neuropsychology 13 (1996): 1193–99.

  3. B. Milner, “Memory Impairment Accompanying Bilateral Hippocampal Lesions,” in Psychologie De L’hippocampe, eds, P. Passouant (Paris, France: Centre National de la Recherche Scientifique, 1962), 257-72.

  4. Ibid.

  5. S. Corkin, “Tactually-Guided Maze Learning in Man: Effects of Unilateral Cortical Excisions and Bilateral Hippocampal Lesions,” Neuropsychologia 3 (1965): 339–51.

  6. E. K. Miller and J. D. Cohen, “An Integrative Theory of Prefrontal Cortex Function,” Annual Review of Neuroscience 24 (2001): 167–202; available online at web.mit.edu/ekmiller/Public/www/miller/Publications/Miller_Cohen_2001.pdf (accessed September 2012).

  7. S. Corkin, “Acquisition of Motor Skill after Bilateral Medial Temporal-Lobe Excision,” Neuropsychologia 6 (1968): 255–65; available online at web.mit .edu/bnl/pdf/Corkin%201968.pdf (accessed September 2012).

  8. Ibid.

  9. Ibid.

  10. Ibid.

  11. Ibid.

  12. Ibid.

  13. Ibid.

  14. G. Ryle, “Knowing How and Knowing That,” in The Concept of Mind (London: Hutchinson’s University Library, 1949), 26–60; full text available online at tinyurl.com/8kqedyj (accessed September 2012).

  Decades after Ryle’s book was published, the philosophical distinction between “knowing how” and “knowing that” made its way into the artificial intelligence community. As discussed at the beginning of Chapter 5, artificial-intelligence research has often helped to further theories about the brain because it deals with the practical task of programming computers to function like human brains. The resulting solutions can give neuroscientists models to test and predict how the brain works. In the 1970s, artificial-intelligence researchers used the terms procedural and declarative to describe two ways of representing knowledge. In 1975, Terry Winograd published an article, “Frame Representations and the Declarative/Procedural Controversy” (in Representation and Understanding: Studies in Cognitive Sciences, ed. D. G. Bobrow, et al. [New York: Academic Press], 185–210), which outlined an argument between the proceduralists and the declarativists: “The proceduralists assert that our knowledge is primarily a ‘knowing how.’ The human information processor is a stored program device, with its knowledge of the world embedded in the programs. What a person (or robot) knows about the English language, the game of chess, or the physical properties of his world is coextensive with his set of programs for operating with it” (p. 186). In other words, knowledge consists of the specific routines that guide our behavior. “The declarativists, on the other hand, do not believe that knowledge of a subject is intimately bound with the procedures for its use. They see intelligence as resting on two bases: a general set of procedures for manipulating facts of all sorts, and a set of specific facts describing particular knowledge domains.” This way of viewing knowledge sees it as information rather than as a set of operations. Winograd advocated blurring the distinction between the two kinds of representations, and proposed taking the middle ground between declarative and procedural knowledge by specifying how particular declarative statements would be used. His idea was to attach procedures to facts in long-term memory.

  In contrast, John Anderson argued for a fundamental difference between procedural and declarative knowledge. In his 1976 book Language, Memory, and Thought (Hillsdale, NJ: Psychology Press), with reference to Ryle, Anderson noted three distinguishing features. The first is that declarative knowledge is something we either have or lack, whereas procedural knowledge can be acquired gradually, a little at a time. A second distinction, he wrote, “is that one acquires declarative knowledge suddenly by being told whereas one acquires procedural knowledge gradually by performing the skill” (p. 117). The third distinctive feature is that we can tell someone about our declarative knowledge, but cannot explain our procedural knowledge.

  While theorists debated how distinct these two kinds of knowledge really were, computer scientist Patrick Winston suggested a compromise. In his 1977 book Artificial Intelligence (Reading, MA: Addison-Wesley), Winston wrote, “There are arguments for and against the procedural and declarative positions on how knowledge should be stored. In most situations, the best plan is to face the problems in a bipartisan way drawing on talents from both sides of the aisle” (p. 393). Humans need procedural and declarative knowledge to function in everyday life, and the brain allocates different processes and circuits for obtaining and storing these two kinds of information. Milner had already revealed this biological distinction fifteen years earlier when she reported Henry’s mirror-tracing results.

  See also Milner, “Memory Disturbance after Bilateral Hippocampal Lesions.”

  15. M. Victor and A. H. Ropper, Adams and Victor’s Principles of Neurology, 7th ed. (New York: McGraw-Hill, Medical Pub. Division, 2001).

  16. Ibid.

  17. Additional cognitive testing strengthened our conclusion that the mirror-tracing deficit we uncovered in Parkinson patients was truly a learning disorder. To rule out the possibility that the patients’ slow learning was due to deficits in processing spatial layouts or basic motor functions, we asked them to perform additional tests to examine these other abilities. When our data analyses took all these other test scores into account, they still showed a significant learning deficit. This finding strengthens the view that mirror tracing is supported by a memory circuit that depends on intact neurotransmission in the striatum.

  18. M. J. Nissen and P. Bullemer, “Attentional Requirements of Learning: Evidence from Performance Measures,” Cognitive Psychology 19 (1987): 1–32.

  19. D. Knopman and M. J. Nissen, “Procedural Learning Is Impaired in Huntington’s Disease: Evidence from the Serial Reaction Time Task,” Neuropsychologia 29 (1991): 245–54.

  20. A. Pascual-Leone et al., “Procedural Learning in Parkinson’s Disease and Cerebellar Degeneration,” Annals of Neurology 34 (1993): 594–602; J. N. Sanes et al., “Motor Learning in Patients with Cerebellar Dysfunction,” Brain 113 (1990): 103–20.

  21. T. A. Martin et al., “Throwing while Looking through Prisms. I. Focal Olivocerebellar Lesions Impair Adaptation,” and “II. Specificity and Storage of Multiple Gaze–Throw Calibrations,” Brain 119 (1996): 1183–98, 1199–211.

  22. R. Shadmehr and F. A. Mussa-Ivaldi, “Adaptive Representation of Dynamics during Learning of a Motor Task,” Journal of Neuroscience 14 (1994): 3208–24; available online at www.jneurosci.org/content/14/5/3208.full.pdf+html (accessed September 2012).

  One influential model from neuropsychology, which Daniel Willingham proposed in 1
998, explains the stages through which motor-skill learning progresses. According to this theory, motor-skill learning engages two independent modes, one unconscious and the other conscious. The unconscious mode subsumes three motor control processes that function outside of awareness: selecting spatial targets for movement, sequencing these targets, and transforming them into muscle commands. The conscious, attention-demanding mode supports motor-skill learning by selecting goals to change the environment, selecting targets for movement, and assembling a sequence of targets. The conscious mode is exercised when a person imitates the performance of an expert. Learning advances through the interaction of the unconscious and conscious modes. Willingham’s model allowed the researcher to make predictions about different learning stages and processes and their neural underpinnings. It did not, however, illuminate the mechanisms of how we learn motor skills step by step. See D. B. Willingham, “A Neuropsychological Theory of Motor Skill Learning,” Psychological Review 105 (1998): 558–84.

  23. M. Kawato and D. Wolpert, “Internal Models for Motor Control,” Novartis Foundation Symposium 218 (1998): 291–304.

  24. Ibid.

  25. Ibid.

  26. H. Imamizu and M. Kawato, “Brain Mechanisms for Predictive Control by Switching Internal Models: Implications for Higher-Order Cognitive Functions,” Psychological Research 73 (2009): 527–44.

  27. T. Brashers-Krug et al., “Consolidation in Human Motor Memory,” Nature 382 (1996): 252–55; available online at tinyurl.com/8hhuga3 (accessed September 2012).

  28. R. Shadmehr et al., “Time-Dependent Motor Memory Processes in Amnesic Subjects,” Journal of Neurophysiology 80 (1998): 1590–97; available online at web.mit.edu/bnl/pdf/Shadmehr.pdf (accessed September 2012).

  29. Ibid.

  30. Ibid.

  31. Ibid.

  32. Ibid.

  33. A. Karni et al., “The Acquisition of Skilled Motor Performance: Fast and Slow Experience-Driven Changes in Primary Motor Cortex,” Proceedings of the National Academy of Sciences 95 (1998): 861–68.