Article | Published:

Representation of retrieval confidence by single neurons in the human medial temporal lobe

Nature Neuroscience volume 18, pages 10411050 (2015) | Download Citation

Abstract

Memory-based decisions are often accompanied by an assessment of choice certainty, but the mechanisms of such confidence judgments remain unknown. We studied the response of 1,065 individual neurons in the human hippocampus and amygdala while neurosurgical patients made memory retrieval decisions together with a confidence judgment. Combining behavioral, neuronal and computational analysis, we identified a population of memory-selective (MS) neurons whose activity signaled stimulus familiarity and confidence, as assessed by subjective report. In contrast, the activity of visually selective (VS) neurons was not sensitive to memory strength. The groups further differed in response latency, tuning and extracellular waveforms. The information provided by MS neurons was sufficient for a race model to decide stimulus familiarity and retrieval confidence. Together, our results indicate a trial-by-trial relationship between a specific group of neurons and declared memory strength in humans. We suggest that VS and MS neurons are a substrate for declarative memories.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Foundations of Human Memory (Oxford University Press, New York, 2012).

  2. 2.

    & Judging confidence influences decision processing in comparative judgments. Psychon. Bull. Rev. 10, 177–183 (2003).

  3. 3.

    & Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009).

  4. 4.

    , & The comparative psychology of uncertainty monitoring and metacognition. Behav. Brain Sci. 26, 317–339, discussion 340–373 (2003).

  5. 5.

    & A computational framework for the study of confidence in humans and animals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 1322–1337 (2012).

  6. 6.

    , & Inference and computation with population codes. Annu. Rev. Neurosci. 26, 381–410 (2003).

  7. 7.

    , , & Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).

  8. 8.

    , & The medial temporal lobe. Annu. Rev. Neurosci. 27, 279–306 (2004).

  9. 9.

    , & Category-specific visual responses of single neurons in the human medial temporal lobe. Nat. Neurosci. 3, 946–953 (2000).

  10. 10.

    , & Human medial temporal lobe neurons respond preferentially to personally relevant images. Proc. Natl. Acad. Sci. USA 106, 21329–21334 (2009).

  11. 11.

    & Visual object recognition. Annu. Rev. Neurosci. 19, 577–621 (1996).

  12. 12.

    Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition. Neuron 27, 205–218 (2000).

  13. 13.

    , & Single-trial learning of novel stimuli by individual neurons of the human hippocampus-amygdala complex. Neuron 49, 805–813 (2006).

  14. 14.

    , & Activity of human hippocampal and amygdala neurons during retrieval of declarative memories. Proc. Natl. Acad. Sci. USA 105, 329–334 (2008).

  15. 15.

    & Differential neuronal encoding of novelty, familiarity and recency in regions of the anterior temporal lobe. Neuropharmacology 37, 657–676 (1998).

  16. 16.

    & The effects of stimulus novelty and familiarity on neuronal activity in the amygdala of monkeys performing recognition memory tasks. Exp. Brain Res. 93, 367–382 (1993).

  17. 17.

    , , & Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 464, 903–907 (2010).

  18. 18.

    & Signal Detection Theory and Psychophysics (Wiley, 1966).

  19. 19.

    , , , & Recognition memory and the human hippocampus. Neuron 37, 171–180 (2003).

  20. 20.

    , & Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo. J. Neurosci. Methods 154, 204–224 (2006).

  21. 21.

    , , & Differences in mnemonic processing by neurons in the human hippocampus and parahippocampal regions. J. Cogn. Neurosci. 18, 1654–1662 (2006).

  22. 22.

    , , , & A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

  23. 23.

    & Computation of measures of effect size for neuroscience data sets. Eur. J. Neurosci. 34, 1887–1894 (2011).

  24. 24.

    , , , & The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006).

  25. 25.

    Decision Processes in Visual Perception (Academic Press, New York, 1979).

  26. 26.

    , & Single neuron activity in human hippocampus and amygdala during recognition of faces and objects. Neuron 18, 753–765 (1997).

  27. 27.

    et al. Sparse and distributed coding of episodic memory in neurons of the human hippocampus. Proc. Natl. Acad. Sci. USA 111, 9621–9626 (2014).

  28. 28.

    Simple memory: a theory for archicortex. Philos. Trans. R. Soc. Lond. B Biol. Sci. 262, 23–81 (1971).

  29. 29.

    & Detection Theory (Lawrence Associates, Mahwah, New Jersey, 2005).

  30. 30.

    et al. Object decoding with attention in inferior temporal cortex. Proc. Natl. Acad. Sci. USA 108, 8850–8855 (2011).

  31. 31.

    Dual-process theory and signal-detection theory of recognition memory. Psychol. Rev. 114, 152–176 (2007).

  32. 32.

    & Global matching models of recognition memory: How the models match the data. Psychon. Bull. Rev. 3, 37–60 (1996).

  33. 33.

    & The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

  34. 34.

    A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

  35. 35.

    et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).

  36. 36.

    , , & Characterizing interneuron and pyramidal cells in the human medial temporal lobe in vivo using extracellular recordings. Hippocampus 17, 49–57 (2007).

  37. 37.

    , & Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55, 131–141 (2007).

  38. 38.

    et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc. Natl. Acad. Sci. USA 109, 1731–1736 (2012).

  39. 39.

    , & Large identified pyramidal cells in macaque motor and premotor cortex exhibit “thin spikes”: implications for cell type classification. J. Neurosci. 31, 14235–14242 (2011).

  40. 40.

    , , , & Short duration waveforms recorded extracellularly from freely moving rats are representative of axonal activity. Front. Neural Circ. 7, 181 (2013).

  41. 41.

    , & Action potential initiation and propagation in rat neocortical pyramidal neurons. J. Physiol. (Lond.) 505, 617–632 (1997).

  42. 42.

    The human amygdala and Memory. in The Human Amydala (eds. Whalen, P.J. & Phelps, E.A.) 177–203 (The Guilford Press, New York, 2009).

  43. 43.

    , , , & Novelty as a dimension in the affective brain. Neuroimage 49, 2871–2878 (2010).

  44. 44.

    Metamemory. in Learning and Memory: a Comprehensive Reference (ed. Roediger, H.L.) 349–362 (Elsevier, Oxford, 2008).

  45. 45.

    Evolution of metacognition. in Handbook of Metamemory and Memory (eds. Dunlovsky, J. & Bjork, R.) 29–46 (Psychology Press, New York, 2008).

  46. 46.

    Rhesus monkeys know when they remember. Proc. Natl. Acad. Sci. USA 98, 5359–5362 (2001).

  47. 47.

    & Honey bees selectively avoid difficult choices. Proc. Natl. Acad. Sci. USA 110, 19155–19159 (2013).

  48. 48.

    & Metacognition in the rat. Curr. Biol. 17, 551–555 (2007).

  49. 49.

    & Metacognition in monkeys during an oculomotor task. J. Exp. Psychol. Learn. Mem. Cogn. 37, 325–337 (2011).

  50. 50.

    , & Recollection-like memory retrieval in rats is dependent on the hippocampus. Nature 431, 188–191 (2004).

  51. 51.

    The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).

  52. 52.

    A comparison of current measures of the accuracy of feeling-of-knowing predictions. Psychol. Bull. 95, 109–133 (1984).

  53. 53.

    , & Testing global memory models using ROC curves. Psychol. Rev. 99, 518–535 (1992).

  54. 54.

    , & Using noise signature to optimize spike-sorting and to assess neuronal classification quality. J. Neurosci. Methods 122, 43–57 (2002).

  55. 55.

    , , , & Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

  56. 56.

    , , , & Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).

  57. 57.

    & Hippocampal network dynamics constrain the time lag between pyramidal cells across modified environments. J. Neurosci. 28, 13448–13456 (2008).

  58. 58.

    & Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol. Methods 8, 434–447 (2003).

  59. 59.

    The neural decoding toolbox. Front. Neuroinform. 7, 8 (2013).

  60. 60.

    & Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10, 173–185 (2009).

  61. 61.

    et al. Single-neuron correlates of atypical face processing in autism. Neuron 80, 887–899 (2013).

  62. 62.

    , , & Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. J. Neurophysiol. 75, 1806–1814 (1996).

Download references

Acknowledgements

We thank J. Kaminski, R. Adolphs, C. Anastassiou, U. Maoz, J. Wertheimer and W. Einhaeuser for discussion, Z. Fu for spike sorting, C. Heller for performing some of the surgeries, the staff of the Epilepsy Monitoring Units at Huntington Memorial Hospital and Cedars-Sinai for invaluable assistance, particularly J. Schmidt. We thank K. Birch and H. Babu for assistance with patient care and surgery, and L. Philpott and M.-T. Le for neuropsychological testing. This work was supported by the Cedars-Sinai Medical Center Department of Neurosurgery (to U.R.), National Institute of Mental Health Conte Center at Caltech (P50 MH094258), and the Gustavus and Louise Pfeiffer Research Foundation (to U.R.).

Author information

Affiliations

  1. Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA.

    • Ueli Rutishauser
    • , Shengxuan Ye
    • , Matthieu Koroma
    •  & Adam N Mamelak
  2. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.

    • Ueli Rutishauser
    •  & Jeffrey M Chung
  3. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.

    • Ueli Rutishauser
  4. Computation & Neural Systems Program, California Institute of Technology, Pasadena, California, USA.

    • Ueli Rutishauser
    • , Shengxuan Ye
    •  & Oana Tudusciuc
  5. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.

    • Ueli Rutishauser
  6. Departement de Biologie, Ecole Normale Superieure de Cachan, Cachan, France.

    • Matthieu Koroma
  7. Division of Humanities and Social Science, California Institute of Technology, Pasadena, California, USA.

    • Oana Tudusciuc
  8. Department of Neurosurgery, Huntington Memorial Hospital, Pasadena, California, USA.

    • Ian B Ross

Authors

  1. Search for Ueli Rutishauser in:

  2. Search for Shengxuan Ye in:

  3. Search for Matthieu Koroma in:

  4. Search for Oana Tudusciuc in:

  5. Search for Ian B Ross in:

  6. Search for Jeffrey M Chung in:

  7. Search for Adam N Mamelak in:

Contributions

U.R. and A.N.M. designed the experiments. U.R. and O.T. performed experiments. U.R., M.K. and S.Y. performed analysis. A.N.M. and I.B.R. performed surgery. J.M.C. provided patient care. U.R. and A.N.M. wrote the paper. All of the authors discussed the results at all stages of the project.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ueli Rutishauser.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Tables 1–4

  2. 2.

    Supplementary Methods Checklist

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nn.4041