Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Dissociated functional significance of decision-related activity in the primate dorsal stream

Abstract

During decision making, neurons in multiple brain regions exhibit responses that are correlated with decisions1,2,3,4,5,6. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision making7,8,9. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making2,10, with strong correlations between their response fluctuations and the animal’s choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and choices, and had firing rate patterns consistent with their sensory role in motion encoding1. The behavioural impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behaviour, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision making was stable over trials and sessions and was robust to changes in stimulus type and task geometry, arguing against several forms of compensation. Thus, decision-related signals in LIP do not appear to be critical for computing perceptual decisions, and may instead reflect secondary processes. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations do not necessarily offer direct access to the neural computations underlying decisions.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Task and neural responses during direction discrimination.
Figure 2: Psychophysical performance before and after neural inactivations in areas MT and LIP.
Figure 3: Performance in control tasks following LIP inactivation.

References

  1. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996)

    Article  CAS  PubMed  Google Scholar 

  2. Shadlen, M. N. & Newsome, W. T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001)

    Article  CAS  PubMed  Google Scholar 

  3. Gu, Y., DeAngelis, G. C. & Angelaki, D. E. A functional link between area MSTd and heading perception based on vestibular signals. Nature. Neurosci. 10, 1038–1047 (2007)

    Article  CAS  PubMed  Google Scholar 

  4. Ding, L. & Gold, J. I. The basal ganglia’s contributions to perceptual decision making. Neuron 79, 640–649 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Liu, S., Gu, Y., DeAngelis, G. C. & Angelaki, D. E. Choice-related activity and correlated noise in subcortical vestibular neurons. Nature Neurosci. 16, 89–97 (2013)

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  7. Pitkow, X., Liu, S., Angelaki, D. E., DeAngelis, G. C. & Pouget, A. How can single sensory neurons predict behavior? Neuron 87, 411–423 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D. & Brody, C. D. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife 4, 8166 (2015)

    Article  Google Scholar 

  9. Cumming, B. G. & Nienborg, H. Feedforward and feedback sources of choice probability in neural population responses. Curr. Opin. Neurobiol. 37, 126–132 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Brunton, B. W., Botvinick, M. M. & Brody, C. D. Rats and humans can optimally accumulate evidence for decision-making. Science 340, 95–98 (2013)

    Article  CAS  ADS  PubMed  Google Scholar 

  11. Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007)

    Article  CAS  PubMed  Google Scholar 

  12. Britten, K. H., Shadlen, M. N., Newsome, W. T. & Movshon, J. A. Responses of neurons in macaque MT to stochastic motion signals. Vis. Neurosci. 10, 1157–1169 (1993)

    Article  CAS  PubMed  Google Scholar 

  13. Mazurek, M. E., Roitman, J. D., Ditterich, J. & Shadlen, M. N. A role for neural integrators in perceptual decision making. Cereb. Cortex 13, 1257–1269 (2003)

    Article  PubMed  Google Scholar 

  14. Hess, R. & Murata, K. Effects of glutamate and GABA on specific response properties of neurones in the visual cortex. Exp. Brain Res. 21, 285–297 (1974)

    Article  CAS  PubMed  Google Scholar 

  15. Newsome, W. T. & Paré, E. B. A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J. Neurosci. 8, 2201–2211 (1988)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chowdhury, S. A. & DeAngelis, G. C. Fine discrimination training alters the causal contribution of macaque area MT to depth perception. Neuron 60, 367–377 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nature Neurosci. 17, 1784–1792 (2014)

    Article  CAS  PubMed  Google Scholar 

  19. Wardak, C., Olivier, E. & Duhamel, J.-R. A deficit in covert attention after parietal cortex inactivation in the monkey. Neuron 42, 501–508 (2004)

    Article  CAS  PubMed  Google Scholar 

  20. Balan, P. F. & Gottlieb, J. Functional significance of nonspatial information in monkey lateral intraparietal area. J. Neurosci. 29, 8166–8176 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wilke, M., Kagan, I. & Andersen, R. A. Functional imaging reveals rapid reorganization of cortical activity after parietal inactivation in monkeys. Proc. Natl Acad. Sci. USA 109, 8274–8279 (2012)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  22. Kerkhoff, G. Spatial hemineglect in humans. Prog. Neurobiol. 63, 1–27 (2001)

    Article  CAS  PubMed  Google Scholar 

  23. Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  24. Crowe, D. A. et al. Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition. Nature Neurosci. 16, 1484–1491 (2013)

    Article  CAS  PubMed  Google Scholar 

  25. Sarma, A., Masse, N. Y., Wang, X.-J. & Freedman, D. J. Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices. Nature Neurosci. 19, 143–149 (2016)

    Article  CAS  PubMed  Google Scholar 

  26. Hanks, T. D., Ditterich, J. & Shadlen, M. N. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nature Neurosci. 9, 682–689 (2006)

    Article  CAS  PubMed  Google Scholar 

  27. Freedman, D. J. & Assad, J. A. Experience-dependent representation of visual categories in parietal cortex. Nature 443, 85–88 (2006)

    Article  CAS  ADS  PubMed  Google Scholar 

  28. Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  29. Heitz, R. P. & Schall, J. D. Neural chronometry and coherency across speed-accuracy demands reveal lack of homomorphism between computational and neural mechanisms of evidence accumulation. Phil. Trans. R. Soc. B 368, 20130071 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  31. Meister, M. L. R., Hennig, J. A. & Huk, A. C. Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J. Neurosci. 33, 2254–2267 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997)

    Article  CAS  PubMed  Google Scholar 

  33. Eastman, K. M. & Huk, A. C. PLDAPS: a hardware architecture and software toolbox for neurophysiology requiring complex visual stimuli and online behavioral control. Front. Neuroinform. 6, 1 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  34. Liu, Y., Yttri, E. A. & Snyder, L. H. Intention and attention: different functional roles for LIPd and LIPv. Nature Neurosci. 13, 495–500 (2010)

    Article  CAS  PubMed  Google Scholar 

  35. Zirnsak, M., Chen, X., Lomber, S. G. & Moore, T. Effects of reversible inactivation of parietal cortex on the processing of visual salience in the frontal eye field. Proc. Conference Soc. Neurosci. (2015)

  36. Patel, G. H. et al. Topographic organization of macaque area LIP. Proc. Natl Acad. Sci. USA 107, 4728–4733 (2010)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  37. Wichmann, F. A. & Hill, N. J. The psychometric function: I. Fitting, sampling, and goodness of fit. Percept. Psychophys. 63, 1293–1313 (2001)

    Article  CAS  PubMed  Google Scholar 

  38. Gnadt, J. W. & Andersen, R. A. Memory related motor planning activity in posterior parietal cortex of macaque. Exp. Brain Res. 70, 216–220 (1988)

    CAS  PubMed  Google Scholar 

  39. Kelly, R. C. et al. Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex. J. Neurosci. 27, 261–264 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tolias, A. S. et al. Recording chronically from the same neurons in awake, behaving primates. J. Neurophysiol. 98, 3780–3790 (2007)

    Article  PubMed  Google Scholar 

  41. Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  42. Pillow, J. W., Shlens, J., Chichilnisky, E. J. & Simoncelli, E. P. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings. PLoS One 8, e62123 (2013)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  43. Celebrini, S. & Newsome, W. T. Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J. Neurosci. 14, 4109–4124 (1994)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Noudoost, B. & Moore, T. A reliable microinjectrode system for use in behaving monkeys. J. Neurosci. Methods 194, 218–223 (2011)

    Article  PubMed  Google Scholar 

  45. Martin, J. H. Autoradiographic estimation of the extent of reversible inactivation produced by microinjection of lidocaine and muscimol in the rat. Neurosci. Lett. 127, 160–164 (1991)

    Article  CAS  PubMed  Google Scholar 

  46. Arikan, R. et al. A method to measure the effective spread of focally injected muscimol into the central nervous system with electrophysiology and light microscopy. J. Neurosci. Methods 118, 51–57 (2002)

    Article  CAS  PubMed  Google Scholar 

  47. Yttri, E. A., Wang, C., Liu, Y. & Snyder, L. H. The parietal reach region is limb specific and not involved in eye-hand coordination. J. Neurophysiol. 111, 520–532 (2014)

    Article  PubMed  Google Scholar 

  48. Heiss, J. D., Walbridge, S., Asthagiri, A. R. & Lonser, R. R. Image-guided convection-enhanced delivery of muscimol to the primate brain. J. Neurosurg. 112, 790–795 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lewis, J. W. & Van Essen, D. C. Mapping of architectonic subdivisions in the macaque monkey, with emphasis on parieto-occipital cortex. J. Comp. Neurol. 428, 79–111 (2000)

    Article  CAS  PubMed  Google Scholar 

  50. Chapman, B., Zahs, K. R. & Stryker, M. P. Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex. J. Neurosci. 11, 1347–1358 (1991)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kubanek, J., Li, J. M. & Snyder, L. H. Motor role of parietal cortex in a monkey model of hemispatial neglect. Proc. Natl Acad. Sci. USA 112, E2067–E2072 (2015)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank R. Krauzlis, C. Brody, E. Seidemann, L. Cormack, and R. Aldrich for comments on the manuscript. We thank the Brody laboratory (particularly C. Brody and J. Erlich) for inspiring the experiments, the Mauk laboratory (particularly M. Mauk, F. Riusech, and H. Halverson) for assistance with muscimol preparation, and K. Mitchell for animal support. This research was supported by the Howard Hughes Medical Institute International Student Research Fellowship to L.N.K., the McKnight Foundation grant to J.W.P., the National Eye Institute (R01-EY017366) grant to both J.W.P, and A.C.H., and the National Institutes of Health under Ruth L. Kirschstein National Research Service Awards T32DA018926 from the National Institute on Drug Abuse and T32EY021462 from the National Eye Institute.

Author information

Authors and Affiliations

Authors

Contributions

L.N.K., J.L.Y. and A.C.H. designed the experiments. L.N.K. and J.L.Y. collected behavioural and electrophysiological data. L.N.K. and J.L.Y. performed pharmacological inactivations. L.N.K. analysed behavioural data. J.L.Y. analysed electrophysiological data. J.W.P. and A.C.H. guided data analysis. All authors discussed the results and wrote the manuscript.

Corresponding author

Correspondence to Leor N. Katz.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Location of LIP recording and muscimol infusion sites.

a, b, The recording (blue circles) and infusion sites (red) for monkey N (a) and monkey P (b) along the medial-lateral (M/L) and posterior-anterior (P/A) axes within the chamber (demarcated by the ovals). Electrode and cannula tracks are represented by the grey lines (with a small jitter on the x–y plane for better visualization). The mean infusion depths were 7.12 ± 1.15 mm (monkey N) and 7.03 ± 1.39 mm (monkey P) (the microdrive was zeroed below dura mater and just above the cortical surface). Given the estimated spread of muscimol described in the main text, the inactivations targeted a substantial territory of the ventral portion of LIP49. Even though a functional distinction with depth has been proposed34, we emphasize that the critical component of our protocol was targeting the precise locations at which we measured canonical decision-related activity in LIP.

Extended Data Figure 2 Direction discrimination sensitivity is restored when motion is placed outside of the inactivated MT field.

a. Illustration of MT inactivation along with the estimated inactivated field (grey cloud), for two experimental geometries: motion stimulus placed inside the inactivated MT field (top) and motion placed outside the inactivated MT field (bottom). b. Average psychophysical data for baseline and muscimol treatment pairs (grey and green, respectively, same data as Fig. 2c, n = 6; 3 in monkey N; 3 in monkey P) and psychophysical data collected during muscimol treatment, with the motion stimulus outside of the inactivated MT field (orange, n = 3). Direction discrimination sensitivity is restored to baseline levels in these sessions. Error bars on points show ± 1 s.e.m. over all trials.

Extended Data Figure 3 No relationship between effect magnitude in control task, effect magnitude in direction discrimination task, and muscimol mass.

a, b, The relationship between the effect of LIP inactivation in the free-choice task (that is, shift in proportion of contralateral choices from baseline to muscimol treatment) and the effect of LIP inactivation in the direction discrimination task on sensitivity (percentage change in psychometric function slope, a) and bias (shift in normalized motion strength, b). R2 and associated P values of a Pearson correlation are indicated on individual plots (n = 21; 12 in monkey N; 9 in monkey P). Orange data points indicate sessions in which muscimol was infused from two cannulae simultaneously into LIP. c–e, Dose–response functions between muscimol mass and the effect in the direction discrimination task on slope (c, same units as a), bias (d, same units as b), and the effect in the free-choice task (e, same units as a, b). For e, we used free-choice sessions that took place on the same days as the direction discrimination task (n = 21) along with an additional 13 sessions that took place during other inactivation experiments under similar conditions (n = 34 in total; 14 in monkey N; 20 in monkey P; as in Fig. 3d). R2, associated P values and regression lines are indicated on the plots (linear regression).

Extended Data Figure 4 Time course of accuracy and bias within sessions.

Accuracy and bias in the direction discrimination task were computed over time by taking a running mean of correct and contralateral choices, respectively (sliding window of 40 trials). a, Inactivation in area MT (n = 6, green curve; 3 in monkey N; 3 in monkey P) had a clear and consistent impact on behavioural accuracy compared to baseline (n = 6, grey), but did not have systematic effects on bias (bottom), consistent with our results from the fitted psychometric functions (main text). Panels show data from trial 40 (sliding window size) to the median trial length of each group of experiments (variable session lengths contribute to increased variability at later trials). Error bars show ± 1 s.e.m. between sessions. b, Inactivations in area LIP (n = 21, blue curve; 12 in monkey N; 9 in monkey P) yielded no systematic trends in either accuracy (top) or bias (bottom) compared to baseline (n = 21, grey), indicating that within-session compensation is unlikely. Panel format same as in a. We also investigated whether compensation may have taken place before we began collecting the ‘inactivation’ data set, or during the first 10–30 instruction (warm-up) trials. On 13 of the 21 LIP inactivation sessions, we collected a third data set (in addition to the standard paired baseline and inactivation data sets), in which psychophysical performance was monitored during the time muscimol was being infused (during infusion, orange curve). No systematic changes in accuracy or bias were observed in this exploratory data set either, further arguing against compensation on the time scales of our manipulations and measurements.

Extended Data Figure 5 Psychophysical performance in the direction discrimination task across sessions.

Panels show data from monkey P (left) and monkey N (right), for all baseline and treatment pairs: muscimol (blue, n = 21), saline (unfilled grey, n = 6) and sham (filled grey, n = 3). Each pair consists of two sessions that took place in close succession (typically on consecutive days), at a similar time of day, after a similar number of preceding tasks and trials, and is represented by two markers connected by a line. Additional control pairs with no saline/sham manipulation (n = 16) are not presented, for visual clarity. a, Psychometric function slope over sessions. No significant change in slope was present over time, evaluated by linear regression, for either monkey P (P = 0.22) or N (P = 0.63). When considering the difference in slope between baseline and treatment pairs, monkey P exhibited a small decrease (regression line slope = −0.07, P = 0.023). However, a similar effect was seen in the interleaved controls (saline and sham, grey markers), indicating that this pattern likely reflects nonspecific trends in performance across back-to-back pairs of experiments. Monkey N had no significant change (P = 0.92). b, Psychometric function midpoint over sessions. No significant change was observed in the session-to-session midpoint values, evaluated by linear regression, for either monkey P (P = 0.44) or monkey N (P = 0.24). When considering the difference in midpoint value for each data set pair over time (that is, muscimol treatment – baseline), no significant change was detected either (P = 0.98 and P = 0.4 for monkey P and N, respectively). The x axis dates are in the year, month, date, yyyymmdd format.

Extended Data Figure 6 Psychophysical performance for all individual baseline and treatment session pairs.

ac, All pairs of baseline and treatment sessions for all treatment types: muscimol, saline, and sham, (control pairs with no saline/sham manipulation are similar but not presented, for visual clarity) for all variants of the direction discrimination task: standard geometry (a), both targets in inactivated field (b), and Newsome dots (c), for both LIP and MT inactivation. In all panels, the abscissa represents motion strength towards the direction contralateral to the LIP under study, the ordinate represents the proportion of contralateral choices. The grey curve is baseline, and the coloured curve is treatment. The first panel in each section presents mean psychophysical performance for each monkey over sessions. Subsequent panels present individual session pairs. Error bars are s.e.m. over all trials.

Extended Data Table 1 Parametric and nonparametric analysis of psychophysical data, for two- and four-parameter psychometric functions
Extended Data Table 2 Infusion details for all treatment sessions

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Katz, L., Yates, J., Pillow, J. et al. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016). https://doi.org/10.1038/nature18617

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature18617

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing