LETTERS Free choice activates a decision circuit between frontal and parietal cortex

LETTERS Free choice activates a decision circuit between frontal and parietal cortex
Vol 453 | 15 May 2008 | doi:10.1038/nature06849
LETTERS
Free choice activates a decision circuit between
frontal and parietal cortex
Bijan Pesaran1,3, Matthew J. Nelson2 & Richard A. Andersen2,3
We often face alternatives that we are free to choose between.
Planning movements to select an alternative involves several areas
in frontal and parietal cortex1–11 that are anatomically connected
into long-range circuits12. These areas must coordinate their activity to select a common movement goal, but how neural circuits
make decisions remains poorly understood. Here we simultaneously record from the dorsal premotor area (PMd) in frontal
cortex and the parietal reach region (PRR) in parietal cortex to
investigate neural circuit mechanisms for decision making. We
find that correlations in spike and local field potential (LFP) activity between these areas are greater when monkeys are freely making choices than when they are following instructions. We propose
that a decision circuit featuring a sub-population of cells in frontal
and parietal cortex may exchange information to coordinate activity between these areas. Cells participating in this decision circuit
may influence movement choices by providing a common bias to
the selection of movement goals.
According to theories of decision making, we make choices by
selecting the alternative that is most valuable to us13. How much
we value each alternative is revealed by our choices. If we value
swimming as much as running, we will choose to do both instead
of always choosing one over the other. Although actions with similar
values can lead to different choices, only one choice can be made at a
time. Planning a movement to select an alternative activates many
areas of the brain. How does the brain decide what to do? PMd and
PRR plan reaching arm movements14 and are directly connected12.
We therefore studied these areas to identify a neural circuit for deciding where to reach. We trained two monkeys to do a free search task
and an instructed search task (Fig. 1a, b). In both tasks, monkeys
made a sequence of reaches to visual targets for rewards of juice. The
key manipulation was that, in the free search task, the three targets
were visually identical circles, and the monkey could search in any
sequence (Fig. 1a); whereas in the instructed search task, the three
targets were a circle, a square and a triangle, and the monkey had to
search in a fixed sequence (Fig. 1b). To control other sensory, motor
and reward-related factors, we carefully matched the two tasks by
yoking the sequences presented in the instructed task to the monkey’s
choices in the free search task (see Methods, Supplementary Results
and Supplementary Fig. 2).
During free search, each monkey’s choices varied, even for identical stimuli (Fig. 1c). In contrast, instructed search movement
sequences did not vary (Fig. 1d). Overall, each monkey developed a
free search strategy and chose between two or three different movement sequences for most search arrays (Supplementary Fig. 3).
Although the tasks we studied could differ in other aspects, like
reward expectancy, attention or overall effort, analysis of each animal’s behaviour indicates that the major difference involves decision
making (Supplementary Results). Free and instructed search involve
different decisions because the alternatives have different values. Free
search involves choosing between movement sequences with similar
values so choices vary from trial to trial (Fig. 1c). Because we reward
only one movement sequence, instructed search involves alternatives
with very different values. Consequently, each monkey repeatedly
makes the same choices (Fig. 1d).
When movement choices vary from trial to trial, PMd and PRR
must coordinate their activity. Analysing spiking and LFP activity
may resolve neural coordination between these areas. Spiking activity measures action potentials from individual neurons. LFP activity
predominantly measures synaptic potentials in a population of neurons near the recording electrode15. Spike–field coherency directly
relates these two signals by measuring how well LFP activity is predicted by action potentials. We therefore measured spike–field
coherency to characterize neural coordination between PMd and
PRR and identify the neurons involved in this coordination.
We made 314 PMd spike–PRR field and 187 PRR spike–PMd field
recordings in two animals during both free and instructed search
a
b
Free search
Instructed search
H
H
c
d
1.00
0.25
H
0.51
H
H
H
H
H
H
1.00
0.23
Sample
configuration
H
H
1.00
H
Figure 1 | Task and behaviour. a, Free search task. Three circular targets
presented at eight potential locations spaced 10u apart around the central
hand position, H. b, Instructed search task. Targets in the instructed search
task were a circle, square and triangle; the monkey had to reach to them in that
order. Each target had an equal, one-third, probability of being the rewarded
target. c, The most frequent movement sequences made in response to an
example configuration during the free search task. The same configuration
elicits three different sequences. d, Instructed search configurations elicit the
same sequence. Probability is shown above each arrow.
1
Center for Neural Science, New York University, New York, New York 10003, USA. 2Computation and Neural Systems Program, California Institute of Technology, Pasadena,
California 91125, USA. 3Division of Biology, California Institute of Technology, Pasadena, California 91125, USA.
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LETTERS
NATURE | Vol 453 | 15 May 2008
tasks (Supplementary Materials). We estimated spike–field coherency between spiking in PMd and LFP activity in PRR using
a 6150 ms analysis window that was stepped through the trial every
10 ms from before the onset of the search array to the time of the first
reach. A highly significant, transient increase in 15-Hz coherence
after search array onset was clearly present, as illustrated in an
example recording in Fig. 2a. Coherence was significant during both
tasks but stronger during free search (Fig. 2b; P , 0.05, t-test).
Coherence between spiking in PRR and LFP activity in PMd revealed
a similar pattern (Fig. 2c, d). In this recording, coherence was only
significant during free search and not during instructed search
(Fig. 2d).
Significant coherence at 15 Hz implies that the timing of action
potentials is correlated with fluctuations in LFP activity. Analysing
the relative phase of activity in PMd and PRR supported this and
revealed correlations in the timing of activity in each area that were
not simply time-locked to search array onset (see Supplementary
Results and Supplementary Fig. 4). Interestingly, the amplitude of
spike and LFP activity, as opposed to their relative timing, did not
predict PMd–PRR coherence. We correlated the strength of the
coherence immediately after search array onset with LFP power
and did not observe a significant correlation (P 5 0.45; F-test).
PMd spike–PRR field coherence
Frequency (Hz)
Free
100
Instructed
80 Search
array
Reach
60 onset
40
Search
array
onset
0.3
Reach
0.15
20
0
0
1
Time (s)
2
0
1
Time (s)
2
0
z-Trans. coherence
b
a
30
**
Free
Instructed
20
10
0
0
50
100
Frequency (Hz)
PRR spike–PMd field coherence
Free
Frequency (Hz)
100
80 Search
60
array
onset
Reach
Search
array
onset
0.15
Reach
40
e
4
0.1
0.05
20
0
z-Transformed coherence
Instructed
0
1
Time (s)
2
0
1
Time (s)
2
0
z-Trans. coherence
d
c
10
8
6
4
2
0
-2
0
**
Free
Instructed
50
100
Frequency (Hz)
Population spike–field coherence
15 Hz
3
PMd spike–PRR field
free search
2
PMd spike–PRR field
instructed search
1
PRR spike–PMd field
free search
0
PRR spike–PMd field
instructed search
-1
–100 0
100 200 300 400
Time from search array onset (ms)
Figure 2 | PMd–PRR spike–field coherence. a, b, Example PMd spike–PRR
field coherence: a, Time–frequency coherence every 50 ms during free and
instructed search. Amplitude is colour coded. Activity is aligned to search
array onset (first vertical white bar). Average time of the first reach (second
vertical white bar). White horizontal bar shows analysis window for
b. b, Coherence line plot for free (black) and instructed (red) search tasks.
Coherence is z-transformed. Significant difference at 15 Hz (**P , 0.05;
t-test). c, d, Example PRR spike–PMd field coherence. e, Population average
15 Hz PMd–PRR spike–field coherence every 10 ms. PMd spike–PRR field
coherence (solid); PRR spike–PMd field coherence (dashed). Free search
(black); instructed search (red). Coherence is z-transformed before
averaging; 95% confidence intervals, Bonferroni-corrected (shaded).
Table 1 | Population PMd spike–PRR field coherence
Centre-out
Free or instructed
23/221 (10%)
74/314 (24%)
Free only
Instructed only
Free and instructed
31/74 (42%)
20/74 (27%)
22/74 (22%)
Linear regression of spike–field coherence against the change in firing
rate immediately after search array onset also revealed that coherence
was not simply related to the firing rate (r 2 5 0.06, P 5 0.14). Cells
with an increase in firing rate generally had the greatest coherence.
However, coherence also increased for some cells whose firing rate
decreased or did not change.
Spike–field correlations were present only between select pairs of
recording sites. Across the population, 74 PMd spike–PRR field
recordings (74/314, 24%) contained statistically significant coherence at 15 Hz after search array onset during either task (P , 0.05;
t-test; Table 1). A similar proportion of PRR spike–PMd field recordings were significant (43/187, 23%; P , 0.05; Table 2). In both cases,
spike–field coherence was most prevalent during free search. The
fraction of correlated recordings significantly increased between sites
with overlapping (less than 20u) response fields (P , 0.05; binomial
test; 54% of PMd spike–PRR field recordings, 45% of PRR spike–
PMd field recordings).
To test whether spike–field coherence between PMd and PRR is
specific to decision making, we measured coherence during two control experiments. First, we measured spike–field coherence during a
single-target centre-out task, instructing monkeys to move to a single
peripheral target. In this task, there was no choice between targets. The
proportions of recordings with significant spike–field coherence fell
dramatically (Tables 1 and 2). Only 10% (23/221) of PMd spike–PRR
field recordings and 9% (13/138) of PRR spike–PMd field recordings
had significant coherence. Second, during both search tasks, we found
that saccades are reliably made after search array onset (see Supplementary Results). To test whether spike–field coherence was due to
these eye movements, we measured coherence in one animal during a
variant of the search tasks that involved enforced fixation. Even during
fixation, spike–field coherence was significant after search array onset
and was strongest during free search (Supplementary Results, Supplementary Fig. 5 and Supplementary Tables 1 and 2). The population
average spike–field coherence across all cells recorded during each task
reinforced the selectivity for the search tasks (Supplementary Fig. 6).
These control experiments demonstrate that spike–field coherence
between PMd and PRR is associated with making a decision.
LFP activity was not only correlated with spiking activity in the
other area. Within-area spike–field coherence was also significant
(Supplementary Results and Supplementary Figs 7 and 8). Because
spiking was coherent with locally recorded LFP activity, correlations
in LFP activity between areas may capture the correlation we observe.
Partial spike–field coherence analysis16 addresses this concern
(Supplementary Methods and Supplementary Fig. 9). In each example
recording, partial spike–field coherence remained significant after
accounting for local LFP activity (P , 0.05, t-test). Significant partial
spike–field coherence was also present across the population (74% of
PMd spike–PRR field partial coherence and 70% of PRR spike–PMd
field partial coherence; see Supplementary Results). Therefore, spike–
field coherence between PMd and PRR directly relates the activity of
individual neurons with distant LFP activity.
Table 2 | Population PRR spike–PMd field coherence
Centre-out
Free or instructed
13/138 (9%)
43/187 (23%)
Free only
Instructed only
Free and instructed
21/43 (49%)
12/43 (28%)
9/43 (21%)
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NATURE | Vol 453 | 15 May 2008
Spike–field coherence gives two independent measures of the neuronal coordination between PRR and PMd. This may indicate how
activity flows across the circuit. We estimated the population average
coherence for each of the populations that showed coherence at 15 Hz
in either search task and compared them (Fig. 2e). Across each population, PMd-PRR spike–field z-score coherence (see Supplementary
Methods) was stronger during free search than instructed search
(P , 0.01, Bonferroni-corrected t-test). Importantly, PMd spike–
PRR field coherence started about 30 ms earlier than PRR spike–
PMd field coherence.
Assuming that LFP activity is predominantly synaptic, this suggests that PMd is activated before PRR during search and that PMd
spiking is reflected in PRR LFP activity before PRR spiking is reflected
in PMd LFP activity (see Supplementary Discussion). The activity is
at a relatively low frequency, about 15 Hz, and is transient, about
350 ms. Our time resolution is limited, but the correlation can
involve only a few 15-Hz cycles. Because action potentials are propagated between areas, one attractive possibility is that spike–field
coherence measures signals in a sub-population of neurons that
travel across this circuit first from PMd to PRR and then back from
b
PMd example
PRR example
Free
Instructed
100
Rate (Hz)
Rate (Hz)
80
60
40
50
20
0
c
0
200
Time (ms)
0
400
0
d
PMd average
200
Time (ms)
400
PRR average
15
12
8
Rate (Hz)
Rate (Hz)
10
6
4
10
5
2
f
15
10
5
0
–100
0
100
200
200
PRR
PMd
80
a
b
PMd spiking
PRR spiking
40
0
In
st
ru
Time (ms)
120
ee
Rate (Hz)
20
Centre-out
PRR
PMd
0
100
Time (ms)
Fr
25
0
–100
200
Latency (ms)
e
0
100
Time (ms)
ct
ed
C
en
tre
-o
ut
0
–100
Figure 3 | Spike response latencies. a, Example PMd neuron response to
free search (black) and instructed search (red). Activity is aligned to search
array onset. Movement to the cell’s preferred direction. b, Example PRR
neuron. c, Population average PMd spike response for cells. Activity is
baseline subtracted; s.e.m. (shaded). d, Population average PRR spike
response. e, Population average PRR and PMd spike responses during
centre-out task to the preferred direction. f, Population response latencies
for PMd and PRR during free search, instructed search and the centre-out
task. Error bars, 95% confidence intervals.
Choice probability
a
PRR to PMd in a ‘handshake’. Consistent with this possibility, the
30 ms latency between the spike–field coherence measurements
(Fig. 2e) is a half-cycle at 15 Hz.
PMd and PRR spiking activity lets us examine when each area
becomes active. We recorded 115 PMd and 39 PRR neurons responsive to search array onset to measure response latency in each area.
PMd spiking responded significantly earlier than PRR spiking in both
search tasks (PMd instructed search, 64 6 6 ms (mean 6 s.e.m.); free
search, 79 6 5 ms. PRR instructed search, 90 6 10 ms; free search,
109 6 11 ms; Fig. 3). We then estimated response latency for 110
PMd neurons and 120 PRR neurons recorded in both animals during
the centre-out task. PRR cue response latencies were significantly
shorter in this task than in either of the search tasks (P , 0.05; permutation test); and PMd and PRR response latencies did not differ
(PMd, 63 6 5 ms; PRR, 70 6 6 ms; P 5 0.51, Wilcoxon test; Fig. 3e).
This suggests that the response latency difference between PRR and
PMd is specific to making a decision.
Because spike–field correlations are strongest during decision
making, the sub-population of coherent neurons may encode the
upcoming movement choice. If so, cells with significant spike–field
correlations should predict the movement choice earlier than cells
that do not. We analysed this with a receiver-operating characteristic
of the firing rate during free search (Supplementary Methods). We
calculated the average choice probability separately for correlated
and uncorrelated PRR and PMd neurons. In both areas, correlated
neurons predict the movement choice after search array onset during
the period of greatest spike–field coherence (Fig. 4a, b; see also
Fig. 2e). Later in the trial, uncorrelated cells predict the movement
choice as accurately as correlated cells. Neurons with long-range
correlations may, therefore, exchange information about movement
choice between PMd and PRR.
In summary, correlations between PMd and PRR are activated by
decision making. Coherence is strongest during free search and is
weaker during instructed search. Far less coherence is present during
a simpler centre-out task, and the pattern of coherence is unaffected
by freely made eye movements. This shows that decision making is
distributed across a frontal–parietal circuit and that top-down signals
from PMd influence decisions in this circuit.
Why is coherence stronger during free search? This could be due to
the nature of the decision. Choices were variable during free search.
In contrast, the same choices were made repeatedly during instructed
search (Fig. 1). Decision making can be modelled by races underlying
the selection of each alternative17. These races must be closer during
free search because choices are more variable. Therefore, the difficulty of the decision may underlie coherence between PMd and
PRR. Cognitive control mechanisms are activated to select between
alternative actions. Prefrontal, medial frontal and cingulate cortex
are involved in these mechanisms18–20 and could modulate frontal–
parietal coherence during decision making.
0.7
0.6
0.6
0.5
–0.5 0
Significant spike-field
coherence
Insignificant spike-field
coherence
0.5
0.5
1
1.5
–0.5 0
0.5
1
1.5
Time from search array onset (s)
Figure 4 | Receiver-operating characteristic choice probability estimated
from the firing rate for neurons with and without significant PMd–PRR
spike–field coherence. a, Population average choice probability for
correlated (solid) and uncorrelated (dashed) PMd neurons; 95% confidence
intervals (shaded). b, Same for PRR neurons.
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NATURE | Vol 453 | 15 May 2008
During search, the flow of activity across frontal and parietal
cortex may reflect the process of deciding. Information rises fastest
in PMd (Fig. 3f), so it cannot be driven by PRR14 and must take
alternative routes, possibly through the thalamus21. Information
may go from frontal to parietal cortex22 and then back in a
‘handshake’ of increased communication (Fig. 2e) that reflects the
decision. This transient coordination may reflect how long the
decision takes. Subsequent activity may reflect movement planning
after the decision (see Supplementary Discussion). Oscillations and
synchronization in frontal and parietal cortex exist during attention
and movement preparation4,23–28. Correlations at specific frequencies
could be a signature of these cognitive processes29. We have identified
a decision circuit in which frontal–parietal communication occurs at
relatively low frequencies. The neurons participating in this circuit
could play an important role in deciding where to reach.
METHODS SUMMARY
Two male rhesus monkeys (Macaca mulatta) participated in the experiments.
We recorded single-unit and LFP activity from PMd and PRR using Pt/Ir electrodes controlled by multiple-electrode microdrives (Thomas Recordings). Each
monkey was trained to perform a reach search for juice rewards either by freely
making choices or by following instructions. Correlations between spiking and
LFP activity within and between PMd and PRR were estimated using multitaper
spectral methods4,30. All surgical and animal care procedures were done in
accordance with National Institutes of Health guidelines and were approved
by the California Institute of Technology Animal Care and Use Committee.
Full Methods and any associated references are available in the online version of
the paper at www.nature.com/nature.
Received 2 January 2007; accepted 22 February 2008.
Published online 16 April 2008.
1.
Romo, R. & Schultz, W. Neuronal activity preceding self-initiated or externally
timed arm movements in area 6 of monkey cortex. Exp. Brain Res. 67, 656–662
(1987).
2. Platt, M. L. & Glimcher, P. W. Neural correlates of decision variables in parietal
cortex. Nature 400, 233–238 (1999).
3. Gold, J. I. & Shadlen, M. N. Representation of a perceptual decision in developing
oculomotor commands. Nature 404, 390–394 (2000).
4. Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P. & Andersen, R. A. Temporal
structure in neuronal activity during working memory in macaque parietal cortex.
Nature Neurosci. 5, 805–811 (2002).
5. Sugrue, L. P., Corrado, G. S. & Newsome, W. T. Matching behavior and the
representation of value in the parietal cortex. Science 304, 1782–1787 (2004).
6. Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor
cortex: specification of multiple direction choices and final selection of action.
Neuron 45, 801–814 (2005).
7. Gail, A. & Andersen, R. A. Neural dynamics in monkey parietal reach region reflect
context-specific sensorimotor transformations. J. Neurosci. 26, 9376–9384
(2006).
8. Pesaran, B., Nelson, M. J. & Andersen, R. A. Dorsal premotor neurons encode the
relative position of the hand, eye, and goal during reach planning. Neuron 51,
125–134 (2006).
9. Quian Quiroga, R., Snyder, L. H., Batista, A. P., Cui, H. & Andersen, R. A. Movement
intention is better predicted than attention in the posterior parietal cortex.
J. Neurosci. 26, 3615–3620 (2006).
10. Scherberger, H. & Andersen, R. A. Target selection signals for arm reaching in the
posterior parietal cortex. J. Neurosci. 27, 2001–2012 (2007).
11. Yang, T. & Shadlen, M. N. Probabilistic reasoning by neurons. Nature 447,
1075–1080 (2007).
12. Johnson, P. B., Ferraina, S., Bianchi, L. & Caminiti, R. Cortical networks for visual
reaching: physiological and anatomical organization of frontal and parietal lobe
arm regions. Cereb. Cortex 6, 102–119 (1996).
13. Kreps, D. M. A Course in Microeconomic Theory Ch. 2 (Princeton Univ. Press,
Princeton, 1990).
14. Wise, S. P., Boussaoud, D., Johnson, P. B. & Caminiti, R. Premotor and parietal
cortex: corticocortical connectivity and combinatorial computations. Annu. Rev.
Neurosci. 20, 25–42 (1997).
15. Mitzdorf, U. Current source-density method and application in cat cerebral
cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev. 65,
37–100 (1985).
16. Halliday, D. M. et al. A framework for the analysis of mixed time series/point
process data—theory and application to the study of physiological tremor, single
motor unit discharges and electromyograms. Prog. Biophys. Mol. Biol. 64, 237–278
(1995).
17. Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. D. 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).
18. Goldberg, G. Supplementary motor area structure and function: review and
hypotheses. Behav. Brain Sci. 8, 567–616 (1985).
19. Kerns, J. G. et al. Anterior cingulate conflict monitoring and adjustments in
control. Science 303, 1023–1026 (2004).
20. Daw, N. D. & Doya, K. The computational neurobiology of learning and reward.
Curr. Opin. Neurobiol. 16, 199–204 (2006).
21. Schmolesky, M. T. et al. Signal timing across the macaque visual system.
J. Neurophysiol. 79, 3272–3278 (1998).
22. Cisek, P. Integrated neural processes for defining potential actions and deciding
between them: a computational model. J. Neurosci. 26, 9761–9770 (2006).
23. Bressler, S. L., Coppola, R. & Nakamura, R. Episodic multiregional cortical
coherence at multiple frequencies during visual task-performance. Nature 366,
153–156 (1993).
24. Murthy, V. N. & Fetz, E. E. Synchronization of neurons during local field potential
oscillations in sensorimotor cortex of awake monkeys. J. Neurophysiol. 76,
3968–3982 (1996).
25. Riehle, A., Grun, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate
modulation differentially involved in motor cortical function. Science 278,
1950–1953 (1997).
26. Scherberger, H., Jarvis, M. J. R. & Andersen, R. A. Cortical local field potential
encodes movement intentions. Neuron 46, 347–354 (2005).
27. Rickert, J. et al. Encoding of movement direction in different frequency ranges of
motor cortical local field potentials. J. Neurosci. 25, 8815–8824 (2005).
28. Buschman, T. J. & Miller, E. K. Top-down versus bottom-up control of attention in
the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).
29. Fries, P. A mechanism for cognitive dynamics: neuronal communication through
neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).
30. Mitra, P. P. & Pesaran, B. Analysis of dynamic brain imaging data. Biophys. J. 76,
691–708 (1999).
Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements This work was supported by the National Eye Institute, the
National Institute of Mental Health, the Defense Advanced Research Projects
Agency BioInfoMicro program, a Career Award in the Biomedical Sciences from
the Burroughs Wellcome Fund (B.P.), a James D. Watson Investigator Program
Award from NYSTAR (B.P.) and a Sloan Research Fellowship (B.P.). We thank:
N. Daw, H. Dean and D. Heeger for comments; T. Yao for editorial assistance;
K. Pejsa and N. Sammons for animal care; and V. Shcherbatyuk and M. Walsh for
technical assistance.
Author Contributions B.P., M.J.N. and R.A.A. designed the experiment and wrote
the paper. B.P. and M.J.N. collected the data. B.P. performed the data analysis.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. Correspondence and requests for materials should be
addressed to B.P. ([email protected]).
409
©2008 Nature Publishing Group
doi:10.1038/nature06849
METHODS
Experimental preparation. Two male rhesus monkeys (Macaca mulatta) participated in the experiments. Each animal was first implanted with a head cap and
eye coil under general anaesthesia. In a second surgery, recording chambers were
implanted in frontal and posterior parietal cortex in the right hemisphere of each
animal. Structural magnetic resonance imaging identified the position of the
arcuate sulcus and intraparietal sulcus and guided placement of the recording
chambers to give access to cortex medial to each sulcus. In both animals, PMd
recordings were made within the cortical gyrus within 1.5 mm of the cortical
surface, and PRR recordings were made within the intraparietal sulcus 4–9 mm
below the cortical surface.
Behavioural tasks. For all tasks, reaches were made with the left arm on a touchsensitive screen (ELO Touch Systems). Visual stimuli were presented on an LCD
display (LG Electronics) placed behind the touch screen. All trials began with the
illumination of a central circle which the animal needed to touch with his hand
and hold for a baseline period (about 500 ms).
In the search tasks, after a baseline hold period (0.5–1 s), three targets were
presented on a 3 3 3 grid (spaced 10u) of eight possible locations around the start
point. After a delay period (1–1.5 s) the monkey was given a ‘go’ signal to reach to
one of the three targets. Only one of the three targets triggered a juice reward
when touched. If the monkey did not reach to the target that gave the reward, he
was allowed to make additional reaches to targets after subsequent hold periods
(0.5–1 s). Additional reaches were allowed until the reward was received. Targets
were extinguished once they were touched. An auditory tone signalled the ‘go’
signal for each reach. A different set of three targets from the eight possible
locations appeared for each trial, and the target that gave the reward was chosen
from these three targets with equal probability. This stimulus–reward configuration set ensured that the monkey did not repeatedly perform the same stereotyped sequence of movements. This elicited choices by releasing constraints
instead of intensively training the subject to overcome biases and avoid stereotyped choices. If the animal reached for the wrong shape in the instructed search
task, the trial was aborted. The animal first knew it was in a free search or
instructed search trial when the search array was illuminated.
The free and instructed search tasks were yoked in an interleaved design to
match the sensory-, motor- and reward-related contingencies. We did this by
requiring the monkey to perform an initial set of free search trials in a block
(typically 50). The search array configurations were selected at random from the
set of 56 possible configurations. We counted the number of times each search
array configuration was presented and the number of times each possible movement sequence was made during the free search task. After the initial set of free
search trials was performed, we began to randomly interleave instructed search
trials. During this phase of the session, the probability of a given trial being a free
search or instructed search task was balanced so that after 200 total trials an equal
number of trials from each task would be successfully completed. Search array
configurations for the free search task continued to be selected at random. Search
array configurations for the instructed search task were drawn from the probability distribution defined by the set of search configurations presented in
the preceding free search trials that were successfully completed. To match the
motor contingencies in the instructed search trials to the free search trials, the
order of the movement sequences instructed by the search array was drawn from
a probability distribution defined by the set of movement choices made in the
preceding free search trials. To reduce the number of trials needed to estimate
these movement sequence probabilities and to prevent the generation of stereotyped movement sequences, we matched only the first element of the instructed
movement sequence with the monkey’s choices and allowed potential mismatch
for the second and third elements of the instructed movement sequence. All
probability distributions were updated after each successful trial. Eye movements were unconstrained and, on a subset of experimental sessions (53 sessions
in monkey E, 15 sessions in monkey Z), were monitored using a scleral search
coil (CNC Engineering).
A variant of the search tasks with enforced fixation was also tested in one
animal (monkey E). In this variant, the search tasks were identical except that the
monkey needed to maintain fixation at the current touch location throughout
the trial. As a result, the only eye movements that were allowed were made at the
time of a reach movement.
In the centre-out task, a single target was presented at one of eight peripheral
locations on a 3 3 3 grid (spaced 10u) around the start point. After a delay period
(1–1.5 s) the monkey reached for the target and was then given a juice reward.
Fixation was enforced during the period after acquisition of the start point until
the end of the delay period. At this time, gaze was unconstrained and both
monkeys made a coordinated saccade to the target of the reach movement.
©2008 Nature Publishing Group
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