Neuron

Article

Decoding a Perceptual DecisionProcess across CortexAdrian Hernandez,1 Veronica Nacher,1 Rogelio Luna,1 Antonio Zainos,1 Luis Lemus,1 Manuel Alvarez,1 Yuriria Vazquez,1

Liliana Camarillo,1 and Ranulfo Romo1,*1Instituto de Fisiologıa Celular-Neurociencias, Universidad Nacional Autonoma de Mexico, 04510 Mexico, D.F., Mexico

*Correspondence: rromo@ifc.unam.mxDOI 10.1016/j.neuron.2010.03.031

SUMMARY

Perceptual decisions arise from the activity ofneurons distributed across brain circuits. But, decod-ing the mechanisms behind this cognitive operationacross brain circuits has long posed a difficultproblem. We recorded the neuronal activity of diversecortical areas, while monkeys performed a vibrotac-tile discrimination task. We find that the encoding ofthe stimuli during the stimulus periods, workingmemory, and comparison periods is widely distrib-uted across cortical areas. Notably, during thecomparison and postponed decision report periodsthe activity of frontal brain circuits encode both theresult of the sensory evaluation that corresponds tothe monkey’s possible choices and past informationon which the decision is based. These results sug-gest that frontal lobe circuits are more engaged inthe readout of sensory information from workingmemory, when it is required to be compared withother sensory inputs, than simply engaged in motorresponses during this task.

INTRODUCTION

In its simplest formulation, a perceptual decision results from

the interaction between past and current sensory information.

A major problem in this formulation involves understanding

how brain circuits represent past and current sensory events

and how these representations are linked to perceptual

reports (Romo and Salinas, 1999). Previously, we addressed

this problem using a vibrotactile discrimination task (Hernan-

dez et al., 1997). In this task, trained monkeys compare infor-

mation of the first stimulus frequency (f1) temporarily stored in

working memory to the current sensory information of the

second stimulus frequency (f2) to form a decision, i.e., whether

f2 > f1 or f2 < f1, and to immediately report their perceptual

evaluation by pressing one of two push buttons. Because

this sequence depends on discrimination of highly simplified

stimuli, the neuronal activity of diverse cortical areas can be

examined during the same behavior (Brody et al., 2003; Chow

et al., 2009; Hernandez et al., 2000, 2002; Jun et al., 2010;

Luna et al., 2005; Machens et al., 2005; Romo et al., 1999,

300 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

2002, 2003, 2004; Romo and Salinas, 2003; Salinas et al.,

2000).

The task used in these studies simulates the behavioral

condition in which the decision based on a sensory evaluation

is immediately reported through a voluntary movement (Hernan-

dez et al., 1997). There are, however, behavioral conditions in

which a perceptual decision can be postponed for later report.

But, in theory, once the subject reaches a decision, this becomes

categorical, no matter whether it must be reported immediately

or reported later. If postponed, memory circuits may store the

categorical decision for later report (de Lafuente and Romo,

2005; Shadlen and Newsome, 1996). However, an alternative

could be that the memory circuits store not only the categorical

decision, but also the information on which the decision is

based (Lemus et al., 2007). This last possibility could be

extremely advantageous since it gives flexibility for the deci-

sion-making process. In this case, it is possible that the deci-

sion is revised or updated as long as there is time for it to be

reconsidered.

In a variant of the vibrotactile discrimination task, in which

monkeys were asked to postpone their decision report, we found

that the activity of medial premotor cortex (MPC, presupplemen-

tary motor area, and supplementary motor cortex) neurons

during this period encodes both the result of the sensory evalu-

ation (which corresponds to the monkey’s two possible choices)

and past information on which the decision is based (Lemus

et al., 2007). These responses could switch back and forth with

remarkable flexibility across the postponed decision report

period. Moreover, these responses covaried with the animal’s

decision report. Thus, the MPC circuits appear critically suited

to integrate and reorganize all of the elements associated with

decision making in this task. Furthermore, they reflect the flexi-

bility that is needed when a perceptual decision must be either

immediately reported (Hernandez et al., 2002) or postponed for

later report (Lemus et al., 2007).

This result prompted us to further explore whether the

neuronal responses recorded during the postponed decision

period are a unique property of the MPC circuit (Lemus et al.,

2007) or whether similar processes are also present in other

cortical areas of the parietal and frontal lobes during this variant

of the task. To further investigate this question, we recorded the

neuronal activities of diverse cortical areas while trained

monkeys reported a postponed decision based on previous

sensory evaluation. In this task, monkeys must hold f1 in working

memory and must compare it to the current sensory stimulus (f2)

and must postpone the decision report until a cue triggers the

Figure 1. Discrimination Task

(A) Sequence of events during discrimination

trials. The mechanical probe is lowered, indenting

the glabrous skin of one digit of the restrained

hand (pd); the monkey places its free hand on an

immovable key (kd); the probe oscillates vertically,

at the base stimulus frequency (f1); after a fixed

delay (3 s), a second mechanical vibration is deliv-

ered at the comparison frequency (f2); after

another fixed delay (3 s) between the end of f2

and probe up (pu), the monkey releases the key

(ku) and presses either a lateral or a medial push-

button (pb) to indicate whether the comparison

frequency was higher or lower than the base,

respectively.

(B) Stimulus set used during recordings. Each box

indicates a base/comparison frequency stimulus

pair. The number inside the box indicates overall

percentage of correct trials for that (f1, f2) stimulus pair, except when the stimulus pair was identical (22 Hz; we plotted the number of times that animal pressed

the lateral push button).

(C) Psychophysical performance when f1 was maintained fixed at 22 Hz and f2 was variable (red curve), and when f2 was fixed at 22 Hz and f1 was variable (green

curve). D.L. is the discrimination threshold in Hz.

(D) Top view of the monkey brain and the cortical areas recorded during perceptual discrimination (orange spots). Recordings were made in primary somatosen-

sory cortex (S1) and secondary somatosensory cortex (S2) contralateral to the stimulated hand (left hemisphere) and in primary motor cortex (M1) contralateral to

the responding hand/arm (right hemisphere). Recordings were made contralateral and ipsilateral to the stimulated fingertip in prefrontal cortex (PFC), ventral pre-

motor cortex (VPC), medial premotor cortex (MPC), and dorsal premotor cortex (DPC).

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motor report, i.e., whether f2 > f1 or f2 < f1. Clearly, the neuronal

processes associated with the postponed decision report and

the task components that precede it can be analyzed across

diverse cortical areas.

Here we report the extent to which the stimulus identity is

encoded across diverse cortical areas in this task. We found

that the encoding of f1 and f2 through all task periods is widely

distributed across cortical areas. We also found that the activity

of frontal lobe circuits encodes both the result of the sensory

evaluation and past information on which those choices are

based. Notably, the activity of primary motor cortex (M1) showed

processes similar to those observed in the premotor areas

(ventral premotor cortex, VPC; dorsal premotor cortex, DPC;

and MPC) and prefrontal cortex (PFC), both during the compar-

ison and postponed decision report periods. These results

suggest that frontal lobe neurons have the capacity to encode

during the comparison and postponed decision report periods

both the final result of the sensory evaluation and past informa-

tion about it.

Here we also document the nature of the neuronal responses

during the stimuli and their interactions. In addition to the stan-

dard discrimination test, the neuronal activity of all cortical areas

was studied when the stimuli were delivered but monkeys were

not requested to perform the task. Under this condition, most

neurons across the cortical areas no longer encode information

about the stimuli and their interactions during these trials. The

only areas that responded in this case were S1 and S2. This

would suggest that those cortical areas central to S1 that encode

information about the stimuli are more likely associated with

the sensory evaluation, than engaged simply in encoding the

sensory stimulus. We also tested each neuron in a simpler

task, in which trials proceeded exactly as in the vibrotactile

task, but the stimuli were not delivered to the skin and the move-

ments were guided by visual cues. Neurons responded during

movement execution but not during the periods preceding it.

These control tests show that the neuronal responses from all

the cortical areas studied, except for S1, reflect both the active

comparisons between f1 and f2 and the execution of the motor

choice that is specific to the context of the vibrotactile discrimi-

nation task.

RESULTS

Optimal Conditions for Studying PerceptualDiscriminationFour monkeys (Macaca mulatta) were trained to discriminate the

difference in frequency between two consecutive vibrotactile

stimuli, f1 and f2 delivered to one fingertip (Figure 1A). Monkeys

were asked to report discrimination after a fixed delay period of

3 s between the end of f2 and the cue that triggered the motor

report (probe up, pu in Figure 1A). This delay period thus sepa-

rates the comparison between the two stimuli from the motor

response. In this task, monkeys must hold f1 in working memory,

must compare the current sensory input f2 to the memory trace

of f1, and must postpone the decision until the sensory cue

triggers the motor report. Animals were trained to perform the

task up to their psychophysical thresholds (Figures 1B and C).

After training, we recorded the activity of single neurons from

diverse cortical areas while the monkeys performed the task

(Figure 1D). These recordings were made in primary somatosen-

sory cortex (S1), secondary somatosensory cortex (S2), PFC,

VPC, DPC, and MPC contralateral to the stimulated finger and

in PFC, VPC, DPC, MPC, and M1 contralateral to the responding

hand/arm. All neurons were recorded using the stimulus set of

Figure 1B. In these trials, the comparison frequency (f2) can be

judged higher or lower than f1. Thus, the neuronal responses

across trials can be analyzed as functions of f1, f2, f2 � f1, or

as functions of the monkey’s two possible motor choices.

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Neuronal Responses across Cortical Areas duringPerceptual DiscriminationWe recorded from 2509 neurons (Table 1) that had average firing

rates that were significantly different from their firing rates during

a pre-trial control period (500 ms immediately before probe

down; pd in Figure 1A; p < 0.01, Wilcoxon rank-sum test [Siegel

and Castellan, 1988]). Some of these neurons enhanced or

reduced their firing rates (222/2509, 9%), but their firing rate

did not depend significantly on the applied stimulus frequencies

or the animal’s decision report (Experimental Procedures). Here

we focus exclusively on the 91% of neurons (n = 2287) that were

stimulus dependent (Romo et al., 1999), although not necessarily

during the stimulus presentation periods: stimulus-dependent

modulations could occur also during the working memory,

comparison and postponed decision report periods of the task

(Table 1). This is illustrated for an example neuron recorded in

the MPC contralateral to the stimulated finger (Figure 2A). The

discharge rate of this neuron during the delay period between

f1 and f2 varied as a monotonic function of f1. This neuron dis-

charged more strongly for the lower frequency and decreased

its firing rate steadily for increasing f1. We refer for this type of

response as ‘‘negative monotonic’’ (Romo et al., 1999). Other

neurons had discharge rates that varied in the opposite direc-

tion. We refer to those as ‘‘positive monotonic’’ (Romo et al.,

1999). Thus, for this example neuron the analysis showed that

f1 appeared to be encoded directly with various strengths and

at various periods of time during the delay period between f1

and f2. Neurons with positive or negative modulations were

also recorded in S2 contralateral to the stimulated hand and

bilaterally in PFC, VPC, DPC, and MPC. Neurons from these

areas could respond exclusively to the f1 presentation, or

encode f1 during the delay period between f1 and f2 (Figure 2A)

or during both periods. It is also worth mentioning that the large

majority of S1 neurons encoded f1 almost exclusively in a posi-

tive monotonic fashion during the first stimulus period only, and

that none of the M1 neurons showed any modulation of their

firing rates as a function of f1 during the f1 presentation or during

the delay period between f1 and f2 (Figure 3 and see Figure S1

available online).

As the task progressed, trials for the example neuron of

Figure 2A can be divided into two types: those on which f2 > f1

(black label) and those on which f2 < f1 (gray label). During the

final 200 ms of f2 presentation and early component (1 s) of

the postponed decision report period of our example neuron,

the firing rates were modulated by both f1 and f2. The main

determinant of the firing rate was not, however, the particular

values that f1 or f2 took on any given trial. Instead, it was simply

whether the trial belonged to the f2 > f1 group or f2 < f1 group.

This corresponds to the monkey’s two possible choices, repre-

sented during the comparison and postponed decision report

periods. Not only did f1 modulate the response to f2 in this

neuron, but even more notably, this happened such that by

the end of f2 and during the early component of the postponed

decision period, the responses became mostly correlated

with the monkey’s choice. This type of response that occurred

during the comparison and postponed decision report periods

was observed in area S2 contralateral to the stimulated hand

and bilaterally in PFC, VPC, DPC, and in M1 contralateral to

302 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

the responding arm (Figure 3), but not in S1. In brief, the

stimulus identity of f1 and f2 could be encoded in a positive

(S1, S2, PFC, VPC, DPC, and MPC) or negative (S2, PFC, VPC,

DPC, and MPC) monotonic fashion during the stimulus pre-

sentations. The f2 > f1 and f2 < f1 responses during the f2 and

postponed decision report periods recorded in S2, PFC, VPC,

MPC, and M1 suggest that they result from an interaction

between the two stimuli. This requires, however, understanding

precisely how f1 and f2 are encoded and how these interactions

are computed during the comparison and postponed decision

report periods.

Decoding Perceptual Discrimination across CorticalAreasTo further estimate the representation of f1 and f2 and their

interactions during the task components, we used multivariate

regression analysis. For each neuron, we modeled the firing

rate’s dependency on f1 and f2. In principle, the response during

f2 could be an arbitrary linear function of both f1 and f2: firing

rate(t) = a1(t) f1 + a2(t) f2 + a3(t) (Draper and Smith, 1966; Hernan-

dez et al., 2002; Press et al., 1992; Romo et al., 2002, 2004). In

this formulation, t represents time, and the coefficients a1 and

a2 serve as direct measurements of firing rate dependence on

f1 and f2, respectively. These measurements were calculated

in sliding windows of 200 ms moving in steps of 20 ms. To illus-

trate this analysis, the resulting coefficients a1 and a2 and

their interactions were plotted in panels C and D of Figure 2 for

the example neuron as a function of time. We also plotted the

values of a1 and a2 against each other to compare the responses

at different points during the task (Figure 2C). Three lines are

of particular relevance in these plots: points that fall on the

a1 = 0 axis represent responses that depend on f2 only (red dots

in Figure 4); points that fall on a2 = 0 axis represent responses

that depend on f1 only (green dots in Figure 4), and points that

fall near the a2 = �a1 line represent responses that are a func-

tion of f2� f1 only (black and blue dots in Figure 4). This last con-

sideration is of particular importance since the sign of the differ-

ence between f1 and f2 determines correct task performance.

However, the result of the analysis is not restricted to these three

conditions. For example, in those hypothetical cases when the

modulation imposed by f1 and f2 results in f1 + f2, the point

would fall close to a1 = a2 line. In this case, the memory of

f1 is added to the f2 representation, but this result was rarely

observed. Importantly, the larger area of the plane represents

those responses where the strengths of a1 and a2 are signifi-

cantly different from zero and significantly different from each

other. This plot can also reveal whether one of the two stimulus

frequencies is more strongly represented than the other (ja1jsja2j; a1 s 0; a2 s 0; black dots in panels C and D of Figure 2 and

black dots of Figure 4).

The population analysis of each cortical area shows that most

of the neurons encode f1 in a positive or negative monotonic

manner at various coefficients strengths. This result agrees

with results obtained from a large data base recorded in

monkeys reporting discrimination immediately after f2 (Romo

et al., 2004). As before, most S1 neurons encode f1 during the

stimulus presentation in a positive monotonic fashion whereas

the rest of cortical areas—except for M1 neurons which did

Table 1. Database

Task Component

Area Responsive Tuned f1 delay f1 � f2 f2 delay f2 � pu mt

S1 189 f1 59 + (31.2%)

5 � (2.6%)

f2 61 + (32.2%)

4 � (2.1%)

c

d

S2 426 f1 49 + (11.5%) 28 + (6.5%) 6 + (1.4%) 8 + (1.8%) 4 + (0.9 %)

45 � (10.5%) 21 � (4.9%) 5 � (1.1%) 6 � (1.4%) 3 � (0.7%)

f2 57 + (13.3%) 31 + (7.2%) 6 + (1.4%)

54 � (12.6%) 25 � (5.8 %) 5 � (1.1%)

c 34 (7.9%) 15 (3.5%) 12 (2.8%)

d 21 (4.9%) 26 (6.1%) 23 (5.4%)

VPC 375 f1 48 + (12.8%) 63 + (16.8%) 76 + (20.2%) 31 + (8.2%) 14 + (3.7%)

36 � (9.6%) 45 � (12.0%) 57 � (15.2%) 26 � (6.9%) 12 � (3.2%)

f2 47 + (12.5%) 27 + (7.2%) 15 + (4.0%)

30 � (8.0%) 25 � (6.6%) 7 � (1.8%)

c 62 (16.5%) 11 (2.9%) 5 (1.3%)

d 108 (28.8%) 68 (18.1%) 16 (4.3%)

PFC 358 f1 25 + (6.9%) 49 + (13.7%) 25 + (6.9%) 14 + (3.9%) 5 + (1.4%)

26 � (7.7%) 42 � (11.7%) 21 � (5.8%) 9 � (2.5%) 8 � (2.2%)

f2 35 + (9.7%) 27 + (7.5%) 11 + (3.0%)

21 � (8.0%) 31 � (8.7%) 13 � (3.6%)

c 53 (14.8%) 26 (7.3%) 3 (0.8%)

d 82 (22.9%) 72 (20.1%) 16 (4.5%)

MPC 494 f1 29 + (5.9%) 47 + (9.5%) 33 + (6.7%) 39 + (7.9%) 19 + (3.8%)

24 � (4.9%) 39 � (7.9%) 28 � (5.7%) 32 � (6.5%) 14 � (2.8%)

f2 63 + (12.8%) 67 + (13.6%) 16 + (3.2%)

41 � (8.3%) 59 � (11.9%) 17 � (3.4%)

c 53 (10.7%) 42 (8.5%) 13 (2.6%)

d 83 (16.8%) 74 (15.0%) 45 (9.1%)

DPC 164 f1 10 + (6.0%) 12 + (7.3%) 3 + (1.8%) 9 + (5.5%) 4 + (2.4%)

12 � (7.3%) 8 � (4.9%) 6 � (3.7%) 12 � (7.3%) 5 � (3.0%)

f2 15 + (9.1%) 11 + (6.7%) 9 + (5.5%)

16 � (9.8%) 13 � (7.9%) 10 � (6.1%)

c 9 (5.5%) 3 (1.8%) 3 (1.8%)

d 4 (2.4%) 9 (5.5%) 16 (9.8%)

M1 281 f1 2 + (0.7%) 3 + (1.0%) 3 + (1.0%) 17 + (6.0%) 4 + (1.4%)

3 � (1.0%) 4 � (1.4%) 7 � (2.4%) 24 � (8.5%) 6 � (2.1%)

f2 25 + (8.8%) 49 + (17.4%) 14 + (4.9%)

27 � (9.6%) 52 � (18.5%) 17 � (6.0%)

c 15 (5.3%) 27 (9.6%) 15 (5.3%)

d 17 (6.0%) 35 (12.5%) 29 (10.3%)

Recorded, n = 2509. Responsive, n = 2287 (91%). f1, first stimulus; f2 second stimulus; pu, probe up; mt, movement time; S1, primary somatosensory

cortex; S2, second somatosensory cortex; VPC, ventral premotor cortex; PFC, prefrontal cortex; MPC, medial premotor cortex; DPC, dorsal premotor

cortex; M1, primary motor cortex. Tuned to stimulus frequency with positive (+) or negative (�) slopes; c, tuned to stimuli with differential activity for

f2 > f1 or f2 < f1; d, differential activity to f2 > f1 or f2 < f1.

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Figure 2. Responses of a MPC Neuron

during the Discrimination Task and Control

Tests

(A) Raster plots of responses during the discrimi-

nation task. This neuron responded with an f1

negative monotonic fashion to the increasing stim-

ulus frequency f1 during the delay period between

f1 and f2 and during the early delay period

between the end of f2 and the beginning of the

decision motor report (pu). Each row of ticks is

a trial, and each tick is an action potential. Trials

were delivered in random order (10 trials per stim-

ulus pair). Labels at left indicate f1:f2 stimulus

pairs. Black indicates f2 > f1; gray indicates f2 < f1.

(B) Firing rate modulation (mean ± SEM) as a func-

tion of f1 or f2.

(C) Resulting coefficient values for f1 (a1, green)

and f2 (a2, red) for panels in (B).

(D) Coefficients values as functions of time. Green

and red traces correspond to a1 and a2, respec-

tively. Filled circles indicate significant values.

Black circles indicate points at which a1 and a2

were significant and of different magnitudes, but

had opposite signs; these are partially differential

(c) responses. Blue circles indicate points at which

a1 and a2 were significant and of similar magni-

tude but had opposite signs; these are fully differ-

ential (d) or categorical responses.

(E) Responses of the same neuron when the same

set of stimuli (A) was delivered to the fingertip, but

discrimination was restricted, just by removing the

key and the interrupt target switches. Thus, in this

condition the animal remained alert—by rewarding

with drops of liquid at different times—but was no

longer using the stimuli to indicate discrimination

with the free hand/arm. Under this test condition,

the neuron does not encode information about

the stimuli.

(F) Choice probability indices as function of time

during the discrimination task. Filled circles are

significant values that deviated from 0.5 (green

for f1 values; black for c values; and blue for

d values of D).

(G) Choice probability index for the same neuron tested in the light instruction task. Under this condition, the choice probability indices were calculated by

comparing the response distributions for lateral versus medial push button presses. Arm movements in this situation were identical to those in the vibrotactile

discrimination task but were cued by visual stimuli.

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not encode f1 at all—do so in dual form: positive or negative

monotonic encoding (green dots in Figure 4). These encodings

are also observed during the delay period between f1 and f2,

except for S1 and M1 neurons, which do not show any encoding

during this period (green dots in Figure 4). During the comparison

period, most S1 neurons encoded f2 in a positive monotonic

fashion (red dots in Figure 4), whereas most of the cortical areas

encoded the current stimulus f2, but also f1 (Figure 4). Again, the

encoding of f1 and f2 could be positive or negative monotonic.

But, in addition, the information of f1 and f2 is combined to

generate a differential response (blue dots in Figure 4). An inter-

esting observation is that some neurons during the comparison

period switched from an f1 encoding to a combination of f2

and f1 (black dots in Figure 4), and then to a differential response

(blue dots in Figure 4). During the postponed decision report,

many neurons from PFC, VPC, MPC, DPC, and M1 encoded

information on which the decision is based and the resulting

304 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

operation, f2 > f1 or f2 < f1 (Figure 4). Again, S1 did not show

any sign of participation in the postponed decision report

(Figure 4).

Dynamics of the Perceptual Discrimination Processacross Cortical AreasFigure 4 shows the number of neurons with significant a1 and

a2 coefficients during the relevant components of the task.

However, it does not tell much about the dynamics of the popu-

lation response of each cortical area across the task compo-

nents. An analysis of the coefficients as functions of time shows

that all cortical areas studied here encoded f1 during the f1

presentation, except M1 (green traces in Figure 5A). The earliest

response began in S1 compared to S2, PFC, VPC, MPC, and

DPC, then S2 responded earlier than PFC, VPC, MPC, and

DPC, and finally PFC and VPC responded earlier than MPC

and DPC (Figure 6; p < 0.01 between response distributions;

Figure 3. Responses of a M1 Neuron during

the Discrimination Task and Control Tests

(A) Raster plots of responses during the discrimi-

nation task. This neuron responded with a negative

monotonic fashion to the increasing f2 stimulus

frequency during the delay period between the

end of f2 and the beginning of the decision motor

report (pu). Each row of ticks is a trial, and each

tick is an action potential. Trials were delivered in

random order (10 trials per stimulus pair). Labels

at left indicate f1:f2 stimulus pairs. Black indicates

f2 > f1; gray indicates f2 < f1.

(B) Firing rate modulation (mean ± SEM) as a func-

tion of f1 or f2.

(C) Resulting coefficient values for f1 (a1, green)

and f2 (a2, red) for panels in (B).

(D) Coefficients values as functions of time. Green

and red traces correspond to a1 and a2, respec-

tively. Red filled circles indicate significant f2

values.

(E) Responses of the neuron when the same set of

stimuli (panel A) was delivered to the fingertip, but

discrimination was restricted, just by removing the

key and the interrupt target switches. Thus, in this

condition the animal remained alert—by rewarding

with drops of liquid at different times—but was no

longer using the stimuli to indicate discrimination

with the free hand/arm. Under this test condition,

the neuron does not encode information about

f2, as shown in (D).

(F) Choice probability indices as function of time.

Filled circles are significant values that deviated

from 0.5 for responses of (D).

(G) Choice probability index for the same neuron

tested in the light instruction task. Because in

this test condition animals did not show incorrect

responses, the choice probability index was

calculated by comparing the response distribu-

tions for lateral versus medial push button

presses. Arm movements in this situation were

identical to those in the vibrotactile discrimination

task, but were cued by visual stimuli.

See also Figure S1.

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Wilcoxon rank-sum test; Siegel and Castellan, 1988). Therefore,

the f1 encoding seems to proceed in a serial fashion, although

there is broad overlap in the response latencies of these cortical

areas (Figure 6). This would suggest that all these cortical

areas are engaged in f1 processing, and it is natural to ask

whether they also hold f1 information during the delay period

between f1 and f2. The results show that S2, PFC, VPC, DPC,

and MPC encode f1 at various coefficient strengths and at

various times during the delay period of this task (green traces

in Figure 5A). It is quite interesting to note that more neurons

in PFC, VPC, and MPC than in S2 and DPC are engaged in

encoding f1 during the working memory component just imme-

diately before the f2 presentation, when the comparison takes

place.

All cortical areas studied here encoded information about f2

(red traces in Figure 5A). The earliest response began in S1,

continued in S2, then PFC and VPC, and finally DPC, MPC,

and M1 (Figure 6; p < 0.01). Except for S1, we also observed

f1 signals in all cortical areas during presentation of f2 (green

traces in Figure 5A). The presence of f1 information is essential

for the comparison process in this task. We observed that

some neurons reflected the comparison, the difference between

f2 and f1 (blue traces in Figure 5A) and some others that switched

from an f1 encoding to a combination with f2 (black traces in Fig-

ure 5A). Except for S1, these comparison signals were observed

in all cortical areas studied here. These differential responses

were significantly (p < 0.01) delayed in comparison to f1 and f2

signals (Figure 6). Also, all frontal lobe areas, including M1,

showed information about f2 (red traces in Figure 5A), f1 (green

traces in Figure 5) during the comparison period, and during

the delay period between the end of f2 and the cue that triggered

the motor report (black and blue traces in Figure 5A). Thus,

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Figure 4. Population Coefficient Values

across Cortical Areas during the Different

Components of the Discrimination Task

Each point represents one neuron with at least one

coefficient significantly different from zero. We

analyzed five periods: f1 (500 ms), delay between

f1 and f2 (3000 ms), f2 (500 ms), delay between

the end of f2 and pu (3000 ms), and during a period

posterior to pu (1000 ms). For each neuron, we

identified a 200 ms bin with the highest modulation

during each period. n = number of neurons.

Green and red circles correspond respectively

to neurons with significant a1 coefficients only or

a2 coefficients only. Black circles correspond to

neurons with both significant a1 and a2 coeffi-

cients of opposite signs but of significantly

different magnitudes; these are partially differen-

tial responses (c). Blue circles correspond to

neurons with both significant a1 and a2 coeffi-

cients, but of opposite signs and statistically equal

magnitude; these are fully differential or categor-

ical responses encoding f2 � f1 (d). S1, primary

somatosensory cortex; S2, secondary somato-

sensory cortex; VPC, ventral premotor cortex;

PFC, prefrontal cortex; MPC, medial premotor

cortex; DPC, dorsal premotor cortex; M1, primary

motor cortex.

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during the postponed decision report period these cortical

circuits maintain in working memory all the elements associated

with this cognitive operation. However, we noticed that there

were more neurons in MPC, DPC, and M1 than in PFC and

VPC that carried information about f2 during the postponed deci-

sion period (red traces in Figure 5A), indicating that the most

recent information sensory information (f2) is more likely to be

kept in working memory than immediately-preceding sensory

information (f1).

State-Dependent Responses across Cortical AreasCortical population dynamics illustrated in Figure 5A shows that

neurons of diverse cortical areas encode the different task

components. But, to what extent are these neuronal events

associated with the task components and animal’s sensory eval-

uation?

Do these events occur only during the task execution or are

irrespective of the animal’s state? To answer these questions,

in addition to the standard test, many of the neurons that

306 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

encoded information about the stimuli

and motor choice were also tested in

a variant of the task (Experimental Proce-

dures). In this test, the neuronal activity

of these cortical areas was studied

when the stimuli were delivered but

monkeys were not requested to perform

the task. Under this condition, most

neurons across the cortical areas no

longer encoded information about the

stimuli and their interactions during the

task components (Figure 5B). The only

areas that responded in this case were S1 and S2. This would

suggest that those cortical areas central to S1 that encoded

information about the stimuli are more likely associated with

the sensory evaluation, than engaged in encoding the stimuli.

Choice Signals across Cortical AreasResponses during correct trials alone did not allow us to deter-

mine to what extent (f2 � f1)-dependent responses were corre-

lated with the sensory stimuli, or with the monkey’s action

choice. For each neuron of each cortical area we sorted the

responses into correct and errors trials and calculated a choice

probability index as a function of time (Britten et al., 1996; Green

and Swets, 1966; Romo et al., 2002). This quantified for each

stimulus (f1, f2) pair whether neuronal responses during error

trials were different from responses during correct trials (panel

F in Figures 2 and 3). If the responses are exclusively stimulus

dependent, they should show no differences between correct

and errors trials, except when the differences expected at

chance level result of the intrinsic variability of the neural activity.

Figure 5. Cortical Population Dynamics

during the Discrimination Task

(A) Percentage of neurons with significant co-

efficients as a function of time. Green and red

traces correspond to a1 and a2 coefficients,

respectively. Black traces indicate percentage of

neurons with a1 and a2 coefficients of opposite

sign but of different magnitudes. These neurons

combine differential response with a sensory

component. Blue traces indicate percentage of

neurons with coefficients a1 and a2 of opposite

sign but similar magnitude; these produce a differ-

ential signal.

(B) Percentage of neurons that responded during

passive stimulation. All these neurons are part of

the populations studied in (A). S1, primary somato-

sensory cortex; S2, secondary somatosensory

cortex; VPC, ventral premotor cortex; PFC, pre-

frontal cortex; MPC, medial premotor cortex; DPC,

dorsal premotor cortex; M1, primary motor cortex.

n = number of neurons.

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Decision Making across Cortex

In contrast, if the responses were dependent on the animal’s

choice, then they should vary according to which button the

monkey chose to press (panel F in Figures 2 and 3). For all

cortical areas, we computed a choice probability index sepa-

rately for the neurons that responded as a function of f1 (green

traces in Figure 7A), f2 (red traces in Figure 7A) or that depended

specifically on f2 – f1 (black and blue traces in Figure 7A). The

result of this analysis shows that very few neurons from S1

reflect significant differences between correct and error trials.

In contrast, many neurons from S2, PFC, VPC, DPC, MPC, and

M1 predicted in their activity the animal’s choice. These

responses began during the f1 stimulus presentation for areas

S2, PFC, VPC, DPC, and MPC, and were also present during

the delay period between f1 and f2 for areas PFC, VPC, MPC,

DPC, and M1, just before f2 presentation. During the f2 period,

all these areas except S1 had significant choice probability

indices (Figures 7A and S2). These signals were maintained

during the postponed decision report, during the delay between

the end of f2 and the cue that triggered the motor response

(Figures 7A and S2). These results suggest that neuronal activity

that correlates with error responses are widely distributed across

cortical areas, and that an error could be due to a failure to

encode the sensory inputs and their interactions during the

task components.

In addition to analyzing error trials, we tested each neuron in

a variant of the task, in which trials proceeded exactly as in the

vibrotactile task, but the stimuli were not delivered to the skin

and the movements were guided by visual cues (Experimental

Procedures). Thus, movements were triggered by visual cues.

Neurons responded exactly as in Figure 7A during movement

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execution but not during the period

preceding it (Figure 7B). These control

tests show that some of the neuronal

responses from all the cortical areas

studied, except for S1, reflect the active

comparison between f1 and f2 whereas

some other neurons encode the motor choice that is specific

to the context of the vibrotactile discrimination task.

Neuronal Correlates of Bias Behavior across CortexIn the task used here, the decision must result of the difference

between f1 and f2 (choice = f(f2 – f1)). However, the discrimina-

tion thresholds plotted in Figure 1C suggest that subjects could

base their decisions by paying more attention to the weight of f2

than to the weight of f1 (choice = f(w2*f2 – w1*f1)). To further esti-

mate the weights w2 and w1, we used multivariate regression

analysis and plotted the subject’s performance as the percen-

tage of f2 > f1 correct responses for each stimulus pair—we

used only stimulus pairs labeled red and green in Figure 1C. Thus,

the resulting coefficients a1 and a2 of the multivariate regres-

sion analysis correspond to w1 and w2, respectively. If during

the decision subjects gave more weight to f2 than to the weight

of f1, f2 must be reported higher than f1. This is what the anal-

ysis shows on the data of Figure 1C (a1 = 4.3, a2 = 5.6; r =

a1/a2 = 0.76). If this analysis is carried out for each experiment

of the 312 reported here, we obtain a geometric mean of 0.79

(Figure 8A). On the other hand, we know that those neurons

with sensory differential activity (labeled in black in Figures 4

and 5) show coefficients a1 and a2 significant different from

zero and between each other. Therefore, it is natural to ask

whether the activity of these neurons is correlated with the

behavioral bias illustrated in Figure 8A. For example, if we use

the a1 and a2 values of the MPC neurons for those periods of

times that show the condition described above and calculate

the ratio a1/a2, we obtain the histogram of Figure 8B. This plot

shows that the larger percentage of neural responses (77%;

14, April 29, 2010 ª2010 Elsevier Inc. 307

Figure 6. Box Plots Illustrate Response Latency Distributions for f1

(Green), f2 (Red), Comparison (c, Black), and Differential (d, Blue)

across Cortical Areas

These boxes have lines at the lower quartile, median, and upper quartile

values. The whiskers are lines extending from each end of the boxes to

show the extent of the rest of the data. A comparative analysis (Wilcoxon

rank-sum test; Siegel and Castellan, 1988) of the response latencies between

the cortical areas showed that the f1 and f2 began earlier in S1 (p < 0.01) than in

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308 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

data distribution to the left relative to 1 in Figure 8B) corresponds

to a1 < a2 responses. Such proportion of bias can not be ob-

tained by chance (p < 0.001; binomial test [100, 55, and 0.05]).

Interestingly, there is a strong relationship between the direc-

tion and magnitude of the bias behavior. To further establish in

which cortical area this bias is generated, we estimated the

neuronal bias (a1/a2) for each neuron of each cortical area using

a sliding window of 200 ms duration moving in steps of 20 ms

(S2, m = 0.79; VPC, m = 0.82; PFC, m = 0.81; MPC, m = 0.81;

M1, m = 0.86)—beginning at f2 onset and ending at probe up

that triggered the decision motor report—and compared this

value against the behavioral bias (a1/a2) obtained simulta-

neously in the same experiment. The results are shown

in Figure 8C. Except for S1, we found that a large fraction of

these neurons correlated with the behavioral bias. This neural

bias was more evident in the PFC, MPC, DPC, and M1 than in

S2 (Figure 8C). Thus, when one of the two stimulus frequencies

is more strongly represented than the other, it could bias

the psychophysical performance in this task. This interpretation

is consistent with the fact that f2 is more strongly represented

during the comparison and postponed decision periods than f1

(Figure 5).

DISCUSSION

To understand perceptual discrimination, we need to know

where in the brain are the physical relevant variables encoded

and what are their relative contributions to the final percept.

Our study focuses on this problem using highly simplified stimuli,

in which the neuronal responses from diverse cortical areas can

be examined while trained monkeys executed the same task.

Although not sufficiently exhaustive, this study shows how

cortical circuits are associated with perceptual discrimination.

For example, our results show that S1 is essentially sensory

and M1 is not necessarily primarily associated with motor

outputs only. Also, those cortical areas that receive the S1 inputs

combine the sensory representations of S1 with sensory signals

stored in working memory. Notably, these cortical areas encode

at various strengths and times the stimulus parameters of both

past and current sensory information on which the perceptual

decision report is based. Moreover, the sensory, memory and

comparison signals are gradually conveyed to the frontal lobe

S2, PFC, VPC, MPC, DPC, and M1 (f1 was not present in M1). The response

latencies for f1 and f2 in S2 (p < 0.01) began earlier than PFC, VPC, MPC,

DPC, and M1. The response latencies for f1 and f2 began earlier in PFC and

VPC (p < 0.01) than in MPC, DPC and M1. We found no differences in the

response latencies for f1 and f2 between MPC, DPC, and M1 (p > 0.01).

All f1 and f2 response latencies in all these cortical areas began earlier

(p < 0.01) than the comparison (c) and differential responses (d). We found

no statistical differences (p > 0.01) between the comparison and differential

responses across the cortical areas. L, left hemisphere (contralateral to the

stimulated hand); R, right hemisphere (ipsilateral to the stimulated hand).

Recordings in primary somatosensory cortex (S1) and secondary somatosen-

sory cortex (S2) were made contralateral to the stimulated hand (left hemi-

sphere) and in primary motor cortex (M1) contralateral to the responding

hand/arm (right hemisphere). Recordings were made bilaterally in prefrontal

cortex (PFC), ventral premotor cortex (VPC), medial premotor cortex (MPC),

and dorsal premotor cortex (DPC).

Figure 7. Correlation between Neuronal

Responses of Diverse Cortical Areas and

Behavioral Choice

(A) Percentage of neurons that had significant

choice probability indices as a function of time.

Green trace: neurons that encoded information

about f1; red trace: neurons that carried informa-

tion about f2; black trace: partially differential

neurons that carried information about f1 and f2

(c); blue trace: fully differential neurons that carried

information specifically about f2 � f1 only (d). See

also Figure S2.

(B) Percentage of the neurons in (A) that showed

significant choice probability indices during the

visual control task. In this test, animals had to

follow a visual cue to produce the motor choice

response. S1, primary somatosensory cortex;

S2, secondary somatosensory cortex; PFC, pre-

frontal cortex; VPC, ventral premotor cortex;

MPC, medial premotor cortex; DPC, dorsal premo-

tor cortex; M1, primary motor cortex. n = number of

neurons.

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Decision Making across Cortex

circuits that in turn drive the motor circuits for a movement

execution. Although this suggests a feedforward processing

beginning in S1 and ending in M1, this seems unlikely given feed-

back/recurrent communications between cortical and subcor-

tical areas (Lamme and Roelfsema, 2000). This problem is

currently addressed by recording the simultaneous activity of

neurons distributed across cortical circuits engaged in the task

used here (Hernandez et al., 2008).

This study shows how distinct cortical areas contribute to the

entire sequence of the processing steps that link sensation and

decision making. One could argue that the neuronal events

recorded in frontal lobe circuits during this task reflect other

processes, such as preparation for a future action, particularly

during the postponed decision report. This seems unlikely, how-

ever, because (1) delay responses between f1 and f2 depended

on f1 regardless of subsequent movements; (2) responses

during the postponed decision period often reflected f1 or f2

information; (3) choice probability indices indicated that there

were significant differences between correct versus error

trials—except for S1, variability in the responses of those S2

and frontal neurons associated with encoding the stimuli corre-

lates with the behavioral choice, although less stronger than

for the differential responses; (4) when the same movements

were guided by visual cues the differential activity disappeared,

except for some neurons that maintained their differential activity

during movement execution (Figure 7B); and (5) except for S1, all

these processes are dependent on active stimulus comparisons,

because they disappeared when subjects were not engaged in

solving the task (Figure 5B). We found it surprising that during

the comparison and postponed decision period some M1

neurons encoded information on which the decision is based.

This result could suggest that M1 is engaged in the readout of

sensory information from working memory, when it is required

to be compared with other sensory inputs, than engaged simply

in a motor response in this task. However, considering the

activity observed in other cortical areas notably in PFC, VPC,

MPC, and DPC during the same task, it would seem that this

process involved conjoined activity of these cortical areas, not

only during the postponed decision report, but also during the

task components preceding it. Thus, a comparison of the

strengths (Figure 4), dynamics (Figure 5A), and latencies (Fig-

ure 6) of the f1 and f2 responses and their interactions across

cortical areas is instructive.

Our results show that the strength of the f1 responses during

the stimulus period is stronger in S1 and gradually decreasing

in S2, PFC, VPC, MPC, and DPC (green dots in Figure 4). Also,

more neurons with f1 responses were recorded in S1, S2, PFC

and VPC than in MPC and DPC (green traces in Figure 5). This

suggests that f1 is preferentially encoded in some of the cortical

areas studied during this task. Accurate performance of the task

can be consistent only with a sensory percept elicited during the

f1 period. The lifetime of the percept directly induced by f1 could

not be measured, if it were not kept in working memory. It there-

fore remained possible that the lifetime of a quantitative, induced

percept was confined to the period of stimulation. Our results

show that the induced percept can be quantitatively memorized

as illustrated in Figures 4 and 5. Although the strength of this

signal varies across areas (green dots in Figure 4), all of them

except S1 and M1 store the value of f1 at different strengths

and times during the working memory component of the task

(green dots in Figure 4 and green traces in Figure 5A). These

results are in accord with the proposal that there is a large

cortical network that dynamically stores sensory information

during working memory (Fuster, 1997; Romo et al., 2004).

During the comparison period, f2 is processed similarly by the

same cortical areas and also in M1 (red dots in Figure 4 and red

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Figure 8. Neuronal Correlates of Bias Behavior

(A) Distribution of coefficients a1/a2 ratios (312 experiments in four animals),

obtained from linear regression analysis to the behavioral data. The histogram

shows that coefficient a2 has a stronger weight than coefficient a1.

(B) Bin distribution ratios for coefficients a1/a2 for neurons from medial premo-

tor cortex (MPC) that showed coefficients a1 and a2 significantly different from

zero and from each other. For each MPC neuron, we estimated the ratio

between weights a1/a2 in a sliding window of 200 ms moving in steps of

20 ms, beginning during the onset of the comparison period and ending during

the probe up that triggers the decision report. All these neurons showed the

properties described in B and illustrated in Figures 4 and 5 (black dots and

traces, respectively). This panel shows that coefficient a2 was more often

higher than coefficient a1, and consequently there are more bins to the left

relative to 1.

(C) Distribution of bin ratios for behavioral bias/neuron bias. For each neuron of

each cortical area, the resulting value a1/a2 of each cortical neuron was

compared against the behavioral value a1/a2 obtained simultaneously in the

same experiment. Data from primary somatosensory cortex (S1) are not

shown, since there are no neurons that show the properties described in (B).

DPC, dorsal premotor cortex; PFC, prefrontal cortex; M1, primary motor

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310 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

traces in Figure 5A). Again, accurate performance of the task can

be consistent only with a sensory percept elicited during the f2

period. But, it is during the f2 period that the comparison

between stored (f1) and ongoing sensory information (f2) takes

place. During this period, f2 must not only be present, but f1

too, as shown in Figure 4 (red and green dots) and Figure 5

(red and green traces). The comparison between f1 and f2 is

observed in S2, VPC, PFC, MPC, DPC, and M1, again at various

strengths across these cortical areas (black dots in Figure 4 and

black traces in Figure 5). Some of these comparison signals

evolve into a signal that is consistent with the animal’s motor

choice (blue dots in Figure 4 and blue traces in Figure 5). During

the postponed report period, the activity of all cortical areas

except S1 encodes both the result of the comparison and past

information on which the decision is based (Figures 4 and 5A)

and covaried with the animal’s decision report (Figure 7A).

During the comparison and postponed delay periods, more

neurons in MPC, DPC, and M1 encoded f2 (red traces in Fig-

ure 5A) than information about f1 (green traces in Figure 5A)

and comparison signals (black and blue traces in Figure 5A).

This would suggest that these frontal circuits are more likely to

store recent sensory information (f2) than immediately preceding

sensory information (f1) during this task. Consistent with this

observation is the fact that the lifetime of the percept kept in

working memory seems to impact the decision report, as

observed in Figures 2B and 2C. These results suggest that

frontal lobe circuits do not simply wait for the result of a sensory

evaluation to be communicated but that actively participate in

this process. Although highly speculative, we suggest that main-

taining in working memory the original stimulus information on

which the decision is based could serve to continuously update

the postponed decision report in this task, and that very likely

depends on the conjoined activity of these cortical areas.

Assuming that neurons from distinct cortical circuits coordi-

nate their activities to solve this perceptual discrimination task,

we wonder how these events evolve in time. The comparative

analysis of the response latencies of f1, f2, and comparison

signals could shed some light on this problem. For instance,

compare S1 and S2: their response latencies were significantly

different (p < 0.01), with the f1 and f2 signals beginning earlier

in S1 than in S2 (Figure 6). This type of comparative analysis

also shows that the response latencies of S2 began significantly

earlier (p < 0.01) than in VPC, PFC, MPC, and DPC. This would

suggest that S2 could send information about the stimuli to these

frontal lobe circuits because their response types are quite

similar to S2 (Figures 4 and 5A). The question is whether frontal

lobe circuits receive at the same time S2 inputs or at different

times. An analysis of the response latencies for f1 and f2 showed

that the PFC and VPC respond significantly earlier (p < 0.01) than

DPC, MPC and M1 (f1 was not present in M1), with no significant

differences between PFC and VPC (p > 0.01). This would suggest

that the PFC and VPC receive the S2 inputs and that very likely

cortex; S2, secondary somatosensory cortex; VPC, ventral premotor cortex;

m, geometric mean (vertical line in each histogram). Gray bars in the histograms

show percentage of bins close to 1 (arbitrary range, 1 ± 0.22). A value of 1

means close correspondence between neuronal activity and behavioral

report.

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distribute them to DPC, MPC, and M1. However, further studies

are needed to establish whether the functional connectivity

between these cortical circuits proceeds in such order in the

task used here.

The comparative analysis of the response latencies shows, in

this task, that sensory information processing proceeds in

a serial order. An important observation is that we did not find

significant differences (p > 0.01) in the response latencies

between the comparison and differential signals in the cortical

areas that showed these responses (Figure 6). The response

latencies for the comparison and differential signals were all

significantly (p < 0.01) delayed in comparison to the f1 and f2

signals. These findings suggest that S1 generates first a neural

representation of the stimulus (positive monotonic encoding)

and that then the S2 circuit transforms it into a dual representa-

tion (positive and negative monotonic encoding). The S2 repre-

sentations could be then used by the frontal lobe circuits to

not only encode the sensory information but also for the decision

process (Romo et al., 2003). Also, these findings suggest that

frontal lobe circuits coordinate the sensory, memory and deci-

sion components of the task, but that these processes are first

coordinated in PFC and VPC (Romo et al., 2004). These interpre-

tations, however, need further support by analyzing the neuronal

responses simultaneously recorded across cortical areas during

this task (Hernandez et al., 2008).

A fundamental problem posed by the results obtained in this

study is whether the neuronal signals across the task compo-

nents are associated with decision making or motor planning.

But, is there any difference between making a decision and plan-

ning a motor action that can be also postponed? In this respect,

the study by Gold and Shadlen (2000) is particularly revealing.

They recorded neural activity and injected microstimulation

current in the frontal eye field of monkeys performing a two-

alternative visual motion discrimination task. They found that

this area gradually accumulates evidence for motion in one or

another direction, such that the process of forming a decision

and motor preparation seem to be indistinguishable. The deci-

sion process studied here seems to proceed as it were part of

an encompassing, established plan. In this respect, decision

making could be conceived as the creation of a highly flexible

motor plan that can be delayed (if the action requires a go signal

to be triggered) or rapidly reconfigured (if the motor output is not

specified ahead of time), for example (Hernandez, et al., 2002;

Lemus et al., 2007; Romo et al., 2002, 2004). In fact, the stronger

signals that come closest to encoding the output of a decision

making process in our task have been found in areas involved

in motor actions: PFC, VPC, MPC, DPC, and M1. These results

fit quite well with the interpretation that those frontal lobe circuits

that show preparatory activity during delay periods not only

encode the planning of motor actions but encode also informa-

tion on which the motor action is based (Carpenter et al., 1999;

Hoshi and Tanji, 2004; Mushiake et al., 2006; Ohbayashi et al.,

2003; Shima et al., 2007).

Intuitively, the circuits that generate motor commands should

stand at the other end of the decision making processes

because their output needs to be expressed physically. Consis-

tent with this conjecture is the fact that some neurons from the

frontal lobe circuits respond differentially during the movement

execution in the light instruction task (Figure 7B), but not during

the periods that preceded it. This result is very similar to that

reported by Salinas and Romo (1998) in a categorization task.

Another possibility is that motor planning in this task is main-

tained in other circuits, for example, in the spinal cord (Prut

and Fetz, 1999). In this case weak signals sent from the cortical

lobe circuits could activate the execution of the motor plan in this

task. This conjecture is supported by the fact that the frontal lobe

circuits studied in our task send projections to the motor circuits

of the spinal cord (Dum and Strick, 1991; He et al., 1993).

However, few studies have explored the functional role of frontal

lobe neurons that project to the spinal cord during cognitive

tasks (Kraskov et al., 2009). Thus, further studies are needed

to explore the functional roles of frontal lobe circuit neurons

that send projections to the spinal cord, and whether spinal

motor circuits receive an instruction signal to execute the motor

plan in this task.

Our study is different from other paradigms used to investigate

perceptual discrimination processes (de Lafuente and Romo,

2005; Newsome et al., 1989). They have shown how neurons

from distinct cortical areas vary their discharges as a function

of varying a sensory stimulus and of the behavioral responses

(de Lafuente and Romo, 2006; Gold and Shadlen, 2007). Our

study focused on how neurons from several cortical areas

respond during decision making based on the evaluation of

two stimuli. In our task, decision making arises from the interac-

tion between stored information of f1 and current f2. Thus, the

fundamental mechanism behind decision making based on the

evaluation of one single stimulus or about two stimuli resides

on understanding the contribution of memory: how is it com-

bined with the current sensory input to produce a decision?

Although quite speculative, it is very likely that the working

memory signals associated with the delay period of our task is

a reflection of the stimulus recall triggered by the sensory

inputs. In this vein, it could be possible that the synapses of

the neuronal networks associated with the discrimination task

store the ‘‘task rule’’ (Mongillo et al., 2008), in which case the

scalar analog stimuli trigger synaptic graded responses, which

are then reflected in the neuronal firing rates. This mechanism

could be responsible for encoding the stimuli during the stimulus

periods, working memory and decision periods in cortical areas

central to S1.

In brief, this study shows how the dynamics of distinct cortical

circuits contribute to perceptual discrimination. However, to

reveal the flow of information between these circuits and the

operations described in our task, it would be desirable to simul-

taneously record the neuronal events (Hernandez et al., 2008).

Experiments of this type would reveal how neuronal populations

of distinct brain circuits joint efforts, in real time, to solve percep-

tual discrimination.

EXPERIMENTAL PROCEDURES

Discrimination Task

This study was performed on four male monkeys, Macaca mulatta, 5–7 kg. The

sensory discrimination task used here has already been described (Lemus

et al., 2007 and Figure 1). The monkey sat on a primate chair with its head fixed.

The right hand was restricted through a half-cast and kept in palm-up position.

The left hand operated an immovable key (elbow at �90�) and two push

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Decision Making across Cortex

buttons in front of the animal, 25 cm away from the shoulder and at eye level.

The centers of the switches were located 7 and 10.5 cm to the left of the

midsagittal plane. In all trials, the monkey first placed the left hand and later

projected to one of the two switches. Stimuli were delivered to the skin of

the distal segment of one digit of the right, restrained hand, via a computer-

controlled stimulator (2 mm round tip, BME Systems). The initial indentation

was 500 mm (pd in Figure 1A). Vibrotactile stimuli were trains of short mechan-

ical pulses. Each of these pulses consisted of a single-cycle sinusoid lasting

20 ms. Stimulation amplitudes were adjusted to produce equal subjective

intensities (Hernandez et al., 1997; Mountcastle et al., 1990). During trials,

two vibrotactile stimuli (f1 and f2) were delivered consecutively to the glabrous

(hairless) skin of one fingertip, separated by a fixed interstimulus delay period

of 3 s. The monkey was asked to report discrimination of the two stimuli after

a fixed delay period of 3 s between the end of f2 and a cue signal that triggered

the beginning of the motor response (pu in Figure 1A). Discrimination was indi-

cated by pressing one of two pushbuttons with the left hand (lateral push

button for f2 > f1, medial push button for f2 < f1). The animal was rewarded

for correct discriminations with a drop of liquid. Performance was quantified

through psychometric techniques (Figures 1B and C). Monkeys were handled

according to the institutional standards of the National Institutes of Health and

Society for Neuroscience.

Because we were interested in finding cortical activity related to sensory

events, it was crucial to minimize or eliminate modulatory effects arising

from the well known dependence on arm movement direction (Georgopoulos

et al., 1988; Schwartz et al., 1988) or on parameter that covary with it. The

setup was thus arranged to filter out the classic directionally tuned responses.

The distance between target switches was 3.5 cm, and these were 18 cm away

from the immovable key. Thus, the difference between medial and lateral

movements was�11�. On average, the directional cells reported by Schwartz

et al. (1988, their Figure 13) fire at frequencies that range between �5 and

25 spikes/s, corresponding to their antipreferred and preferred directions,

respectively. Therefore, on average, directional cells modulate their firing rates

by�20 spikes/s when movement direction changes 180�. The expected effect

on an 11� change in direction is thus on the order of 1 spike/s. Under these

conditions some activity related to arm motion may be expected, but should

be practically identical for the two arm movements.

Visual Instruction Task

During recordings, we used a simpler control task. Trials in this test began

exactly as described above and in Figure 1A, except that when the probe

touched the skin, one of the target switches was illuminated. The monkey

had to respond by holding the immovable key. Then, after a long delay period

(8–9 s) the light was turned off and the probe was simultaneously lifted off from

the skin and triggered the hand/arm movement. The monkey was rewarded for

pressing the previously illuminated pushbutton. Arm movements in this situa-

tion were identical to those in the vibrotactile discrimination task, but were

cued by visual stimuli.

Passive Stimulation

In this condition, the same set of stimuli (Figure 1B) were delivered to the

fingertip while recording cortical neurons from diverse cortical areas, but

discrimination was restricted, just by removing the key and the interrupt target

switches. Thus, in this condition the animal remained alert—by rewarding with

drops of liquid at different times—but was no longer using the stimuli to indi-

cate discrimination with the free hand/arm.

Recordings

Neuronal recordings were obtained with an array of seven independent micro-

electrodes (2–3 MU; Romo et al., 1999) inserted into each cortical area, contra-

lateral (left hemisphere) or ipsilateral (right hemisphere) to the stimulated hand,

except for S1 in which recordings were contralateral to the stimulated hand

and in M1 (arm region) contralateral to the responding hand/arm. We collected

data using the stimulus set of Figure 1B, usually 10 trials per stimulus pair.

We used well-established electrophysiological and anatomical criteria to

distinguish between cortical areas (Figure 1C; Hernandez et al., 2002; Romo

et al., 1999, 2002, 2004; Salinas et al., 2000; Salinas and Romo, 1998). For

example, in S1 we recorded single neurons with cutaneous receptive fields

312 Neuron 66, 300–314, April 29, 2010 ª2010 Elsevier Inc.

confined to the distal segments of the glabrous skin of fingertips 2, 3 or 4

and had quickly adapting properties. All neurons recorded in S2 had large

cutaneous receptive fields confined to the hand contralateral to the recording

site. All neurons in MPC were recorded in the pre-SMA. Pre-SMA is located

rostral to a line passing from the midline to the posterior edge of the arcuate

sulcus (Matsuzaka et al., 1992). Neuronal activity from VPC was recorded in

area F5 (Rizzolatti et al., 1988). Recordings in PFC were made anterior to the

arcuate sulcus and lateral to the principal sulcus (Romo et al., 1999). All record-

ings in DPC were made in the arm region of F2 (Rizzolatti and Luppino 2001).

This region is in front of M1 (F1), lateral to the central dimple, posterior to F7

and genu of arcuate sulcus (Rizzolatti and Luppino, 2001). Recordings in M1

were confined to the arm region of M1, confined to the anterior bank and crown

of the central sulcus, medial to the level of the posterior genu of the arcuate

sulcus and lateral to the central dimple (Rizzolatti and Luppino 2001). On all

recordings sessions in M1, acceptable penetrations sites were first identified.

The criterion was that, throughout the penetration track (maximum depth of

2000 mm), neurons were found that responded both during the task and to

passive movements of the contralateral arm. The passive responses had to

be related to shoulder and elbow joints; when they were associated with wrist

and finger movements the penetration was discarded. If these conditions were

met, then other neurons with different characteristics but recorded in the same

penetration were also studied and considered in the analysis. Recordings sites

changed from session to session and the locations of the penetrations were

used to construct surface maps of all the penetrations in each cortical area.

This was done by marking the edges of the small chamber (7 mm in diameter)

placed above each cortical area.

Data Analysis

We considered a neuron’s response as task-related if during any of the rele-

vant periods (f1, delay between f1 and f2, f2, delay between f2 and pu, reaction

time [RT] or movement time ([MT]) its mean firing rate was significantly different

from a control period of equal duration (500 ms) but preceding the initial probe

indentation at the beginning of each trial (Wilcoxon test, p < 0.01; Siegel and

Castellan, 1988). By definition, f1 and f2 correspond to the base and compar-

ison periods, respectively. The first delay was divided into consecutive inter-

vals of 500 ms beginning at the end of f1 and up to the beginning of f2. Similar

intervals were used for the second delay between the end of f2 and pu. The RT

was the period from the end of pu to the beginning of the key up (ku; Figure 1A).

The MT was the period from the end of ku to the beginning of the push button

press (pb; Figure 1A).

To estimate the a1, a2, and a3 coefficient values through multivariate regres-

sion analysis (Draper and Smith, 1966; Press et al., 1992), we used the 18 stim-

ulus frequency pairs labeled in green and red in Figure 1B. The correlation

coefficient between these stimulus pairs is zero, indicating that f1 and f2 are

absolutely linearly independent. After finding the best-fit coefficients a1 and

a2, differences between fitted and measured responses to the individual (f1,

f2) stimulus pairs were calculated, resulting in a full 2D covariance matrix of

errors (Press et al., 1992). Coefficients were considered significantly different

from (0, 0) if they were more than two standard deviations away. Neuronal

responses were defined unambiguously as dependent on f1 or f2 if the

coefficients of the planar fit were within two standard deviations of either the

a2 = 0 or the a1 = 0 lines, respectively. Responses were considered dependent

on f2 � f1 if the coefficients were more than 2 standard deviations away from

these two lines and within 2 standard deviations of the a2 = �a1 line

(Figure 2C). Responses not satisfying this criterion were classified as ‘‘mixed.’’

The dynamics of these coefficients was analyzed using a sliding window of

200 ms duration moving in steps of 20 ms.

The beginning of the f1 tuned response latency was estimated for each

neuron by identifying the first three consecutive 20 ms bins after f1 onset, in

which a1 was significantly different from zero and a2 was not significantly

different from zero. We then obtained a post-stimulus-time-histogram (PSTH,

500 ms immediately before f1 onset until the end of f1 using a time resolution

of 1 ms). The PSTH was smoothed using a 3-point moving average filter. We

then obtained the mean and standard deviation from the PSTH corresponding

to the 500 ms immediately before stimulus onset. From this, we identified the

first bin after the stimulus onset with a value higher to the mean and two

standard deviations of the control period (p < 0.01). The time of this bin was

Neuron

Decision Making across Cortex

considered as the response latency. The beginning of the f2 tuned responses

was similarly estimated for each neuron as for the f1 tuned response, but a2

was significantly different from zero and a1 was not significantly different

from zero. The beginning of the comparison response (c; black dots and traces

in Figures 2–5) was estimated as before but, (1) a1 and a2 were both signifi-

cantly different from zero; (2) either a1 or a2 was two standard deviations

away from the a2 = �a1 line.. The beginning of the differential response

(d; blue dots and traces in Figures 2–5) was estimated similarly as for the

comparison responses by identifying the first of three consecutive 20 ms

bins in which the coefficients a1 and a2 were significantly different from zero

and both coefficients were of equal magnitudes, within two standard devia-

tions of the a2 = �a1 line.

The choice probability index was calculated using methods from signal

detection theory (Green and Swets, 1966). This quantity measures the overlap

between two response distributions, in this case between correct and error

responses for each stimulus (f1, f2) pair. We restricted the analysis to those

stimulus (f1, f2) pairs for which the animals had between 30% and 70% of

errors. Notice that a value of 0.5 indicates full overlap, while 1 and 0 indicates

completely separate distributions (Figures 2 and 3). Thus, the choice proba-

bility index quantifies selectivity for one or the other outcome of the discrimi-

nation process. To compute it at different times, we used a sliding window

of 200 ms duration moving in 20 ms steps, beginning 1000 ms immediately

before f1 and ending 1000 ms immediately after the animal reported the

comparison between f2 and f1. To establish the significance of the choice

probability values, the neuronal responses in each time window were shuffled,

such that correct and error trials were randomized, and new choice probability

indices for the shuffled data were generated (permutation test). By comparing

the indices from the shuffled and unshuffled data and repeating the process

1000 times, we estimated the probability of obtaining choice probability values

as large or larger than those observed initially (with the unshuffled data) just by

chance (Figures 7A and S2). In the light instruction task, choice probability

indices were calculated by comparing the response distributions for lateral

versus medial push button presses (Figure 7B).

SUPPLEMENTAL INFORMATION

Supplemental Information includes two figures and can be found with this

article online at doi:10.1016/j.neuron.2010.03.031.

ACKNOWLEDGMENTS

The research of R.R. was partially supported by an International Research

Scholars Award from the Howard Hughes Medical Institute, and grants from

the Direccion del Personal Academico de la Universidad Nacional Autonoma

de Mexico and the Consejo Nacional de Ciencia y Tecnologıa. We thank Carlos

Brody, Andres Ojeda, and Emilio Salinas for invaluable comments.

Accepted: March 24, 2010

Published: April 28, 2010

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