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The detection of stimuli is critical for an animal’s survival . However, it is not adaptive for an animal to respond automatically to every stimulus that is present in the environment [2–5]. Since the prefrontal cortex (PFC) plays a key role in executive function [6–8], we hypothesized that PFC activity should be involved in context-dependent responses to uncommon stimuli. To test this hypothesis, monkeys participated in a same-different task, a variant of an oddball task . During this task, a monkey heard multiple presentations of a “reference” stimulus that was followed by a “test” stimulus and reported whether these stimuli were the same or different. While they participated in this task, we recorded from neurons in the ventrolateral prefrontal cortex (vPFC; a cortical area involved in aspects of non-spatial auditory processing [9, 10]). We found that vPFC activity was correlated with the monkeys’ choices. This finding demonstrates a direct link between single neurons and behavioral choices in the PFC on a non-spatial auditory task.
Two rhesus monkeys participated in the same-different task (Fig. 1A) using morphed versions of the prototype spoken words bad and dad (Figure 1B). Figure 1C shows the behavioral performance of the two monkeys. The data shown in this figure were generated from all of the recording sessions reported in this study. The monkeys reliably reported that the 0% – 40% morph test stimuli were different than the reference stimulus and that the 60% – 100% morph test stimuli were the same as the reference stimulus. The monkeys’ reports on the 50% morphs were, in general, intermediate between their reports of the lower-percentage and the upper-percentage morphs. The monkeys’ performance during this task is consistent with a large literature of human and animal studies that tested the perceptual boundaries of human phonemes (ba and da) [11–15].
We recorded from 91 vPFC neurons while the monkeys participated in the same-different task (Figure 1A). For 53 of these 91 neurons, we collected blocks of data in which both bad and dad were the reference stimulus. In the other 38 neurons, we only collected blocks of data in which either bad (22 neurons) or dad was the reference stimulus (16 neurons). 67 of the 91 neurons were classified as “auditory” [16–18]; these neurons had reliably different firing rates during the 500-msec period that began with test-stimulus onset than during the 500-msec period that occurred prior to test-stimulus onset (t-test, p < 0.05).
The response profiles of two vPFC neurons are shown in Figure 2. For the vPFC neuron in Figure 2A, when the test stimulus was a 0% – 50% morph, this neuron had a high firing rate (cool-blue colors). In contrast, when the test stimulus was a 60% – 100% morph, the neuron had a relatively lower firing rate (red/purple colors).
Which aspects of the task could this vPFC neuron be coding? Since the stimulus-presentation dynamics in our same-different task are similar to that used in oddball tasks and stimulus-specific adaptation [2, 19], stronger “pop-out” vPFC responses might reflect the automatic detection  of uncommon test stimuli. Therefore, stronger responses could reflect test stimuli that are acoustically distinct from the reference stimulus (i.e., the 0% – 80% morphs). However, since this neuron responds weakly to several of the test stimuli (see Figure 2A), its response pattern does not reflect the presence of acoustically distinct test stimuli.
Another neuron with a different type of response profile is shown in Figure 2B. Unlike the neuron in Figure 2A, this vPFC neuron had a low firing rate (cool-blue colors) when the test stimulus was a 0% – 50% morph and a high firing rate (red/purple colors) when the test stimulus was a 60% – 100% morph. This neuron’s response profile, like that in Figure 2A, is also incompatible with the idea that vPFC neurons automatically signal the detection of acoustically-distinct test stimuli with strong pop-out responses : this neuron had a low firing rate when the response and test stimuli were acoustically distinct. Relatively few neurons (n = 4/67) had response profiles like that shown in Figure 2B; most (n = 63/67) had response profiles similar to that shown in Figure 2A.
Instead of the automatic detection of uncommon stimuli, we hypothesize that vPFC activity may be correlated with the monkey’s choices (behavioral reports; see Figure 1C). For the neuron in Figure 2A, stronger responses might reflect trials when the monkey reports that the reference and test stimuli are perceptually—as opposed to acoustically—distinct (i.e., different), whereas weaker responses might reflect trials when he reports that they are perceptually similar (i.e., the same). For the neuron in Figure 2B, weaker responses might code the trials when the two stimuli are the same and strong responses might code the trials when the stimuli are different. This hypothesis is tested directly by a series of population analyses in the next section.
Does vPFC activity reflect what the monkeys should choose or does it reflect the monkeys’ actual choices? To gain insight into whether neural activity reflects what the monkey should choose, a neurometric analysis [20, 21] (see Supplemental Material) was conducted. This analysis tests whether an ideal observer can use vPFC activity to predict the differences between test stimuli and whether this activity co-varies with the monkeys’ behavioral reports. However, as seen in the Supplemental Material, the results of the neurometric analysis indicate that vPFC activity is not a good predictor of the test stimulus and, hence, is not a good predictor of what the monkey should choose.
To test the hypothesis that vPFC activity reflects the monkeys’ actual choices, we calculated the choice probability (CP) [22–24]. On a neuron-by-neuron-basis and using both successful and error trials, we first formed two distributions. One distribution contained the test-stimulus-period firing rates from trials when the monkey reported that the reference and test stimuli were the same. The second distribution contained the firing rates when the monkey reported that the stimuli were different. From these two distributions, a receiver-operating-characteristic curve was generated; the area under this curve is a neuron’s CP . The CP values from different neurons and from different variations of the analysis were grouped together to form different population distributions of the CP values; to minimize the differences between different neurons’ firing rates, firing rates were normalized with a z-score.
If vPFC activity reflects the automatic detection of acoustically-uncommon test stimuli, neural activity should not be modulated by the monkeys’ choices. Under this hypothesis, the CP should equal 0.5. On the other hand, if vPFC activity reflects the monkeys’ choices, the CP should be > 0.5 or < 0.5 if vPFC activity, on average, increases or decreases, respectively, when the monkeys report that the reference and test stimuli are different.
We first calculated the “grand” CP . In this analysis, the “same” and “different” distributions were formed using the data generated from all of the potential test-stimulus morph values (i.e., 0% – 100%). The data in Figure 3A represent the grand-CP values generated when the reference stimulus was bad, whereas the data in Figure 3B represent the grand-CP values generated when the reference stimulus was dad. The mean grand-CP values from both distributions were reliably greater than 0.5 (t-test; p < 0.05). This result is consistent with the hypothesis that vPFC activity during the same-different task reflects the monkeys’ choices.
This population-level result was also seen at the single-neuron level. When the reference stimulus was bad, 33 of the 56 neurons had grand-CP values reliably larger than 0.5 (permutation test, p <0.05); this proportion of neurons is reliably greater than chance (binomial test, p < 0.05). Similarly, when the reference stimulus was dad, a significant proportion of vPFC neurons (n = 26/55; binomial test, p < 0.05) had grand-CP values that were reliably larger than 0.5 (permutation test, p <0.05). We did not find a reliable population (p > 0.05) of vPFC neurons with significant CP values < 0.5.
Next, we considered whether the results of the grand-CP analysis might be biased by particular test-stimulus morph values. The CP values might have been biased toward large values during those trials when the monkeys’ choices were “easy” (i.e., those trials when the reference and test stimuli were very different or identical) and there were few error trials. In contrast, the CP values might have been biased toward values equaling 0.5 during those trials when the monkeys choices were “hard” (i.e., those trials when the reference and test stimuli were similar) and there were relatively more error trials.
To eliminate this possibility, we calculated the CP values from the neural data generated during easy trials (0%, 20%, 80%, and 100% morphs) and during hard trials (40% – 60% morphs). The population distributions of these easy- (Fig. 3C) and hard-CP (Fig. 3D) values were both reliably greater than 0.5 (t-test; p < 0.05). Thus, vPFC neurons code the monkeys’ choices for both easy and hard morph values.
Finally, we examined, at the population level, the grand-CP time course. Figure 4A shows this analysis when the data were aligned relative to test-stimulus onset. As expected, when the reference stimuli were presented (i.e., time < 0), the mean CP value was not reliably different than 0.5. However, following test-stimulus onset, the CP increased and became reliably > 0.5. The average CP value remained > 0.5 following test-stimulus offset for another ~250 msec before returning to a value of 0.5.
To test how the grand CP is modulated before the monkeys report their choices, we realigned the data relative to the onset of the two LEDs. As seen in Figure 4B, the CP preceding LED onset remained elevated. Additionally, there was a slight increase in the CP following LED onset that correlates with the monkeys’ saccade to one of the two LEDs. This CP increase was not wholly related to any potential spatial tuning of neural activity during the saccade period: saccade-related activity in our population of vPFC neurons was not, in general, spatially tuned (data not shown).
If vPFC activity reflects the automatic detection of acoustically uncommon stimuli, we would expect that vPFC activity would habituate with repeated presentations of the reference stimuli as seen in stimulus-specific adaptation studies [19, 25]. We found that, on average, vPFC activity was not modulated by the number of reference stimuli and hence, did not habituate (bad: F(6,383) = 1.51, p > 0.05; dad: F(6,376) = 1.25, p > 0.05). We also tested whether a vPFC neuron’s response to the test stimulus was dependent on the number of reference stimuli. As the number of reference stimuli increases, the probability that the next stimulus is a test stimulus also increases. To test this possibility, we sorted the average test-stimulus firing rates as a function of the number of reference stimuli that preceded test-stimulus onset (see Supplemental Figure 3). We did not find a main effect for the number of reference stimuli (bad: F(2,1105) = 1.68, p > 0.05; dad: F(2,1082) = 0.43, p > 0.05), but there was a main effect for the morph percentage on the test-stimulus firing rates (bad: F(6,1105) = 29.83, p < 0.05; dad: F(1,1082) = 8.89, p < 0.05); this latter result indicates that test-stimulus firing rates were modulated by the morph percentage as seen in Figure 2. Finally, we asked whether the context in which the prototype stimuli were presented (i.e., as a reference or test stimulus) modulated vPFC activity. To test this issue, we calculated, on a neuron-by-neuron basis, an index that quantified how similarly a neuron responded to a prototype when it was the reference stimulus versus when it was the test stimulus (top rows of Supplemental Fig. 4A and 4B). Since these index-value distributions were not reliably different than zero (t-test; p > 0.05), vPFC neurons, on average, responded comparably to a prototype when presented as reference or test stimulus. In contrast, when the reference and test stimuli were different prototypes (see the bottom rows of Supplemental Fig. 4A and 4B), the average index value was reliably different than zero (p < 0.05) indicating that vPFC neurons responded differently to the two prototypes.
Whereas it is clearly adaptive to detect uncommon or novel stimuli, it is not adaptive to respond to all of these stimuli since this detection might divert key attentional and neural resources away from a critical task. Indeed, neural representations of uncommon stimuli are reduced as the attentional demands of an ongoing task increase [26–30]. Alternatively, attention can facilitate the detection of uncommon tones from the background [31, 32]. Thus, the perceptibility of uncommon stimuli is under considerable cognitive control and is not purely an automatic response.
Since the PFC plays a key role in executive function , it is natural to hypothesize that it might also contribute significantly to the adaptive processes that allow an animal to contextually respond to the presence of uncommon stimuli. The PFC might mediate this role through top-down mechanisms that flexibly modulate the neural circuits involved with the detection of novel stimuli [30, 33, 34]. Several lines of evidence support a role for the PFC in contextually-dependent detection of uncommon or novel stimuli. Familiarity, for example, may be a modulating factor: when familiar stimuli, which are inherently not novel, occur in unfamiliar situations, they differentially modulate PFC neurons . Second, PFC activity is correlated with decisions on the commonality of a stimulus and the subsequent re-allocation of neural resources . Third, using a delayed match-to-sample task that was similar, but not identical, to our same-different task, Miller and colleagues reported that PFC neurons are actively engaged in the decision-making process of whether two stimuli are the same or different . Finally, the current data (see Figures 3 and and4)4) indicate that test-period vPFC activity reflects the monkeys’ choices during a same-different task.
Previous work from our laboratory and others have suggested that the pathway leading from the primary auditory cortex to the superior temporal gyrus and ultimately to the vPFC is dedicated to processing the non-spatial aspects of auditory stimuli [9, 10, 16, 38]. However, since these studies did not test neural activity while monkeys were participating in an auditory behavior, it was not known whether the vPFC and other areas in this pathway are actively engaged in auditory cognition. Here, we demonstrate directly that the vPFC plays an important role in aspects of non-spatial auditory cognition: vPFC activity reflects the decision-making processes that monkeys make during a non-spatial auditory task. Unfortunately, our data are too preliminary to offer insight into the mechanism of this decision-making process; though, future studies may be able to shed more light on these mechanisms. Finally, our results further emphasize the role of the PFC in the maintenance and retrieval of abstract rules [6–8, 39, 40] and are consistent with a more general literature describing a role for the PFC in decision making [41–44].
We recorded from neurons in the vPFC from one male and one female rhesus monkey (Macaca mulatta). Under isofluorane anesthesia, the monkeys were implanted with a scleral search coil, head-positioning cylinder, and a recording chamber. vPFC recordings were obtained from the male rhesus’ left hemisphere and from the female’s right hemisphere. All recordings were guided by pre- and post-operative magnetic resonance images of each monkey’s brain. The Dartmouth Institutional Animal Care and Use Committee approved the experimental protocols.
The prototype stimuli were the spoken words bad and dad. In humans, these stimuli differ in their place of articulation. The prototypes were digitized recordings an American adult female and were provided by Dr. Michael Kilgard. Morphed versions of the prototypes were created using the STRAIGHT  software package, which is run in the Matlab (The Mathworks Inc.) programming environment. Morphing was accomplished by calculating the shortest trajectory between the fundamental and formant frequencies of the two prototypes. Morphed versions of the two prototypes were created at 20%, 40%, 50%, 60%, and 80% of the distance along this trajectory. Spectrograms of the two prototypes and some of the morphed stimuli are shown in Figure 1.
As schematized in Figure 1, the task began with 2 – 4 presentations of a “reference” stimulus that was followed by the presentation of a “test” stimulus. The manner in which stimuli were presented in this task is similar to that seen in other studies of stimulus novelty such as oddball tasks and stimulus-specific adaptation [2, 19]. The reference and test stimuli were 500 msec in duration and the inter-stimulus interval averaged 1600 msec. The stimuli were presented from a speaker (Pyle, PLX32) that was in front of the monkey at a level of 70 dB SPL. The reference stimulus was always one of the two prototype words. The test stimulus was a morph of one of the two prototypes. The 100% morph was operationally defined to be the same prototype as the reference stimulus; therefore, the 0% morph was the other prototype. 500-msec after test-stimulus offset, two LEDs were illuminated. If the test stimulus was a 0% – 40% morph, the monkeys were rewarded when they successfully reported that the reference and test stimuli were different by making a saccade to the LED that was 20° to the right of the speaker. If the test stimulus was a 60% – 100% morph, the monkeys were rewarded when they successfully reported that the reference and test stimuli were the same by making a saccade to the LED that was 20° to the left of the speaker. When the test stimulus was a 50% morph, which has been shown to be a perceptual boundary [13, 14], the monkeys were rewarded randomly based on their overall performance level .
Single-unit extracellular recordings were obtained with tungsten microelectrodes (Frederick Haer & Co.) seated inside a stainless-steel guide tube. The electrode and guide tube were advanced into the brain with a hydraulic microdrive (Narishige MO-95). The electrode signal was amplified (Bak MDA-4I) and band-pass filtered (Krohn-Hite 3700,) between 0.6 – 6.0 kHz. Single-unit activity was isolated using a two-window, time-voltage discriminator (Bak DDIS-1). The time of occurrence of each action potential was stored for on- and off-line analyses.
The vPFC was identified by its anatomical location and its neurophysiological properties [18, 47]. The vPFC is located anterior to the arcuate sulcus and Area 8a and lies below the principal sulcus. vPFC neurons were further characterized by their strong responses to auditory stimuli.
Once a neuron was isolated, the monkeys participated in blocks of trials of the same-different task. Since vPFC neurons respond broadly to a wide range of auditory stimuli , we did not tailor the reference and test stimuli to the neuron’s response characteristics. In each block of trials, there were 6 trials in which the test stimulus was a 0% morph, 6 trials in which the test stimulus was a 100% morph, and 2 trials each of the remaining morphs. The test stimulus was chosen in a balanced pseudorandom order. We report those neurons in which we were able to collect data from ≥ 5 successful blocks of trials using one prototype as the reference stimulus.
We would like to thank John Pezaris, Tony Zador, Anne Krendl, Jung Hoon Lee, and Heather Hersh for helpful comments on the preparation of this manuscript, Ashlee Ackelson and Selina Davis for excellent technical assistance, and Farshad Chowdhury and Lauren Wool for assistance with preliminary aspects of data collection. BER was supported by an NRSA grant from the NIMH-NIH. YEC was supported by grants from the NIDCD-NIH and the NIMH-NIH.
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