The emergence of consciousness from brain activity constitutes one of the great riddles in biology. It is commonly assumed that only the conscious perception of the presence of a stimulus elicits neuronal activation to signify a “neural correlate of consciousness,” whereas the subjective experience of the absence of a stimulus is associated with a neuronal resting state. Here, we demonstrate that the two subjective states “stimulus present” and “stimulus absent” are represented by two specialized neuron populations in crows, corvid birds. We recorded single-neuron activity from the nidopallium caudolaterale of crows trained to report the presence or absence of images presented near the visual threshold. Because of the task design, neuronal activity tracking the conscious “present” versus “absent” percept was dissociated from that involved in planning a motor response. Distinct neuron populations signaled the subjective percepts of “present” and “absent” by increases in activation. The response selectivity of these two neuron populations was similar in strength and time course. This suggests a balanced code for subjective “presence” versus “absence” experiences, which might be beneficial when both conscious states need to be maintained active in the service of goal-directed behavior.

Our sense of number rests on the activity of neurons that are tuned to the number of items and show great invariance across display formats and modalities. Whether numerosity coding becomes abstracted from local spatial representations characteristic of visual input is not known. We mapped the visual receptive fields (RFs) of numerosity-selective neurons in the pFC and ventral intraparietal area in rhesus monkeys. We found numerosity selectivity in pFC and ventral intraparietal neurons irrespective of whether they exhibited an RF and independent of the location of their RFs. RFs were not predictive of the preference of numerosity-selective neurons. Furthermore, the presence and location of RFs had no impact on tuning width and quality of the numerosity-selective neurons. These findings show that neurons in frontal and parietal cortices integrate abstract visuospatial stimuli to give rise to global and spatially released number representations as required for number perception.

Neural oscillations in distinct frequency bands in the prefrontal cortex (pFC) are associated with specialized roles during cognitive control. How dopamine modulates oscillations to structure pFC functions remains unknown. We trained macaques to switch between two numerical rules and recorded local field potentials from pFC while applying dopamine receptor targeting drugs using microiontophoresis. We show that the D1 and D2 family receptors (D1Rs and D2Rs, respectively) specifically altered internally generated prefrontal oscillations, whereas sensory-evoked potentials remained unchanged. Blocking D1Rs or stimulating D2Rs increased low-frequency theta and alpha oscillations known to be involved in learning and memory. In contrast, only D1R inhibition enhanced high-frequency beta oscillations, whereas only D2R stimulation increased gamma oscillations linked to top–down and bottom–up attentional processing. These findings suggest that dopamine alters neural oscillations relevant for executive functioning through dissociable actions at the receptor level.

Crows quickly learn arbitrary associations. As a neuronal correlate of this behavior, single neurons in the corvid endbrain area nidopallium caudolaterale (NCL) change their response properties during association learning. In crows performing a delayed association task that required them to map both familiar and novel sample pictures to the same two choice pictures, NCL neurons established a common, prospective code for associations. Here, we report that neuronal tuning changes during learning were not distributed equally in the recorded population of NCL neurons. Instead, such learning-related changes relied almost exclusively on neurons which were already encoding familiar associations. Only in such neurons did behavioral improvements during learning of novel associations coincide with increasing selectivity over the learning process. The size and direction of selectivity for familiar and newly learned associations were highly correlated. These increases in selectivity for novel associations occurred only late in the delay period. Moreover, NCL neurons discriminated correct from erroneous trial outcome based on feedback signals at the end of the trial, particularly in newly learned associations. Our results indicate that task-relevant changes during association learning are not distributed within the population of corvid NCL neurons but rather are restricted to a specific group of association-selective neurons. Such association neurons in the multimodal cognitive integration area NCL likely play an important role during highly flexible behavior in corvids.

In everyday situations, quantitative rules, such as “greater than/less than,” need to be applied to a multitude of magnitude comparisons, be they sensory, spatial, temporal, or numerical. We have previously shown that rules applied to different magnitudes are encoded in the lateral PFC. To investigate if and how other frontal lobe areas also contribute to the encoding of quantitative rules applied to multiple magnitudes, we trained monkeys to switch between “greater than/less than” rules applied to either line lengths (spatial magnitudes) or dot numerosities (discrete numerical magnitudes). We recorded single-cell activity from the dorsal premotor cortex (dPMC) and cingulate motor cortex (CMA) and compared it with PFC activity. We found the largest proportion of quantitative rule-selective cells in PFC (24% of randomly selected cells), whereas neurons in dPMC and CMA rarely encoded the rule (6% of the cells). In addition, rule selectivity of individual cells was highest in PFC neurons compared with dPMC and CMA neurons. Rule-selective neurons that simultaneously represented the “greater than/less than” rules applied to line lengths and numerosities (“rule generalists”) were exclusively present in PFC. In dPMC and CMA, however, neurons primarily encoded rules applied to only one of the two magnitude types (“rule specialists”). Our data suggest a special involvement of PFC in representing quantitative rules at an abstract level, both in terms of the proportion of neurons engaged and the coding capacities.

Whether nonhuman primates can decouple their innate vocalizations from accompanied levels of arousal or specific events in the environment to achieve cognitive control over their vocal utterances has been a matter of debate for decades. We show that rhesus monkeys can be trained to elicit different call types on command in response to arbitrary visual cues. Furthermore, we report that a monkey learned to switch between two distinct call types from trial to trial in response to different visual cues. A controlled behavioral protocol and data analysis based on signal detection theory showed that noncognitive factors as a cause for the monkeys' vocalizations could be excluded. Our findings also suggest that monkeys also have rudimentary control over acoustic call parameters. These findings indicate that monkeys are able to volitionally initiate their vocal production and, therefore, are able to instrumentalize their vocal behavior to perform a behavioral task successfully.

Animals and humans share an evolutionary ancient quantity representation which is characterized by analog magnitude features: Discriminating magnitudes becomes more difficult with increasing set sizes ( size effect ) and with decreasing distance between two numerosities ( distance effect ). Humans show these effects even with number symbols. We wondered whether monkeys would show the same psychophysical effects with numerical signs and addressed this issue by training three monkeys to associate visual shapes with numerosities. We then confronted the monkeys with trials in which they had to match these visual signs with each other. The monkeys' performance in this shape versus shape protocol was positively correlated with the numerical distance and the magnitudes associated with the signs. Additionally, the monkeys responded significantly slower for signs with higher assigned numerical values. These findings suggest that the numerical values imprint their analog magnitudes characteristics onto the associated visual sign in monkeys, an effect that we also found reflected in the discharges of prefrontal neurons. This provides evidence for a precursor of the human number symbol knowledge.

There is general agreement that nonverbal animals and humans endowed with language possess an evolutionary precursor system for representing and comparing numerical values. However, whether nonverbal numerical representations in human and nonhuman primates are quantitatively similar and whether linear or logarithmic coding underlies such magnitude judgments in both species remain elusive. To resolve these issues, we tested the numerical discrimination performance of human subjects and two rhesus monkeys ( Macaca mulatta ) in an identical delayed match-to-numerosity task for a broad range of numerosities from 1 to 30. The results demonstrate a noisy nonverbal estimation system obeying Weber's Law in both species. With average Weber fractions in the range of 0.51 and 0.60, nonverbal numerosity discriminations in humans and monkeys showed similar precision. Moreover, the detailed analysis of the performance distributions exhibited nonlinearly compressed numerosity representations in both primate species. However, the difference between linear and logarithmic scaling was less pronounced in humans. This may indicate a gradual transformation of a logarithmic to linear magnitude scale in human adults as the result of a cultural transformation process during the course of mathematical education.

Monkeys have been introduced as model organisms to study neural correlates of numerical competence, but many of the behavioral characteristics of numerical judgments remain speculative. Thus, we analyzed the behavioral performance of two rhesus monkeys judging the numerosities 1 to 7 during a delayed match-to-sample task. The monkeys showed similar discrimination performance irrespective of the exact physical appearance of the stimuli, confirming that performance was based on numerical information. Performance declined smoothly with larger numerosities, and reached discrimination threshold at numerosity “4.” The nonverbal numerical representations in monkeys were based on analog magnitudes, object tracking process (“subitizing”) could not account for the findings because the continuum of small and large numbers shows a clear Weber fraction signature. The lack of additional scanning eye movements with increasing set sizes, together with indistinguishable neuronal response latencies for neurons with different preferred numerosities, argues for parallel encoding of numerical information. The slight but significant increase in reaction time with increasing numerosities can be explained by task difficulty and consequently time-consuming decision processes. The behavioral results are compared to single-cell recordings from the prefrontal cortex in the same subjects. Models for numerosity discrimination that may account for these results are discussed.