Quantitative knowledge guides vital decisions in the life of animals and humans alike. The posterior parietal cortex in primates has been implicated in representing abstract quantity, both continuous (extent) and discrete (number of items), supporting the idea of a putative generalized magnitude system in this brain area. Whether or not single neurons encode different types of quantity, or how quantitative information is represented in the neuronal responses, however, is unknown. We show that length and numerosity are encoded by functionally overlapping groups of parietal neurons. Using a statistical classifier, we found that the activity of populations of quantityselective neurons contained accurate information about continuous and discrete quantity. Unexpectedly, even neurons that were nonselective according to classical spike-count measures conveyed robust categorical information that predicted the monkeys' quantity judgments. Thus, different information-carrying processes of partly intermingled neuronal networks in the parietal lobe seem to encode various forms of abstract quantity.monkey ͉ single-unit recording ͉ statistical classifier T he questions ''how many?'' and ''how much?'' refer to two different types of quantity. Abstract quantity may be discrete and enumerable, thus referring to the number of elements, as opposed to continuous and uncountable quantity such as spatial extent (1). Behavior based on abstract quantitative parameters is clearly adaptive; for instance, understanding how much drinking water is around (2, 3) or how many individuals belong to an opponent party (4, 5) guide vital decisions in the life of animals and humans alike.The conceptual similarity between discrete and continuous quantity is perceptually reflected by behavioral interference phenomena. In a number comparison task, for example, choosing the numerically larger number takes significantly longer if the numeral is smaller in size compared with the numerically smaller number (e.g., in the comparison 2 versus 7) (6, 7). This number-size interference implies interactions on the neuronal level when discrete and continuous quantities are processed. The parietal cortex has recently been implicated in the representation of different types of quantity information (8-10). Functional imaging studies in humans suggest that anatomical vicinity (7, 11-13) or even a common magnitude system (14) for the representation of numerical (discrete) and spatial (continuous) quantity in the parietal cortex might be responsible for behavioral interference phenomena between numerical and spatial quantity (15,16).How continuous quantity is encoded by single nerve cells, and how it relates to numerosity representations, however, remains unknown. We analyzed the response properties of individual neurons in the fundus of the intraparietal sulcus (IPS) of monkeys simultaneously engaged in numerosity and length discrimination tasks. Using a neural network classification technique, we investigated to what extent the responses of small populations of neuro...

Humans and animals can nonverbally enumerate visual items across time in a sequence or rapidly estimate the set size of spatial dot patterns at a single glance. We found that temporal and spatial enumeration processes engaged different populations of neurons in the intraparietal sulcus of behaving monkeys. Once the enumeration process was completed, however, another neuronal population represented the cardinality of a set irrespective of whether it had been cued in a spatial layout or across time. These data suggest distinct neural processing stages for different numerical formats, but also a final convergence of the segregated information to form most abstract quantity representations.

Memory-based decisions are often accompanied by an assessment of choice certainty, but the mechanisms of such confidence judgments remain unknown. We studied the response of 1065 individual neurons in the human hippocampus and amygdala while neurosurgical patients made memory retrieval decisions together with a confidence judgment. Combining behavioral, neuronal and computational analysis, we identified a population of memory-selective (MS) neurons whose activity signaled stimulus familiarity and confidence as assessed by subjective report. In contrast, the activity of visually selective (VS) neurons was not sensitive to memory strength. The groups further differed in response latency, tuning, and extracellular waveforms. The information provided by MS neurons was sufficient for a race model to decide stimulus familiarity and retrieval confidence. Together, this demonstrates a trial-by-trial relationship between a specific group of neurons and declared memory strength in humans. We suggest that VS and MS neurons are a substrate for declarative memories.

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The human amygdala plays a key role in recognizing facial emotions and neurons in the monkey and human amygdala respond to the emotional expression of faces. However, it remains unknown whether these responses are driven primarily by properties of the stimulus or by the perceptual judgments of the perceiver. We investigated these questions by recording from over 200 single neurons in the amygdalae of 7 neurosurgical patients with implanted depth electrodes. We presented degraded fear and happy faces and asked subjects to discriminate their emotion by button press. During trials where subjects responded correctly, we found neurons that distinguished fear vs. happy emotions as expressed by the displayed faces. During incorrect trials, these neurons indicated the patients' subjective judgment. Additional analysis revealed that, on average, all neuronal responses were modulated most by increases or decreases in response to happy faces, and driven predominantly by judgments about the eye region of the face stimuli. Following the same analyses, we showed that hippocampal neurons, unlike amygdala neurons, only encoded emotions but not subjective judgment. Our results suggest that the amygdala specifically encodes the subjective judgment of emotional faces, but that it plays less of a role in simply encoding aspects of the image array. The conscious percept of the emotion shown in a face may thus arise from interactions between the amygdala and its connections within a distributed cortical network, a scheme also consistent with the long response latencies observed in human amygdala recordings.human single unit | medial temporal lobe | limbic system | hippocampus | intracranial

Summary The human amygdala is critical for social cognition from faces, as borne out by impairments in recognizing facial emotion following amygdala lesions [1] and differential activation of the amygdala by faces [2–5]. Single-unit recordings in the primate amygdala have documented responses selective for faces, their identity, or emotional expression [6, 7], yet how the amygdala represents face information remains unknown. Does it encode specific features of faces that are particularly critical for recognizing emotions (such as the eyes), or does it encode the whole face, a level of representation that might be the proximal substrate for subsequent social cognition? We investigated this question by recording from over 200 single neurons in the amygdalae of seven neurosurgical patients with implanted depth electrodes [8]. We found that approximately half of all neurons responded to faces or parts of faces. Approximately 20% of all neurons responded selectively only to the whole face. Although responding most to whole faces, these neurons paradoxically responded more when only a small part of the face was shown compared to when almost the entire face was shown. We suggest that the human amygdala plays a predominant role in representing global information about faces, possibly achieved through inhibition between individual facial features.

Summary People with autism spectrum disorder (ASD) show abnormal processing of faces. A range of morphometric, histological, and neuroimaging studies suggest the hypothesis that this abnormality may be linked to the amygdala. Here for the first time we recorded from single neurons within the amygdalae of two rare neurosurgical patients with ASD. While basic electrophysiological response parameters were normal, there were specific and striking abnormalities in how individual facial features drove neuronal response. Compared to control patients, a population of neurons in the two ASD patients responded significantly more to the mouth, but less to the eyes. Moreover, we found a second class of face-responsive neurons whose responses to faces appeared normal. The findings confirm the amygdala’s pivotal role in abnormal face processing by people with ASD at the cellular level, and suggest that dysfunction may be traced to a specific subpopulation of neurons with altered selectivity for the features of faces.

The ability to understand and manipulate quantities ensures the survival of animals and humans alike. The frontoparietal network in primates has been implicated in representing, along with other cognitive abilities, abstract quantity. The respective roles of the prefrontal and parietal areas and the way continuous quantities, as opposed to discrete ones, are represented in this network, however, are unknown. We investigated this issue by simultaneously analyzing recorded single-unit activity in the prefrontal cortex (PFC) and the fundus of the intraparietal sulcus (IPS) of two macaque monkeys while they were engaged in delayed match-to-sample tasks discriminating line length and numerosity. In both areas, we found anatomically intermingled neurons encoding either length, numerosity, or both types of quantities. Even though different sets of neurons coded these quantities, the representation of length and numerosity was similar within the IPS and PFC. Both length and numerosity were coded by tuning functions peaking at the preferred quantity, thus supporting a labeled-line code for continuous and discrete quantity. A comparison of the response characteristics between parietal and frontal areas revealed a larger proportion of IPS neurons representing each quantity type in the early sample phase, in addition to shorter response latencies to quantity for IPS neurons. Moreover, IPS neurons discriminated quantities during the sample phase better than PFC neurons, as quantified by the receiver operating characteristic area. In the memory period, the discharge properties of PFC and IPS neurons were comparable. These single-cell results are in good agreement with functional imaging data from humans and support the notion that representations of continuous and discrete quantities share a frontoparietal substrate, with IPS neurons constituting the putative entry stage of the processing hierarchy.