Semantic associations between signs and numerical categories in the prefrontal cortex

Abstract

The utilization of symbols such as words and numbers as mental tools endows humans with unrivalled cognitive flexibility. In the number domain, a fundamental first step for the acquisition of numerical symbols is the semantic association of signs with cardinalities. We explored the primitives of such a semantic mapping process by recording single-cell activity in the monkey prefrontal and parietal cortices, brain structures critically involved in numerical cognition. Monkeys were trained to associate visual shapes with varying numbers of items in a matching task. After this long-term learning process, we found that the responses of many prefrontal neurons to the visual shapes reflected the associated numerical value in a behaviorally relevant way. In contrast, such association neurons were rarely found in the parietal lobe. These findings suggest a cardinal role of the prefrontal cortex in establishing semantic associations between signs and abstract categories, a cognitive precursor that may ultimately give rise to symbolic thinking in linguistic humans.

Figure 1. Task Protocols

(A) Dot protocol designed as delayed match-to-sample task. The monkeys were required to release a lever if sample and test displays contained the same number of items, or to keep holding it otherwise (probability = 0.5). The dots' position and size were varied between sample and test display and changed in each trial. (B) Shape protocol designed as delayed association task. Task conditions were identical to those of the dot protocol, but during the sample period the numerical information was cued by an Arabic numeral. The numerals' size and position changed in each trial. (C and D) Standard (first row) and control (second to fourth row) stimuli. (C) In the dot protocol, we controlled for non-numerical cues (dot circumference, area, configuration, and density). (D) Four different fonts (“Arial” in the standard protocol; “Times New Roman,” “Souvenir BT,” and “Lithograph Light” in the controls) were presented in the shape protocol.

Figure 2. Behavioral Performance

(A and B) Dot protocol. (C and D) Shape protocol. The curves show how often the monkeys judged the first test and sample numerosity to be equal. The numerical value in the first test display is shown on the x-axis. Each color stands for a certain numerosity shown during the sample period. Average performance for each numerosity is shown in gray as percentage correct (chance level = 50%).

Figure 3. Neural PFC Responses

(A–E) Neuron 1 showed highest firing rates for numerical value two in the dot (A) and shape (B) protocols in the early sample phase. Top panels in (A) and (B) show dot raster histograms (each dot represents an action potential); bottom panels are the corresponding color-coded spike density histograms (averaged and smoothed with a 100-ms Gaussian kernel for illustrative purposes only). The first 500 ms indicates the fixation period. Black vertical lines mark sample onset (500 ms) and offset (1,300 ms). (C) Tuning functions in the sample period for the dot and shape protocols, calculated from the raw firing rates in a 400-ms latency shifted window. (D) Time course of original CCs (red) and chance CCs (SPs, blue). (E) Time course of discriminability between CCs and SP quantified as the AUROC. The horizontal bar above the x-axis indicates the time interval of significant cross-correlation between tuning to the dot and shape protocols; in this period, the neuron associated numerical values in the two protocols. The black dashed line depicts the threshold (mean of ROC values derived during fixation period ± three standard deviations). The gray dashed line represents chance level (0.5). (F–J) Neuron 2 exhibiting four as preferred numerical value in the sample and delay period. Same layout as in (A–E); tuning functions were derived from the second ANOVA window of the sample period. (I and J) Neuron 2 associated numerical values in both protocols throughout the entire sample and delay period. (K–O) Neuron 3 exhibited two as preferred numerical value in the delay period. Same layout as in (A–E); tuning functions were derived from the second ANOVA window of the delay period.

Figure 4. Association Neurons in the PFC

(A) Diagram showing the temporal evolution of significant (as determined by a sliding ROC analysis; see Materials and Methods) cross-correlations between the tuning functions of 157 individual neurons to the dot and shape protocols. Top panel: Number of association neurons as a function of time. Bottom panel: Time course of association of each individual neuron. Each horizontal line corresponds to one single neuron. Periods of significant correlation are marked in black. Data are sorted by the first time of significant cross-correlation. Time is aligned to the midpoint of the 100-ms sliding windows. Data from example neurons 1–3 in Figure 3 are indicated by gray triangles. (B) Distribution of response-latency-corrected time points at which neurons started to associate numerical values. (C) Population tuning curves. Normalized discharges and averaged tuning curves of all association neurons to the dot (gray) and shape (black) protocols. Data are plotted as a function of numerical distance from the preferred numerical value.

Figure 5. Error Trial Analysis for PFC Neurons

(A) Temporal profile of CCs during correct trials (upper panel) and error trials (bottom panel) for the same association neurons (running average rectangular filter, window size five data points). Neurons are sorted by time of maximal correlation. (B) Time course of mean CCs across cells for correct and error trials (running average rectangular filter, window size five data points; shaded areas ± standard error of the mean).

Figure 6. Quantitative Summary and Comparison of Neuronal Response Classes in PFC and IPS

(A and B) Venn diagrams summarizing the results from the two-factor ANOVA and cross-correlation analysis in the PFC (A) and IPS (B). Data from the sample and delay period are combined. Numbers correspond to the numbers of neurons selective for each class. (C) Lateral view of a monkey brain. Circles represent schematic locations of recording sites in the frontal and parietal lobe. AS, arcuate sulcus; CS, central sulcus; PS, principal sulcus; STS, superior temporal sulcus; LS, lateral sulcus. (D) Frequency of association neurons. Proportions correspond to the added numbers of neuron classes (i.e., association neurons, ANOVA-selective neurons for both protocols, and ANOVA-selective neurons for shape or dot protocol; ***, p < 0.001).

Figure 7. Time Course of Association Strength

The association strength (measured as the AUROC) of 157 association PFC (A) and eight association IPS (B) neurons. AUROC values were sorted independently for each time bin. Higher AUROC values indicate stronger association, i.e., more similar tuning functions to the dot and shape protocols. Black lines correspond (from right to left) to sample onset and offset. Time is aligned to the midpoint of the sliding windows.