Two processes support visual recognition memory in rhesus monkeys

Abstract

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Fig. 1.

ROCs (A and D), zROCs (B and E), and probability density functions (C and F) for the unequal variance signal detection (UVSD) and dual process (DP) models, respectively. Recognition memory ROCs in humans are typically curvilinear and asymmetrical to the negative diagonal (black curves in A and D). The UVSD model assumes that novel and repeated (old) items are represented by two Gaussian functions along the dimension of memory strength, with the variance of the old-item distribution being greater than the variance of the novel-item distribution (C). The DP model assumes that two independent processes, recollection and familiarity, contribute to recognition memory functions. Recollection is assumed to be a threshold process, whereas familiarity is assumed to be an equal variance signal detection process (F). Specifically, a certain proportion of old items (the distribution of all old items is indicated by the gray shading in F) is assumed to exceed that threshold and, therefore, is recognized with high confidence on the basis of recollection. When recollection fails, recognition is assumed to be based on familiarity. Purely on their own, an equal variance signal detection process will produce an ROC that is curvilinear and symmetrical to the negative diagonal, and a threshold process will produce a linear ROC (D; both ROCs shown in gray). The UVSD model predicts linear zROCs (B), with a slope smaller than one, reflecting the ratio of the SDs of the new- and old-item distributions. The DP model predicts U-shaped zROCs (E) as a result of the threshold process.

Fig. 2.

(A) Timing of the running recognition task. Images were presented on a computer screen, one at a time, with equally probable presentation of old and new images. (B) Response bias was pseudorandomly manipulated in blocks of 200 stimuli. The histogram shows the juice reward (in number of drops; y axis) obtainable for correct “old” (black) and correct “new” (white) responses as a function of the five experimental bias manipulations (x axis). The symbols at the bottom indicate the cues that were presented immediately before each image as a function of bias level in effect on that block (Methods, Behavioral Procedure).

Fig. 3.

ROCs (Left) and zROCs (Right) for each of the four monkeys. Areas under the ROC curves (AUCs) are given in Left. (Monkey MU) For the long interval, the ROC was significantly curvilinear (F = 117, P < 0.005), and the zROC was significantly nonlinear (F = 45, P < 0.05). For the medium interval, the ROC was significantly curvilinear (F = 75456, P < 0.001), and the zROC was significantly nonlinear (F = 89, P < 0.05). For the short interval, the ROC was significantly curvilinear (F = 184, P < 0.001), and the nonlinearity of the zROC approached significance (F = 18, P = 0.052). (Monkey MI) For the long interval, the ROC was significantly curvilinear (F = 268, P < 0.001), and the nonlinearity of the zROC approached significance (F = 11, P < 0.079). For the medium interval, the ROC was significantly curvilinear (F = 10518, P < 0.001), and the zROC was significantly nonlinear (F = 19, P < 0.05). For the short interval, the ROC was significantly curvilinear (F = 225, P < 0.001), and the zROC was significantly nonlinear (F = 30, P < 0.05). (Monkey KN) The most extreme “new” bias level was associated with a lower hit rate and higher false alarm rate than the neighboring bias level for all intervals. Because analyzing ROCs assumes constant discriminability across bias levels, which is not the case here, these extreme data points (shown in gray) were excluded from analysis. For the long interval, the ROC was significantly curvilinear (F = 334, P < 0.005), and the zROC was not significantly nonlinear (F = 0.004, P > 0.9). For the medium interval, the ROC was significantly curvilinear (F = 7118, P < 0.001), and the zROC was significantly nonlinear (F = 34191, P < 0.005). For the short interval, the ROC was significantly curvilinear (F = 1326, P < 0.001), and the nonlinearity of the zROC approached significance (F = 87, P = 0.068). (Monkey RU) For all delays, hit rate was 1.0 for the extreme “old” bias level and was set to 0.997 for analysis. For the long interval, the ROC was significantly curvilinear (F = 353, P < 0.001), and the zROC was significantly nonlinear (F = 22, P < 0.05). For the medium interval, the most extreme “new” bias level (shown in gray) was excluded from analysis because of a lower hit rate and higher false alarm rate than the neighboring bias level (see Monkey KN for rationale). The ROC was significantly curvilinear (F = 19188, P < 0.001), and the zROC was not significantly nonlinear (F = 3.9, P > 0.25). For the short interval, the ROC was significantly curvilinear (F = 2115, P < 0.001), and the nonlinearity of the zROC approached significance (F = 15, P = 0.06).The data points that were excluded from analysis are displayed in gray.

Fig. 4.

Comparison of R² as goodness of fit measures for the UVSD (x axis) and DP models (y axis). Eleven of twelve ROCs were better fit by the DP model than by the UVSD model, and one was fit equally well by both models.