Hippocampal damage impairs recognition memory broadly, affecting both parameters in two prominent models of memory

Abstract

Declarative memory is thought to rely on two processes: recollection and familiarity. Recollection involves remembering specific details about the episode in which an item was encountered, and familiarity involves simply knowing that an item was presented even when no information can be recalled about the episode itself. There has been debate whether the hippocampus supports only recollection or whether it supports both processes. We approached this issue in a relatively theory-neutral way by fitting two prominent models that have been used to describe recognition memory: dual process signal detection and unequal variance signal detection. Both models yield two parameters of interest when fit to recognition memory data. The dual process signal detection model yields estimates of recollection (r) and familiarity (d'). The unequal variance signal detection model yields estimates of the ratio of the variance of target and foil memory strength distributions (σtarget/σfoil) and the difference in the means of the two distributions (d). We asked how the two parameters of each model were affected by hippocampal damage. We tested five patients with well-characterized bilateral lesions thought to be limited to the hippocampus and age-matched controls. The patients exhibited a broad memory deficit that markedly reduced the value of both parameters in both models. In addition, the pattern of results exhibited by the patients was recapitulated in healthy controls as the delay between learning and testing was extended. Thus, hippocampal damage impairs both component processes of recognition memory.

Fig. 1.

Parameter estimates for recognition memory performance of controls (CON) and patients with hippocampal lesions (H) based on two prominent models. Both models yield two parameters of interest. The DPSD model yields estimates of familiarity and recollection. Familiarity is a discriminability estimate, d′ (A), and recollection is a probability estimate, r (B). The UVSD model yields d, a discriminability estimate (A) and the ratio of the SD of the target distribution to the SD of the foil distribution, σ_target/σ_foil (B). All estimates were lower for the patients than controls. Error bars show SEM.

Fig. 2.

Parameter estimates for recognition performance of controls (CON, n = 9) as a function of retention delay. The DPSD model yields estimates of familiarity, d′ (A) and a probability estimate labeled recollection, r (B). The UVSD model yields d, a discriminability estimate (A) and the ratio of the SD of the target distribution to the SD of the foil distribution, σ_target/σ_foil (B). In both models, the two parameters decrease as time passes after learning.

Fig. 3.

Parameter estimates for recognition performance of controls tested 1 or 7 d after learning (CON 1 d, n = 5; CON 7 d, n = 7) based on two models. Corresponding estimates from Fig. 1 for controls (CON, n = 11) and patients with hippocampal lesions (H, n = 5) tested 3 min after learning are shown for comparison. At 1 d after learning, control performance was numerically better than performance of the patients tested 3 min after learning whereas, at 7 d after learning, control performance was numerically better than that of patients. The DPSD model yields estimates of familiarity, d′ (A) and a probability estimate labeled recollection, r (B). The UVSD model yields d, a discriminability estimate (A) and the ratio of the SD of the target distribution to the SD of the foil distribution, σ_target/σ_foil (B). All parameter estimates were higher for controls tested at 1 d after learning than for patients tested after 3 min. This pattern was reversed when controls were tested after 7 d. Error bars show SEM.