Supplementary Notes
Least-squares curve-fitting model
Estimates of recollection and familiarity were derived using a least-squares method
(Yonelinas et al., 1998). The model equation assumes that recognition reflects the
contribution of recollection (R) and an independent familiarity process (F). The model
can be represented by the following equations:
(1) P(‘old’|old) = R + (1-R)Fold,
(2) P(‘old’|new) = Fnew,
where Fold = Ф(d’/2 – ci), and
where Fnew = Ф(-d’/2 – ci),
in which the variable d’ reflects the distance between the equal-variance Gaussian
strength distributions for old and new items, ci reflects the response criterion at point i,
and Ф is the cumulative response function.
Linear regression of z-transformed scores
The hit and false alarm rates of each rat on each of the 5 bias levels were converted to z
scores to determine the symmetry of the ROC curves. We calculated the z-score
corresponding to the area under the normal distribution (mean = 0; standard deviation =
1) for each hit and false alarm rate, and these z-scores were fitted with a linear regression
to plot the shape of the corresponding z-ROC. A perfectly symmetrical ROC will have a
slope of 1.0 in z space (Yonelinas et al., 1998; Wickens, 2002), whereas asymmetric
ROCs have a slope less than 1.0 (Yonelinas et al., 1998). One-sample t-tests were used to
compare the z-ROC slopes of each group with 1.0. Confirming our observations in ROC
space, z-transformed linear approximations (Fig S1) of the preoperative and the control at
30 min groups had a slope < 1 (preop: 0.751, t11 = -3.172, p < 0.05; controls: 0.744, t5 = 7.33959, p < 0.05), whereas the slope of the hippocampal group did not differ from 1.0
(1.086; t5 = 0.098, p > 0.05).
Con-F
We used the estimates of R and d’ of the control ROC to estimate the shape of their ROC
if the recollection component were removed. For each bias level, we calculated the
probability of hits solely predicted by familiarity (Fold) by rearranging equation 1 [Fold =
(P(‘old’|old) – R)/(1-R)].
Analyses of variance (ANOVAs) and subsequent planned t tests on recollection and
familiarity estimates
To compare the raw scores for postoperative performance of controls and animals with
hippocampal lesions, we performed repeated-measures ANOVAs on the raw hit and false
alarm rates observed across bias levels. False alarm rates did not differ between the two
groups. However, the same analysis performed on hit rates revealed a significant Group
X Bias level interaction, confirming the observation that the deficit of hippocampal
animals is greater at bias levels associated with low false alarm rates (Fig 1e).
Subsequent planned comparisons further explored the nature of the deficit by comparing
recollection and familiarity estimates between groups using one-tailed t-tests. Also,
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estimates of familiarity and recollection cannot be negative, so comparisons of these
estimates against zero also involved one-tailed comparisons.
Familiarity estimates
To facilitate comparison with recollection, which is measured as a probability, we
converted the d’ value associated with each rat’s ROC into familiarity estimates. The
estimates were obtained by calculating the probability of a hit given a false alarm rate of
0.08, such that the familiarity estimates would be close to the value of preoperative
recollection estimates (~ 0.38; see Fig 1d).
Yonelinas, A.P., Kroll, N.E., Dobbins, I., Lazzara, M. & Knight, R.T. Recollection and familiarity deficits
in amnesia: convergence of remember-know, process dissociation, and receiver operating characteristic
data. Neuropsychology 12, 323-339 (1998).
Wickens, T.D. Elementary Signal Detection Theory (Oxford University Press, Oxford, 2002).
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