Extended Data Fig. 7: Variation in representational fidelity with the same d’ by separating on strength of strongest memory signal.

Simulation from TCC illustrating how signal detection can predict variance in representational fidelity as a function of confidence even with a fixed d’ (see also⁴²). Some studies used to support variability of information across individual items or trials have done so by using a confidence metric⁵¹. While variability and confidence are distinct from one another, in a large amount of research they are inextricably linked. An interesting advantage and implication of signal detection-based models is that they naturally predict confidence data⁶⁴. In particular, the strength of the winning memory match signal is used as the measure of memory strength–and confidence–in signal detection models of memory. Thus, even with a fixed d’ value for all items, TCC naturally predicts varying distributions relative to confidence. This likely explains some of the evidence previously observed in the literature that when distinguishing responses according to confidence, researchers found support for variability in precision among items / trials. Note that this occurs in TCC even though d’ is fixed in this simulation–that is, all trials are generated from a process with the same signal-to-noise ratio. Thus, variability in responses as a function of confidence (or related effects, like improved performance when participants choose their own favorite item to report²³) are not evidence for variability in d’ in TCC, but simply a natural prediction of the underlying signal detection process. Of course, it is possible d’ may also vary between items, which remains an open question.