Task Performance and Mechanistic Identification
Task performance alone is insufficient for mechanistic identification because comparable task/validation error can arise from multiple distinct circuit solutions that differ in their biological/mechanistic plausibilit...
Task performance alone is insufficient for mechanistic identification because comparable task/validation error can arise from multiple distinct circuit solutions that differ in their biological/mechanistic plausibility , and the training objective/metric does not reliably arbitrate between them.[:cite[1]{ln=2}], [:cite[2]{ln=4}], [:cite[3]{ln=2}] Concretely, this paper reports that similar task performance does not reliably identify biologically plausible circuit solutions , with “best performing” models’ mechanistic/functional insights not stable across nominally identical retraining runs .[:cite[1]{ln=2}], [:cite[4]{ln=1}] It further notes that different model clusters can have overlapping validation task error distributions , so even the lowest error models may include clusters that do not correspond well to experimentally observed neural tuning.[:cite[4]{ln=2}] The instability is tied to underdetermination : when multiple realizable implementations achieve similar error, the objective function itself cannot arbitrate between them , making mechanistic interpretation contingent on selection choices that are underdetermined by the training procedure .[:cite[3]{ln=2}] The authors also emphasize that validation task error differences can be too small to reliably distinguish candidate circuit solutions (e.g., cluster mean errors differing by < 0.08 across new ensembles).[:cite[5]{ln=4}] Finally, even when alternative post hoc selection rules can sometimes recover better biological agreement, the paper describes such rules as ad hoc and not robust to small changes in performance metrics—again indicating that task performance alone does not provide a principled, stable basis for mechanistic identification.[:cite[6]{ln=1}], [:cite[6]{ln=4}]