Triangle completion is a task widely used to study human path integration, an important navigation method relying on idiothetic cues. Systematic biases (compression patterns in the inbound responses) have been well documented in human triangle completion. However, the sources of systematic biases remain controversial. We used cross-validation modeling to compare three plausible theoretical models that assume that systematic errors occur in the encoding outbound path solely (encoding-error model), executing the inbound responses solely (execution-error model), and both (bi-component model), respectively. The data for cross-validation modeling are from a previous study (Qi et al., 2021), in which participants learned three objects’ locations (one at the path origin, that is, home) very well before walking each outbound path and then pointed to the objects’ original locations after walking the outbound path. The modeling algorithm used one inbound response (i.e., response to the home) or multiple inbound responses (i.e., responses to two non-home locations and the home) for each outbound path. The algorithm of using multiple inbound responses demonstrated that the bi-component model outperformed the other models in accounting for the systematic errors. This finding suggests that both encoding the outbound path and executing the inbound responses contribute to the systematic biases in human path integration. In addition, the results showed that the algorithm using only the home response could not distinguish among these three models, suggesting that the typical triangle-completion task with only the home response for each outbound path cannot determine the sources of the systematic biases.