In anesthesia research it is an open question how general anesthetics lead to loss of consciousness (LOC). It has been proposed that LOC may be caused by the disruption of cortical information processing, preventing information integration. Therefore, recent studies investigating information processing under anesthesia focused on changes in information transfer, measured by transfer entropy (TE). However, often this complex technique was not applied rigorously, using time series in symbolic representation, or using TE differences without accounting for neural conduction delays, or without accounting for signal history. Here, we used current best-practice in TE estimation to investigate information transfer under anesthesia: We conducted simultaneous recordings in primary visual cortex (V1) and prefrontal cortex (PFC) of head-fixed ferrets in a dark environment under different levels of anesthesia (awake, 0.5% isoflurane, 1.0 % isoflurane). To elucidate reasons for changes in TE, we further quantified information processing within brain areas by estimating active information storage (AIS) as an estimator of predictable information, and Lempel-Ziv complexity (LZC) as an estimator of signal entropy. Under anesthesia, we found a reduction in information transfer (TE) between PFC and V1 with a stronger reduction for the feedback direction (PFC to V1), validating previous results. Furthermore, entropy (LZC) was reduced and activity became more predictable as indicated by higher values of AIS. We conclude that higher anesthesia concentrations indeed lead to reduced inter-areal information transfer, which may be partly caused by decreases in local entropy and increases in local predictability. In revealing a possible reason for reduced TE that is potentially independent of inter-areal coupling, we demonstrate the value of directly quantifying information processing in addition to focusing on dynamic properties such as coupling strength.