A third option is to argue that causal information itself can explain biological phenomena, and no additional concept of information is needed. Biological systems, on this view, can be adapted to send or receive signals that carry causal information. Elevated prolactin, for example, does covary with pregnancy, and that is no accident. Likewise, the production of milk is a designed response to the registration of these elevated levels at the mammary glands. While it is true that lactation carries as much causal information about prolactin levels as those levels carry about milk flow, there is a physical asymmetry, hence directionality, between source and receiver. (After birth, suckling stimulates local production of prolactin, so the physical asymmetry is the signal of pregnancy from the pituitary gland to the breasts, tuning them for lactation). Brian Skyrms’ work has been very important in returning causal information to centre stage, in part because his work shows that sender-receiver signalling systems need not be cognitively sophisticated agents (Skyrms 2010). Sending and receiving causal information can emerge and stabilise amongst simple systems; certainly amongst systems no more complex than a cell.