Backchannel responses are often phatic expressions, primarily serving a social or meta-conversational purpose, such as signifying the listener's attention, understanding, sympathy, or agreement, rather than conveying significant information.
The term was coined by Victor Yngve in 1970, in the following passage: "In fact, both the person who has the turn and his partner are simultaneously engaged in both speaking and listening.
[6] Usually, the way backchannel is used would be a person telling a story or explaining something to one or more individuals, involved in a conversation, who would respond to them with short verbal messages or non-verbal body language.
[2] Confusion or distraction can occur during an intercultural encounter if participants from both parties are not accustomed to the same backchannel norms.
[12] A non-lexical backchannel is a vocalized sound that has little or no referential meaning but still verbalizes the listener's attention, and that frequently co-occurs with gestures.
In both of these cases, Goodwin argues that the backchannels focus only on addressing some aspect of the immediately preceding utterance rather than the larger conversation itself.
[15] Research in 2000 has pushed back on the notion of backchannels, in which the listener's role is merely to receive information provided by the speaker.
Bavelas, Coates, and Johnson[16] put forth evidence that listeners' responses help shape the content of the speaker's utterances.
The researchers asked independent reviewers to code the verbal and visual responses of the narration events as generic or specific.
[17] In 2017, Kyoto University's Graduate program of Informatics began developing a robot to assist individuals, more specifically the elderly, with mental health through the use of attentive listening.
They utilized backchannel generation as a method for the robot to have some form of feedback to feel like a real conversation.
[19] This study was a part of a new method of "discourse detection" and "statistical modeling" that allowed them to have such a large sample size, giving the possibility of generalizing this data to larger communities.