Language is acquired within a complex, interactiveenvironment. A key question for cognitive science is whetherand how different types of environmental cues might affectthe learning and processing of language. In this paper, weexplore the role of feedback as a possible cue in a novelactive artificial language learning task: The Picture GuessingGame. Subjects were instructed to guess which scenecorrectly displayed the meaning of a spoken sequence ofunfamiliar monosyllabic words. After their response, eitherpositive, negative, or no feedback was provided. Theprediction was that feedback would help the subject toeventually learn the vocabulary, syntax, and semantics of theartificial language. The results indeed showed that feedback(both positive and negative) is beneficial and necessary toattain a certain level of learning. Interestingly, the datashowed that positive feedback may be particularly helpful forthe learner, promoting more in-depth learning of the artificiallanguage.

Language processing depends on the integration of bottom-upinformation with top-down cues from several differentsources—primarily our knowledge of the real world, ofdiscourse contexts, and of how language works. Previousstudies have shown that factors pertaining to both the senderand the receiver of the message affect the relative weighting ofsuch information. Here, we suggest another factor that maychange our processing strategies: perceptual noise. Wehypothesize that listeners weight different sources of top-downinformation more in situations of perceptual noise than innoise-free situations. Using a sentence-picture matchingexperiment with four forced-choice alternatives, we show thatdegrading the speech input with noise compels the listeners torely more on top-down information in processing. We discussour results in light of previous findings in the literature,highlighting the need for a unified model of spoken languagecomprehension in different ecologically valid situations,including under noisy conditions.