= Cohort model
A <cohort model> explains <spoken word recognition> through an evolving set of candidate words, the <lexical cohort>. An initial stretch of speech activates plausible candidates; later acoustic evidence and, depending on the model version, contextual information constrain recognition. <William D. Marslen-Wilson> and <Alan Welsh>'s 1978 study connected this account with experimental manipulations of continuous speech.<Processing interactions and lexical access during word recognition in continuous speech by William D. Marslen-Wilson and Alan Welsh>[Introduction and Abstract; cohort framework and task comparisons]{ref}
For an illustrative English vocabulary, the opening sounds of “candle”, “candy” and “canister” initially overlap. Hearing the next consonant can remove “canister” while leaving the other two plausible; later sounds separate them. The candidates depend on pronunciation and the listener's vocabulary, not merely on matching written letters.
A deliberately simplified noise-free rule is
$$
C_{t+1}=\{w\in C_t:\ w\text{ remains compatible with the speech heard so far}\}.
$$
Here $C_t$ is the candidate set after the evidence available at step $t$. This filtering illustration is not a fitted psychological model: noisy speech, graded activation and recovery from misleading evidence require additional assumptions. A <uniqueness point> concerns when the remaining acoustic prefix distinguishes a word within a specified vocabulary; it need not coincide with the listener's measured response time.
The original task comparisons motivate testing a model on intermediate behaviour and contextual effects, not just final accuracy.<Processing interactions and lexical access during word recognition in continuous speech by William D. Marslen-Wilson and Alan Welsh>[Abstract; distinct effects in shadowing and mispronunciation detection]{ref}
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