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classifier to discriminate labial and alveolar place of articulation in the stop consonant syllables [pa] vs. [ta]. They demonstrated generalization in the same neural areas to fricatives distinguished by the same labial vs. alveolar contrast, [fa] vs. [sa]. Similarly, they showed generalization across manner of articulation. Training on a stop vs. fricative pair, [pa] vs. [fa], generalized to [ta] vs. [sa] (see also Guediche et al., 2018 for similar generalization results for the feature voicing).

      Aphasic patients show patterns consistent with these findings. In particular, they have more difficulties discriminating minimal pair nonwords and minimal pair words that differ by a single feature compared to stimulus pairs distinguished by two features. Additionally, they have greater difficulties discriminating stimuli distinguished by place of articulation compared to voicing (Blumstein, Baker, & Goodglass, 1977).

       Lexical access

      In a seminal paper, Luce and Pisoni (1998) showed that access to an auditorily presented lexical target is affected by the word’s lexical neighborhood. A target word that is in a dense neighborhood, that is, where a large number of words share phonetic segments with the target word, is more difficult to access than a word in a sparse neighborhood, that is, where a few words share phonetic segments with the target word. These findings support a functional architecture in which the lexicon is a network where words compete for access with each other based on their phonological similarity. The more words are phonologically similar, the greater the competition, and the increased difficulty in ultimately selecting the lexical target word in word recognition.

Density is determined by computing the number of words that result from substituting, adding, or deleting a segment in the target word. Thus, these findings in themselves do not speak to whether features are a part of the lexical representation of words. However, several studies (Luce, 1986; Goldinger, Luce, & Pisoni, 1989; Luce et al., 2000) examined word recognition by investigating priming for word pairs that were maximally similar or maximally dissimilar phonologically. The metric used to determine similarity was based on subject judgments of the degree of similarity between individual consonants and vowels in the target and other words in the lexicon. Although the number of feature differences was not expressly determined, results showed that maximally similar prime target pairs resulted in slowed reaction times compared to maximally dissimilar word pairs. For example, a prime‐target pair that was maximally similar, for example, fawn [fɔn] and thumb [θəm] (see Luce et al., 2000) produced slowed reaction time latencies in a shadowing task compared to a prime‐target pair that was maximally dissimilar, for example, cheat and thumb. Comparing fawn and thumb, both consonant contrasts [f]–[θ] and [n]–[m] are distinguished by place of articulation, and [ɔ] and [ə] are distinguished by vowel height. In contrast, comparing cheat and thumb, [č]–[θ] are distinguished by manner and place of articulation, [t]–[m] are distinguished by manner, place, and voicing, and [i] and [ə] are distinguished by vowel height, tenseness, and frontness. Thus, in this case, the number of features shared (or the minimal number of features distinguished) appears to increase phonological competition between prime and target stimulus pairs.

      Perhaps more compelling is evidence that shows that features play a role not only in accessing the lexical (phonemic‐phonetic) representation of words but also in influencing access to the meaning of words. In particular, the magnitude of semantic priming in a lexical decision task is influenced by the feature distance between a nonword and a prime real word that is semantically related to a target (Connine, Blasko, & Titone, 1993; Milberg, Blumstein, & Dworetzky, 1988). For example, nonwords that are distinguished from a real word by a single feature, for example, gat is distinguished from cat by the feature voicing, show a greater magnitude of priming for semantically related words, for example, dog, than nonword primes that are distinguished by two or more features, for example, wat (Milberg, Blumstein, & Dworetzky, 1988). Using the visual world paradigm, Apfelbaum, Blumstein, and McMurray (2011) showed that greater semantic priming occurred for words semantically related to a visual target as a function of the lexical density of the target. Greater semantic priming occurred for low‐density compared to high‐density word targets presumably because the greater number of competitor words in high‐density neighborhoods ultimately reduced the activation of the lexical semantic network of the target word, resulting in less semantic priming compared to target words from low‐density neighborhoods.

It is always possible that feature effects for adults could reflect learning and experience with the language rather than fundamental, intrinsic properties of language. However, compelling evidence that features serve as the building blocks for phonological and ultimately word representations comes from the developmental literature. In an auditory word discrimination task, Gerken, Murphy, and Aslin (1995) showed that three‐ to four‐year‐old children were sensitive to the degree of mismatch between a target word and a nonword that varied in the extent of feature overlap. Either the stimuli differed by a single feature segment or by two feature segments. Such graded sensitivity to feature attributes in accessing words was shown in even younger children. Using a preference looking paradigm, White and Morgan (2008) presented 19‐month‐old toddlers with a visual presentation of two objects corresponding to a familiar word or an unfamiliar word. When an auditory stimulus was presented that named the familiar object or was one‐ (place), two‐ (place and voicing), or three‐feature (place, voicing, and manner) mispronunciations from the initial consonant of the familiar object, the toddlers showed, as do adults (White et al., 2013), graded sensitivity to the degree of mismatch, with progressively fewer looks to the familiar object as the feature distance between the correctly named familiar object and the mispronounced stimuli increased.

      Taken together, these findings provide further evidence that (1) features are used in mapping from sounds to words; (2) importantly, even nonwords access the lexicon, with activation of a lexical entry a function of the number of overlapping features shared; and (3) the activation of a lexical entry is graded, the extent of the activation being a function of the goodness of fit between the auditory input and its lexical phonological (feature) representation; and (4) the degree to which a lexical representation is activated has a cascading effect on the degree of activation of its lexical semantic network.

Features: Binary or graded

      In linguistic theory, features are binary; that is, they are either present (+) or absent (−). For example, the stop consonant [d] would be marked as [−nasal] to contrast it from the nasal consonant [n] which is marked [+nasal].The preceding section on “Feature dimensions” in speech perception and lexical access is consistent with this view. However, it has long been known that membership in phonetic categories is not all‐or‐none. Rather, there is a range of values associated with parameters or features of speech with some values appearing to be more representative than others. On the face of it, it would suggest then that, in speech perception, features may be graded. What is critical is whether such graded representations influence not only speech‐perception processes but also lexical access processes. We now turn to this question.

      As we shall see, experimental evidence challenges the view that features are binary and rather supports the view that features are graded. This has consequences not only for speech‐perception processes but also for the mapping from phonetic category representations to lexical representations and for ultimately accessing the lexical semantic network.

       Speech perception

Asking participants to categorize a continuum of speech stimuli varying in equal acoustic steps from one phonetic category to another results in a categorical‐like function, for example, [d] to [t] varying in 10 millisecond (ms) voice‐onset time steps. There is a range of stimuli consistently categorized as [d] and a range of stimuli consistently categorized as [t], with one or two stimuli at the edges of or on the boundary between the two categories less consistently categorized. Nonetheless, it turns out that not all members within a phonetic category are perceived equally.

      Using more sensitive measures than phonetic categorization including reaction time (Pisoni & Tash, 1974) and judgments

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