Phonological Priming and Lexical Access in Spoken Word Recognition   Christine P. Malone Minnesota State University Moorhead

Problems How do the processes occurring during early stages of spoken word recognition affect single-word shadowing (naming) performance? How is a string of incoming phonetic features mapped onto a remembered lexical item? How does phonological information influence the organization of lexical items in memory? It is generally agreed that during spoken word recognition, listeners automatically evaluate the unfolding input by activating a set of potential lexical candidates, which then compete for recognition. However, numerous questions remain about how the set of possible candidates is established, what characteristics these candidates share with the incoming stimulus, and how the activated set is evaluated during real-time processing. For example, little is known about how potential candidates are related in terms of phonological information (i.e., shared sounds), especially when the incoming stimulus consists of more than one syllable. Thus, it is not clear whether as the word motivate is heard, the word recognition system considers all words containing matching sounds and matching relative location (e.g., motorist, innovate) or whether all words with the same sounds, regardless of relative location are considered (e.g., demote, innovate). Further, does candidate activation occur regardless of the lexicality of the unfolding stimulus? Questions like these have important implications for how we understand and model the word recognition system.

Background on Activation A set of potential lexical candidates is activated, which then compete for recognition. The incoming sound pattern determines the potential candidates. Degree of activation is determined by match between the potential candidates and the unfolding sensory input. Explain deviation point. Explain set of candidates as proposed by cohort theory.

Connectionist Models Difficulty obtaining facilitation following beginning phonological overlap across different tasks (Connine, Blasko, & Titone, 1993). Theoretical interest turned from cohort to connectionist theory. Multi-level architecture composed of simple processing units, called nodes. Adapted from visual word recognition. Connine, Blasko, & Titone (1993)—obtained facilitation in cross-modal priming paradigm when minimum change nonwords were primes, whether initial or medial phonemes were altered. Overall goodness-of-fit explanation. This can be explained with the stimuli For experiment 1 or at the predictions part.

Levels of Speech Processing Feature Level—Break speech into phonetic features (e.g., voiced/voiceless) Phoneme Level--Interpret stream of features and produce a pre-lexical representation. Lexical Level--Identify the word. WORD LETTER FEATURE INPUT

Priming and the Naming Task Shadowing task (naming) involves lexical processing, but is relatively unaffected by postlexical processing. Effect of having recognized the prime on recognizing the target? Phonological priming--assess differential levels of residual activation when manipulating phonological overlap and lexicality of prime. Measurement of the activation is taking place immediately after its presumed to occur

Experiment 1 Stimuli Match Mismatch Target: motivate   Match Mismatch Early Overlap motorist demote Late Overlap innovate atrium Unrelated vocalist vocalist

Method This slide describes materials Method This slide describes materials. Note: add other slides for Participants, Design, and Procedure Auditory priming paradigm, 100 ms ISI Single word shadowing (or naming) task, each list contained 8 EM, 8 EMM, 8 LM, 8 LMM, and 8 Unrelated pairs. Digitally recorded stimuli (22kHz, 16-bit) using SoundEdit and presented via PsyScope. Voice-activated reaction times recorded from target onset until beginning of vocal response.

Shadowing Latencies Reaction times to LM pairs were consistently faster than those to LMM pairs Reaction times were significantly faster following LM pairs as compared to Unrelated pairs (i.e., facilitation). On average, response latencies following Early pairs were equal to those following Unrelated pairs.

Conclusions Shared beginnings slowed naming of target (inhibition) for word and nonword targets. Potential candidates are inhibited based on matching beginning information, supporting connectionist architecture.

Applications Questions regarding the lexicon architecture have important implications for how we understand and model the word recognition system. Empirical data is useful for scientists studying language processing, as well as for scientists developing speech recognition systems.

Questions?