Academic journal article Memory & Cognition

Perception and Recognition Memory of Words and Werds: Two-Way Mirror Effects

Academic journal article Memory & Cognition

Perception and Recognition Memory of Words and Werds: Two-Way Mirror Effects

Article excerpt

We examined associative priming of words (e.g., TOAD) and pseudohomophones of those words (e.g., TODE) in lexical decision. In addition to word frequency effects, reliable base-word frequency effects were observed for pseudohomophones: Those based on high-frequency words elicited faster and more accurate correct rejections. Associative priming had disparate effects on high- and low-frequency items. Whereas priming improved performance to high-frequency pseudohomophones, it impaired performance to low-frequency pseudohomophones. The results suggested a resonance process, wherein phonologic identity and semantic priming combine to undermine the veridical perception of infrequent items. We tested this hypothesis in another experiment by administering a surprise recognition memory test after lexical decision. When asked to identify words that were spelled correctly during lexical decision, the participants often misremembered pseudohomophones as correctly spelled items. Patterns of false memory, however, were jointly affected by base-word frequencies and their original responses during lexical decision. Taken together, the results are consistent with resonance accounts of word recognition, wherein bottom-up and top-down information sources coalesce into correct, and sometimes illusory, perception. The results are also consistent with a recent lexical decision model, REM-LD, that emphasizes memory retrieval and top-down matching processes in lexical decision.

Studies of word perception are often framed in terms of signal detection theory (SDT; Green & Swets, 1966), especially studies focused on lexical decision (Balota & Chumbley, 1984) or semantic priming (Rhodes, Parkin, & Tremewan, 1993). SDT provides a metaphorical description of decision making (including lexical decisions) and methods for data analysis. Moreover, SDT provides a conceptual framework that separates the "bottom-up" collection of sensory information from "top-down" decision processes that follow. In the word perception literature, most studies focus on response time (RT) data, using such tasks as speeded naming or lexical decision. RT is typically the primary measure in lexical decision because accuracy is high, although modified methods focus on error rates (Hintzman & Curran, 1997). Nevertheless, in experimental procedures involving two-alternative classification, as in lexical decision, SDT can help estimate sensitivity and bias, potentially guiding the interpretation of some experimental manipulation. And even when performance is highly accurate, the SDT framework may help explain RT patterns (Balota & Chumbley, 1984; Gordon, 1983; Lewellen, Goldinger, Pisoni, & Greene, 1993).

For example, Schvaneveldt and McDonald (1981) examined semantic priming across three detection tasks, examining both errors and RTs. Different tests were chosen, focusing participants' attention to different "levels" of lexical analysis: Some participants tried to detect gaps in single letters in words, others looked for rotated letters within words, and others made lexical decisions. In every task, words and nonwords were preceded by neutral, unrelated, or related primes. Also, each task involved either tachistoscopic (50-msec) stimulus presentation or longer, response-terminated presentation. The most interesting results arose in lexical decision: Participants discriminated words (TIGER) from nonwords that differed by single, noninitial letters (TIGAR). Semantic priming sped up lexical decisions when targets remained in sight. With brief exposures, however, priming mainly increased false alarms (FAs) to nonwords. Schvaneveldt and McDonald suggested that a verification process occurs in lexical decision (cf. Paap, Newsome, McDonald, & Schvaneveldt, 1982). Specifically, they proposed that an early stage of word perception is purely bottom-up, as visual features are extracted. Top-down processes, characterized as spelling verification, are relatively late arriving (as in the cascade model; McClelland, 1979). …

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