Phrase frequency effects in free recall: Evidence for redintegration

Are lexical representations graded or discrete?

Most research on mental lexical representations (lemmas) assumes they are discrete and correspond in number to a word's number of distinct meanings. Thus, homophones (bat), whose meanings are unrelated, have separate lemmas for each meaning (one for baseball bat, another for flying bat), whereas polysemes (paper), whose senses are related, have shared lemmas (the same lemma for printer paper and term paper). However, most aspects of cognition are thought to be graded, not discrete; could lemmas be graded too? We conducted a preregistered picture-word interference study with pictures of words whose meanings ranged from unrelated (homophones) to very related (regular polysemes). Whereas semantic competitors to picture names slow picture naming, semantic competitors to non-depicted meanings of homophones facilitate naming, suggesting distinct lemmas for homophones' meanings. We predicted that competitors to non-depicted senses of polysemes would slow naming, as polysemes' depicted and non-depicted senses presumably share a lemma. Crucially, we aimed to examine the transition from facilitation to inhibition: two groupings (where competitors to non-depicted senses led to facilitation for words with two lemmas but inhibition for words with one lemma) would imply that lemmas are indeed discrete. But a transition that varies continuously by sense relatedness would imply that lemmas are graded. Unexpectedly, competitors to non-depicted senses of both homophones and polysemes facilitated naming. Although these results do not indicate whether lemmas are graded or discrete, they do inform a long-standing question on the nature of polysemes, supporting a multiple-lemma (vs. core-lemma) account.

Good-enough language production

Our ability to comprehend and produce language is one of humans' most impressive skills, but it is not flawless. We must convey and interpret messages via a noisy channel in ever-changing contexts and we sometimes fail to access an optimal combination of words and grammatical constructions. Here, we extend the notion of good-enough (GN) comprehension to GN production, which allows us to unify a wide range of phenomena including overly vague word choices, agreement errors, resumptive pronouns, transfer effects, and children's overextensions and regularizations. We suggest these all involve the accessing and production of a 'GN' option when a more-optimal option is inaccessible. The role of accessibility highlights the need to relate memory encoding and retrieval processes to language comprehension and production.

Models of Language and Multiword Expressions

Traditional accounts of language postulate two basic components: words stored in a lexicon, and rules that govern how they can be combined into meaningful sentences, a grammar. But, although this words-and-rules framework has proven itself to be useful in natural language processing and cognitive science, it has also shown important shortcomings when faced with actual language use. In this article, we review evidence from language acquisition, sentence processing, and computational modeling that shows how multiword expressions such as idioms, collocations, and other meaningful and common units that comprise more than one word play a key role in the organization of our linguistic knowledge. Importantly, multiword expressions straddle the line between lexicon and grammar, calling into question how useful this distinction is as a foundation for our understanding of language. Nonetheless, finding a replacement for the foundational role the words-and-rules approach has played in our theories is not straightforward. Thus, the second part of our article reviews and synthesizes the diverse approaches that have attempted to account for the central role of multiword expressions in language representation, acquisition, and processing.

Disentangling Semantic Composition and Semantic Association in the Left Temporal Lobe

Although composing two words into a complex representation (e.g., "coffee cake") is conceptually different from forming associations between a pair of words (e.g., "coffee, cake"), the brain regions supporting semantic composition have also been implicated for associative encoding. Here, we adopted a two-word magnetoencephalography (MEG) paradigm which varies compositionality ("French/Korean cheese" vs "France/Korea cheese") and strength of association ("France/French cheese" vs "Korea/Korean cheese") between the two words. We collected MEG data while 42 English speakers (24 females) viewed the two words successively in the scanner, and we applied both univariate regression analyses and multivariate pattern classification to the source estimates of the two words. We show that the left anterior temporal lobe (LATL) and left middle temporal lobe (LMTL) are distinctively modulated by semantic composition and semantic association. Specifically, the LATL is mostly sensitive to high-association compositional phrases, while the LMTL responds more to low-association compositional phrases. Pattern-based directed connectivity analyses further revealed a continuous information flow from the anterior to the middle temporal region, suggesting that the integration of adjective and noun properties originated earlier in the LATL is consistently delivered to the LMTL when the complex meaning is newly encountered. Taken together, our findings shed light into a functional dissociation within the left temporal lobe for compositional and distributional semantic processing.SIGNIFICANCE STATEMENT Prior studies on semantic composition and associative encoding have been conducted independently within the subfields of language and memory, and they typically adopt similar two-word experimental paradigms. However, no direct comparison has been made on the neural substrates of the two processes. The current study relates the two streams of literature, and appeals to audiences in both subfields within cognitive neuroscience. Disentangling the neural computations for semantic composition and association also offers insight into modeling compositional and distributional semantics, which has been the subject of much discussion in natural language processing and cognitive science.

Chunking and data compression in verbal short-term memory

Short-term verbal memory is improved when words can be chunked into larger units. Miller (1956) suggested that the capacity of verbal short-term memory is determined by the number of chunks that can be stored in memory, rather than by the number of items or the amount of information. But how does the improvement due to chunking come about, and is memory really determined by the number of chunks? One possibility is that chunking is a form of data compression. It allows more information to be stored in the available capacity. An alternative is that chunking operates primarily by redintegration. Chunks exist only in long-term memory, and enable the corresponding items in short-term memory to be reconstructed more reliably from a degraded trace. We review the data favoring each of these views and discuss the implications of treating chunking as data compression. Contrary to Miller, we suggest that memory capacity is primarily determined both by the amount of information that can be stored but also by the underlying representational vocabulary of the memory system. Given the limitations on the representations that can be stored in verbal short-term memory, chunking can sometimes allow the information capacity of short-term memory to be exploited more efficiently. (202 words).