Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater
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Abstract Rapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.Keywords:
Chunking (psychology)
Long-term memory
Inspired by Shimomura & Kumada (2011), we investigated if visual working memory load will affect subitizing and enumeration performance. Our dual-task paradigm used a rapid serial visual presentation (RSVP) stream, in which we first taxed participants working memory with a spatial location memory task followed by an enumeration task. We hypothesized that as we increased the difficulty of the working memory task, we would see reduced enumeration and subitizing performance. The RSVP stream consisted of participants being asked to remember a 3x3 matrix containing 3, 5, or 8 alphabet characters, presented for 1000ms. Next, Gabor patches ranging from 0 to 9 were presented for 133ms, and participants immediately keyed in a response. After submitting their response, a second matrix of letters similar or identical to the first was presented indefinitely until participants responded if they thought the matrix was the same or different as the first. This design both divided the participants’ attention and taxed their spatial working memory for the duration of the trial. Results show that accuracy decreased with numerosity and working memory load. Interestingly, these factors also seem to be independent, suggesting that interactions between perceptual numerosity and working memory depends on the type of working memory task. Meeting abstract presented at VSS 2013
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In the current study we examined the relationship between working memory capacity, inhibition/susceptibility to interference and fluid intelligence, measured by the Raven's Progressive Matrices (PM38), comparing groups of young (aged 18–35), young-old (aged 65–74), and old-old (aged 75–86) participants. Groups were administered two working memory tasks tapping into different mechanisms involved in working memory. The ability to control for irrelevant information was measured both considering memory errors (intrusion errors) in a working memory task and an index of susceptibility to interference obtained with a variant of the Brown-Peterson task. Regression analyses showed that the classical working memory measure was the most potent predictors of the Raven's score. Susceptibility to interference and intrusions errors contributed, but to a lower extent, to the Raven explained variance. These results confirm that working memory shares cognitive aspects with the fluid intelligence measure considered, whereas the role of inhibition to Raven scores is still in need of better evidence.
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Short-term verbal memory is improved when words in the input 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, and not by the number of items or the amount of information. But how does the improvement due to chunking come about? Is memory really determined by the number of chunks? One possibility is that chunking is a form of data compression. Chunking 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 items in the input which correspond to chunks 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 by the amount of information that can be stored. However, 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.
Chunking (psychology)
TRACE (psycholinguistics)
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Abstract Rapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.
Chunking (psychology)
Long-term memory
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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).
Chunking (psychology)
Long-term memory
Engram
TRACE (psycholinguistics)
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Many general linguistic theories and language processing frameworks have assumed that language processing is largely a chunking procedure and that it is underpinned and constrained by our memory limitations. Despite this general consensus, the distinction between short-term memory (STM) and working memory (WM) limitations as they relate to language processing has remained elusive. To resolve this issue, we propose an integrated memory- and chunking-based metric of parsing complexity, in which STM limitations of 7±2 (Miller, 1956a) are relevant to the Momentary Chunk Number (MCN), while WM limitations of 4±1 (Cowan, 2001) are relevant to the Mean Momentary Chunk Number (MMCN). Examples of concrete calculations of our new metric are presented vis-à-vis Liu’s MDD metric and Hawkins’ IC-to-word Ratio metric. Related methodology issues are also discussed. We conclude the paper by echoing some recently repeated calls (O'Grady, 2012 & 2017; Gómez-Rodríguez et al., 2019; Wen, 2019) to include STM and WM limitations as part and parcel of the language device (LD; cf. Chomsky, 1957) in that their impacts are ubiquitous and permeating in all essential linguistic domains ranging from phonology to grammar, discourse comprehension and production. (PDF) Short-term and working memory capacity and the language device: Chunking and parsing complexity. Available from: https://www.researchgate.net/publication/340490956_Short-term_and_working_memory_capacity_and_the_language_device_Chunking_and_parsing_complexity [accessed May 31 2021].
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