The effects of word frequency and context variability in cued recall

Criss, Amy H.; Aue, William R.; Smith, Larissa L.

doi:10.1016/j.jml.2010.10.001

Cited by 49 publications

(79 citation statements)

References 84 publications

(155 reference statements)

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“…That is, as we discussed before in the item recognition section (see also Reder et al, 2007), the trade-off between lower contextual competition for LF words and their encoding disadvantage can mask the positive effects of the former, and the negative effects of the latter. Consistent with these claims, Criss et al (2011) found in Exp. 3 that when all words have high context diversity, the effect of the cue frequency is removed or reversed.…”

Section: Effects Of Word Frequency On Cued-recall and Source Memorysupporting

confidence: 83%

“…The difference in context diversity between HF and LF cues was bigger in the high context diversity condition (F(1, 261) = 12.68, p < .001). Since the difference in context diversity between HF and LF cues is bigger in the High context diversity condition, the model would predict that the beneficial effect of HF cues would be removed or even reversed, which is what Criss et al (2011) found. Figure 16 shows the fits of the SAC model to the data from Criss et al (2011), implementing these assumptions.…”

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 59%

“…Two recent comprehensive studies on how word frequency affects cued-recall provide additional support for our position (Criss et al, 2011;Madan et al, 2010). Both studies evaluated separately the effect of cue and target frequency by manipulating them orthogonally.…”

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 76%

“…When it comes to the cue, however, Madan et al (2010) found no effect of word frequency, while Criss et al (2011) found that while HF cues were more effective than LF cues, the effect was much smaller than that for the target. Criss et al (2011) found those results surprising and argued that most memory models would predict a benefit for LF cues, similar to single item recognition, either because they better match their distinctive features in the memory trace (McClelland & Chappell, 1998;Shiffrin & Steyvers, 1997), or because they have fewer preexperimental associations (Dennis & Humphreys, 2001;Reder et al, 2000). Our explanation for the weaker, but positive effect of cue frequency is that since LF cues take more resources to encode, there are fewer left for forming the cue-target association.…”

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 93%

“…While Criss et al (2011) claimed to have equated context diversity in Experiment 3, the SUBTLEX context diversity rating revealed that this was not the case (see Table 4). A 2x2 ANOVA revealed that HF cues had more context diversity compared to LF cues in both the Low and the High context diversity conditions (F(1, 261) = 72.87, p < .001).…”

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 96%

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Frequency Effects on Memory: A Resource-Limited Theory

Popov¹,

Reder²

2018

Preprint

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We present a review of frequency effects in memory, accompanied by a theory of memory, according to which the storage of new information in long-term memory (LTM) depletes a limited pool of working memory (WM) resources as an inverse function of item strength. We support the theory by showing that items with stronger representations in LTM (e.g. high frequency items) are easier to store, bind to context, and bind to one another; that WM resources are involved in storage and retrieval from LTM; that WM capacity is greater for stronger, more familiar stimuli. We present a novel analysis of preceding item strength, in which we show from eight existing studies that memory for an item is higher if during study it was preceded by a stronger item (e.g. a high frequency word; HF). This effect is cumulative (the more prior items are HF, the better), continuous (memory proportional to word frequency of preceding item), interacts with current item strength (larger for weaker items) and interacts with lag (decreases as the lag between the current and prior study item increases). A computational model that implements the theory is presented, which accounts for these effects. We discuss related phenomena that the model/theory can explain.

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Section: Effects Of Word Frequency On Cued-recall and Source Memorysupporting

confidence: 83%

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 59%

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 76%

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 93%

Section: Effects Of Word Frequency On Cued-recall and Source Memorymentioning

confidence: 96%

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Frequency Effects on Memory: A Resource-Limited Theory

Popov¹,

Reder²

2018

Preprint

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Reactivity from judgments of learning is not only due to memory forecasting: evidence from associative memory and frequency judgments

Maxwell

Huff

2022

Metacognition Learning

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Research has shown that judgments of learning (JOLs) often produce a reactive effect on the learning of cue-target pairs in which target recall differs between participants who provide item-based JOLs at study versus those who do not. Positive reactivity, or the memory improvement found when JOLs are provided, is typically observed on related pairs, while no reactivity is commonly found on unrelated pairs. In four experiments, we examined JOL reactivity effects by comparing JOL and no-JOL groups to other groups who engaged in relational-type encoding/judgment tasks. Experiment 1 replicated positive JOL reactivity effects with related pairs with an extension to symmetrically related pairs. Next, Experiment 2 found that providing judgments of associative memory-a task that does not involve memory predictions-yielded equivalent reactivity patterns as JOLs. Experiment 3 replicated this reactivity pattern using a frequency of co-occurrence judgment task. Finally, In Experiment 4, a similar positive reactivity pattern was found using a relational encoding task when compared to a standard JOL. Collectively, our results suggest that previous JOL reactivity patterns are not solely due to memory forecasting processes via JOLs and likely reflect relational encoding that is strategically applied towards related, but not unrelated pairs. Keywords Metamemory • Judgments of learning • Reactivity • Relational encoding • Associative direction • Cued-recall An individual's ability to accurately monitor the progress of their own learning is critical for successful retention. Effective monitoring allows individuals to adjust their study strategies to maximize memory performance (Nelson & Narens, 1990) and provides insights on how best to allocate memorial resources to optimize learning (Soderstrom et al., 2015; see also Bjork, 1999, for a review). Empirically, information about learning processes can R code used for data screening and analyses as well as all applicable stimuli and data files have been made available on our OSF page (https:// osf. io/ 8yvn3/).

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