| Literature DB >> 29513731 |
Abstract
Models of verbal working memory that incorporate active memory maintenance, long-term memory networks, and attention control have been developed. Current studies suggest that semantic representations of words, evoked via long-term memory networks, are actively maintained until they are needed to fulfill a role. In other words, it is possible that some mechanism actively refreshes semantic representations of words, analogous to but independently from articulatory rehearsal which refreshes phonological representations. One valuable piece of evidence is a double dissociation, observed in a dual task paradigm in which manual tapping disrupted a semantic memory task while articulatory suppression disrupted a phonological memory task. However, in that study, the secondary tasks could have competed not only with the maintenance but also with the encoding activities. Additionally, the study items in the phonological memory tasks were words; hence, the discriminability of the memory tasks is doubtful. The present study, therefore, examined a potential double dissociation in situations where the secondary tasks could not compete with encoding, using a modified phonological memory task. Furthermore, this article discusses a potential mechanism for maintaining semantic representations.Entities:
Mesh:
Year: 2018 PMID: 29513731 PMCID: PMC5841779 DOI: 10.1371/journal.pone.0193808
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Serial position curves for both conditions in Experiments 1A-1C.
Mean recognition rate on the memory tasks for each condition, as well as Bayes factor (BF) and effect size for dual task in Experiments 1A, 1B, and 1C.
| Condition | ||||||
|---|---|---|---|---|---|---|
| Single ( | Dual ( | BF | Partial η2 | |||
| Exp.1A: Tapping (2/sec) | ||||||
| Synonym | 0.86 | (0.8) | 0.78 | (0.11) | 196.50 | .74 |
| Nonword | 0.53 | (0.13) | 0.54 | (0.14) | 0.35 | .04 |
| Exp.1B: AS (2/sec) | ||||||
| Synonym | 0.85 | (0.10) | 0.69 | (0.18) | 25.00 | .60 |
| Nonword | 0.55 | (0.08) | 0.39 | (0.08) | 82.26 | .69 |
| Exp.1C: AS (1/sec) | ||||||
| Synonym | 0.78 | (0.14) | 0.77 | (0.13) | 0.55 | .13 |
| Nonword | 0.60 | (0.14) | 0.49 | (0.12) | 24.19 | .60 |
Note. AS = Articulatory suppression. Decrement was calculated by subtracting single from dual task condition. The data was analyzed through a Bayesian ANOVA (BANOVA: [44]), using the BayesFactor package [45] in R software [46]. Partial η2 is based on ANOVAs separately conducted on each secondary task condition of each task (df = 1,11). The chance of performing each task correctly was 0.2.
The results of ANOVAs in Experiments 1A, 1B, and 1C.
| Exp.1A: Tapping | Exp.1B: AS | Exp.1C: AS | |||||||
|---|---|---|---|---|---|---|---|---|---|
| η | η | η | |||||||
| Memory task | 68.38 | .86 | 54.11 | .83 | 51.53 | .82 | |||
| Dual task | 4.82 | .30 | 36.03 | .77 | 14.61 | .57 | |||
| Memory task × Dual task | 24.55 | .69 | 0.02 | < .01 | 13.24 | .55 | |||
Note.
*** p < .001.
** p < .01. The dfs of the main effects and the interaction were 1,11.
Fig 2Serial position curves for both conditions in Experiments 2A.
Mean recognition rate on the memory tasks for each condition, as well as Bayes factor (BF) and effect size for dual task in Experiments 2A and 2B.
| Condition | ||||||
|---|---|---|---|---|---|---|
| Single ( | Dual ( | BF | Partial η2 | |||
| Experiment 2A | ||||||
| Tapping (2/sec) | ||||||
| Synonym | 0.82 | (0.14) | 0.78 | (0.15) | 1.11 | .25 |
| Nonword | 0.51 | (0.13) | 0.44 | (0.12) | 1.73 | .31 |
| AS (1/sec) | ||||||
| Synonym | 0.82 | (0.12) | 0.77 | (0.13) | 4.75 | .44 |
| Nonword | 0.63 | (0.12) | 0.42 | (0.16) | 5774.41 | .87 |
| Experiment 2B | ||||||
| Tapping (1.5/sec) | ||||||
| Synonym | 0.71 | (0.09) | 0.60 | (0.12) | 462.71 | .78 |
| Nonword | 0.44 | (0.08) | 0.45 | (0.09) | 0.30 | .01 |
| AS (1.5/sec) | ||||||
| Synonym | 0.67 | (0.12) | 0.70 | (0.11) | 0.42 | .08 |
| Nonword | 0.49 | (0.10) | 0.36 | (0.05) | 55.05 | .66 |
Note. AS = Articulatory suppression. The decrement was calculated by subtracting single from dual task condition. The data was analyzed through a BANOVA. Partial η2 is based on ANOVAs separately conducted on each secondary task condition for each task (df = 1,11).
The results of ANOVAs in Experiments 2A and 2B.
| Exp.2A: ISI | Exp.2B: delay | |||||
|---|---|---|---|---|---|---|
| η | η | |||||
| Secondary task type | 0.28 | .01 | 0.08 | < .01 | ||
| Memory task | 92.04 | .81 | 89.03 | .80 | ||
| Dual task | 84.11 | .79 | 0.91 | .04 | ||
| Secondary task type × Memory task | 0.97 | .04 | 15.60 | .41 | ||
| Secondary task type × Dual task | 11.89 | .35 | 0.03 | < .01 | ||
| Memory task × Dual task | 8.93 | .29 | 0.87 | .04 | ||
| Three-way Interaction | 5.08 | .19 | 27.57 | .56 | ||
Note.
*** p < .001.
** p < .01.
* p < .05. The dfs of the main effects and the interaction were 1,22.
Fig 3Serial position curves for both conditions in Experiments 2B.