Powered by WordPress. Skip to content. Find the answer to this question here. Super convenient online flashcards for studying and checking your answers! Correct Answer Below Reveal the answer to this question whenever you are ready. Select one: a. The group session lasted on average 15 minutes, and the updating task lasted about 20—25 minutes.
The two sessions were non-consecutive, in order to avoid possible fatigue effects. The vocabulary and nonverbal reasoning subtests, taken from the Primary Mental Aptitude Battery [ 32 ] were presented to the whole class group during a school day; both have a four alternative multiple-choice structure.
The vocabulary subtest has 30 items and the nonverbal reasoning subtest, 25 items. Participants had time constraints for both subtests; specifically, 5 minutes for the vocabulary and 6 minutes for the nonverbal reasoning.
The task we used in the current paper was described in detail previously, in [ 14 ]. As in [ 14 ] the stimuli were sub-lexical units between two consonants of the Latin alphabet. The association was based on LTM consonant representation; that is, on the basis of their combined frequency in the spoken Italian language.
We evaluated this from the lexicon of frequency of Italian spoken language [ 33 ], a corpus of about , lemmas collected in four main Italian cities, emerging from different subgroups of discourse.
High and low frequency lemmas were selected. Low frequency ranged from 0 to 2 i. High frequency lemmas had at least 3 occurrences in the corpus. That said, we should note there is no specific frequency information for consonant couples, only for lemmas of the corpus. In addition, low frequency associations, typically, were in the middle of the lemma, whereas high frequency lemmas were at the beginning of the lemma.
In addition, we checked the corpus to find occurrences of low frequency sub-lexical associations in different lemmas, in order to preclude their presence in high frequency lemmas. We included in the low frequency sub-lexical associations those one occurring in low frequency lemmas only. Strong and weak associations between consonants were controlled in order to avoid obviously familiar or meaningful couplets.
Each association was legal, and thus possible at the sub-lexical level of the Italian language [ 14 ]. As described in [ 14 ] and in order to avoid ceiling i.
Some letters were overrepresented relative to others, but we controlled for this bias by randomizing these across association strengths. Further, the position of the sub-lexical unit within the triplet i. We did not control for potential position effects, as it was shown in a previous experiment that position did not interact with either updating or strength see [ 14 ], Experiment 2.
The third letter of each triplet was another consonant, which was always unrelated to the other two. Specifically, the link between the sub-lexical unit and the third letter was always linguistically impossible in Italian e.
This was done in order to avoid any LTM strong or weak or some other meaningful way association between these letters. Next, they were instructed to update part of the association, that is, to remove the item C and substitute the G. Thus, the triplet they were now maintaining was GHB. Lastly, they had to maintain the recently updated triplet post-updating maintenance process. In the example, a target probe was presented B , to which they had to give a positive answer. A three factor mixed design was implemented: Strength and Phase were within-participants factors, and Age group between-participants.
The variable Strength had two levels: strong-to-weak and weak-to-weak. Weak-to-weak represented associations between letters occurred where the association was weak at encoding and updated with another weak association e. For each trial, we considered two main phases of encoding i. Although the trial was constituted of four phases, only encoding and updating phases i. Here, no updating occurred, and maintenance alone was required throughout the trials. Procedure was described in detail previously [ 14 , 26 ].
The task was administered on a standard PC and consisted of four phase subject-paced trials, where participants pressed the spacebar to start each trial, and after each phase, in order to proceed with the task.
In each phase, triplets were always displayed in the centre of the screen. Each trial started with an encoding phase Phase 1; see an example with letters in Fig 1 , where a strong-to-weak association is represented , where participants had to memorize the first triplet of consonants e. A pre-updating maintenance phase followed Phase 2 , where three pluses were displayed; this indicated that the previously encoded triplet had to be actively maintained.
Then, at updating Phase 3 , participants had to substitute the no-longer-relevant information here, C with newly relevant information here, G. Concurrently, they needed to maintain previously relevant detail here, H-B , thus, updating the triplet i. Finally, a post-updating maintenance Phase 4 ended the sequence, to control for recency biases. See also Fig 1. Only one letter of the triplet had to be updated; this letter could be presented in any position of the triplet i.
Position was balanced across trials, and only new consonants were presented across each phase. At the end of each trial Phase 5 , participants were presented with a probe recognition task: a single red consonant was displayed in the centre of the screen. Here, they had to indicate whether this belonged to the most-recently studied triplet or not. They responded by pressing one of two keys on the keyboard; one M for Yes for target probes requiring a positive answer i. For these, we included both lures i.
Afterwards, each participant was presented with a practice block of eight trials to familiarize themselves with the task. One hundred and twenty trials were then presented shared equally in four blocks.
We recorded subject-paced RT at each of the four phases, in addition to probe recognition accuracy at Phase 5. Participants performed accurately on an average of As expected, participants were very good in completing the task and very few errors were produced.
Accuracy was analysed to verify adequate performance, but the main focus of the analysis was on RT. We ran a mixed 2 x 3 ANOVA, with Strength weak-to-weak, strong-to-weak as within-participants factor and Age Group younger children, older children as between-participants factor on mean accuracy rates of target, lures and negative responses.
Only subject-paced RTs for trials that ended with correct probe recognition were analysed. In addition, updating measures in particular, indexes of RT at the updating step , were highly inter-correlated, suggesting good reliability of the task. We used a mixed-effects model approach to test our hypotheses; the most important advantage of such models is that they allow simultaneous consideration of all factors that may contribute to understanding the structure of the data [ 35 ].
Raw RTS were logarithmically transformed to normalize them. These factors comprise not only the standard fixed-effects factors controlled by the experimenter in our case, age group and strength but also random-effects factors; that is, factors whose levels are drawn at random from a population in our case, children. To test the effect of age group younger children, older children and strength strong-to-weak, weak-to-weak on the variables of online RT, and offline RT three mixed-models were used: one for online RT with encoding and updating phases as additional factors , another one for RT of correctly detected target probes, and a third for RT of correctly rejected lures.
See specific details in the subsections below. Results for each dependent variable are presented below. For planned comparisons, Tukey correction was used to control the Type I error rate. A linear mixed-effects model was constructed with 3-way interactions between Age Group younger children, older children , Strength strong-to-weak, weak-to-weak , and Phase encode, update.
No other interaction reached significance. A linear mixed-effects model was constructed with 2-way interactions between Age Group younger children, older children and Strength strong-to-weak, weak-to-weak.
The model revealed a significant effect of Strength, F 1, No other effect reached significance. First, we conducted a control analysis with Strength weak-to-weak, strong-to-weak , and Probe lure, negative as within-participant factors and Age Group younger children, older children as between-participant factor, for lures vs.
In addition, to test our hypotheses more specifically, a linear mixed-effects model was constructed with 2-way interactions between Age Group younger children, older children and Strength strong-to-weak, weak-to-weak and was run on lures only, as these represent a measure of the ability to inhibit irrelevant information once completed the updating task.
The model revealed a significant effect of Age Group, F 1, In addition, we found a main effect of Strength, F 1, The two-way Strength by Age Group interaction reached significance, F 1, Subsequently, post-hoc comparisons showed that rejection of a lure from a strong-to-weak association needed longer RTs compared to weak-to-weak association , but only for older children, t Rejection of a lure from a strong-to-weak association did not differ from a weak-to-weak condition in younger children, t Therefore, we do not predict any specific vocabulary-related effect.
However, in order to control for the role of vocabulary in the process examined, we ran the same mixed-effect models, covarying for vocabulary. In this study, our aim was to investigate how LTM associations affect updating development. Updating is a complex activity that involves inhibition at different levels such as from the same lists set, or from previous lists [ 9 ], with the distinguishing component of the item-removal process [ 16 , 18 ].
More specifically here, we analysed how the strength of LTM association between items affects updating from a developmental perspective.
Typically, the literature on adults shows enhanced recall for strongly associated items; the stronger the pre-existing association in LTM, the better the performance in WM. For updating, a somewhat different process is indicated i.
In this case, the opposite was shown: the stronger the pre-existing association, the harder it is to dismantle it [ 26 ]. In addition, the first notable difference between updating and recall i. Indeed, recall involves maintenance of information only; whereas updating entails a further item-removal component.
Therefore, it is reasonable to assume that an additional operation i. However, results comparing updating performance compared to recall have demonstrated the reverse effect; that is a cost rather than a benefit. This difference is likely to be due to the nature of updating, an essential process in adaptation of WM content to new elements.
A recent model of updating [ 9 ] showed that updating develops via two main components of inhibition, one more related to control of inhibition from same lists; another one of inhibition from previous lists. The former, shows fewer developmental differences, the latter also called PI control in [ 9 ] shows greater age-related differences.
In our view, the task used here with children is suitable for consideration of both components in terms of processing speed an index useful in studying development via more subtle and fine-grained measurement. In fact, in the current task, each participant needs to maintain information and inhibit it, when no longer relevant, by substituting with new information during the tasks same list inhibition component.
In particular, in accordance with [ 9 ] model, we found different outcomes consistent with the measures considered. Accordingly, the online RT showed a global age-related effect older children faster than younger children , but not specific for strength with which letter were associated in fact, no interaction.
This finding could be accounted for, if we consider the development of self-monitoring i. That is, monitoring skills develop between 7 to 10 years, and subtle but important improvements are found over the primary school years [ 38 ]. In particular, it has been shown that children from 8 years of age are more accurate in judgment of learning when given after a delay of about 2 minutes, than immediately after study [ 39 ].
For this reason, we believe we did not find age-related effects relative to strength for self-paced RTs and thus, failed to replicate the effects found with adults [ 14 , 26 ]. Conversely, for offline inhibitory control i. Therefore, we found that online inhibition component was less affected by developmental change: younger children are able to perform updating tasks successfully.
The real challenge in updating i. Here, in fact there is no need for self-regulation i. The modulation of association strength development in older children but not in younger could be well accounted by the development of both lexico-orthographic knowledge and executive mechanisms that can work simultaneously [ 5 , 6 ].
This finding supports claims that the ability to inhibit irrelevant information is a fundamental mechanism that underlines many other developmental changes [ 40 , 41 , 42 ]. However, we believe the novelty of the current study lies in the specificity of the experimental manipulation.
Notably, these results indicated that, from 10 years onward, children found highly familiar stimuli such as letters more intrusive and difficult to control when strongly associated. Therefore, although we find that older children are less susceptible to interference, it seems that they are more sensitive to strong and weakly associated stimuli, similarly to performance in adults [ 14 , 26 ]. In addition, it could be useful to administer the task to children with specific learning disorders in order to show possible modulation of WM performance by LTM knowledge.
Specifically, the task could then be useful to implement ad hoc measures to train children to remediate identified weaknesses, both in educational and clinical settings. In conclusion, the present study demonstrated how WM updating is affected by LTM strength of association in a developmental sample. A significant cost of dismantling and updating strong associations was shown, and this effect was independent from age; all children from 7 to 10 years were comparably sensitive to association strength.
In addition, results allowed us to differentiate age-related effects for interference control in updating of strong LTM associations; older children but not younger were more susceptible to interference from strongly-associated information.
We wish to thank all children and schools participating in the study. We also thank Beatrice Colombani for her help with data collection. Actively scan device characteristics for identification. Use precise geolocation data. Select personalised content. Create a personalised content profile. Measure ad performance.
Select basic ads. Create a personalised ads profile. Select personalised ads. Apply market research to generate audience insights. Measure content performance. Develop and improve products. List of Partners vendors.
Long-term memory refers to the storage of information over an extended period. This type of memory tends to be stable and can last a long time—often for years. Long-term memory can be further subdivided into two different types: explicit conscious and implicit unconscious memory. If you can remember something that happened more than just a few moments ago, whether it occurred just hours ago or decades earlier, then it is a long-term memory.
Long-term memory is usually divided into two types— explicit and implicit. Long-term memories are often outside of the conscious mind. This information is largely outside of our awareness but can be called into working memory to be used when needed. Some memories are relatively easy to recall, while others are much harder to access. Through the process of association and rehearsal, the content of short-term memory can become long-term memory. Long-term memories can last for a matter of days to as long as many decades.
There are a number of factors that can influence how long information endures in long-term memory:. Not all long-term memories are created equal. While some memories spring to mind quickly, others are weaker and might require prompts or reminders to bring them into focus. Information that is of greater importance leads to a stronger recall. You can usually remember important events such as your wedding day with much greater clarity and detail than you can more ordinary days.
The information-processing model of memory characterizes human memory as much like a computer. Information enters short-term memory a temporary store , and then some of this information is transferred into long-term memory a relatively permanent store , much like information being saved to the hard disk of a computer.
Memories that are frequently accessed become stronger and easier to recall. Accessing these memories over and over again strengthens the neural networks in which the information is encoded, leading to the easier recollection of the information.
0コメント