encoding

Repetition is behind our improved memory for emotional events

December, 2011

A new study concludes that positive events tend to be remembered better than negative, but the more important finding is that being repeatedly reminded of the event is the main factor behind improved memory for emotional experiences.

Certainly experiences that arouse emotions are remembered better than ones that have no emotional connection, but whether negative or positive memories are remembered best is a question that has produced equivocal results. While initial experiments suggested positive events were remembered better than negative, more recent studies have concluded the opposite.

The idea that negative events are remembered best is consistent with a theory that negative emotion signals a problem, leading to more detailed processing, while positive emotion relies more heavily on general scripts.

However, a new study challenges those recent studies, on the basis of a more realistic comparison. Rather than focusing on a single public event, to which some people have positive feelings while others have negative feelings (events used have included the OJ Simpson trial, the fall of the Berlin Wall, and a single baseball championship game), the study looked at two baseball championships each won by different teams.

The experiment involved 1,563 baseball fans who followed or attended the 2003 and 2004 American League Championship games between the New York Yankees (2003 winners) and the Boston Red Sox (2004 winners). Of the fans, 1,216 were Red Sox fans, 218 were Yankees fans, and 129 were neutral fans. (Unfortunately the selection process disproportionately collected Red Sox fans.)

Participants were reminded who won the championship before answering questions on each game. Six questions were identical for the two games: the final score for each team, the winning and losing pitchers (multiple choice of five pitchers for each team), the location of the game, and whether the game required extra innings. Participants also reported how vividly they remembered the game, and how frequently they had thought about or seen media concerning the game.

Both Yankee and Red Sox fans remembered more details about their team winning. They also reported more vivid memories for the games their team won. Accuracy and vividness were significantly correlated. Fans also reported greater rehearsal of the game their team won, and again, rehearsal and accuracy were significantly correlated.

Analysis of the data revealed that rehearsal completely mediated the correlation between accuracy and fan type, and partially mediated the correlation between vividness and fan type.

In other words, improved memory for emotion-arousing events has everything to do with how often you think about or are reminded of the event.

PTSD, for example, is the negative memory extreme. And PTSD is characterized by the unavoidable rehearsal of the event over and over again. Each repetition makes memory for the event stronger.

In the previous studies referred to earlier, media coverage provided a similarly unavoidable repetition.

While most people tend to recall more positive than negative events (and this tendency becomes greater with age), individuals who are depressed or anxious show the opposite tendency.

So whether positive or negative events are remembered better depends on you, as well as the event.

When it comes down to it, I'm not sure it's really a helpful question - whether positive or negative events are remembered better. An interesting aspect of public events is that their portrayal often changes over time, but this is just a more extreme example of what happens with private events as well — as we change over time, so does our attitude toward those events. Telling friends about events, and receiving their comments on them, can affect our emotional response to events, as well as having an effect on our memory of those events.

Reference: 

[2591] Breslin, C. W., & Safer M. A.
(2011).  Effects of Event Valence on Long-Term Memory for Two Baseball Championship Games.
Psychological Science. 22(11), 1408 - 1412.

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Cannabis disrupts synchronized brain activity

November, 2011

Effects of a cannabis-like drug on rats explain why cannabis is linked to schizophrenia and how it might impair cognition, as well as supporting our understanding of how working memory works.

Research into the effects of cannabis on cognition has produced inconsistent results. Much may depend on extent of usage, timing, and perhaps (this is speculation) genetic differences. But marijuana abuse is common among sufferers of schizophrenia and recent studies have shown that the psychoactive ingredient of marijuana can induce some symptoms of schizophrenia in healthy volunteers.

Now new research helps explain why marijuana is linked to schizophrenia, and why it might have detrimental effects on attention and memory.

In this rat study, a drug that mimics the psychoactive ingredient of marijuana (by activating the cannabinoid receptors) produced significant disruption in brain networks, with brain activity becoming uncoordinated and inaccurate.

In recent years it has become increasingly clear that synchronized brainwaves play a crucial role in information processing — especially that between the hippocampus and prefrontal cortex (see, for example, my reports last month on theta waves improving retrieval and the effect of running on theta and gamma rhythms). Interactions between the hippocampus and prefrontal cortex seem to be involved in working memory functions, and may provide the mechanism for bringing together memory and decision-making during goal-directed behaviors.

Consistent with this, during decision-making on a maze task, hippocampal theta waves and prefrontal gamma waves were impaired, and the theta synchronization between the two was disrupted. These effects correlated with impaired performance on the maze task.

These findings are consistent with earlier findings that drugs that activate the cannabinoid receptors disrupt the theta rhythm in the hippocampus and impair spatial working memory. This experiment extends that result to coordinated brainwaves beyond the hippocampus.

Similar neural activity is observed in schizophrenia patients, as well as in healthy carriers of a genetic risk variant.

The findings add to the evidence that working memory processes involve coordination between the prefrontal cortex and the hippocampus through theta rhythm synchronization. The findings are consistent with the idea that items are encoded and indexed along the phase of the theta wave into episodic representations and transferred from the hippocampus to the neocortex as a theta phase code. By disrupting that code, cannabis makes it more difficult to retain and index the information relevant to the task at hand.

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Working memory capacity not 4 but 2+2

October, 2011

A monkey study finds that our very limited working memory capacity of around 4 items reflects two capacities of two items. The finding has practical implications for information presentation.

In the study, two rhesus monkeys were given a standard human test of working memory capacity: an array of colored squares, varying from two to five squares, was shown for 800 msec on a screen. After a delay, varying from 800 to 1000 msec, a second array was presented. This array was identical to the first except for a change in color of one item. The monkey was rewarded if its eyes went directly to this changed square (an infra-red eye-tracking system was used to determine this). During all this, activity from single neurons in the lateral prefrontal cortex and the lateral intraparietal area — areas critical for short-term memory and implicated in human capacity limitations — was recorded.

As with humans, the more squares in the array, the worse the performance (from 85% correct for two squares to 66.5% for 5). Their working memory capacity was calculated at 3.88 objects — i.e. the same as that of humans.

That in itself is interesting, speaking as it does to the question of how human intelligence differs from other animals. But the real point of the exercise was to watch what is happening at the single neuron level. And here a surprise occurred.

That total capacity of around 4 items was composed of two independent, smaller capacities in the right and left halves of the visual space. What matters is how many objects are in the hemifield an eye is covering. Each hemifield can only handle two objects. Thus, if the left side of the visual space contains three items, and the right side only one, information about the three items from the left side will be degraded. If the left side contains four items and the right side two, those two on the right side will be fine, but information from the four items on the left will be degraded.

Notice that the effect of more items than two in a hemifield is to decrease the total information from all the items in the hemifield — not to simply lose the additional items.

The behavioral evidence correlated with brain activity, with object information in LPFC neurons decreasing with increasing number of items in the same hemifield, but not the opposite hemifield, and the same for the intraparietal neurons (the latter are active during the delay; the former during the presentation).

The findings resolve a long-standing debate: does working memory function like slots, which we fill one by one with items until all are full, or as a pool that fills with information about each object, with some information being lost as the number of items increases? And now we know why there is evidence for both views, because both contain truth. Each hemisphere might be considered a slot, but each slot is a pool.

Another long-standing question is whether the capacity limit is a failure of perception or  memory. These findings indicate that the problem is one of perception. The neural recordings showed information about the objects being lost even as the monkeys were viewing them, not later as they were remembering what they had seen.

All of this is important theoretically, but there are also immediate practical applications. The work suggests that information should be presented in such a way that it’s spread across the visual space — for example, dashboard displays should spread the displays evenly on both sides of the visual field; medical monitors that currently have one column of information should balance it in right and left columns; security personnel should see displays scrolled vertically rather than horizontally; working memory training should present information in a way that trains each hemisphere separately. The researchers are forming collaborations to develop these ideas.

Reference: 

[2335] Buschman, T. J., Siegel M., Roy J. E., & Miller E. K.
(2011).  Neural substrates of cognitive capacity limitations.
Proceedings of the National Academy of Sciences.

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Brain prosthetic restores learning capability in rats

September, 2011

Effective patterns of neural activity replayed via an artificial device inserted in the hippocampus restores lost learning capability and even improves learning in normal rats.

In the experiment, rats learned which lever to press to receive water, where the correct lever depended on which lever they had pressed previously (the levers were retractable; there was a variable delay between the first and second presentation of the levers). Microelectrodes in the rats’ brains provided data that enabled researchers to work out the firing patterns of neurons in CA1 that resulted from particular firing patterns in CA3 (previous research had established that long-term memory involves CA3 outputs being received in CA1).

Normal neural communication between these two subregions of the hippocampus was then chemically inhibited. While the rats still remembered the general rule, and still remembered that pressing the levers would gain them water, they could only remember which lever they had pressed for 5-10 seconds.

An artificial hippocampal system that could reproduce effective firing patterns (established in earlier training) was then implanted in the rats’ brains and long-term memory function was restored. Furthermore, when the ‘memory prosthetic’ was implanted in animals whose hippocampus was functioning normally, their memory improved.

The findings open up amazing possibilities for ameliorating brain damage. There is of course the greatly limiting factor that effective memory traces (spatiotemporal firing patterns) need to be recorded for each activity. This will be particularly problematic for individuals with significant damage. Perhaps one day we will all ‘record’ ourselves as a matter of course, in the same way that we might put by blood or genetic material ‘in case’! Still, it’s an exciting development.

The next step will be to repeat these results in monkeys.

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Running faster changes brain rhythms associated with learning

September, 2011

A mouse study finds that gamma waves in the hippocampus, critically involved in learning, grow stronger as mice run faster.

I’ve always felt that better thinking was associated with my brain working ‘in a higher gear’ — literally working at a faster rhythm. So I was particularly intrigued by the findings of a recent mouse study that found that brainwaves associated with learning became stronger as the mice ran faster.

In the study, 12 male mice were implanted with microelectrodes that monitored gamma waves in the hippocampus, then trained to run back and forth on a linear track for a food reward. Gamma waves are thought to help synchronize neural activity in various cognitive functions, including attention, learning, temporal binding, and awareness.

We know that the hippocampus has specialized ‘place cells’ that record where we are and help us navigate. But to navigate the world, to create a map of where things are, we need to also know how fast we are moving. Having the same cells encode both speed and position could be problematic, so researchers set out to find how speed was being encoded. To their surprise and excitement, they found that the strength of the gamma rhythm grew substantially as the mice ran faster.

The results also confirmed recent claims that the gamma rhythm, which oscillates between 30 and 120 times a second, can be divided into slow and fast signals (20-45 Hz vs 45-120 Hz for mice, consistent with the 30-55 Hz vs 45-120 Hz bands found in rats) that originate from separate parts of the brain. The slow gamma waves in the CA1 region of the hippocampus were synchronized with slow gamma waves in CA3, while the fast gamma in CA1 were synchronized with fast gamma waves in the entorhinal cortex.

The two signals became increasingly separated with increasing speed, because the two bands were differentially affected by speed. While the slow waves increased linearly, the fast waves increased logarithmically. This differential effect could have to do with mechanisms in the source regions (CA3 and the medial entorhinal cortex, respectively), or to mechanisms in the different regions in CA1 where the inputs terminate (the waves coming from CA3 and the entorhinal cortex enter CA1 in different places).

In the hippocampus, gamma waves are known to interact with theta waves. Further analysis of the data revealed that the effects of speed on gamma rhythm only occurred within a narrow range of theta phases — but this ‘preferred’ theta phase also changed with running speed, more so for the slow gamma waves than the fast gamma waves (which is not inconsistent with the fact that slow gamma waves are more affected by running speed than fast gamma waves). Thus, while slow and fast gamma rhythms preferred similar phases of theta at low speeds, the two rhythms became increasingly phase-separated with increasing running speed.

What’s all this mean? Previous research has shown that if inputs from CA3 and the entorhinal cortex enter CA1 at the same time, the kind of long-term changes at the synapses that bring about learning are stronger and more likely in CA1. So at low speeds, synchronous inputs from CA3 and the entorhinal cortex at similar theta phases make them more effective at activating CA1 and inducing learning. But the faster you move, the more quickly you need to process information. The stronger gamma waves may help you do that. Moreover, the theta phase separation of slow and fast gamma that increases with running speed means that activity in CA3 (slow gamma source) increasingly anticipates activity in the medial entorhinal cortex (fast gamma source).

What does this mean at the practical level? Well at this point it can only be speculation that moving / exercising can affect learning and attention, but I personally am taking this on board. Most of us think better when we walk. This suggests that if you’re having trouble focusing and don’t have time for that, maybe walking down the hall or even jogging on the spot will help bring your brain cells into order!

Pushing speculation even further, I note that meditation by expert meditators has been associated with changes in gamma and theta rhythms. And in an intriguing comparison of the effect of spoken versus sung presentation on learning and remembering word lists, the group that sang showed greater coherence in both gamma and theta rhythms (in the frontal lobes, admittedly, but they weren’t looking elsewhere).

So, while we’re a long way from pinning any of this down, it may be that all of these — movement, meditation, music — can be useful in synchronizing your brain rhythms in a way that helps attention and learning. This exciting discovery will hopefully be the start of an exploration of these possibilities.

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Preventing interference between topics or skills

September, 2011

Learning two tasks or subjects one after another typically leads to poorer remembering of the first. A new study indicates the cause and suggests a remedy.

Trying to learn two different things one after another is challenging. Almost always some of the information from the first topic or task gets lost. Why does this happen? A new study suggests the problem occurs when the two information-sets interact, and demonstrates that disrupting that interaction prevents interference. (The study is a little complicated, but bear with me, or skip to the bottom for my conclusions.)

In the study, young adults learned two memory tasks back-to-back: a list of words, and a finger-tapping motor skills task. Immediately afterwards, they received either sham stimulation or real transcranial magnetic stimulation to the dorsolateral prefrontal cortex or the primary motor cortex. Twelve hours later the same day, they were re-tested.

As expected from previous research, word recall (being the first-learned task) declined in the control condition (sham stimulation), and this decline correlated with initial skill in the motor task. That is, the better they were at the second task, the more they forgot from the first task. This same pattern occurred among those whose motor cortex had been stimulated. However, there was no significant decrease in word recall for those who had received TMS to the dorsolateral prefrontal cortex.

Learning of the motor skill didn't differ between the three groups, indicating that this effect wasn't due to a disruption of the second task. Rather, it seems that the two tasks were interacting, and TMS to the DLPFC disrupted that interaction. This hypothesis was supported when the motor learning task was replaced by a motor performance task, which shouldn’t interfere with the word-learning task (the motor performance task was almost identical to the motor learning task except that it didn’t have a repeating sequence that could be learned). In this situation, TMS to the DLPFC produced a decrease in word recall (as it did in the other conditions, and as it would after a word-learning task without any other task following).

In the second set of experiments, the order of the motor and word tasks was reversed. Similar results occurred, with this time stimulation to the motor cortex being the effective intervention. In this case, there was a significant increase in motor skill on re-testing — which is what normally happens when a motor skill is learned on its own, without interference from another task (see my blog post on Mempowered for more on this). The word-learning task was then replaced with a vowel-counting task, which produced a non-significant trend toward a decrease in motor skill learning when TMS was applied to the motor cortex.

The effect of TMS depends on the activity in the region at the time of application. In this case, TMS was applied to the primary motor cortex and the DLPFC in the right hemisphere, because the right hemisphere is thought to be involved in integrating different types of information. The timing of the stimulation was critical: not during learning, and long before testing. The timing was designed to maximize any effects on interference between the two tasks.

The effect in this case mimics that of sleep — sleeping between tasks reduces interference between them. It’s suggested that both TMS and sleep reduce interference by reducing the communication between the prefrontal cortex and the mediotemporal lobe (of which the hippocampus is a part).

Here’s the problem: we're consolidating one set of memories while encoding another. So, we can do both at the same time, but as with any multitasking, one task is going to be done better than the other. Unsurprisingly, encoding appears to have priority over consolidation.

So something needs to regulate the activity of these two concurrent processes. Maybe something looks for commonalities between two actions occurring at the same time — this is, after all, what we’re programmed to do: we link things that occur together in space and time. So why shouldn’t that occur at this level too? Something’s just happened, and now something else is happening, and chances are they’re connected. So something in our brain works on that.

If the two events/sets of information are connected, that’s a good thing. If they’re not, we get interference, and loss of data.

So when we apply TMS to the prefrontal cortex, that integrating processor is perhaps disrupted.

The situation may be a little different where the motor task is followed by the word-list, because motor skill consolidation (during wakefulness at least) may not depend on the hippocampus (although declarative encoding does). However, the primary motor cortex may act as a bridge between motor skills and declarative memories (think of how we gesture when we explain something), and so it may this region that provides a place where the two types of information can interact (and thus interfere with each other).

In other words, the important thing appears to be whether consolidation of the first task occurs in a region where the two sets of information can interact. If it does, and assuming you don’t want the two information-sets to interact, then you want to disrupt that interaction.

Applying TMS is not, of course, a practical strategy for most of us! But the findings do suggest an approach to reducing interference. Sleep is one way, and even brief 20-minute naps have been shown to help learning. An intriguing speculation (I just throw this out) is that meditation might act similarly (rather like a sorbet between courses, clearing the palate).

Failing a way to disrupt the interaction, you might take this as a warning that it’s best to give your brain time to consolidate one lot of information before embarking on an unrelated set — even if it's in what appears to be a completely unrelated domain. This is particularly so as we get older, because consolidation appears to take longer as we age. For children, on the other hand, this is not such a worry. (See my blog post on Mempowered for more on this.)

Reference: 

[2338] Cohen, D. A., & Robertson E. M.
(2011).  Preventing interference between different memory tasks.
Nat Neurosci. 14(8), 953 - 955.

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Why spaced practice is better

September, 2011

New mouse research helps explain why the spacing effect occurs.

I’ve spoken often about the spacing effect — that it’s better to spread out your learning than have it all massed in a block. A study in which mice were trained on an eye movement task (the task allowed precise measurement of learning in the brain) compared learning durability after massed training or training spread over various spaced intervals (2.5 hours to 8 days, with 30 minute to one day intervals). In the case of massed training, the learning achieved at the end of training disappeared within 24 hours. However learning gained in spaced training did not.

Moreover, when a region in the cerebellum connected to motor nuclei involved in eye movement (the flocculus) was anesthetized, the learning achieved from one hour of massed training was eliminated, while learning achieved from an hour of training spaced out over four hours was unaffected. This suggests that the memories had been transferred out of the flocculus (to the vestibular nuclei) within four hours.

However, when protein synthesis in the flocculus was blocked, learning from spaced training was impaired, while learning from massed training was not. This suggests that proteins synthesized in the flocculus play a vital part in the transfer to the vestibular nuclei.

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Theta brainwaves improve remembering

September, 2011

New research suggests that successful retrieval depends not only on retrieval cues, but also on your preceding brain state.

What governs whether or not you’ll retrieve a memory? I’ve talked about the importance of retrieval cues, of the match between the cue and the memory code you’re trying to retrieve, of the strength of the connections leading to the code. But these all have to do with the memory code.

Theta brainwaves, in the hippocampus especially, have been shown to be particularly important in memory function. It has been suggested that theta waves before an item is presented for processing lead to better encoding. Now a new study reveals that, when volunteers had to memorize words with a related context, they were better at later remembering the context of the word if high theta waves were evident in their brains immediately before being prompted to remember the item.

In the study, 17 students made pleasantness or animacy judgments about a series of words. Shortly afterwards, they were presented with both new and studied words, and asked to indicate whether the word was old or new, and if old, whether the word had been encountered in the context of “pleasant” or “alive”. Each trial began with a 1000 ms presentation of a simple mark for the student to focus on. Theta activity during this fixation period correlated with successful retrieval of the episodic memory relating to that item, and larger theta waves were associated with better source memory accuracy (memory for the context).

Theta activity has not been found to be particularly associated with greater attention (the reverse, if anything). It seems more likely that theta activity reflects a state of mind that is oriented toward evaluating retrieval cues (“retrieval mode”), or that it reflects reinstatement of the contextual state employed during study.

The researchers are currently investigating whether you can deliberately put your brain into a better state for memory recall.

Reference: 

[2333] Addante, R. J., Watrous A. J., Yonelinas A. P., Ekstrom A. D., & Ranganath C.
(2011).  Prestimulus theta activity predicts correct source memory retrieval.
Proceedings of the National Academy of Sciences. 108(26), 10702 - 10707.

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Negative emotion can enhance memory for tested information

September, 2011

Images designed to arouse strong negative emotion can improve your memory for information you’re learning, if presented immediately after you’ve been tested on it.

In a recent study, 40 undergraduate students learned ten lists of ten pairs of Swahili-English words, with tests after each set of ten. On these tests, each correct answer was followed by an image, either a neutral one or one designed to arouse negative emotions, or by a blank screen. They then did a one-minute multiplication test before moving on to the next section.

On the final test of all 100 Swahili-English pairs, participants did best on items that had been followed by the negative pictures.

In a follow-up experiment, students were shown the images two seconds after successful retrieval. The results were the same.

In the final experiment, the section tests were replaced by a restudying period, where each presentation of a pair was followed by an image or blank screen. The effect did not occur, demonstrating that the effect depends on retrieval.

The study focused on negative emotion because earlier research has found no such memory benefit for positive images (including images designed to be sexually arousing).

The findings emphasize the importance of the immediate period after retrieval, suggesting that this is a fruitful time for manipulations that enhance or impair memory. This is consistent with the idea of reconsolidation — that when information is retrieved from memory, it is in a labile state, able to be changed. Thus, by presenting a negative image when the retrieved memory is still in that state, the memory absorbs some of that new context.

Reference: 

[2340] Finn, B., & Roediger H. L.
(2011).  Enhancing Retention Through Reconsolidation.
Psychological Science. 22(6), 781 - 786.

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Childhood amnesia shifts with time

August, 2011

A new study finds that the earliest memories children can recall shifts with time, providing support for the theory that children’s memories don’t consolidate in the way adults’ memories do.

Childhood amnesia — our inability to remember almost everything that happened to us when very young — is always interesting. It’s not as simple as an inability to form long-term memories. Most adults can’t remember events earlier than 3-4 years (there is both individual and cultural variability), even though 2-year-olds are perfectly capable of remembering past events (side-note: memory durability increases from about a day to a year from age six months to two years). Additionally, research has shown that young children (6-8) can recall events that happened 4-6 years previously.

Given that the ability to form durable memories is in place, what governs which memories are retained? The earliest memories adults retain tend to be of events that have aroused emotions. Nothing surprising about that. More interesting is research suggesting that children can only describe memories of events using words they knew when the experience occurred — the study of young children (27, 33 or 39 months) found that, when asked about the experimental situation (involving a "magic shrinking machine") six months later, the children easily remembered how to operate the device, but were only able to describe the machine in words they knew when they first learned how to operate it.

Put another way this isn’t so surprising: our memories depend on how we encode them at the time. So two things may well be in play in early childhood amnesia: limited encoding abilities (influenced but not restricted to language) may mean the memories made are poor in quality (whatever that might mean); the development of encoding abilities means that later attempts to retrieve the memory may be far from matching the original memory. Or as one researcher put it, the format is different.

A new study about childhood amnesia looks at a different question: does the boundary move? 140 children (aged 4-13) were asked to describe their three earliest memories, and then asked again two years later (not all could provide as many as three early memories; the likelihood improved with age).

While more than a third of the 10- to 13-year-olds described the same memory as their very earliest on both occasions, children between 4 and 7 at the first interview showed very little overlap between the memories (only 2 of the 27 4-5 year-olds, and 3 of the 23 6-7 year-olds). There was a clear difference between the overlap seen in this youngest group (4-7) and the oldest (10-13), with the in-between group (8-9) being placed squarely between the two (20.7% compared to 10% and 36%).

Moreover, children under 8 at the first interview mostly had no overlap between any of the memories they provided at the two interviews, while those who were at least 8 years old did. For the oldest groups (10-13), more than half of all the memories they provided were the same.

The children were also given recall cues for memories they hadn’t spontaneously recalled. That is, they were told synopses of memories belonging to both their own earlier memories, and other children’s earlier memories. Almost all of the false memories were correctly rejected (the exceptions mostly occurred with the youngest group, those initially aged 4-5). However, the youngest children didn’t recognize over a third of their own memories, while almost all the oldest children’s memories were recognized (90% by 8-11 year-olds; all but one by 12-13 year-olds). Their age at the time of the event didn’t seem to affect the oldest or the very youngest groups, but 6-9 year-olds were more likely to recall after cuing events that happened at least a year later than those events that weren’t recalled after cuing.

In general, the earliest memories were several months later at the follow-up than they had been previously. The average age at the time of the earliest memory was 32 months, and 39.6 months on the follow-up interview. This shift in time occurred across all ages. Moreover, for the very earliest memory, the time-shift was even greater: a whole year.

In connection with the earlier study I mentioned, regarding the importance of language and encoding, it is worth noting that by and large, when the same memories were recalled, the same amount of information was recalled.

There was no difference between the genders.

The findings don’t rule out theories of the role of language. It seems clear to me that more than one thing is going on in childhood amnesia. These findings bear on another aspect: the forgetting curve.

It has been suggested that forgetting in children reflects a different function than forgetting in adults. Forgetting in adults matches a power function, reflecting the fact that forgetting slows over time (as is often quoted, most forgetting occurs in the first 24 hours; the longer you remember something, the more likely you are to remember it forever). However, there is some evidence that forgetting in children is best modeled in an exponential function, reflecting the continued vulnerability of memories. It seems they are not being consolidated in the way adults’ memories are. This may be because children don’t yet have the cognitive structures in place that allow them to embed new memories in a dense network.

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