Why some memories do not last a lifetime: dynamic long-term retrieval in changing environments

Dunlap, Aimee S.; McLinn, Colleen M.; MacCormick, Holly A.; Scott, Matthew E.; Kerr, Benjamin

doi:10.1093/beheco/arp102

Cited by 34 publications

(26 citation statements)

References 51 publications

(70 reference statements)

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“…This has the advantage of being eminently testable: the annual autocorrelation coefficient corresponding to our parameter r could feasibly be directly estimated from any kind of environmental time-series data. If the annual autocorrelation coefficient is not of the order of 0.95 or above, then individuals will do better on average by assuming that the environment will regress to the mean than they do by taking their early-life experience as representative of the world into which they will mature (see [59], for conceptually related results on the optimal duration of memories).…”

Section: Discussionmentioning

confidence: 99%

The evolution of predictive adaptive responses in human life history

2013

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Many studies in humans have shown that adverse experience in early life is associated with accelerated reproductive timing, and there is comparative evidence for similar effects in other animals. There are two different classes of adaptive explanation for associations between early-life adversity and accelerated reproduction, both based on the idea of predictive adaptive responses (PARs). According to external PAR hypotheses, early-life adversity provides a 'weather forecast' of the environmental conditions into which the individual will mature, and it is adaptive for the individual to develop an appropriate phenotype for this anticipated environment. In internal PAR hypotheses, early-life adversity has a lasting negative impact on the individual's somatic state, such that her health is likely to fail more rapidly as she gets older, and there is an advantage to adjusting her reproductive schedule accordingly. We use a model of fluctuating environments to derive evolveability conditions for acceleration of reproductive timing in response to early-life adversity in a long-lived organism. For acceleration to evolve via the external PAR process, early-life cues must have a high degree of validity and the level of annual autocorrelation in the individual's environment must be almost perfect. For acceleration to evolve via the internal PAR process requires that early-life experience must determine a significant fraction of the variance in survival prospects in adulthood. The two processes are not mutually exclusive, and mechanisms for calibrating reproductive timing on the basis of early experience could evolve through a combination of the predictive value of early-life adversity for the later environment and its negative impact on somatic state.

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Section: Discussionmentioning

confidence: 99%

The evolution of predictive adaptive responses in human life history

2013

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“…Nevertheless, some animals form LTM after a single experience suggesting that they may have evolved to rely on the value of the learned information more readily than other animals, rather their having evolved superior learning and memory abilities [7]. Several factors have been described that play a role in learning and memory including the high energetic costs of memory formation [8,9], the effects of age, physiological state, longevity, stress and the number of lifetime experiences [10][11][12][13][14]. This review focuses on aspects of variation in the importance and reliability of the learned information.…”

Section: Highlightsmentioning

confidence: 99%

The complexity of learning, memory and neural processes in an evolutionary ecological context

Smid

Vet

2016

Current Opinion in Insect Science

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The ability to learn and form memories is widespread among insects, but there exists considerable natural variation between species and populations in these traits. Variation manifests itself in the way information is stored in different memory forms. This review focuses on ecological factors such as environmental information, spatial aspects of foraging behavior and resource distribution that drive the evolution of this natural variation and discusses the role of different genes and neural networks. We conclude that at the level of individual, population or species, insect learning and memory cannot be described as good or bad. Rather, we argue that insects evolve tailor-made learning and memory types; they gate learned information into memories with high or low persistence. This way, they are prepared to learn and form memory to optimally deal with the specific ecologies of their foraging environments. Highlights:• Environmental variation, spatial foraging behavior and the distribution of resources are factors that drive the evolution of differences in prepared learning • Considerable heritable variation exists in numerous learning and memory traits • Insects have evolved tailor-made memory types, adapted to the specific ecology of their foraging environment • The foraging gene is an important gene underlying natural variation in learning and memory • Dopaminergic signaling may drive the adaptive gating of information into different memory formsThe use of previous experience to optimize behavior in an adaptive manner is obviously of great benefit to all animals, including insects [1,2]. However, this does not mean that insects should learn and remember information from all possible experiences they encounter. Studies on diverse insect species have revealed the daunting complexity of different forms of memory each with different stabilities, including short-, mid-, anesthesia resistant-and long term memory (STM, MTM, ARM and LTM), e.g. [3][4][5][6]. In Fig. 1 we provide a basic description of these memory forms and their abbreviations. Why does learning not always result in the formation of LTM? To ensure the reliability of learned information, most animals require multiple, spaced experiences before information is stored as LTM. Nevertheless, some animals form LTM after a single experience suggesting that they may have evolved to rely on the Accepted in Current Opinion in Insect Science, CC-BY-NC-ND

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“…A few models have addressed this problem by modeling the fitness value of memory, typically linking this to environmental variability in a relatively obvious way: long-lasting memory does not pay when environments change quickly because information go ‘out of date’ when things change quickly (e.g. Anderson and Schooler 1991; Dunlap et al 2009). These models commonly consider how a memory system should integrate old and new experience.…”

Section: Introductionmentioning

confidence: 99%

Tracking a changing environment: optimal sampling, adaptive memory and overnight effects

Dunlap

Stephens

2012

Behavioural Processes

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Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 hour period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 hours. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24 hour retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this ‘specialness’ depends on the subject’s prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time.

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Why some memories do not last a lifetime: dynamic long-term retrieval in changing environments

Cited by 34 publications

References 51 publications

The evolution of predictive adaptive responses in human life history

The evolution of predictive adaptive responses in human life history

The complexity of learning, memory and neural processes in an evolutionary ecological context

Tracking a changing environment: optimal sampling, adaptive memory and overnight effects

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