Explain how environmental factors such as learning context, noise, lead, poverty and technology influence memory and attention, evaluate natural experiments and replications through the concepts, and apply them to scenarios.
Each is in the Psychology glossary, with every other term in the subject.
The theory that natural environments allow the capacity for effortful, directed attention to recover, because they hold attention without requiring effort.
The limited capacity for attention, working memory and self-control available at any moment, which can be reduced by preoccupying concerns such as worry about money.
The use of physical actions or external tools, such as notes, reminders, devices or the internet, to reduce the cognitive demands of a task, especially on memory.
Better recall when the external environment at retrieval matches the environment in which the information was learned.
The principle, proposed by Endel Tulving, that memory is best when the cues present at retrieval match those encoded together with the information at learning.
The tendency to remember less of information that one expects to be able to find again online, while remembering better where it can be found.
Better recall when a person’s internal state at retrieval, such as mood, arousal or the effect of a drug, matches their state at learning.
Explain what an environmental factor is and describe the main kinds that influence cognitive processes.
Explain context-dependent and state-dependent memory with reference to the encoding specificity principle.
Describe and evaluate research on the effects of noise and of lead exposure on memory and learning.
Describe and evaluate Mani et al.’s (2013) research on poverty and cognitive function, including attempts to replicate it.
Discuss research on the influence of digital technology on memory, including the Google effect and its replication record.
Evaluate research on environmental influences on cognition with reference to causality, measurement and bias.
Apply research on environmental influences to suggest changes that could improve a cognitive process.
The environment in which a person learns and lives affects how well they attend, learn and remember. Some influences act within minutes, such as the room in which information is learned or the presence of a phone on the desk; others act over months or years, such as aircraft noise at school, lead in drinking water or the constant pressure of not having enough money. Because many of these factors can be changed by policy, design or habit, research on them has direct practical value.
Explanations of environmental influences draw on the cognitive approach, which describes the processes of encoding, storage and retrieval that the environment affects, and on the biological approach, because some environmental factors, such as lead, act on the brain directly. Social and economic conditions decide who is exposed, which links the topic to the sociocultural approach
With a free account, Augment remembers where you stopped and what you have practiced.
An environmental factor is any influence on behaviour or cognition that comes from outside the person rather than from their genes or biology. For cognitive processes, environmental factors can be grouped by where they come from: the immediate learning context (the place, sounds and smells present during learning, and the person’s physical state), the physical environment (noise, toxins, green space), the economic environment (poverty and financial pressure) and the technological environment (smartphones, the internet).
Environmental factors act on cognition in different ways. Some become part of the memory itself, so that recall is easier when the same environment is present again. Some compete for limited attention or working memory, so that less capacity is left for learning. Others damage the brain over time, as with lead. Naming the mechanism is the key to explaining an environmental influence rather than just stating it.
When people learn something, they store more than the target information. Features of the setting and of their own internal state are encoded with it. Endel Tulving’s encoding specificity principle states that a retrieval cue is effective only to the extent that it matches information encoded along with the target. Recall is therefore best when the conditions at retrieval match the conditions at encoding.
Two kinds of match are distinguished. In context-dependent memory, the cues are external, such as a room, a place or background music. In state-dependent memory, the cues are internal, such as mood, level of arousal or the effect of a drug. Both describe forgetting caused by a lack of the right retrieval cues: the information is stored, but cannot be reached.
Godden and Baddeley tested context-dependent memory in a natural setting with very different contexts. A small group of divers learned lists of 36 unrelated words, heard from a recording, either on the beach or underwater, and then recalled them either in the same environment or in the other one. Recall was clearly better when the learning and recall environments matched than when they did not, in both directions.
The study has high mundane realism for a memory experiment, because the divers used a skill they practised regularly, and its design rules out the explanation that one environment is simply better for learning. However, the sample was small and the change of context was extreme. A further experiment by the same researchers showed that the disruption of moving between land and water did not explain the effect. In a later meta-analysis, Smith and Vela (2001) found that environmental context effects are reliable across many studies, but smaller when people focus on the material rather than on their surroundings, and reduced when people mentally reinstate the learning context before recall.
Goodwin et al. (1969) asked male volunteers to carry out memory tasks either sober or after drinking alcohol, and tested them 24 hours later in the same or the opposite state. On recall tasks, participants who had learned while intoxicated remembered more when they were intoxicated again than when they were sober, although recognition was not affected by the change of state. The finding does not mean that alcohol helps learning; the effect concerns only the match between the states at learning and at recall.
Chronic noise can affect learning in several ways. It competes for attention, it masks speech so that teachers and classmates are harder to hear, and children exposed to it for long periods may learn to filter out sound in general, which may impair the processing of speech and reading. These mechanisms predict effects on reading and on long-term memory for material learned at school.
Hygge, Evans and Bullinger (2002) used the move of Munich’s airport as a natural experiment. Before the new airport opened and the old one closed, they recruited children living near both sites, together with control groups from quiet areas matched for socioeconomic status. A total of 326 children, with a mean age of 10.4 years, were tested three times: once before the switch and twice after it.
After the switch, long-term memory and reading declined in the children newly exposed to noise near the new airport and improved in the children near the old airport, whose noise had stopped. Short-term memory also improved after the old airport closed, and speech perception declined in the newly exposed group. The design is strong for causality: the same children were tested before and after a change they did not choose, with control groups, and the effects went in both directions.
The RANCH project extended this work across three countries. Clark et al. (2006) studied 2,010 children aged 9–10 at 89 schools around airports in Amsterdam, Madrid and London. Aircraft noise at school was linked to poorer reading comprehension even after adjusting for socioeconomic variables, while road traffic noise was not, and the pattern was similar in all three countries. Because the data are correlational, they cannot prove causation, but the consistency across countries and the exposure–effect relationship support the causal conclusion of the Munich study.
Sources: Hygge, Evans & Bullinger (2002), _Psychological Science; Clark et al. (2006), American Journal of Epidemiology (RANCH)._
Lead is a metal that was once widely used in petrol, paint and water pipes. It is a neurotoxin: it damages developing neurons and affects the brain systems that support attention, learning and memory, especially in young children, whose brains are developing quickly and who absorb more of the lead they swallow. Lead is therefore an environmental factor that acts through biology, an example of how the approaches interact.
Lanphear et al. (2005) pooled data from 1,333 children in seven longitudinal cohort studies in several countries, followed from birth or infancy to between 5 and 10 years of age. After adjusting for other variables, higher blood lead was associated with lower IQ scores. An increase in blood lead from 2.4 to 30 micrograms per decilitre was associated with a loss of 6.9 IQ points, and the loss was steepest at the lowest levels.
The finding has had a large effect on policy. Because the studies are observational, lead exposure could be confounded with poverty, poor housing and other disadvantages that also affect cognitive development. The researchers adjusted for these variables, and the evidence from animal experiments and from the effects of removing lead from petrol supports a causal interpretation. Ethical responsibility means that a causal experiment in children would never be acceptable, so this convergence of observational and animal evidence is the best evidence available.
YouTubeMani, Mullainathan, Shafir and Zhao aimed to test whether poverty itself reduces cognitive function. Earlier research had shown that poor people more often make choices that keep them poor, such as missing medical appointments or taking expensive loans. The researchers proposed that worrying about money uses up limited mental resources, which they called cognitive bandwidth, leaving less for other tasks.
The research had two parts. In a laboratory experiment, shoppers in a mall in New Jersey were randomly given an easy or a hard hypothetical problem: their car needed a repair that would cost either a small or a large amount, and they were asked how they would pay for it. They then completed Raven’s Progressive Matrices, a test of reasoning, and a spatial compatibility task, a test of cognitive control. Their income was recorded at the end, so that the effect of the scenario could be compared for poorer and richer participants.
In a field study, 464 small sugarcane farmers living in 54 villages in India, who earned most of their income from sugarcane, were tested twice in 2010: before the harvest, when money is scarce and many farmers are in debt, and after the harvest, when they have just been paid. The same tests of reasoning and cognitive control were used. Because the same farmers were tested in both conditions, this was a repeated measures design within a natural experiment.
In the laboratory, the hard scenario lowered the reasoning and cognitive control scores of poorer participants but not those of richer participants; with the easy scenario the two groups did not differ. The researchers estimated the effect on poorer participants as equivalent to about 13 IQ points. In the field study, the same farmers performed worse on the cognitive tests before the harvest than after it.
The researchers argued that the difference was not caused by differences in time available, nutrition or work effort, which they measured. Farmers reported more stress before harvest, but stress did not account for the difference in cognitive performance. They concluded that poverty itself reduces cognitive capacity because concerns about money take up mental resources.
Financial scarcity, an environmental factor, appeared to reduce the availability of attention and working memory for other tasks. This suggests that some of the poor decisions linked to poverty may be consequences of poverty rather than causes of it, and that policies that make life more complex for poor people, such as long forms or complicated rules, impose a further “cognitive tax”.
The combination of a randomised laboratory experiment and a within-subjects field study is a strength: the experiment shows that thinking about money can cause a drop in performance, and the field study shows the effect in a real change of income. The field study cannot rule out every seasonal difference between before and after harvest, and practice on the tests could improve the second scores; the researchers reported that their results were robust to learning effects.
Wicherts and Scholten (2013) reanalysed the laboratory data and argued that the effect depended on dividing participants into “rich” and “poor” groups. When income was treated as a continuous variable, they found no reliable effect, and they suggested that ceiling effects in short, easy tests produced spurious interactions. Mani et al. replied that the interaction remained reliable when the experiments were pooled and income was continuous. The debate shows how decisions about measurement and analysis can change a conclusion.
Carvalho, Meier and Wang (2016) randomly assigned low-income households in the United States to complete an online survey before or after payday. Before payday, people were more present-biased in choices about money, but there was no difference in cognitive function, in risk-taking or in the quality of their decisions. O’Donnell et al. (2021) repeated 20 studies from the wider scarcity literature and found very mixed results, with some clear successes and some clear failures.
The original idea remains influential, but its evidence is weaker than first claimed. The case illustrates the risk of publication bias and the value of replication before a finding is used for policy.
Sources: Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty impedes cognitive function. _Science, 341(6149), 976–980. Wicherts & Scholten (2013), Science; Carvalho, Meier & Wang (2016), American Economic Review._
Digital devices change what people need to remember. Cognitive offloading is the use of a physical action or an external tool, such as writing a note, setting a reminder or saving a file, to reduce the demands on memory. It is not new (shopping lists and diaries are forms of it), but smartphones and search engines make it constant and effortless.
Sparrow, Liu and Wegner (2011) reported four studies suggesting that when people expect to be able to look information up later, they remember the information itself less well and remember instead where to find it. When participants typed trivia statements and were told the statements would be saved in named folders, they were more likely to recall the folder than the statement. This pattern was named the Google effect.
The effect fits the idea of transactive memory, in which people in a couple or team remember who knows what instead of remembering everything themselves. However, in a large preregistered project, Camerer et al. (2018) attempted to replicate 21 experiments published in Nature and Science, and the Sparrow et al. finding was among those that did not replicate. The Google effect therefore remains a plausible but uncertain influence of technology on memory.
YouTubeWard et al. (2017) reported that the mere presence of a person’s own smartphone on the desk reduced available cognitive capacity on tasks measuring working memory and reasoning, an effect they called “brain drain”. The proposed mechanism was that resisting the pull of the phone uses up attention. A preregistered direct replication by Ruiz Pardo and Minda (2022) found no difference between participants whose phones were on the desk, in a pocket or bag, or in another room. Like the Google effect, the brain drain effect is widely cited but weakly supported.
Environmental demands can strengthen cognitive structures as well as weaken them. Maguire et al. (2000) used structural MRI to compare licensed London taxi drivers, who must learn the layout of thousands of streets, with control participants who did not drive taxis. The rear part of the hippocampus was larger in the taxi drivers, and its size correlated with the time spent driving a taxi. A demanding spatial environment seems to have shaped a brain structure that supports spatial memory, an example of neuroplasticity.
According to attention restoration theory, natural environments capture attention gently, without demanding effort, and so allow directed attention, the effortful attention used for study and work, to recover. Urban environments, by contrast, contain stimuli that demand attention, such as traffic. Berman, Jonides and Kaplan (2008) found that walking in nature, or even viewing pictures of nature, improved performance on tests of directed attention, such as repeating digit sequences backwards, more than walking in an urban setting or viewing urban pictures.
The findings suggest simple environmental changes for learning, such as breaks outdoors or views of green space from classrooms. The studies were small and used short-term measures, so the size and duration of the benefit in real schools are not yet clear.
Environmental factors act through cognitive and biological processes, and who is exposed to them depends on social and economic conditions. Poor children are more likely to live near airports and busy roads, in old housing with lead paint, and in families under financial stress, so several environmental factors often add up for the same children. Explanations that focus on one factor alone underestimate their combined effect, and policies that reduce several factors together may have the greatest benefit.
Environmental factor | Process affected | Main evidence | Design and causal strength |
|---|---|---|---|
Learning context | Retrieval | Godden & Baddeley (1975); Smith & Vela (2001) | Experiment; effect reliable but often modest |
Aircraft noise | Long-term memory, reading | Hygge et al. (2002); RANCH | Natural experiment plus multi-country correlational study |
Lead | IQ, attention, learning | Lanphear et al. (2005) | Pooled cohorts; animal evidence; no experiment possible |
Financial scarcity | Attention, working memory | Mani et al. (2013) | Experiment and natural experiment; disputed, not replicated |
Internet and phones | Memory, attention | Sparrow et al. (2011); Ward et al. (2017) | Experiments; key findings failed to replicate |
Nature | Directed attention | Berman et al. (2008) | Small experiments; short-term effects |
Many environmental factors cannot ethically or practically be manipulated: researchers cannot expose children to lead or noise. Natural experiments, in which a change happens for reasons outside the researchers’ control, are the main solution. The Munich airport move and the harvest cycle allowed the same people to be compared before and after a change. Their limitation is that other things may change at the same time, such as the season or practice on the tests, and participants are not randomly allocated to exposure.
The outcomes in this research are measured in very different ways: recall of word lists, standardised reading tests, IQ scores, Raven’s matrices and short laboratory tasks. A drop on a 10-minute laboratory task, as in some of the scarcity and smartphone studies, may not show any lasting change in real learning, and short, easy tests can produce ceiling effects that distort analyses. The environmental exposure must also be measured: noise is measured in decibels near schools, lead in blood samples, but “financial worry” only indirectly, through a scenario or a season.
Striking claims about modern environments, such as phones reducing intelligence or the internet weakening memory, attract attention and are more likely to be published and publicised. Several have failed in larger, preregistered replications: Sparrow et al. (2011), Ward et al. (2017) and much of the scarcity literature. By contrast, the effects of lead and of aircraft noise have been found repeatedly with large samples in many countries.
Research on environmental influences is closely linked to change. Removing lead from petrol, insulating schools against noise and simplifying forms for people in poverty are all changes justified partly by this research. That gives researchers a responsibility to communicate uncertainty honestly: policies built on findings that later fail to replicate waste resources, while delays in acting on strong findings, such as those on lead, cause harm.
Environmental influences on cognitive processes appear in Paper 1 Section B, where a scenario describes a place, a policy or a habit, and in Section C, where the concept is often causality, measurement or change.
For a Section C question, choose two or three environmental factors with evidence of different strength, so that the essay can make a reasoned judgement rather than listing studies.
Environmental factors are influences from outside the person; they affect cognition by becoming retrieval cues, by competing for attention and working memory, or by changing the brain.
The encoding specificity principle explains context-dependent and state-dependent memory: recall is better when cues at retrieval match those at encoding, as with Godden and Baddeley’s divers.
In the Munich airport natural experiment, children’s long-term memory and reading declined with new aircraft noise and improved when noise stopped.
Lanphear et al. (2005) found lower IQ with higher childhood blood lead, with the steepest losses at the lowest levels and no safe threshold.
Mani et al. (2013) proposed that financial scarcity reduces cognitive bandwidth; the finding is disputed and was not replicated by Carvalho et al. (2016).
The Google effect and the smartphone “brain drain” are plausible effects of technology on memory and attention, but their key findings failed to replicate.
Demanding or restorative environments can improve cognition, as suggested by research on taxi drivers’ hippocampi and on nature and attention.
Natural experiments are the main way to study causality when manipulation is unethical, and replication is the main test of how far each finding can be trusted.
I can explain what an environmental factor is and the main ways it can affect memory and attention.
I can explain context-dependent and state-dependent memory using the encoding specificity principle.
I can describe and evaluate research on noise and on lead as environmental influences on learning.
I can describe and evaluate Mani et al. (2013), including the critique and the failed replication.
I can discuss research on the Google effect and smartphones, including their replication record.
I can evaluate research on environmental influences with reference to causality, measurement and bias.
I can apply this research to a scenario and suggest an environmental change to improve a cognitive process.