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How indoor environmental quality affects occupants’ cognitive functions: A systematic review

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Abstract

Cognitive functions refer to the set of brain-based skills to execute tasks of various difficulty levels. As people spend substantial time indoors, the indoor environmental quality (IEQ) influences occupants’ cognitive functions and consequently their learning and work performance. Previous studies have commonly examined the effects of IEQ on integrated learning or work performance, rather than specific cognitive skills. The present review decomposes IEQ into five factors—indoor air quality, the thermal environment, lighting, noise, and non-light visual factors. It divided cognition into five categories—attention, perception, memory, language function, and higher order cognitive skills—to better understand the relationship between IEQ and cognition. We conducted a detailed manual review of 66 focused studies and adopted co-occurrence analysis to generate landscapes of the associations between IEQ and cognition factors by analyzing keywords and abstracts of 8133 studies. Overall, results show that poor IEQ conditions are but not always associated with reduced cognition. However, the effects of a specific IEQ factor on different cognitive functions are quite distinct. Likewise, a specific cognitive function could be affected by different IEQ factors to varying degrees. Furthermore, the results suggest extensive inconsistencies in the relevant literature, especially regarding the effects of IAQ or thermal environment on cognition. Additionally, the keyword co-occurrence analysis identified more IEQ factors and cognitive functions emerging in the recent literature. Future studies are recommended to explore the factors causing the inconsistencies that we highlight here.
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How Indoor Environmental Quality Affects Occupants’ Cognitive
Functions: A Systematic Review
Chao Wang1, Fan Zhang2, Julian Wang3, James K. Doyle4, Peter A. Hancock5, Cheuk Ming Mak6,
Shichao Liu1*
1Department of Civil and Environmental Engineering, Worcester Polytechnic Institute, MA, USA
2School of Engineering and Built Environment, Griffith University, Gold Coast, Australia
3Department of Architectural Engineering, Pennsylvania State University, University Park, PA,
USA
4Department of Social Science and Policy Studies, Worcester Polytechnic Institute, MA, USA
5Department of Psychology and Institute for Simulation and Training, University of Central
Florida, Orlando, FL, USA
6Department of Building Services Engineering, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong
* Shichao Liu (sliu8@wpi.edu)
https://doi.org/10.1016/j.buildenv.2021.107647
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Highlights
Effects of IEQ factors on cognition are reviewed
IEQ and cognition are but not always statistically associated
Considerable conflicting results are identified among studies
A specific IEQ factor may have varying effects on different cognitive functions
Abstract
Cognitive functions refer to the set of brain-based skills to execute tasks of various difficulty levels.
As people spend substantial time indoors, the indoor environmental quality (IEQ) influences
occupants’ cognitive functions and consequently their learning and work performance. Previous
studies have commonly examined the effects of IEQ on integrated learning or work performance,
rather than specific cognitive skills. The present review decomposes IEQ into five factorsindoor
air quality, the thermal environment, lighting, noise, and non-light visual factors. It divided
cognition into five categoriesattention, perception, memory, language function, and higher order
cognitive skillsto better understand the relationship between IEQ and cognition. We conducted
a detailed manual review of 66 focused studies and adopted co-occurrence analysis to generate
landscapes of the associations between IEQ and cognition factors by analyzing keywords and
abstracts of 8,133 studies. Overall, results show that poor IEQ conditions are but not always
associated with reduced cognition. However, the effects of a specific IEQ factor on different
cognitive functions are quite distinct. Likewise, a specific cognitive function could be affected by
different IEQ factors to varying degrees. Furthermore, the results suggest extensive inconsistencies
in the relevant literature, especially regarding the effects of IAQ or thermal environment on
cognition. Additionally, the keyword co-occurrence analysis identified more IEQ factors and
cognitive functions emerging in the recent literature. Future studies are recommended to explore
the factors causing the inconsistencies that we highlight here.
Keywords: Environmental Design, Healthy Buildings, Occupant Satisfaction, Learning
Performance, Productivity, Work Efficiency
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Graphical Abstract
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1. Introduction
Cognitive functions refer to the set of brain-based skills to required execute tasks of various
difficulty levels [1]. They are associated intensively with the mechanisms of learning,
remembering, reasoning, and problem-solving [2]. Each function plays an essential role in
processing new information. Research in neuroscience has been stated that cognitive performance
is associated with the activities of specific brain centers. For instance, the activation of frontal and
parietal areas is directly associated with sustained attention performance [3].
As people now spend a substantial amount of time indoors learning and/or working, particularly
in the lockdown of the pandemic, IEQ could significantly affect occupants’ cognitive functions
and therefore their learning and work performance. Prior reviews have [46] classified IEQ factors
into indoor air quality (IAQ), thermal environment, light, acoustic, office and layout, biophilia and
views, look and feel, and location and amenities, to name a series of the major influences.
There is a substantial body of research showing that poor indoor air quality [7], ventilation [8,9],
thermal conditions [10,11], light [12], noise [13,14], and room layout [15] can profoundly degrade
learning and work performance. Nevertheless, the findings of these studies, and other substantial
ones on this topic [1619], do not fundamentally differentiate between types of cognitive tasks.
However, this is essential as the impacts of IEQ may vary significantly between cognitive tasks.
For instance, previous research indicates that, compared with complex tasks, simple tasks, for
example, might be less susceptible to environmental noise and heat [20,21]. Obviously, different
learning/work tasks rely upon different cognitive functions. For instance, the presidents or chief
operating officers of large corporations might require stronger skills in decision making and
planning, while customer service representatives, in a call center, who handle customer complaints
should be able to excel at auditory perception and emotion recognition. Similarly, reasoning skills
are more involved in the process of learning mathematics compared to foreign languages. It is
difficult to associate IEQ and learning or work performance without specifying each of the
cognitive activities involved.
In the contemporary indoor environment, success in learning and work is mainly determined by
cognitive performance as opposed to physical performance (e.g., strength, endurance, balance).
Understanding the influences of various IEQ factors on each cognitive function is the key to
estimating how differently a chief officer could be susceptible to poor IEQ from the vulnerability
of a service representative in a call center. Unlike previous reviews that examine learning/work
performance as a whole [22,23], the present study focuses on specific cognitive functions that
underpin various learning/work activities, it aims to provide a multidisciplinary and
comprehensive survey of research associated with cognitive functions influenced by IEQ. Another
motivation is the insufficiency of qualitative and/or quantitative summaries of massive numbers
of studies (in the thousands) that may not directly focus on IEQ and cognition, but still shed light
on the patterns of their relationship. To fill this gap, this review work applies keyword co-
occurrence analysis to extract knowledge from thousands of identified and relevant published
papers.
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2. Categories of IEQ factors and Cognitive functions
In this work, we synthesized a large panoply of previous reported work and grouped IEQ factors
into five categories (IAQ, thermal environment, noise, lighting, and non-light visual factors), we
just posed these with five cognitive functions into the categories (attention, perception, memory,
language function, and higher order cognitive skills). Social cognition has been identified but not
discussed in this review due to limited number of studies identified. Indoor environmental factors
that do not ubiquitously exist were not explicitly considered in this review. These include transients
such as music and natural-based soundscapes. However, we acknowledge that these factors may
serve to improve cognition (e.g., working memory [24], verbal memory [25], spatial reasoning
[26], speed of spatial processing [27]), albeit the literature is still rather equivocal concerning a
number of their effects [2832]. Additionally, this review does not consider the cognitive
development of children that might be affected by IEQ [33]. Figure 1 lists the main categories and
subcategories of IEQ factors and cognitive functions identified in the literature. Next section
provides an overview of the basic concepts of IEQ factors and cognitive functions.
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Figure 1. Summarized categories of IEQ and cognitive functions based on the literature; The factors in bold are explicitly studied in the literature
concerning the IEQ-cognition-interaction.
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2.1 Indoor Environmental Quality
2.1.1 Indoor air quality
Indoor air quality (IAQ) is a critical factor that affects both the health and productivity of space’s
occupants [34]. Indoor air pollutants include carbon dioxide (CO2) [35], sulfur dioxide (SO2) [36],
nitric oxide (NO) [37], nitrogen dioxide (NO2) [38], volatile organic compounds (VOCs) [39],
semi-volatile organic compounds (SVOCs) [40], levels of particulate matter (PM) [41], biological
contaminants [42,43] among many others. Practically, ventilation and indoor CO2 concentration
are used as an indicator or proxy for diverse levels of indoor air quality [4446]. A 1000 ppm
increase in CO2 concentration decreases 0.5-0.9% of annual average daily attendance, which is
equivalent to a relative 10-20% increase in student absences [47]. Each of these pollutants can
influence both acts of cognition as well as rates of learning.
2.1.2 Thermal environment
Thermal environment is the physical environment that can affect heat transfer in the indoor. It
influences the thermal perception of an individual and through that, the thermal comfort of
occupants. Thermal comfort is the subjective evaluation of a thermal environment [48] and is
mainly influenced by four physical parameters (air temperature, mean radiant temperature, air
velocity, and relative humidity). These physical values are concentrated with two personal
variables (clothing insulation and activity level) [48]. These go together with other factors such as
gender [49], age [50,51], culture [52], exposure time [53], and physiological adaption [54]. The
complexity of these influencing factors results in various prediction models, including but not
limited to predicted mean vote (PMV) a predicted percentage dissatisfaction (PPD) model [55],
an adaptive thermal comfort model [53,56], and the recent personal thermal comfort [5760]
relying on machine learning principles. The thermal environment exerts fairly consistent and
predictable effects on some elements of cognition, especially toward the outer bounds of tolerance
[61].
2.1.3 Noise
Indoor noise can come from sources inside the building or sources external to it. Internal sources
can consist of conversations of occupants [62], indoor operating equipment [63], and air
distribution systems [64], while outdoor noise transmitted into indoor spaces can emanate from
road traffic [65,66], aircraft [66,67], outdoor construction [68] and outdoor components of the
heating, ventilation, and air conditioning (HVAC) [69]. Noise from traffic, aircraft, public, or
equipment generates a complex sound assemblage that can negatively impact memory [12,70,71].
Even speech from other classrooms in school can influence students’ memory in adjacent classes
[72]. Occupants’ perceptions are affected by both energy intensity and distribution of acoustical
stimuli [73].
2.1.4 Lighting
Lighting plays a critical role in synchronizing humans' endogenous and night pacemakers with the
environment. As the most powerful zeitgeber synchronizing our endogenous circadian rhythm
with the environment, light has been previously described as one of the agents involved in
improving cognitive performance [74]. Light quality for visual comfort is primarily characterized
by photometric variables [7577], glare [7880], and light color temperature [81,82]. Literature
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regarding the effects of lighting on cognition has focused on photometric parameters (i.e.,
luminance, illuminance, color temperature, color rendering).
Artificial light is produced by electrical means such as lamps and light fixtures, while daylight is
the combination of all direct or indirect sunlight. Daylight is considered as the best light source for
color rendering and closely and unsurprisingly matches the human visual response [83]. It is a kind
of trigger that motivates biological activities. Whenever possible, building design typically tries to
use daylight as the source of illumination, because of its excellent color rendering provides higher
satisfaction [84] and supports for stable circadian rhythms [85]. It also helps occupants to generate
an active sense of pleasantness and brightness, which is positive for occupants’ comfort and
productivity [86,87].
The enhancement of occupants' alertness and performance can be improved by light exposure
through a “non-visual” photoreception system depending on melanopsin expressing retinal
ganglion cells (mRGCs) [88]. It also has been reported in recent years that human alertness,
cognitive performance, and mood can be affected by non-visual lighting effects related to spectrum
distribution, timing, and exposure duration, in which certain new metrics have been developed
based on radiometric quantities [8991].
2.1.5 Non-light visual factors
In addition to environment luminance, interior surface textures, spatial design, decoration, interior
color, window views, biophilia, and many other non-light visual factors can influence cognition.
The non-light visual factors in this review include interior color, spatial settings, closeness to
natural views, and landscape. Satisfying non-light visual factors of the indoor environment
positively affects occupants’ cognitive function and overall performance. Humans have ingrained
reactions to different colors, due to our essential relationship with nature. For example, the color
green reminds us of an environment that makes us feel calm and harmonious [92]. Also, indoor
visual interests and opportunities for discovery provide intellectual and cognitive stimulation,
which have been found to foster creative behaviors [93]. Such factors have been considered
influential in restoring attentional resources, as we articulate further below.
Humans tend to seek connections with nature and other living things, as posited by the biophilia
hypothesis [94]. Natural environments have, as we have noted a restorative effect on attention,
according to the attention restoration theory (ART) [95]. A view of natural elements is beneficial
for high workability and job satisfaction [96]. With respect to the visible features of outdoor or
indoor space, landscapes with natural features have a positive effect on cognition and performance.
High school landscapes that lack natural features have been shown to reduce standardized test
scores [97], while landscapes with greater tree coverage ratios show a higher percentage of
proficiency or advancement in reading and mathematics [98].
2.2 Cognitive functions
Cognitive functions can be summarized using a number of different taxonomies. Prior review work
on cognition and human performance has classified cognitive functions into attention, memory,
perceptual-motor performance, judgment, and decision making [2]; while [99] categorized it into
perceptual functions, memory, thinking, and expressive functions. Another categorization
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approach to cognition consists of memory, attention, reasoning, visual perception, language
function, problem-solving, and planning [100]. Among the cognitive functions reported in the
studies we have examined, attention, perception, memory, language function, and higher order
cognitive skills are the most commonly studied when considering associations with IEQ. Each
cognitive function can be further sub-divided as described in Figure 1. For instance, the higher
order cognitive skills consist of problem solving, decision making, reasoning, and others [101].
Other essential cognitions (e.g., social cognition) are also listed (in the unbolded text) but not
studied in this current review.
2.2.1 Attention
Attention is an individual’s ability to concentrate on a particular facet of information
[102]. Attentional processes can be further categorized as sustained attention [103105], selective
attention [106109], and divided attention [110112]. Attentional performance can be assessed
using the Continuous Performance Task (CPT) [113], reaction time [114], Stroop tasks [115], the
attention network test [116], and the dot-probe task [107] among others. For instance, reaction
time is the assessment of motor and mental response speeds, as well as measures of movement
time [117,118]. It is also an important performance measure of multiple cognitive functions
beyond attention [119], such as sensory memory [120].
Attention has a limited capacity. People cannot easily focus on more than one stimulus at a time,
unless experience with the task that has enabled automatic processing [121]. Also, a person might
possess an attentional bias that refers to the tendency of that individual to selectively attending to
a certain category of stimuli in the environment while tending to overlook, ignore, or disregard
other kinds of stimuli [122]. Attentional bias can be influenced by emotion and mood [123,124],
and these moderating effects may confound the association between IEQ and attention. Moreover,
attention could be diverted from stimuli to be remembered by environmental proximal stimuli (e.g.,
conversation in an open-space)[125], making it vulnerable to indoor environmental factors.
2.2.2 Perception
Perception refers to the set of cognitive processes to capture, organize, identify, and interpret the
stimuli received by the sensory organs to understand the presented information in the environment
[126]. It acts as an essential cognitive ability in our lives to connect us with the surrounding world.
While some reports such as [127,128] distinguish perception from cognition, numerous researchers
regard perception as an aspect of overall cognition [129,130]. Perception is different from
sensation. The sensation is the process of detecting our environment, while perception is the
interpretation of what is sensed. Perception is more involved with top-down processing which
itself is influenced by an individual’s expectations and knowledge rather than simply by the
stimulus itself [131].
Perception may be biased as a function of emotion [132], individual differences (such as different
sensitivity to tone sequences [133]), personal context [134], beliefs, and expectations [135] that
might confound the influence of IEQ on perception. For instance, a person’s perception of thermal
comfort might be affected by the opinion of another person sharing the same office.
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There are multiple modes of perception: auditory perception [136], visual perception [137], speech
perception (also a language function), taste perception [138], touch/haptic perception [139], and
olfactory perception [140]. Visual perception is the primary human sense that moderates
surrounding information received by the eyes [141]. Ref [142] concludes that visual perception is
efficient in getting information associated most especially with dynamic variations. Visual stimuli
can be affected by people’s motivational state [143]. For instance, humans’ motivation can
influence the optical system to indicate the content of conscious perception. Speech perception has
a more specific scope than general auditory perception, which refers solely to the ability to receive
and interpret information received by the ear and interpreted by specific language cells in the brain.
2.2.3 Memory
Memory is a function that allows the brain to encode, store, acquire, and retrieve knowledge as
needed [144]. It is a crucial element of cognition that helps us identify who we are, gain new
knowledge, and form a continuity of conscious experience [131,145]. Memory is a component of
the information processing system with both explicit and implicit functions [131]. Explicit memory
refers to instances of conscious recollection, such as a response to a direct request for information
about one’s past. Implicit memory deals with cases when people are asked to perform some tasks
without the use of declarative knowledge [146]. The memory could be subdivided into as many as
256 different categories [147], going from abnormal memory, through terms such as diencephalic
memory, and on to rote memory and sensory memory, and finally to working memory [146].
However, we mainly focus here on broad categories of short-term memory (STM) and long-term
memory (LTM) [149].
External stimuli can be converted to memorized information via roughly three steps [150]. First,
human beings process stimuli through sensory memory that serves as a brief holding system for
the information presented to various sensory systems [151]. Sensory memory is vital for the
listener to integrate incoming acoustic information [120]. Then, the working memory processor
encodes the information, keeps it in mind temporarily, and meanwhile searches and activates data
from previously-stored memories [152]. Finally, the new information is integrated with and then
stored in long-term memory [153].
STM is versatile and supports reasoning and the guidance of decision-making behaviors [154].
When a person is distracted (e.g., by indoor noise or experiencing a cold draft near an exterior
window), information can be rapidly lost from such informative storage. A more modern
conceptualization of STM is working memory, which is a term for the type of memory holding
information for short periods while being manipulated [155]. Working memory involves the
processing of information (such as solving simple arithmetic problems while also remembering
given words during span tasks) as well as the executive control of attention. Besides, sensory
memories, as a type of STM, are the brief holding system for the information presented to the
various sensory systems. Information is thought to be held briefly in each system as it waits for
further processing [151]. Sensory memory is, for example, a vital part of the listener to integrate
incoming acoustic information [120].
LTM is a vast store of knowledge and a record of prior events. Long-term memory also possesses
a lot of subtypes. Distinctions by type of material and mode of presentation include verbal memory,
visual/spatial memory, and olfactory memory, together with procedural memory (also called
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kinesthetic or motor skill memory). Another set of distinctions, in terms of types of declarative (or
explicit) memory, are episodic memory, autobiographical memory, and semantic memory [146].
LTM has a much larger capacity and duration than STM. As such, LTM may be less susceptible
to poor indoor environmental quality.
2.2.4 Language function
Language function involves a set of cognitive skills that enable an individual to effectively
understand and generate language for effective interpersonal communication [156]. It can be
divided into five components, semantics, phonology, morphology, syntax, and pragmatics [157].
Language acquisition is the process by which humans perceive, comprehend, and acquire
information from language [158]. Some examples of language functions include word finding,
language comprehension, repetition, expression, reading, and writing [158]. Memory, attention,
and individual differences are common factors that affect reading and writing abilities. As a
function of language acquisition, speech perception is the process that employs sensory functions
to hear, and then interpret and understand the sounds [159,160].
Speech perception is an integrated result of the recipient's memory, attention, and both passive and
active receipt of signals. The phenomena of short-term memory deficit are common for children
who are poor readers [161]. Speaker’s lip movements act as visual stimuli that affect the auditory
perception of what is said. This process is most apparent when there is a combination of acoustic
information and visual information for a bilabial utterance combined [162]. A perception study
[161] proved that poor readers have a perceptual difficulty with speech perception due to the
material-specific problem. Illusions can also be generated when aural perception becomes
subordinate to what the listener believes they see in the expression of the speaker’s lips.
2.2.5 Higher Order Cognitive Skills
Higher order cognition is a multi-faceted and complex area of research that refers collectively to
the mental processes of reasoning, conceptualization, critical thinking, decision making, and
creativity. Higher order cognition involves the ability to understand and implement the steps
necessary to solve problems, establish new areas of learning, and think creatively [163]. Primary
topics investigated in higher order cognition consists of executive function, reasoning, planning,
and problem solving.
These executive functions are a set of complex cognitive processes that help people manage
thought, skills, and necessary behavior, and action to achieve goals [164]. They are diverse,
correlated, and overlapping. People need these functions to execute goal-oriented behaviors, such
as managing time, focusing on a task, planning, and organizing. The basic executive functions can
involve cognitive inhibition, cognitive flexibility, and emotional control, while reasoning,
planning, problem-solving, and decision making remain higher-order executive functions with the
requirement of several more fundamentally processes working at the same time to support them
[165,166].
Reasoning is regarded as the cognitive process that solves a problem by establishing logical
relationships between different problem elements [167]. It is the central activity in intelligent
thinking. General reasoning skills include inferential reasoning, deductive reasoning, analogical
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reasoning, conditional reasoning, and automated reasoning [168]. Reasoning ability can vary by
gender, age, and are affected by the surrounding environments including IEQ [169171].
People use planning skills to set and achieve goals by developing plans and choosing the
appropriate actions based on the anticipation of consequences [172]. Planning is key in the ability
to make shifts in attention. It is also a vital process for decision making, self-control, and self-
monitoring. Age and gender can be related to differences in planning performance [173]. In one
study younger adults usually made quicker and fewer inappropriate planning moves than older
adults. And girls with the ages of 5 and 17 years have been documented to outperformed boys at
the same age on certain measures of planning [174].
Problem solving is an integrated skill to generate and select solutions for problems. It is related to
mental strategies and heuristics as well as physical health [166]. Previous research found that
indoor environmental factors such as lighting, noise, or thermal environment have established
effects on problem solving [12,169,175]. Other higher order cognitive skills could consist of
judgment and decision making that is the cognitive ability to do a selection among several possible
alternatives [176].
3. Methods
In order to establish systematic effects of IEQ on these orders of cognitive performance, we
conducted a thorough search of the related scientific literature using two methods, a conventional
manual review and keyword co-occurrence analysis. The conventional manual review focused on
the most relevant studies about the explicit association between specific IEQ factors and cognitive
functions. The experimental setup, assessment tools, and the major results were tabulated in detail
after scrutinizing each study. Although arduous and time-consuming, the approach provides an
avenue to meticulously analyze results and serves as one of the most commonly used methods in
review studies [177,178]. There are thousands of studies in the literature involving IEQ and/or
cognition that have only implicitly addressed these same associations. The information in these
studies, though not providing direct evidence-informed decisions, can still shed much light on the
association between IEQ and cognition. Such information can be revealed through the keyword
co-occurrence analysis which we have provided here.
3.1 Conventional manual review
We searched and then gathered the most relevant studies that specifically and explicitly examined
the relationship between IEQ and cognition. These were derived from multiple sources, including
scientific journals, conference proceedings, and relevant books. The searched databases consisted
of Google Scholar, ScienceDirect, Springer, National Center for Biotechnology Information
(NCBI), the American Society of Heating, Refrigerating, and Air-conditioning Engineers
(ASHRAE), and the Proceedings of Indoor Air and Healthy Buildings conferences.
Keywords
We first searched the following keywords, cognitive performance, performance tasks, cognitive
function, productivity, attention, perception, memory, language function, and higher order
cognitive skills for cognition, while using IAQ, ventilation, thermal environment, noise, lighting,
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and non-light visual factors for IEQ factors. We then conducted a follow-up round of searching
for relevant studies by examining the reference lists of each of these collected studies.
Inclusion and exclusion criteria
We refined the papers selected based on the following rules. First, for laboratory studies,
experiments had to have been conducted in well-controlled climate rooms or chambers; for field
studies, environmental factors had to be clearly described and quantified. Studies without
quantitative measurements of IEQ factors were excluded. Studies that did not carry out cognitive
performance tests in different IEQ conditions or report performance test results with statistical
analyses were excluded in the review. Third, we limited the search to concrete cognitive functions;
namely, attention, perception, memory, language function, and higher order cognitive skills.
Performance tests that could be mapped into these five cognitive functions were included here.
Performance tests that did not fall into the above categories or integrated test kits combining
various cognitive functions without reporting individual scores for each function were also
excluded. Table A1 in Appendix I summarizes the cognitive tasks corresponding to different
cognitive functions.
Levels of Association between IEQ and cognition
A preliminary review showed a number of conflicting results for the effects of IEQ factors on
cognition. Some studies reported a statistically significant association (either positive or negative
association); while some reported no clear association between the two. Yet others reported mixed
results of positive associations, no associations and/or negative associations in different tests or
participant categories. To demonstrate the overall quantitative relationship between IEQ factors
and cognition, we, therefore, categorized levels of the statistical association between IEQ factors
and cognition into three ordinal levels ranging between 0 and 2. Here, 0 refers to no statistical
association between IEQ and cognition, meaning that the tested cognitive function was not
significantly different between tested IEQ conditions (p > 0.05). A degraded 1 denoted mixed
association, in which varying levels of statistical association were reported in different
performance tests and/or participant groups; A score of 2 referred to statistical associations,
where consistent positive or negative statistical association (p < 0.05) was reported between IEQ
and cognition. We applied “N/A” to denote the significance level if a study did not report p values.
An assigned score indicates an ordering of the association level.
3.2 Keyword Co-occurrence Analysis
As a particular form of data mining, text mining focuses on handling unstructured or semi-
structured datasets, such as that represented by text documents [179]. It is a well-established
practice that is commonly used to extract patterns and non-trivial knowledge from documents
written in a natural language [180]. In this review, keyword co-occurrence analysis was applied to
assist in literature reviews in retrieving information from large-scale data that is usually too big to
handle manually. Using the method, we were able to retrieve information from unstructured text
and visualize distilled knowledge in a concise form [181]. We first identified 8,133 studies that
mentioned both IEQ and cognition in their abstracts and/or keywords using the following search
logic on Scopus.
(cognition* OR “cognitive function*)
AND
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(“air pollution” OR “air filtration” OR ventilation OR Radon OR “particulate matter” OR PM10
OR PM2.5 OR “black carbon” OR aerosols OR voc OR “volatile organic compound” OR ozone
OR O3 OR asbestos OR pollutant OR “carbon monoxide” OR “carbon dioxide” OR CO2 OR
formaldehyde OR NO2 OR nitrogen dioxide” OR pesticide OR moisture OR “indoor
microorganism” OR “air odor” OR molds OR combustion OR “room temperature” OR “air
temperature” OR “air speed” OR “air velocity” OR “relative humidity” OR “thermal comfort”
OR “heat stress” OR “radiant temperature” OR “room NEAR/15 noise” OR “traffic noise” OR
“airplane noise” OR “speech noise” OR “public noise” OR “machinery noise” OR “equipment
noise” OR music OR lighting OR daylight OR “artificial light” OR “visual comfort” OR biophilia
OR texture OR “spatial shapes” OR glare OR “room NEAR/15 plant” OR greenery OR glare OR
“indoor layout” OR furniture OR furnishing OR “window view” OR “wall color” OR “interior
design” OR “building material” OR vibration)
Then we applied the VOSviewer (visualization of similarities) [182] to construct bibliometric
landscapes that extract a holistic relationship between IEQ and cognition from substantial
bibliographical data (keywords and abstract). The tool provided the visualization of co-
occurrences of scientific topics. For instance, ventilation is highly related to indoor air quality.
Also, through co-occurrence keyword analysis of studies at different periods, we were able to
identify emerging topics in the field.
4. Results
We synthesized the research findings on the influence of IEQ on attention, perception, memory,
language function, and higher order cognitive skills using the conventional manual review of 66
studies and the co-occurrence analysis of keywords and abstracts of 8,133 studies. The
experimental setups and major results of the reviewed studies are summarized in Appendix I Table
A2-A6. Each of these tables summarizes the key findings between one specific cognitive function
and IEQ factors. The table also includes sample size, environmental conditions, and metrics to
evaluate cognitive functions. Please note some studies appear in multiple tables since they have
investigated more than one cognitive function. This section summarizes the major findings of
Appendix I Table A2-A6 and insights from the co-occurrence analysis.
4.1 Relationships identified with a conventional manual review
4.1.1 IEQ’s Effects on Attention
The reviewed studies in Appendix I Table A2 revealed that most IEQ factors, when at disrupting
levels of values, negatively influenced attention in general. However, there is also present evidence
showing that some perceived adverse environments might even elevate attentional or concentration.
For instance, several studies reported enhanced working attention [12] and concentration
performance [170] due to increased temperature and noise levels, respectively.
Indoor Air Quality
Air pollutants negatively impact the neurocognitive functions of occupants during work or learning
processes. Increased levels of annual ozone and particulate matter was related to a decrease in
cognitive performance [183,184]. An increase of 10 ppb in ozone concentration caused a 5.3 years
age-related decline in attentional performance [184]. Higher black carbon (BC) levels had a
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positive association with increased errors of commission and slower hit reaction time (HRT), as
well as mean reaction time for all target responses [185], but the absolute relationship between
pollutant concentration and attention performance was not significant (p > 0.05). Traffic pollution
exposure for adolescents showed an inverse association with their sustained attention and may
therefore assumedly undermine neurobehavioral functions [186].
As an indicator of indoor air quality, CO2 has recently been identified as an indoor pollutant due
to its potential effect on cognitive function [35]. A field study in a primary school concluded that
children showed significantly poorer concentrate levels on the courses when the level of CO2 in
classrooms was high [8]. The increased levels of CO2 led to an approximately 5% decrement on
attentional performance, as reported by the study. Nevertheless, other studies showed little
influence of CO2 level on attention [187,188] Elevated CO2 concentration in the classrooms did
not reduce students’ global short-term attention, although a decrease in the secondary outcome
accuracy (e.g. the total number of characters processed) was found for students exposed to poor
air quality [187]. Ref [188] argued that it might be the bio-effluents, rather than pure CO2 level,
that reduced cognitive performance. Another study employing physiological and
neurophysiological monitoring also reported no effect of CO2 on attention performance [189]. A
critical review of the area concluded that pure CO2 only consistently affects high-level decision-
making performance [190].
Elevated indoor CO2 concentration is primarily derived from insufficient ventilation. Previous
studies have reported improvements in students’ working memory and attention in primary school
buildings at higher ventilation rates [191]. Ref [192] identified a 2.2% improvement in attentional
performance during these higher ventilation rates.
Thermal Environment
Prior studies have shown that attention can be strongly influenced by the thermal environment,
although the direction and magnitude of influence may not be always consistent. Under steady-
state conditions, the attention index of 117 high-school students decreased when they were
thermally uncomfortable [193]. Participants had the highest performance test score at 26
compared with at either 23 or 29 ℃ when a personally controlled fan was available to use [118].
Under thermal transients in Ref [170], concentration performance was significantly and positively
correlated with the rate of temperature increment (p < 0.05) in temperature cycles starting from
22 °C. This implies increased concentration performance when the temperature rises quickly. But
a separate study [194] indicated opposite results that subjects had a better attentional performance
at 16 °C compared to results at 26 °C and 36 °C. Attention tested by using the cursor positioning
test indicated no significant difference in the subjects’ performance in three different thermal
environments [195]. There was also no significant difference of attention in a study [196] which
used a star count test in two temperature conditions of 23 °C and 29 °C. Attention, as assessed by
the Stroop test without feedback, was significantly different between 23 °C and 27 °C [197].
However, the difference was not significant when feedback was provided to the participants. These
sorts of results confirm that at ambient temperature, close to setting, and individual capacities each
exert impactful influences on outcome.
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Noise
The influence of noise on attention is also complicated. High school students worked faster with
high ventilation noise but only at the cost of less accuracy [12]. The results supported a speed-
accuracy trade-off hypothesis that decisions are made slowly with high accuracy or fast with a
high error [198200], contingent upon acoustic surround. Age is a confounding factor when
considering the influence of noise on attention. Elderly people may be more vulnerable to noise.
Listening to speech with multi-talker babble noise, such as in a crowded office, reduces activation
in the auditory cortex but increases memory and attention-related cortical areas (prefrontal and
precuneus regions) for older people [201]. However, noise exposure apparently has little
significant influence on students’ attention performance, at least to a reasonable threshold value
[71,202].
Lighting
The literature has recorded controversial findings as to know if attention is affected by lighting.
The correlated color temperature of 4,300 K resulted in the best-sustained attention performance
for undergraduates using the Chu Attention Test. Also, sustained attention was more affected by
lighting in females than male students [203]. Increasing illuminance from 200 lux to 1500 lux
promoted attention when the room air temperature was 22 °C. But the opposite trend was found at
37 °C. This implies an interactive influence between thermal and visual comfort [204]. A dynamic
lighting system that adjusted lighting color and brightness of computer screens significantly
improved target spotting time in a computer game for both casual gamers and non-gamers [205].
However, the effects of lighting on attention have not been found in other studies. Neither light
color temperature nor lighting intensity influenced the concentration of third-grade children [206].
For example, sustained attention was also independent of lighting conditions for older adults who
were night shift workers [207].
Non-Light Visual Factors
Fisher et al. [208] investigated how classroom decoration affected the ability of children to
concentrate on lesson content. Children were more distracted by highly decorated environments,
spent more time on the task, and gained less knowledge when compared with a relatively plainly
decorated classroom. Colors can stimulate an individual’s physiological and emotional responses
for focal attention and thereby facilitate learning. Pale colors were rated more positively than vivid
ones, due to feeling more calm and relaxed [109, 214]. Additionally, biophilic environments can
promote the attention of occupants. Students’ views of nature or buildings is another factor
influencing attention. Both outdoor natural views [210] and indoor views of plants were reported
to promote students’ attention [211]. In other words, indoor and outdoor visible greenery increases
the ability to concentrate and reduces stress [217, 218]. Significantly better performance of
participants’ attention was reported when a window view is available than when it is unavailable
[214].
4.1.2 IEQ’s Effects on Perception
We summarized in Appendix I Table A3 the major findings as to how IEQ affects perception.
Overall, the accumulated knowledge reports studies focusing on auditory perception and visual
perception. Noise and poor lighting are common stressors for perception.
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In a visual search task, participants showed a significantly different performance, normalized by
mental workload, between warm and neutral conditions, and between warm and cool conditions
[215]. Survey results by Ref [216] demonstrated that façade design affected occupants’ perceived
control over their environments. Uncomfortable environments are through to generate perceptions
of stress and negative attributions about performance [217].
Lee et al. [218] examined the combined effects of color temperature and illuminance in the office
on the visual perception of occupants. They concluded that the less than subjects were visually
disturbed by light during tasks, the more visual comfort they felt. Lighting also affects the
perception of facial surfaces [219]. Observers’ ability to recognize and match faces and objects
was higher for top lighting on the objects than bottom lighting. Berman’s theory [220] states that
elevated color temperature, associated with smaller pupil size can enhance visual acuity. In this
same vein, the performance of a visual perception task on color recognition is higher with the
lighting of higher color temperatures [221].
The negative effects of noise exposure on performance could be attributed, at least in part, to
“learned helplessness”, which is a syndrome of defeat typically resulting from exposure to
uncontrollable circumstances [222]. Occupants might perceive noise to be uncontrollable or have
little perceived control. A socio-acoustic survey observing perceived control over aircraft noise
correlated negatively with identified effects of noise (e.g., disturbances of reading and sleep). This
supports the claim that “learned helplessness” contributes to the effects of noise exposure. In terms
of specifics, the linear exposure-effect association was identified between exposure to chronic
aircraft noise and impaired reading comprehension [71].
4.1.3 IEQ’s Effects on Memory
Appendix I Table A4 catalogs the major findings regarding the impairment of memory due to poor
IEQ. Our review here demonstrated that short-term memory and working memory are most
investigated by previous studies via recall tasks. Overall, results show that memory is generally
associated with most IEQ factors.
Indoor Air Quality
The cross-sectional association between fine particulate concentration levels and cognitive
function in older adults has identified that a higher air pollutant concentration leads to significantly
reduced levels of working memory [223,224]. The incident rate of errors on tests of working
memory shows a ratio of 1.53 with a 10 µg/m3 increase in PM2.5 concentration [223]. Each 10 ppb
increase in annual ozone was associated with decreased short-term memory, equivalent to 5.3 years
of aging-related decline in cognitive performance [184].
Students showed 8% higher picture memory with an increased room ventilation rate that was
associated with lower CO2 levels [192]. Strategic management simulations [9,35,225] were applied
to investigate how indoor CO2 influenced cognitive performance, but its effects on memory were
not reported as the tools were more predictive in domains such as strategy, information usage, and
crisis response. However, the effects of elevated CO2 concentrations on memory performance were
not consistent in some other studies. Neither response time nor accuracy of a picture recognition
task was significantly compromised at approximately 2,900 ppm when compared with 690 ppm
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[119]. A similar conclusion was reported for CO2 at 2,700 ppm versus 700 ppm [189]. Zhang et
al. [188] also did not find any statistical significance in digit span memory scores under
bioeffluents or pure CO2. On the other hand, external oxygen administration was found to improve
memory formation in the first place [226228]. Inhalation of oxygen immediately before learning
a word list increased the average number of words recalled some 10 minutes later [226]. Inhalation
of 100% oxygen for a short time enhanced the memory for names and faces [228]. These findings,
however, were not replicated by other studies that focused more on long-term memory [229,230].
Thermal Environment
The reviewed studies on the effect of thermal environment on memory performance do not report
consistent relationships between the two entities. The extended-U model suggests that memory
performance will remain stable across a broad range but rapidly deteriorates at the thermal
extremes [236, 237]. Students showed the best memory performance when the air temperature
was between 22 °C and 26 °C [10]. Even while exposed to 43.3/27.8 °C (dry/wet bulb temperature),
the short-memory performance for university students did not change significantly, as compared
to a more comfortable condition of 26.7/17.2 °C (dry/wet bulb temperature) [233]. Poorer short-
memory by recalling word lists did occur at 48.9/31.1 °C (dry/wet bulb temperature). Similarly,
the average recall performance did not drop significantly when the chamber air temperature was
between 16.7 and 32.2 °C but did so between 32.2 to 35 °C as individuals began to approach
integrable levels [233]. Zhang and de Dear [170] reported no significant correlation between
thermal environment and memory performance in six temperature cycles. College students
exposed to 25.5 °C, 28 °C and 33 °C did not demonstrate significant memory changes using a
positioning test and letter search test [195]. Neither working memory performance nor long-term
memory performance was significantly impaired when the temperature, was raised from 23 °C to
29 °C [196].
Contradictory results were also reported in the literature regarding the influence of mild
temperature on memory performance. Working memory measured via a forward digit span test
dropped at slightly cooler (21.7 °C) and warmer conditions (28.6 °C) from the neutral condition
(25.2 °C) [215]. Nevertheless, significant reduction only occurred for the hard version of the task
but not the easy one [234], which suggests an interaction with task type. Regression analysis by
Cui et al. [10] showed that long-term memory performance peaked (p < 0.01) at 26 °C in the
temperature range of 22 °C to 32 °C.
The influence on memory due to cooling might not be equivalent to that of heating. Elevated body
core temperatures from 36.6-37.4 °C to 38.8-39.1 °C did not affect memory registration or the
immediate ability to recall digit spans [235], but reduced body core temperatures from 36.7 °C to
34-35°C did induce a loss of approximately 70% of data that could normally be retained from a
memory test [236]. In addition, memory performance in temperature cycles ranging between 21.3
and 31.2 °C was significantly higher than temperature cycles starting from a slightly higher
temperature (23.0-31.5 °C) [170]. The performance of a digital span test increased by 2.8% when
reducing the temperature from 27 °C to 23 °C [197]. However, this increase did not prove
statistically significant.
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Noise
Noise was reported as an environmental stressor that impacted memory in many studies [20, 72,
73, 242]. Noise hinders recall and recognition in student learning. Poor listening conditions due to
background noise and/or long reverberation times, impair memory and learning, even if students
could hear what was said by an instructor [72]. Traffic noise can also worsen performance in both
a search task and a memory task [238]. Stansfeld et al. [71] identified a linear association between
exposure to chronic aircraft noise and impairment of recognition memory through the assessing
2,844 children aged 9 to 10 years. Both intentional and incidental memory were affected by chronic
noise exposure, and school children who were chronically exposed to noise were found
subsequently to be worse at recognition memory, as reported in Ref [202].
Memory involved in complex tasks has proven to be more susceptible to noise compared to that
of simple tasks [20, 244]. In addition to task complexity, one type of noise might be more harmful
than another to memory, especially intermittent noise. Two experiments revealed that background
speech was more detrimental to prose memory than aircraft noise [71, 245]. Furthermore, there
might be interaction effects between noise and illumination on memory. Subjects’ short-term and
long-term memory recall was found to vary with combinations of ventilation noise and illuminance
levels [12, 246]. Interactions were also found between noise and heat on the long-term recall of a
text [12].
Lighting
Long-term memory was enhanced when individuals are exposed to a light color temperature that
induced a less negative mood [169]. The combination of color temperature and illuminance that
best preserved a positive mood increased performance in free recall tasks. Cool-white lighting
impaired the long-term memory recall of a novel text when compared to warm-white lighting [241].
However, the influence of blue-enriched classroom lighting on short-term encoding and retrieval
of memories was not found for high school students [74]. No interactive effects on memory were
reported between light and noise [241], but interaction was found between gender and light color
temperature on mood and long-term memory [169,242].
Non-light Visual Factors
Exposure to green space has beneficial effects on the development of working memory for primary
school children [33] and thus access to these green spaces was associated with improved memory
[243]. Ko et al. [214] reported that Window views influenced different memory associated with
various levels of significance. The working memory test score of the participants in a room with a
window view was 6% higher (p < 0.009) than that in a windowless room. However, no significant
difference was identified for short-term memory by the study. Participants with a major depressive
disorder performed better on memory span tests after walking through a green arboretum, relative
to traffic-heavy streets lined with university and office buildings [244].
4.1.4 IEQ’s Effects on Language Functions
Appendix I Table A5 catalogs the effects of IEQ on language functioning in terms of capacities,
such as reading and writing. Ref [245] investigated whether the combined environmental factors
of light, sound, and temperature in a classroom affected student performance during listening and
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reading tasks. It was reported that indoor sound and temperature had a greater negative influence
on students’ listening and reading tasks when they were outside the comfort zone. However, the
modeled association between reading test scores and ventilation rate did not show any statistical
significance in another preliminary study [246]. The conditions of artificial light were found to
influence the students’ reading performance [206]. It was revealed that focus” lighting consisting
of 1,000 lux illumination and 6500 K color temperature significantly increased students’ oral
reading fluency compared to a “normal” or baseline lighting condition (500 lux with 3,500 K).
Noise effects on recall and recognition are significant [247]. Item difficulty, position, and ability
were not found to interact with these noise effects in the study. Neither did arousal, distraction,
perceived effort, or perceived difficulty in reading and learning mediate the effects on recall and
recognition. Anderson et al. [248] showed that background noise usually disrupts neural timing
and challenging listening conditions disrupted the inability of speech perception. Ref [249]
identified significant effects of reverberation on speech perception of spoken items in classrooms.
Outside noise influences language fluency, which acts as the bridge between sound source and
comprehension [250]. Children’s speech perception and listening comprehension can be
significantly impaired by background speech [251]. Irrelevant speech has a significant influence
on participants’ reading comprehension [252]. Speech recognition was not only influenced by
speech-to-noise ratios (SNRs), but also by thermal conditions as well [253]. Moreover, Wong et
al. [201] reported that age confounds the relationship between noise exposure and speech
perception. Compared to adults, children are more impaired by detrimental listening conditions.
Older adults, who experience reduced activation in the auditory cortex, have increased activation
in attention-related cortical areas. Age and hearing loss were both related to less release from the
effort when increasing the intelligibility of speech in noise, as identified in the same study.
Non-light visual factors also affect language functions such as reading [209]. The color in a private
space affects students’ learning, as well as physiological and emotional states. Vivid colors are
beneficial for students reading, while blue is better for relaxation and calmness.
4.1.5 IEQ’s Effects on Higher Order Cognitive Skills
The listed studies in Appendix I Table A6 describe the association between indoor environmental
factors and different forms of higher order cognitive skills. In general, poor IEQ conditions were
reported to have negative effects on these higher order cognitive skills, but to varying degrees.
However, some studies have found no significant association between IEQ factors and higher order
cognitive skills.
Indoor Air Quality
Occupants performance, which was assessed using, but the speed of addition, response time in a
redirection task, and the error rate of tasks, was reduced when participants were exposed to an
elevated level of CO2 together with bio effluents [188]. The adverse consequence due to high CO2
levels includes the impairment of decision-making performance [35]. Also, the increased response
time has been related to ozone exposure [184]. NOx showed an association with a decline in the
cognitive test scores for visuo-construction, which involves the ability to organize and manipulate
spatial information [254]. An epidemiologic study, using 789 elderly women who attended a
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medical examination in 2007-2009 supported the proposition that lower scores in reasoning were
correlated to particulate air pollution [255].
Thermal environment
Thermal comfort plays an important role in the higher order cognitive skills. A warm environment
can be associated with reduced reaction time. Participants performed tasks more rapidly at 32 °C
compared to other conditions (27, 24, and 19 °C) [99]. This phenomenon was explained by
postulating that participants wanted to finish tasks quickly in the uncomfortable thermal
environments, or that they were activated by elevated internal body temperature [256]. Another
study also reported increased task speed as the temperature ascended [235]. However, findings
were not consistent overall in the literature. For example, a study found that compared to a cooler
temperature of 23 °C or warmer temperature of 29 °C, subjects had the fastest processing speed at
26 °C [118]. This study suggested 26 as the optimum temperature for the optional cognitive
performance. In another recent study [215], significant differences in participants’ addition task
performance were found for a “hard” mode but not for “easy” mode between slightly warm (PMV
=1) and slightly cooler conditions (PMV = -1). In the study, the participants did not show a
significant difference in response time on a choice reaction task for either “hard” or “easy” mode.
Also, the participants’ response time in two reaction tests (“hard” and “easy” modes) was
insignificantly (p > 0.05) differentiated at three PMV conditions (-1, 0, and 1). However, the
difference in response time was statistically significant (p < 0.05) for the Stroop task at the three
PMV conditions. Ref [197] stated that the subjects had neutral comfort at both 23°C and 27°C.
But the reasoning performance, observed at 27°C, decreased by 11.2% compared to performance
at 23°C. The study [195] indicated that only male subjects displayed significant differences in the
four-choice test performance as the temperature increased from 28 °C to 33 °C, as well as the text
typing test when the temperature increased from 25 °C to 28 °C or 33 °C.
Reasoning and planning skills were found to have a significant relationship with the thermal
sensation vote [170]. The study reported that reasoning and planning performance was negatively
correlated to TSV2 and TSV respectively in the warmer temperature cycles starting from 24 °C.
Planning skills were more sensitive to heat than reasoning in the rising temperature. That is, a
higher rate of temperature increment had detrimental effects on planning, but not on reasoning
performance.
Noise
Moderate noise enhances processing difficulties, such as the activation of abstract cognition and
enhancing creative performance [257]. It was also found in the same study that mild noise could
be a trigger for higher leave creativity, while loud noise reduces the extent of information
processing, resulting in cognitive impairment. However, teacher-reported cognition functions of
school children showed no significant effects of ambient noise levels upon executive function
[258].
Lighting
No significant effect of lighting color temperature (3,000 K vs 4,000 K) was found on the
performance of problem solving and judgment [242]. However, another study concluded that
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“warm” white light (3,000 K) was optimal for problem solving [169]. In addition, high-frequency
lighting is perceived as more pleasant than low-frequency lighting and can then enhance problem
solving performance [259].
Non-light visual factors
Mehta and Zhu [260] found that red backgrounds enhance motivation, whereas blue improves
subjects’ creative ability. Blue light enhanced individuals’ purchase intentions toward products
mainly bought for pleasure or enjoyment, indicating that blue lighting is a contributing factor in
participants’ altered purchase intentions. In another study, participants’ planning skills did not
significantly vary when a window view was present or not [214].
4.1.6 Summary of the conventional manual review
Appendix I Tables A2-A6 list the major findings of studies on the association of IEQ factors and
cognition. While detailed and informative, the tabulated results of all the reviewed studies might
not easily generate a clear “big picture”. This is because many studies have reported contradictory
or mixed findings. Therefore, we calculated the percentage of studies that revealed statistically
significant association (with the assigned rating 2), and the percentage of studies showing
mixed association (with the assigned rating “1”) between a particular IEQ factor and a cognitive
function. For example, 36% of the 16 reviewed studies indicated a mixed association (rating 1)
between thermal environment and memory, while only 14% confirmed a statistically significant
association (rating 2). Please note that Table 1 does not distinguish between positive and
negative associations. Even though the statistics is unable to quantify the effect size of each pair
of an IEQ factor and cognitive function, the present approach in Table 1 can still shed lights on
the amount of evidence n the topic and the intensity of research inconsistency across various
disciplines that may not be easily obtained otherwise.
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Table 1. Percentage of studies reporting different leveles of statistical significance for the associations between IEQ and cognition
Perc. of sig.: the percentage of all reviewed studies in Appendix I Tables A2-A6 reporting a significant association only (with the
rating “2”); Perc. of mixed: the percentage of studies revealing a mixed association (with the assigned rating of “1”). The description
of different rating levels can be found in Section 3.1. # of studies: the total number of reviewed studies containing all ratings (“0”,
“1”, “2”, and “NA”).
IAQ
Thermal
environment
Noise
Lighting
Non-light visual
factors
Row average
Perc
. of
sig.
Perc.
of
mixed
# of
studi
es
Perc.
of
sig.
Perc.
of
mixed
# of
studi
es
Perc.
of
sig.
Perc.
of
mixed
# of
studi
es
Perc
. of
sig.
Perc.
of
mixed
# of
studi
es
Perc.
of
sig.
Perc.
of
mixed
# of
studi
es
Perc
. of
sig.
Perc.
of sig.
or
mixed
Attention
20%
20%
6
10%
30%
11
25%
25%
5
33%
34%
6
50%
50%
5
28%
31%
Perception
0
0
1
0
50%
3
NA
NA
0
0
67%
3
NA
NA
0
25%
38%
Memory
0
25%
8
14%
36%
16
71%
29%
8
29%
28%
7
0
100%
1
23%
43%
Language
function
0
0
2
33%
0%
4
67%
33%
10
50%
0%
2
0
100%
1
30%
26%
Higher order
cognitive skills
50%
33%
8
19%
50%
17
20%
40%
5
33%
0%
6
50%
0%
2
34%
25%
Column
average
14%
15%
15%
33%
57%
25%
29%
32%
25%
63%
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Table 1 shows that the most examined IEQ factors in the literature are thermal environment, noise,
and IAQ, while the most studied cognitive functions are memory, high order cognitive skills, and
attention. The research on how IEQ influences perception is quite rare. Overall, for each pair of
IEQ and cognition, a statistically significant association (p <0.05) has been identified by a portion
of studies in the literature.
To interpret the results from Table 1, the sample size (number of studies) in each cell and the
percentage of significant association are both important, as a 100% statistical association reported
in only one study may not carry weight. For pairs of IEQ and cognition with more than 5 studies,
the percentage of studies reporting a significant association (p < 0.05) is 50% between IAQ and
higher order cognitive skills, 67% between noise and language function, and 71% between noise
and memory. In contrast, the percentages of studies showing a significant association is quite small
(< 20%) between IAQ and memory (almost 0%), thermal environment and attention (10%),
thermal environment and memory (14%), and thermal environment and higher order cognitive
skills (19%).
Each row in Table 1 represents the influence of various IEQ variables on a specific cognitive
function. Considering the aggregated effects of all IEQ factors on each cognitive function by
averaging the percentages in a given row, approximately 34% of studies on average imply a
significant association between IEQ and higher order cognitive skills, while the percentage drops
to 30%, 28% and 23% for language functions, attention, and memory, respectively. However, 43%
of studies suggest a mixed association between IEQ and memory, followed by 31% for attention,
26% for language function, and 25% for higher order cognitive skills. The small variations in those
percentage values do not entitle differentiation between the most and least vulnerable cognitive
functions to IEQ. One explanation for this may relate to the difficulty in isolating cognitive
functions, particularly in realistic settings.
For each column of Table 1, the average percentage value over five rows of cognitive functions
can help identify the influence of a particular IEQ factor on holistic cognitive functions.
Approximately 57% of studies found that noise has a significant impact on cognition. Surprisingly,
the percentage of studies reporting statistical significance for both IAQ and thermal environment
are lower than 20% in terms of the effects on cognition. Even considering both the significant
association and mixed association, the percentage is still less than 50%. The results thus suggest
extensive inconsistencies in the relevant literature, especially regarding the effects of IAQ or
thermal environment on cognition.
4.2 Keyword co-occurrence patterns identified by text mining
Figure 2 shows the number of publications and knowledge landscapes obtained from keyword co-
occurrence analysis at different periods. The connection between two circles refers to co-
occurrence instead of statistical association in the same document. A short distance between two
keywords represents high co-occurrence. When two keywords are rarely mentioned together in the
same document, the two circles containing them are therefore distanced. The number of keywords
contained in circles was maximized using a smart local moving algorithm [261]. The size of each
circle represents the percentage of the articles mentioning the corresponding keyword in the circle.
The same circle color represents a clustered category using the mapping technique of visualization
of similarities (VOS) [262].
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The earliest study we found was published in 1932, and since then the number of publications
involving both IEQ and cognition have been growing exponentially in the past few decades, as
shown in Figure 2a. There were 684 papers published in 2019.
Figure 2b, 2c, and 2d show the relation landscape between IEQ factors and cognitive functions by
extracting information from the keywords and abstracts of searched studies, including those
reviewed in the manual review, published within the period of 1932 2010, 2011 2015, and 2016
2020, respectively. During each period, there were approximately 3000 papers published on
average. These results can significantly supplement the detailed manual review described in
Appendix I Tables A2-A6 as well as Table 1. The co-occurrence networks in Figure 2b-2d reveal
two essential patterns. First, the clustering can be summarized into three major topic themes,
cognition (in blue, green, and red), environment (in yellow, aqua, and green), and mediating and
confounding factors (in blue and purple) such as “age”, “gender” and “depression. Second, the
landscapes of keywords in Figure 2b-2d depict the evolution of the topics in terms of cognition
and IEQ. To better quantify the results displayed in the figure, we summarized common topics
sorted on the basis of occurrence frequency during different periods in Table 2 that constitutes a
basis for Figure 2b-2d to further reveal the evolvement of the research field . Topics such as
“sound”, “recognition”, “light”, speech”, and “noise” emerged during 2011 2015, while “air
pollution”, “temperature”, and “mechanical ventilation” have been paid more attention since
2016. A similar patten has been also observed for cognition, such as new keywords of “reading”,
“social cognition”, and “language.” In addition to the two patterns, one can observe that music
related variables frequently appear along with cognition in the literature during each period.
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Figure 2. The number of publications and knowledge landscapes obtained from keyword co-occurrence analysis. a) The temporal
number distribution of publications (The figure does not display the only paper published before 1958); b) keyword co-occurrence
network with publications between 1932 and 2010 (n = 3421); c) keyword co-occurrence network with publications between 2011 and
2015 (n = 2464); d) keyword co-occurrence network with publications between 2016 and 2020 (n = 2956)
Table 2. Summary of the most frequently mentioned topics during different periods
Years 1932~2010
Years 2011~2015
Years 2016~2020
Items
Occurrence
Items
Occurrence
Items
Occurrence
music
662
cognition
683
cognition
950
cognition
585
music
669
music
736
performance
416
exposure
432
cognitive function
547
exposure
384
performance
417
exposure
543
response
325
cognitive function
367
performance
482
cognitive function
314
age
326
age
397
perception
273
memory
310
memory
376
memory
272
response
309
attention
331
attention
239
perception
267
environment
320
environment
220
attention
257
perception
306
disorder
200
environment
257
concentration
236
language
150
disorder
186
disorder
203
concentration
145
concentration
165
learning
203
learning
142
emotion
153
language
184
emotion
115
language
145
cognitive performance
158
recognition
106
sound
121
emotion
145
ventilation
106
adult
113
adult
143
anxiety
103
cognitive performance
108
air pollution
132
cognitive impairment
103
cognitive impairment
102
anxiety
124
depression
103
recognition
100
temperature
112
texture
102
light
99
cognitive ability
110
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music cognition
96
music cognition
92
depression
110
dementia
94
anxiety
89
pesticide
101
cognitive performance
93
speech
88
communication
100
rhythm
93
noise
87
view
99
mood
89
view
86
rhythm
98
sound
88
pesticide
84
mood
97
view
88
mood
83
recognition
95
carbon monoxide
77
texture
82
Alzheimer
93
pesticide
74
communication
79
mechanical ventilation
89
Note: The words in bold are emerging items comparing to the previous period.
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5. Discussion
This review has focused on the association between IEQ factors and the five main categories of
cognitive functioning. The reviewed literature consisted of a mixture of laboratory and field work,
and both cross-sectional and longitudinal studies. Overall, there is a preponderance of the evidence
that almost all IEQ factors, including indoor air quality, thermal environment, noise, lighting, and
non-light visual factors could affect cognitive performance to varying degrees. Different IEQ
factors can have distinct effects on a specific cognitive function. Likewise, a specific IEQ factor
may also exert various impacts, if any, on different cognitive functions. We identify inconsistency,
uncertainties, and confounding factors (such as age, sex, and emotion) in the reviewed studies, and
point out limitations and future directions.
5.1 Inconsistency, uncertainties, and possible explanations
Appendix I Tables A2-A6 demonstrate inconsistency and uncertainties in reviewed studies. For
instance, some experiments indicate that sustained attention is not impaired by aircraft noise [71]
or chronic noise exposure [202], while others [263,264] showed that noise does impair both
attention and recall. Experimental studies of Ref [9] and Ref [188] reported contradictory results
regarding the effects of elevated CO2 levels on cognitive performance. The research evidence on
the effects of lighting on problem-solving is contradictory as well. Ref [169] reported the ‘warm’
white light source at 300 lx illuminance and the ‘cool’ white light source at 1,500 lx illuminance
to be optimal for subjects’ problem solving. However, no significant effect of lighting on problem-
solving performance was found by another similar study [242].
We may distill a principled set of sources for the associated variations and inconsistencies that we
have observed in the assemblage of data. In general, they relate to complexities in the
environmental exposure, variation in the tasks undertaken as representative of both learning and
work performance, significant differences between individuals who display that performance, and
finally methodological barriers to a full and clear exposition of the relationships evaluated. The
factors have been illustrated in Figure 3 for the purpose of ease of discourse. Much of the problem
of inconsistency in results arises as a function of the interaction of these identified influences.
Figure 3. Potential sources of inconsistency and uncertainties related to environments, tasks, and
exposed individuals.
From the input conditions composed of the physical environment through the specification of the
work tasks involved and the variation of the individuals performing such tasks, we can identify
numerous sources of potential inconsistency. Such sources of variability also emanate from the
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function of feedback loops involved in this process, as well as inherent characteristics and
shortfalls in the methods employed to measure response in these varying and disparate sources of
influence. The three majorly identified categories are the realms of quite disparate scientific
disciplines with their own conventions and traditions. For instance, memory has been assessed by
recall tests [223], serial-digit learning tests [184], picture recognition [99], digit span tasks
[117,170,265], interviews through telephones [224], electroencephalography (EEG) [266], and
functional magnetic resonance imaging (fMRI) [201]. In a review, Zhang and colleagues [234]
summarized three common approaches to assess cognitive load/performance. These are primary
tasks, subjective perception, and physiological responses [267]. They pointed out that findings
from these three approaches do not always agree with each other when applied concurrently. In
itself, this can lead to conflicting results in Appendix I Tables A2-A6. Another source of
inconsistencies can be exemplified by different ranges of values of the investigated IEQ factors.
According to the extended-U model [231,232], people can maintain a stable level of performance
over a broad range of environmental stress levels. If the investigated experimental conditions are
within this central plateau area, no performance change might be anticipated. It is, therefore,
unlikely to find any significant relationship between the environmental factor and cognitive
function. However, if the investigated range of environmental stress levels spans beyond this near-
optimum range, a significant change of performance may be identified. For example, Ref [265]
did not find any significant difference in reasoning skills under two temperature conditions of
22 °C and 25 °C. However, a significant reduction in reasoning was found when the temperature
was increased to 30 °C by another similar study [117].
The effects of possible mediators, moderators, confounders, and covariates cannot be ignored as
well, such as skill level, emotion, age, gender [268], personal attitude, mood, past events [269],
and emotion. Previous studies have revealed that performers’ skill levels significantly mediate the
influences of environmental stress on cognitive function [16,270,271]. Performers with higher skill
levels are less susceptible to performance decrements under environmental stress. In addition,
emotion has a mediating effect on cognitive performance [173, 247]. For instance, cognitive
performance was negatively affected by heat, partly because people were less motivated when
feeling uncomfortable [10]. Age is also a confounding variable. Aging can degrade the sensory
and processing functions [271]. Compared to young adults, older adults require a higher-level of
illuminance or thermal comfort to maintain the same attention and perception performance [12,
212]. Age influences speech perception in noise conditions [201]. Furthermore, the effects of
participants' gender have become manifest in many associated aspects between IEQ and cognitive
functions. For example, girls focused much more on a task than boys in experiments with
uncomfortable conditions [193,272]. Males showed better performance on an abstract cognitive
task [272] and performed significantly better than females in problem solving using an embedded
figure task [242]. We discussed in more detail the primary sources of inconsistency (illustrated in
Figure 3) in Appendix II.
5.2 Limitations of the present review
We categorized IEQ factors and cognitive functions according to the terminology in the reviewed
studies. Some performance tests require multiple cognitive functions and thus are difficult to map
into the categories, such as addition, multiplication, and typing. Problem-solving skills involve
both attention and memory. Furthermore, the present review does not include the entire spectrum
of cognition, partially because there is little research identified regarding social cognition,
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visuospatial functions, or motor skills when considering the influence of IEQ factors. Also, many
studies investigated more than one IEQ and/or cognitive factors, thus could carry more weights in
the conclusions of the current analysis. Moreover, some keywords identified in the keyword co-
occurrence analysis may not necessarily reflect the exact context of cognition. For instance,
“attention” is often used in the phrase of pay attention to. Last, this review does not include
studies in languages other than English.
5.3 Recommendation for future research
In addition to the substantial inconsistency in terms of the association between IEQ and cognition,
existing literature lacks sufficient and granular evidence to present a comprehensive understanding
of the underlying mechanism. First, most studies applied the cross-sectional approach. The
consequences of long-term exposure to poor indoor environmental quality thus warrant further
research. Second, most existing studies focus on static environments, while dynamic physical
environments are rarely explored, especially when alliesthesia [273] is experienced by occupants.
Any environmental stimulus that helps to offset the load on the thermo-regulatory system will be
pleasantly perceived, and thus can potentially be used to preserve cognitive functions [234]. Future
research could use physiopsychological sensors, such as electroencephalogram (EEG), functional
magnetic resonance imaging (fMRI) as well as functional near-infrared spectroscopy (fNIRs) to
respond to this challenge. Third, the inherent overlap between different cognitive functions,
interaction effects of IEQ factors [269], and mediating effects of other factors (e.g., emotion, age,
and gender) imply that future research should further decompose each category of IEQ and
cognition, by documenting values of all confounding or mediating variables. Otherwise, the true
effects could be masked by these diverse influences.
In addition, the contribution of some factors remains missing in the literature, e.g. there is almost
no research on how indoor microorganisms such as fungi or molds affect cognition. Research has
also revealed that physical activity level could be associated with cognitive capabilities [274].
Would an office worker with a standing or treadmill desk have better cognitive function than
his/her sedentary colleagues in the same office? More importantly, even though we may possess a
number of dose-response nomograms for the association between IEQ and cognition, we still need
to reference underlying theories and associated modeling and simulation to articulate and complete
the panoply of empirical results that we do possess, and which have been discussed in this present
review.
Albeit any researcher has the flexibility to decide their measurement approach for cognitive
performance, it is always worth considering in the experimental design how to compare results
with previous studies. Existing studies have been conducted mostly in isolated communities with
significantly distinctive measurement protocols to quantify the indoor environment and/or
cognition. Hence, the intrinsic complexity of the IEQ-cognition-causality warrants
multidisciplinary endeavors in developing a unified framework or protocol to permit the synthesis
of “localized” findings. Evidently, such endeavors might involve stakeholders in education
research, social behavior, psychology, building science, and medical or health science.
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6. Summary
This review has examined the effects of indoor environmental quality (IEQ) on cognition that are
documented in a broad range of laboratory and field studies. In this work, IEQ in the literature
consists of five major categories, i.e., indoor air quality, thermal environment, noise, lighting, and
non-light visual factors. The reviewed cognitive functions consist of attention, perception, memory,
language function, and higher order cognitive skills. Thermal environment and noise are the most
studied IEQ factors, while memory and higher order cognitive skills are the most investigated
cognitive functions in the literature based on the manual review.
In general, the reviewed studies demonstrate that poor IEQ is associated with reduced cognitive
performance. However, the effects of a specific IEQ factor on different cognitive functions are
disparate. Inconsistency and uncertainties have been found, possibly owning to distinct assessment
approaches of cognition, different ranges of values of the investigated IEQ factors in the research
design, and ignored confounding or mediating variables. Other variables associated with
environments, tasks, and occupants could potentially contribute as well.
The keyword co-occurrence analysis of 8,133 studies can work alongside and supplement the
conventional manual review to understand the complex network of IEQ and cognitive functions.
The findings suggest an exponential growth of studies and emerging topics related to the
association between IEQ factors and cognitive functions.
Future studies should improve the temporal granularity of the associations between IEQ and
cognition, especially when advanced psychophysiological sensing is available. Also, further
research needs to refine the categories of IEQ and cognition, take confounding or mediating factors
into consideration, and further promote interdisciplinary collaboration.
Conflicts of interest
The authors declare no competing interests.
Acknowledgment
This work was supported by U.S. National Science Foundation (NSF) under Grant No. 1931077
and Grant No. 2028224. The authors appreciate the valuable comments from reviewers and Dr.
Stefano Schiavon of University of California, Berkeley.
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Appendix I
Table A1. Tasks or methods to assess different cognitive functions
Cognitive function
Tasks
Attention
General attention*
Stroop task, Serial-digit learning test, d2-test, Corners’
Continuous Performance test, Standard Toulouse Pieron
questionnaire, Feature match test, Cursor positioning test, Visual
search task, Memory-load search task, Curriculum-based
measurement, Konzentrations-Leistungs test, Zahlen-
Verbindungs test, Necker cube control Test, Symbol digit
modalities test, Norwegian version of the reading span test,
Double trouble test
Sustained attention
Bourdon test, Toulouse-Pieron test, Psychomotor vigilance test,
Chu attention test, Symbol-digit substitution test (SDST),
Directed attention
Symbol digit modalities test (SDMT)
Perception
Acoustic perception
Questionnaire related to the environment
Visual perception
Picture recognition test, Stroop test, Visual search test, Pairing
test, Questionnaire related to visual annoyance, Color recognition
tasks
Memory
General memory*
Picture recognition
Short-term memory
Serial-digit learning test, Word recall test, Digit span tests, Code
substitution and running memory test
Long-term memory
Memory typing test, Text recalling test
Working memory
Subtraction test, Memory span test, 2-Back test, 2-Digit visual
addition/subtraction test, Forward digit span test, Computerized
test, Visual learning test, Spatial span task, Code substitution,
Digit span tests, Operation span task, N-back test, Token search
test
Episodic memory
Telephone interview, The Consortium to Establish a Registry for
Alzheimer's Disease-Neuropsychological Assessment Battery,
Child memory scale
Language
function
Listening
comprehension
Questionnaire related to instruction
Reading
comprehension
Proof-reading test, Suffolk reading scale, Oral reading fluency
test, SAT comprehension test
Speech
comprehension
Speech test, fMRI test, Identification of words and sentence
comprehension, Banford-Kowal-Bench test
Higher
order
cognitive
skills
General higher order
cognitive skills*
CNS Vital signs computerized cognitive test, Cognition test
CERAD-Plus includes the Mini-Mental State Examination
(MMSE), Addition tasks, Attention Deficit Disorder
Questionnaire
Reaction time
Simple reaction time test, Redirection test, Four choice serial test,
Stroop test, Visual signals choice test, Choice reaction time
Reasoning
Alice Heim 4-I test, Logic problem test, Overlapping test,
Grammatical reasoning, Verbal reasoning, Odd-One-Out task,
Event sequence and graphic abstracting task
Decision making
Computer-based test
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Problem solving
Embedded-figure task, She-polish test, Addition task,
Planning
Spatial planning test, Spatial search task
Creativity
Creative thinking test, Remote associates test, Idea-generation
task
Note: Some instruments, such as the Stroop test, can assess more than one cognitive function.
* A specific cognition was not explicitly described in the literature.
† Reaction time is the time elapsed between the onset of a stimulus and a response to it [275]. It
consists of simple reaction time, recognition reaction time, and cognitive reaction time. Since it
could involve multiple cognitive skills, such as information processing, reasoning, and
psychosensory [276], we grouped reaction time together with higher order cognitive skills.
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Table A2. Summary of IEQ on attention
Refer
ence
IEQ vs
Cognition
Sample size & environmental
conditions
Measures of cognitive functions
Major findings
Significan
ce level
[119]
IAQ
vs
Attention
18 school children (age between 10
and 11).
CO2 concentration controlled by
opening or closing the window to
regulate the ventilation; the Mean
CO2 concentration is ranged from
690 ppm to 2909 ppm.
Cognitive Drug Researcher (CDR)
computerized cognitive
assessment system to measure the
subjects’ attention level
The increased levels of CO2 led to a
decrement in the power of attention of
approximately 5% (p = 0.004).
2
[184]
IAQ
vs
Attention
1764 adults (age around 37.5);
Estimated exposure levels to PM10
and ozone-based on ambient
concentrations in the EPA database.
Serial-digit learning test (SDLT)
for testing attention. Symbol-digit
substitution test (SDST) about
coding ability measures an
individual’s sustained attention.
Increased ozone exposure was correlated with
reduced performance in the SDLT test. Each
10-ppb increase in annual ozone was
associated with an increased in SDST and
SDLT scores by 0.16 and 0.56, which was
equal to 3.5 and 5.3 years of aging-related
decline in attention function.
N/A
[187]
IAQ
vs
Concentration
417 school students in total in 20
classrooms with mechanical
ventilation systems; Median CO2
concentration of 1045 ppm and
2115 ppm.
d2-test: a paper-and-pencil test
with 14 rows of characters to
distinguish; The total number of
characters processed for handling
speed and accuracy; The number
of correctly marked target
characters minus incorrectly
marked distractor characters for
concentration assessment.
No significant effect of experimental
condition on concentration performance was
found. No significant effect of experimental
state or median CO2 level on the “total number
of characters processed” could be observed.
The concentration performance was
decreased by 1.11 points at 2115 ppm of CO2
in comparison with 1045 ppm. Concentration
performance, the total number of characters
processed, and total errors changed less than
1.7%.
0
[185]
IAQ
vs
Attention
174 children (46.5% males,
age from 7 to 14).
Estimate the children’s lifetime
exposure to black carbon.
Conners’ Continuous Performance
Test (CPT) for the task-based
computerized assessment of
attention disorders and
neurological functioning.
Exposure to black carbon was associated with
increased commission errors and slower hit
reaction time (HRT). The associations
between BC levels and attention parameters
were significantly different (p < 0.05)
between the middle two BC quartiles and the
first BC quartile. But its association with
omission errors was not statistically
significant. Boys were more susceptible than
1
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girls to potential effects of traffic-related air
pollution in some attention domains.
[188]
IAQ
vs
Attention
25 students (40% males,
age around 23).
Five conditions mixed with three
CO2 levels (500 ppm, 1000 ppm,
and 3000 ppm) and different bio-
effluent concentrations.
d2 test: a paper-and-pencil test
with 14 rows of characters needed
to be distinguished.
No statistically significant effects on
perceived air quality and attention
performance were found by increasing CO2
exposure; Exposure to bio-effluent reduced
perceived air quality, increased the intensity
of reported headache, fatigue, sleepiness, and
difficulty in thinking, reduced speed of
addition, and decreased the number of correct
links made in the cue-utilization test.
0
[189]
IAQ
vs
Attention
31 participants were divided into
four groups.
CO2 concentration in the study
room was controlled at a normal
condition (700 ppm) and a high
condition (2700 ppm).
Shifting attention tasks and Stroop
test were used for the attention
test.
No effect of CO2 on reaction times, complex
attention, simple attention, sustained attention
was found.
0
[99]
Thermal
environment
vs
Attention
24 participants (50% males, mean
age 25 years).
Four temperatures, 19℃, 24℃,
27℃, and 32℃ were considered in
an air-conditioned office with eight
fluorescent lamps.
Letter search tests, memory span
tests, and picture recognition used
in this study were all associated
with subjects’ attention
performance.
No significant effect of temperature on the
attention performance was observed in these
three tests from both response time and
results accuracy.
0
[117]
Thermal
environment
vs
Attention
12 subjects (6 males, average age of
23 years) divided into two groups.
One group was exposed to different
temperatures in a sequence of 22-
30-30-22 °C, while the other group
30-22-22-30 °C.
Computerized test: Stroop - a test
of attentional vitality.
The Stroop test performance significantly (p =
0.01) decreased at 30 °C compared with 22 °C
when feedback for the test was provided. The
performance of the same test was not
significantly different (p = 0.09) between the
two temperatures without feedback provided.