synonym of effect in research

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What is the difference among Effect, Impact, Role, Difference and Relation while selecting a research topic?

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Effect – Synonyms

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The word “effect” has different meanings. One meaning is for example a result which happens in reaction to a change. Overall, the word “effect” is mostly an outcome or consequence of a cause or action.

Another word for “effect” is consequence or result. However, more synonyms will be listed in this article.

On our overview page for synonyms, you can find the best options of synonyms for a vast variety of words that are used in academic writing .

To the overview page for synonyms

Inhaltsverzeichnis

• 1 “Effect” – General synonyms
• 2 “Effect” – Synonyms used in academic writing

“Effect” – General synonyms

The following illustrates other words for “effect” that may be used in everyday conversation as well as in academic writing.

• Consequence
• Effectiveness
• Implication
• Ramification
• Significance

“Effect” synonyms in the sense of result

Synonyms of the word “effect” in the sense of result are:

• Consequences
• Development
• Repercussion

“Effect” synonyms in the sense of impression

Synonyms of the word “effect” in the sense of impression are:

“Effect” synonyms in the sense of purpose

Synonyms of the word “effect” in the sense of purpose are:

“Effect” synonyms in the sense of implementation

Synonyms of the word “effect” in the sense of implementation are:

• Enforcement
• Implementation
• Performance

“Effect” – Synonyms used in academic writing

In an academic context, the word “effect” is mostly used to explain an outcome or a consequence as a result to an action or event. It also can be used to describe the efficacy or effectiveness of something.

However, some synonyms for “effect” used in academic writing will be shown below.

Are you looking for suitable synonyms for “effect” for your academic paper? Have a look at the table below with the top suggestions from our BachelorPrint-Team .

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Affect Vs. Effect — Which one to use when? Know the difference!

Oh! How did I make such a silly blunder! That too in a research article which was reviewed and re-reviewed before we were pretty sure to submit! I was supposed to propose an effect of the indicator on the system and not the affected indicator on the system! Affect, Effect… Why are they so similar and misleading? What a fiasco!

Language is a very specific tool in research publishing. It helps researchers gain the common understanding of content. Moreover, usage of words could change within the context of very specific conditions (research design, statistical analysis), but the reader may observe variations in word usage and meaning across research disciplines.

Table of Contents

Most Commonly Confused Words

Among many researchers in the English language whose usage can confuse both native and nonnative speakers (e.g., fewer vs. less; infer vs. imply), the concepts of affect and effect are particularly vexing. Most common, affect refers to action and is used as a verb. Merriam-Webster offers influence as a synonym for affect. In contrast, effect is most often used as a noun, usually indicating a result. But effect can also be a verb, as in “to effect change.”

Therefore, affect vs. effect is a most commonly confused issue. The problem arises because of its similarity in pronunciation, and especially when both the words are related to change. However, grammatically –

• Affect in a sentence, as a verb, describes the act of producing a change in someone or something.
• Effect in a sentence, as a noun, refers to a change that results when something is done or happens.

Affect Vs. Effect

Use of Affect

Affect is typically a transitive verb and is always used with an object, which means you will always include the name of the person or thing being affected.

Example of Affect: The speed of the reaction was affected by the temperature.

When affect is used as a noun, it can refer to visible emotional response

Example of Affect: The woman’s facial affect indicated that she was distressed by the conversation.

Use of Effect

Effect is typically a noun, which means the result or consequences of a cause or action. The word is often used with an adjective

Example of Effect: The quality of food has a major effect on the state.

When effect is used as a verb, it means “cause something to come to being.” It is used when followed by the word “change.”

Example of Effect: The biology research group effected change through peaceful discussions.

How to Identify the Usage of Effect and Affect?

• The RAVEN ( R emember A ffect is a V erb — E ffect is a N oun) trick is quite famous to remember the difference between effect and affect and identify it.
• Verbs are actions and actions starts with A, and affect is a verb.
• When you do not know how to use affect as a verb, choose a synonym like impact or choose a more specific verb. For example, the weather affected her holiday plans can instead be written as — The weather ruined her holiday plans .
• You could use a grammatical article before nouns, so try identifying the noun effect with an article.
• Use another interesting tip — Accident & Emergency. When you are affected by an accident, the effect is an emergency.
• For every grammar rule, there is an exception. Effect vs. affect has an exception. Affect can be used as a noun when one talks about mood that someone appears to have. Effect can be used as a verb that essentially means “to bring about” or “to accomplish.”

Differentiating Affect Vs. Effect While Writing Your Research

In the aim of a research study, a researcher can describe or observe a particular chain of events, in many cases the investigator wishes to test a specific hypothesis — to determine how a particular situation, behavior, or context affects another. Furthermore, it is common for the hypothesis to specify an independent variable (X) and how it affects the dependent variable (Y). At this point, the investigator is proposing an effect (noun) or outcome that is the result of the ways in which X affects Y.

Once decisions about measurement and definition of X and Y are resolved, the choice of an appropriate statistical analysis further specifies the concept and scope of the proposed effect (noun). Effect in this context describes and quantifies the statistical probability that X is associated with a change in Y. It is a probability, not a certainty. Study designs that incorporate controlled observation methods (e.g., randomized controlled trials) increase the likelihood that the observed results are not due to other factors.

The best way to deal with the confusion in usage of words is to get a thorough grammar check done before any submission. Moreover, there are tools that can run a test, they are easy to access and are less time-consuming, giving reliable results to avoid grammar and language blunders, if any.

Have you ever gotten confused between effect and affect ? Are there any other words that mean different but are spelled or pronounced similarly? Write to us or mention in the comments below about how you deal with such honest language err.

Frequently Asked Questions

"Affect" is commonly used as a verb, meaning to influence or produce a change. "Effect" is primarily used as a noun, representing the result or consequence of an action. However, "effect" can also be used as a verb, meaning to bring about or accomplish something.

"Here are some differences between ""affect"" and ""effect"": 1. The word ""affect"" is a verb, used to describe an action, while ""effect"" is a noun, used to describe a thing or result. 2. Both ""affect"" and ""effect"" relate to consequences and results, but their difference lies in their word class (verb versus noun). 3. In most cases, you will encounter ""affect"" as a verb and ""effect"" as a noun in closely related scenarios involving actions and their consequences. "

"To use ""affect"" and ""effect"" correctly, consider the following guidelines: Affect (verb): Use ""affect"" as a verb when you want to describe the action of influencing or producing a change in something or someone. Effect (noun): Use ""effect"" as a noun when you want to refer to the result, consequence, or outcome of something. Effect (verb): Use ""effect"" as a verb when you want to express bringing about or accomplishing something. "

"""affect"" and ""effect"" can be used as follows: ""Affect"" (as a verb): ""The teacher's instructional methods positively affect students' academic performance."" In this sentence, ""affect"" is used as a verb to describe how the teacher's teaching approaches influence or impact the students' academic performance in a positive manner. ""Effect"" (as a noun): ""The research study found a significant effect of sleep deprivation on cognitive function."" Here, ""effect"" is used as a noun to indicate the result or outcome of sleep deprivation on cognitive function, as observed in the research study."

Helped me very much to understand the differences between affect and effect meanings. Very helpful.

This helped me very much to rember the differences between them….. RAVEN…..ACCIDENT & EMERGENCY

This helped me very much to remeber the differences between them….. RAVEN…..ACCIDENT & EMERGENCY

Very good information and helpful. Like the RAVEN and different meaning’s between affect vs effect.

Thank you for the help, I am still trying to understand the difference between the two but will ALWAYS remember R.A.V.E.N

Simply remeber this: “‘a’ is for action”

Would this be correct ? Were you affected by the flooding ? The flooding had a bad effect on people .

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Other forms: effects; effected; effecting

Effect is the result of an action, as in those “cause and effect ” papers you might write in English class. Your topic could be how your late-night tuba playing (cause) has driven your roommate insane ( effect ).

Another noun use of effect describes an appearance or impression that’s created on purpose, such as the dramatic effect of the bright red walls in your kitchen, or sound effects from your favorite movie. Effect appears less often as a verb, but when it does, it means “produce.” Usually, it’s a noun. It can even refer to your belongings, like when you get kicked out and your former roomie begs you to get all of your personal effects.

• noun a phenomenon that follows and is caused by some previous phenomenon “the magnetic effect was greater when the rod was lengthwise” synonyms: consequence , event , issue , outcome , result , upshot see more see less types: show 30 types... hide 30 types... materialisation , materialization , offspring something that comes into existence as a result aftereffect any result that follows its cause after an interval aftermath , backwash , wake the consequences of an event (especially a catastrophic event) bandwagon effect the phenomenon of a popular trend attracting even greater popularity brisance the shattering or crushing effect of a sudden release of energy as in an explosion butterfly effect the phenomenon whereby a small change at one place in a complex system can have large effects elsewhere, e.g., a butterfly flapping its wings in Rio de Janeiro might change the weather in Chicago by-product , byproduct a secondary and sometimes unexpected consequence change the result of alteration or modification coattails effect (politics) the consequence of one popular candidate in an election drawing votes for other members of the same political party Coriolis effect (physics) an effect whereby a body moving in a rotating frame of reference experiences the Coriolis force acting perpendicular to the direction of motion and to the axis of rotation; on Earth the Coriolis effect deflects moving bodies to the right in the northern hemisphere and to the left in the southern hemisphere dent an appreciable consequence (especially a lessening) domino effect the consequence of one event setting off a chain of similar events (like a falling domino causing a whole row of upended dominos to fall) harvest the consequence of an effort or activity impact , wallop a forceful consequence; a strong effect influence the effect of one thing (or person) on another knock-on effect a secondary or incidental effect branch , offset , offshoot , outgrowth a natural consequence of development product a consequence of someone's efforts or of a particular set of circumstances placebo effect any effect that seems to be a consequence of administering a placebo; the change is usually beneficial and is assumed result from the person's faith in the treatment or preconceptions about what the experimental drug was supposed to do; pharmacologists were the first to talk about placebo effects but now the idea has been generalized to many situations having nothing to do with drugs position effect (genetics) the effect on the expression of a gene that is produced by changing its location in a chromosome repercussion , reverberation a remote or indirect consequence of some action response a result fallout , side effect any adverse and unwanted secondary effect spillover (economics) any indirect effect of public expenditure perturbation (physics) a secondary influence on a system that causes it to deviate slightly purchase a means of exerting influence or gaining advantage wind a tendency or force that influences events reaction a response that reveals a person's feelings or attitude epiphenomenon a secondary phenomenon that is a by-product of another phenomenon depolarisation , depolarization a loss of polarity or polarization type of: phenomenon any state or process known through the senses rather than by intuition or reasoning
• noun a symptom caused by an illness or a medication “the effects of sleep loss” “the effect of the anesthetic” see more see less types: show 4 types... hide 4 types... aftereffect a delayed effect of a drug or therapy bummer a bad reaction to a hallucinogenic drug side effect a secondary and usually adverse effect of a drug or therapy black tongue , furry tongue , hairy tongue a benign side effect of some antibiotics; dark overgrowth of the papillae of the tongue type of: symptom (medicine) any sensation or change in bodily function that is experienced by a patient and is associated with a particular disease
• noun an outward appearance “she retained that bold effect in her reproductions of the original painting” synonyms: impression see more see less types: show 4 types... hide 4 types... figure the impression produced by a person image the general impression that something (a person or organization or product) presents to the public mark the impression created by doing something unusual or extraordinary that people notice and remember tout ensemble a total impression or effect of something made up of individual parts type of: appearance , visual aspect outward or visible aspect of a person or thing
• noun an impression (especially one that is artificial or contrived) “he just did it for effect ” see more see less types: sound effect an effect that imitates a sound called for in the script of a play special effect an effect used to produce scenes that cannot be achieved by normal techniques (especially on film) stage effect a special effect created on the stage type of: belief , feeling , impression , notion , opinion a vague idea in which some confidence is placed
• noun (of a law) having legal validity “the law is still in effect ” synonyms: force see more see less type of: validity , validness the quality of having legal force or effectiveness
• noun the central meaning or theme of a speech or literary work synonyms: burden , core , essence , gist see more see less type of: import , meaning , significance , signification the message that is intended or expressed or signified
• verb produce synonyms: effectuate , set up see more see less types: show 12 types... hide 12 types... accomplish , action , carry out , carry through , execute , fulfil , fulfill put in effect draw , get earn or achieve a base by being walked by the pitcher precipitate bring about abruptly hasten , induce , rush , stimulate cause to occur rapidly serve contribute or conduce to get over to bring (a necessary but unpleasant task) to an end run carry out consummate make perfect; bring to perfection consummate fulfill sexually do , perform get (something) done complete , discharge , dispatch complete or carry out facilitate , help be of use type of: cause , do , make give rise to; cause to happen or occur, not always intentionally
• verb act so as to bring into existence “ effect a change” see more see less types: bring to bear bring into operation or effect carry extend to a certain degree backdate make effective from an earlier date type of: act , move perform an action, or work out or perform (an action)

affect / effect

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Vocabulary lists containing effect

A thorough survey of various textbooks, assignments, content area standards, and examinations yields the following list of words compiled by Jim Burke . You cannot expect to succeed on assignments if you do not understand the directions.

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What is another word for effect ?

Synonyms for effect ɪˈfɛkt ef·fect, this thesaurus page includes all potential synonyms, words with the same meaning and similar terms for the word effect ., english synonyms and antonyms rate these synonyms: 0.0 / 0 votes.

effect noun

An act is strictly and originally something accomplished by an exercise of power, in which sense it is synonymous with deed or effect . Action is a doing . Act is therefore single, individual, momentary; action a complex of acts , or a process, state, or habit of exerting power. We say a virtuous act , but rather a virtuous course of action . We speak of the action of an acid upon a metal, not of its act . Act is used, also, for the simple exertion of power; as, an act of will. In this sense an act does not necessarily imply an external effect , while an action does. Morally, the act of murder is in the determination to kill; legally, the act is not complete without the striking of the fatal blow. Act and deed are both used for the thing done, but act refers to the power put forth, deed to the result accomplished; as, a voluntary act , a bad deed . In connection with other words act is more usually qualified by the use of another noun, action by an adjective preceding; we may say a kind act , though oftener an act of kindness, but only a kind action , not an action of kindness. As between act and deed , deed is commonly used of great, notable, and impressive acts , as are achievement , exploit , and feat .

Festus : We live in deeds , not years; in thoughts, not breaths.

Bailey Festus, A Country Town , sc. 7.

A feat exhibits strength, skill, personal power, whether mental or physical, especially the latter; as, a feat of arms, a feat of memory. An exploit is a conspicuous or glorious deed , involving valor or heroism, usually combined with strength, skill, loftiness of thought, and readiness of resource; an achievement is the doing of something great and noteworthy; an exploit is brilliant, but its effect may be transient; an achievement is solid, and its effect enduring. Act and action are both in contrast to all that is merely passive and receptive. The intensest action is easier than passive endurance.

Synonyms: accomplishment , achievement , act , action , consummation , deed , doing , execution , exercise , exertion , exploit , feat , motion , movement , operation , performance , proceeding , transaction , work

Antonyms: cessation , deliberation , endurance , immobility , inaction , inactivity , inertia , passion (In philosophic sense) , quiescence , quiet , repose , rest , suffering , suspension

Complete Dictionary of Synonyms and Antonyms Rate these synonyms: 2.0 / 1 vote

Synonyms: produce , cause , complete , achieve , accomplish , fulfil , realize , execute , consummate

Antonyms: prevent , obviate , frustrate , mar

Synonyms: result , consequence , issue , pi , goods , chattels , property , movables , commodities

Antonyms: origin , cause

Princeton's WordNet Rate these synonyms: 2.0 / 1 vote

consequence, effect, outcome, result, event, issue, upshot noun

a phenomenon that follows and is caused by some previous phenomenon

"the magnetic effect was greater when the rod was lengthwise"; "his decision had depressing consequences for business"; "he acted very wise after the event"

Synonyms: aftermath , progeny , resolution , issuing , case , government issue , military issue , return , takings , burden , resultant , payoff , consequence , core , take , publication , answer , impression , resultant role , event , upshot , subject , topic , solvent , offspring , egress , gist , outlet , termination , exit , number , essence , yield , moment , emergence , outcome , way out , proceeds , issue , force , solution , final result , import , matter , result , issuance

impression, effect noun

an outward appearance

"he made a good impression"; "I wanted to create an impression of success"; "she retained that bold effect in her reproductions of the original painting"

Synonyms: consequence , impression , event , mental picture , core , force , imprint , feeling , result , burden , opinion , notion , depression , essence , outcome , printing , gist , stamp , upshot , issue , picture , belief

an impression (especially one that is artificial or contrived)

"he just did it for effect"

Synonyms: issue , consequence , result , event , burden , essence , upshot , core , gist , outcome , impression , force

effect, essence, burden, core, gist noun

the central meaning or theme of a speech or literary work

Synonyms: substance , loading , perfume , meat , centre , burden , center , consequence , core , marrow , load , sum , impression , event , heart and soul , core group , upshot , nub , kernel , nitty-gritty , gist , inwardness , pith , heart , nucleus , onus , essence , magnetic core , outcome , encumbrance , issue , force , incumbrance , result

effect, force noun

(of a law) having legal validity

"the law is still in effect"

Synonyms: power , consequence , military unit , event , military group , core , force , force out , military force , force play , strength , gist , violence , result , burden , essence , outcome , force-out , upshot , personnel , forcefulness , issue , impression

• effect verb

a symptom caused by an illness or a drug

"the effects of sleep loss"; "the effect of the anesthetic"

effect, effectuate, set up verb

"The scientists set up a shock wave"

Synonyms: tack together , ensnare , assemble , put , put up , found , install , effectuate , put together , arrange , frame , set up , lay out , tack , instal , launch , put in , gear up , order , ready , entrap , rear , set , fix , pitch , piece , rig , establish , erect , raise , prepare

act so as to bring into existence

"effect a change"

Synonyms: set up , effectuate

Matched Categories

Dictionary of english synonymes rate these synonyms: 0.0 / 0 votes.

Synonyms: consequence , result , issue , event

Synonyms: force , validity , weight , power , efficiency

Synonyms: purport , import , drift , tenor , meaning , general intent

Synonyms: fact , reality , truth

Synonyms: impression ( on the organs of sense )

Synonyms: cause , produce , create

Synonyms: accomplish , achieve , execute , perform , do , consummate , realize , carry , compass , effectuate , bring about , bring to pass , carry out , work out

Synonyms, Antonyms & Associated Words Rate these synonyms: 0.0 / 0 votes

Synonyms: result , outcome , consequence , issue , validity , force , weight , execution , performance , realization , meaning , tenor , purport , intent , ensemble

Synonyms: effectuate , accomplish , realize , achieve , consummate , compass

PPDB, the paraphrase database Rate these paraphrases: 0.0 / 0 votes

List of paraphrases for "effect":

impact , effects , force , fact , purpose , influence , impacts , end , incidence , effectiveness , result , effective , regard , sense , affect , consequence , consequences , implications , practice , meaning , vigour

Suggested Resources

Effect vs. Affect -- In this Grammar.com article you will learn the differences between the words Effect and Affect.

How to pronounce effect?

How to say effect in sign language, usage in printed sources from:  .

• 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Words popularity by usage frequency

How to use effect in a sentence?

Nancy Northup :

This would be the most devastating abortion restrictions allowed to go into effect since Roe v. Wade.

Goa Kerle :

Do you fear doing it? You haven't started feeling the effect of not doing it.

Charles Collyns :

The export effect is certainly significant.

John Koza :

This makes it at least possible to put this into effect by 2020 -- not likely, but possible.

Thomas à Kempis :

Love feels no burden, thinks nothing of trouble, attempts what is above its strength.... It is therefore able to undertake all things, and it completes many things, and warrants them to take effect, where he who does not love would faint and lie down.

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Are we missing a good synonym for effect ?

Image credit, the web's largest resource for, synonyms & antonyms, a member of the stands4 network, free, no signup required :, add to chrome, add to firefox, browse synonyms.com, are you a human thesaurus, which of these is a synonym of sangfroid, nearby & related entries:.

• efface verb
• effaceable adj
• effacement noun
• effect-based
• effect-oriented
• effected adj
• effecter noun

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noun as in collision, force

Strongest matches

Strong matches

• impingement

Weak matches

noun as in effect

• repercussion
• significance
• consequences

verb as in hit with force

Strongest match

Example Sentences

We look for mission-driven founders who believe their companies can make a real and positive impact on the lives of people and patients the world over.

It’s important to recognize the truth of our impacts, to take stock of the enormous destruction we have reaped on this planet’s biodiversity and climate.

Committed investors, engaged philanthropy, and smart state and local policy can create access to capital for marginalized communities that fosters genuine impact and fights poverty.

So it starts as a grant, but then it’s mixed with a convertible note that converts based on how much impact they’re creating, measured on metrics that we advise.

Faulconer and Housing Commission officials see the proposed purchases as a chance to make a major impact on the city’s homelessness crisis.

In this nervous city in an embattled country, even small explosions can have a big impact.

The EPA felt that the State Department had not looked carefully enough at the impact of the pipeline if oil prices fell.

Residents of the neighborhoods where cops are needed the most are mixed on the impact of the apparent slowdown.

Strong currents and winds, however, mean any debris could be drifting up to 31 miles a day eastward, away from the impact zone.

It made a big impact on him and he realized, “Wow, there really is a Santa, at least in the hearts of some people.”

The impact dragged down on the speed of the roadster so that the rear right fender was only crumpled by the brick work.

But "son" had rebounded from the impact like a rubber ball, or the best trained gymnast of his school, as he was.

I saw Johnny Burke's body jerk a bit under the impact of the slugs, but he was too big to be stopped by them.

I felt the impact of that culture in her interested eyes and in the sleek, smart bearing of her utterly relaxed body.

The impact stresses depend so much on local conditions that it is difficult to fix what allowance should be made.

Related Words

Words related to impact are not direct synonyms, but are associated with the word impact . Browse related words to learn more about word associations.

noun as in situation following an event, occurrence

• after-effects
• chain reaction
• eventuality

noun as in hard hit

• knuckle sandwich

noun as in bad end of a situation

noun as in disagreement or fight, often brief

• confrontation
• difference of opinion
• discordance
• embroilment
• have a go at each other
• misunderstanding

Viewing 5 / 33 related words

From Roget's 21st Century Thesaurus, Third Edition Copyright © 2013 by the Philip Lief Group.

Transgenerational effects impact the vulnerability of a host-parasitoid system to rising temperatures.

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Transgenerational effects, where ancestral experience of environmental conditions influences the performance in subsequent generations, are hypothesised to have substantial consequences for responses to climate change. However, any apparent detriment or advantage these processes generate for a focal species may be counteracted by concurrent effects upon interacting species. Using an experimental Drosophila-parasitoid model system, we determined how the parental thermal environment impacts the performance of both hosts and parasitoids. We found substantial responses in both trophic-levels, with potential evidence for both condition-transfer effects and adaptive transgenerational plasticity. We used these results to parameterise discrete-time simulation models to explore how transgenerational effects of thermal conditions and temporal autocorrelation in temperature are expected to impact the time to extinction for this host-parasitoid system under climate change. The models predicted that transgenerational effects would significantly hasten the time to extinction, largely through a reduction in estimated average performance. Under a simple model, there was an additional hastening of extinction derived from the interaction between the coupled transgenerational effects, although no interactive effects were detectable in an alternative model with greater buffering capacity. Our research demonstrates a new mechanism underlying how tightly-coupled interacting species will respond to climate change and highlights the need to quantify and contextualise thermal transgenerational effects.

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The authors have declared no competing interest.

https://doi.org/10.5281/zenodo.13327764

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Common low-calorie sweetener may be riskier for the heart than sugar, study suggests

Another study is raising concern about the safety of the widely used sugar alcohol sweetener erythritol , a low-calorie sugar substitute found in “keto-friendly” foods, baked goods and candies. Researchers from the Cleveland Clinic compared erythritol to typical sugar and found only erythritol caused worrisome cardiovascular effects. 

Although the study was small, it’s the first head-to-head look at people’s blood levels after they consume products with erythritol or sugar (glucose). 

“We compared the results, and glucose caused none of the problems,” said Dr. Stanley Hazen, a cardiologist at the Cleveland Clinic and the lead author of the study, published Thursday morning in the journal Arteriosclerosis, Thrombosis, and Vascular Biology. 

Erythritol is one ingredient on a growing list of nonsugar sweeteners found in low-calorie and sugar-free foods. Erythritol and xylitol are sugar alcohols that are sweet like sugar but with far fewer calories. Erythritol is often mixed with another sweetener, stevia, and xylitol is often found in gum, mouthwash and toothpaste. 

Earlier studies from Hazen’s lab — one published last year and the other in June — found potential links between the sugar alcohols and an increased risk of heart attacks and strokes. The research suggested both sugar alcohols might make blood platelets stickier and therefore more susceptible to clotting and blocking veins or arteries, in turn contributing to heart attacks and strokes.

For the new research, Hazen’s team analyzed the heart effects of erythritol and regular sugar — in this case, simple glucose — by enrolling two groups of healthy middle-aged male and female volunteers: 10 who consumed the erythritol and 10 who consumed sugar.

Both groups fasted overnight. In the morning, their blood was drawn to measure platelet activity. Then, half the volunteers drank glasses of water with 30 grams of glucose mixed in, and half drank glasses of water with 30 grams of erythritol. Hazen said 30 grams of erythritol is an amount typical of erythritol-sweetened foods. 

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Around 30 minutes after each group consumed the sweetened drinks, their blood was drawn and retested. Researchers found the people who consumed erythritol had increased platelet aggregation — meaning the blood was more likely to clot. Adults who drank the normal sugar drink had no changes in platelet aggregation. 

The researchers measured a 1,000-fold increase in blood erythritol levels in the group given the erythritol drink. Those who drank glucose water didn’t have any changes in blood erythritol levels, and their blood glucose levels were only slightly increased. The finding stood out to Hazen, because it far exceeded the trace levels of erythritol that occur naturally in the blood. 

“The amount in sugar substitutes is thousands of folds higher than what is made in our bodies, so to call it ‘natural,’ it’s not,” he said. “Your best recommendation is to avoid the sugar substitutes, and sugar alcohols in particular, because there’s an acute increase in the likelihood of clotting events once you ingest them.”

The Food and Drug Administration considers artificial sweeteners, including erythritol and xylitol, as GRAS, or generally recognized as safe . Hazen hopes mounting evidence about the sugar alcohols might trigger the FDA to look more closely at the data. 

Outside the U.S., the concerns have drawn interest among food regulators. Last year, for instance, the European Food Safety Authority recommended that the European Commission request data about how much erythritol is in food, which could help clarify the risks. 

Do the findings indicate that erythritol is worse overall than high-calorie sugar? Valisa Hedrick, a registered dietitian at Virginia Tech, said a diet high in sugary foods can lead to elevated blood glucose levels that are also linked to stroke and clotting risks. Hedrick wasn’t involved in the Cleveland Clinic study.

The study has several important limitations. Beyond the small number of participants, it measured the effects of erythritol and glucose at only one point in time, as opposed to over months or years of consistent consumption, Hedrick noted.

And the amount of glucose in the sugar water — about 30 grams — is the equivalent of about 120 calories of sugar. Sugary beverages, especially juices and sodas, often contain more sugar. 

For example, a 12-ounce can of Coca-Cola contains 39 grams of sugar, and 12 ounces of Mountain Dew contains 46 grams. 

Michael Goran, a professor of pediatrics at the University of Southern California’s Keck School of Medicine, said it might also be worth comparing erythritol to both fructose and glucose. The combination of fructose and glucose is more typical of sugary juices and sodas than glucose alone, he said. Goran wasn’t part of the new study.

Hazen’s study looked at glucose alone. 

Although the Cleveland Clinic study didn't find negative effects from consuming sugar, the researchers agreed the data doesn’t mean sugar is in the clear. Higher amounts of sugar may cause similar platelet effects, especially in people with diabetes, who can’t effectively regulate high blood glucose.

Hazen’s study focused specifically on healthy people, not people with diabetes.

It could also be important to analyze whether heart effects differ when people consume food with erythritol compared with water with erythritol, said Dr. Michelle Pearlman, a gastroenterologist who is CEO and a co-founder of the Prime Institute in Miami.

“Factors such as protein, fat, fiber and other nutrients might influence this response,” she said. 

Ultimately, said Hedrick of Virginia Tech, the new study underscores the need for more research comparing the health effects of sweeteners versus sugar.

Hazen and his colleagues concluded the research by urging further studies focusing on erythritol’s heart risks, particularly in people already at higher risk of strokes and clotting. 

NBC News contributor Caroline Hopkins is a health and science journalist who covers cancer treatment for Precision Oncology News. She is a graduate of the Columbia University Graduate School of Journalism.  

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Synonyms for Research impact

9 other terms for research impact - words and phrases with similar meaning.

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Why Dropping the E in DEI Is a Mistake

• Enrica N. Ruggs
• Oscar Holmes IV

The Society for Human Resource Management’s decision to remove “equity” from its DEI framework sets a dangerous precedent that flies in the face of decades of research.

The Society for Human Resource Management (SHRM) has decided to remove “equity” from its inclusion, equity, and diversity (IE&D) framework, now promoting “inclusion and diversity” (I&D) instead. This decision sets a dangerous precedent that flies in the face of decades of research about DEI in the workplace. It undermines efforts to create equitable workplaces and ignores the vital role of equity in fostering fairness and addressing systemic barriers faced by marginalized groups. Instead of scaling back their focus on equity, companies should: 1) Commit to achievable equity goals; 2) Implement and track evidence-based DEI policies and practices; and 3) Establish accountability and transparency.

Recently, the Society for Human Resource Management (SHRM), a leading voice of HR professionals, announced that it was abandoning the acronym “IE&D” — inclusion, equity, and diversity — in favor of “I&D.”

• Enrica N. Ruggs , PhD is an associate professor of management in the C. T. Bauer College of Business at the University of Houston. She is a workplace diversity scholar who conducts research on reducing discrimination and bias in organizations and improving workplace experiences for individuals with marginalized identities.
• Oscar Holmes IV , PhD, SHRM-SCP is an associate professor of management at Rutgers University-Camden and the creator and host of the podcast Diversity Matters . In his research he examines how leaders can maximize productivity and well-being by fostering more inclusive workplaces.

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'Affect' vs. 'Effect'

What to Know Affect is usually a verb meaning "to produce an effect upon," as in "the weather affected his mood." Effect is usually a noun meaning "a change that results when something is done or happens," as in "computers have had a huge effect on our lives." There are exceptions, but if you think of affect as a verb and effect as a noun, you’ll be right most of the time.

Affect and effect are two of the most commonly confused words in English, but don’t worry—we’ll help you keep them straight.

The basic difference is this: affect is usually a verb, and effect is usually a noun.

‘Affect’ as a Verb

Affect , when used as a verb, means "to act on or change someone or something."

the drought affected plant growth construction will affect traffic in the area trying not to let emotions affect their decision

Affect also has a sense meaning “to put on a false appearance of (something).”

he affected a French accent

’Effect’ as a Noun

As a noun, an effect is "a change that results when something is done or happens," or "a particular feeling or mood created by something."

the second cup of coffee had no effect he added a scarf to the outfit for effect the law goes into effect next week

A Few Rare Exceptions

There are, however, a few relatively uncommon exceptions, and these are worth knowing about.

Effect can be a verb. As a verb, effect generally means "to cause to come into being" or "accomplish."

the strike effected change within the company

Affect can be a noun. Although its use is primarily found in psychology, the noun affect refers to an observable emotional response.

his affect did not change after hearing the news

But exceptions aside, just stick to the basics: if you think of affect as the verb and effect as the noun, most of the time you’ll be using the word you want.

Want More Commonly Confused Words?

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• Published: 17 August 2024

Neural encoding of linguistic speech cues is unaffected by cognitive decline, but decreases with increasing hearing impairment

• Elena Bolt 1 , 2 &
• Nathalie Giroud 1 , 2 , 3  

Scientific Reports volume  14 , Article number:  19105 ( 2024 ) Cite this article

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The multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of cognitive decline in older adults, taking into account the effects of hearing. Participants without ( \(n = 25\) ) and with ( \(n = 19\) ) early signs of cognitive decline listened to an audiobook while their electroencephalography responses were recorded. Using the mTRF framework, we modeled the relationship between speech input and neural response via different acoustic, segmented and linguistic encoding models and examined the response functions in terms of encoding accuracy, signal power, peak amplitudes and latencies. Our results showed no significant effect of cognitive decline or hearing ability on the neural encoding of acoustic and linguistic speech features. However, we found a significant interaction between hearing ability and the word-level segmentation model, suggesting that hearing impairment specifically affects encoding accuracy for this model, while other features were not affected by hearing ability. These results suggest that while speech processing markers remain unaffected by cognitive decline and hearing loss per se, neural encoding of word-level segmented speech features in older adults is affected by hearing loss but not by cognitive decline. This study emphasises the effectiveness of mTRF analysis in studying the neural encoding of speech and argues for an extension of research to investigate its clinical impact on hearing loss and cognition.

Introduction

The increasing number of dementia patients in our ageing population underlines the urgent need for research that focuses on methods for early detection. Early stages of cognitive decline pose a major challenge for diagnosis and intervention, as early detection is central to effective treatment, making the identification of subtle changes prior to diagnosis particularly important 1 . Language performance, encompassing both comprehension and production, is intricately linked to cognitive functions and often deteriorates in dementia and other forms of cognitive decline 2 , 3 , 4 , 5 . The processing of language in the brain involves complex neural networks across various brain regions, which are vulnerable to early neuropathological changes, especially in conditions like Alzheimer’s disease (AD) 6 . Furthermore, the relationship between cognition and hearing is critically important 7 . Age-related hearing loss is closely linked with cognitive decline and is considered the most common modifiable risk factor for dementia 1 . A significant proportion of older adults with cognitive decline also experience hearing loss 8 , 9 . The manner in which the aging brain processes speech—particularly natural continuous speech that involves ongoing top-down and bottom-up processing 10 —through the auditory system may be key in the early detection of cognitive decline. Behavioral measures have indicated that individuals with early signs of cognitive decline show impaired auditory processing capabilities 11 . Neurophysiological studies have revealed that these early stages of decline are associated with significant changes in neural processing 12 , 13 . Specifically, altered encoding of syllable sounds at both cortical and subcortical levels has been observed in individuals with early cognitive decline, suggesting potential predictive value for such decline 12 . However, in our earlier work where natural speech was used instead of syllable sounds, we were unable to confirm these results 14 .

In our previous work, we used the temporal response functions (TRF) framework to focus on auditory speech encoding as reflected in both subcortical and cortical responses 15 . The TRF, a linearized stimulus-response model, delineates the relationship between speech input and neuronal response, as typically measured by electroencephalography (EEG). These models are particularly adept at quantifying the brain’s response to natural speech over extended listening periods and offer a broader temporal integration range than conventional evoked potentials. Natural speech, which has higher ecological validity than the syllable and click sounds typically used in conventional potentials, forms the basis of our study, increasing the applicability of the results in the real world. The ecological validity of natural speech is underscored by studies showing that cognitive factors, such as focal attention, significantly impact the cortical tracking of speech 16 , 17 . The concept of “neural tracking of speech” refers to the brain’s ability to follow the dynamic properties of the speech signal, which can be captured using TRF models. TRF models are critical for two reasons: they provide an encoding accuracy for the speech feature of interest and a time-delayed neural response function that enables neurophysiological interpretation 18 . Initially, our study focused on the encoding of an acoustic feature present in natural speech, specifically auditory nerve rates derived from speech wave rates—akin to a temporal envelope 14 . However, the flexibility of TRF models allows them to also fit a range of lexical (at the word-level) and sublexical (at the phoneme-level) speech features derived from the speech signal and its temporally aligned transcript, as has been done in several studies 19 , 20 , 21 , 22 . These models can be calculated using impulse vectors that code for, e.g., word or phoneme onsets, or scaled impulse vectors that reflect the surprisal value of a word or phoneme. Furthermore, a TRF model can simultaneously be modeled to multiple speech features, producing multivariate neural response functions—each corresponding to a different speech feature—known as a multivariate TRF (mTRF) 15 . The mTRF models extend beyond acoustic features to encompass linguistic features represented in natural speech. This framework has proven valuable in exploring differential acoustic and linguistic speech tracking in patients with post-stroke aphasia 22 and demonstrating that linguistic representations diminish with increasing age 23 . Inspired by the opportunities offered by the mTRF framework, we have delved deeper into the investigation of linguistic processing.

In this work, we drew on the dataset from our previous study 14 to investigate the neural encoding of linguistic features in natural speech in older adults, focusing on participants with and without putative cognitive decline. Participants were categorized into two groups based on their scores from the Montreal Cognitive Assessment (MoCA) 24 : the normal MoCA group, showing no early signs of cognitive decline, and the low MoCA group, where participants scored below 26 points, the clinical threshold for mild cognitive impairment (MCI). The distribution of MoCA scores and the corresponding group allocation are shown in Fig.  1 A.

Drawing inspiration from the framework established by Kries et al. 22 , we conducted our analysis using five distinct mTRF models. These models covered a spectrum of linguistic features, from basic segmentation of words and phonemes to more complex analyses such as surprisal, frequency, and entropy of (sub)lexical items. Specifically, our models were designed as follows: the acoustic model incorporated the speech envelope and envelope onsets; the word- and phoneme-level segmentation models included the word and phoneme onsets; the linguistic word-level model included word surprisal and word frequency; and the linguistic phoneme-level model included phoneme surprisal and phoneme entropy (see Fig.  2 ). Given the collinearity between features originating from the same speech signal 25 , we regressed the features not of interest from the EEG signal before fitting the mTRF models to isolate the specific feature of interest for each model. Our exploratory approach aimed to determine whether the neural encoding of these linguistic features in natural speech varied between the two participant groups and to understand how these differences might be influenced by hearing loss.

Our analysis began with an evaluation of the encoding accuracy across the mTRF models to assess whether overall neural speech tracking performance was influenced by cognitive decline. We further explored the signal of the time-delayed neural response functions for the two speech features embedded in each mTRF model. This exploration aimed to investigate how the brain processes these features differently in participants with and without early signs of cognitive decline. Additionally, we integrated an assessment of hearing ability by quantifying the four-frequency pure tone average (PTA) 26 to investigate potential interactions between auditory encoding, cognitive decline, and hearing ability. Individual hearing thresholds for each frequency, averaged by ear, are shown in Fig.  1 B, and the interaction between age and PTA is shown in Fig.  1 C. Considering the well-established link between cognitive decline and language processing 2 , 3 , 4 , 5 , we hypothesized that participants exhibiting early signs of cognitive decline would demonstrate altered neural encoding of linguistic features in natural speech, as evidenced by variations in encoding accuracy, compared to those without early signs of cognitive decline. We also anticipated that these differences would manifest in distinct response function signals and that hearing loss would modulate these effects, as seen in previous studies on neural speech processing (see, e.g., 27 , 28 , 29 ).

Montreal Cognitive Assessment (MoCA) scores, audiogram, and age as a function of four-frequency pure-tone average (PTA). ( A ) Distribution of MoCA scores. The dashed line indicates the cutoff score of 26. ( B ) Individual hearing thresholds for each frequency, averaged by ear and colored by MoCA group. PTA values calculated from the audiogram did not differ between groups. ( C ) Age as a function of PTA. The shaded area represents the 95% confidence interval. Age correlated with PTA across all participants. This plot is adapted from the original study 14 .

Overview over the multivariate temporal response function (mTRF) models with speech features. The features are demonstrated using an example sentence: “Botanik gefiel mir, weil ich gern Blätter zerschnitt.” ( I liked botany because I liked cutting up leaves ) from one of the audiobook segments used in the study. The acoustic model included the envelope and envelope onsets derived from the speech wave (teal colored). The word- and phoneme-level segmentation model included the word and phoneme onsets (black), the linguistic word-level model included the word surprisal and word frequency (black), and the linguistic phoneme-level model included the phoneme surprisal and phoneme entropy (gray), all derived from word- and phoneme-level time-aligned transcriptions. The graphical representation was inspired by the one created by Kries et al. 22 .

Effect of MoCA group and PTA on encoding accuracy

To investigate how cognitive decline and hearing ability affect the encoding accuracy of the five different mTRF models—the acoustic, segmentation at the word- and phoneme-level, and linguistic at the word- and phoneme-level models, illustrated in Fig.  2 —we implemented a linear mixed model (LMM) using orthogonal sum contrast coding for the categorical predictors. The results are detailed in Table  1 .

This statistical model accounted for individual differences and the nesting of the five encoding accuracies within participants, yielding the following insights: First, no significant main effect was observed for the MoCA group, indicating that encoding accuracies were comparable between participants with and without early signs of cognitive decline. Second, no significant main effect was detected for PTA, suggesting that hearing ability did not significantly influence encoding accuracy. Third, a significant main effect was found for the mTRF models: the acoustic model, serving as the reference level, exhibited higher encoding accuracy on average compared to the other models, Fig.  3 A. Post-hoc tests confirmed that the encoding accuracies of all other models were significantly lower than that of the acoustic model ( \(p < 0.001\) for all comparisons, see Table S3 ). The interaction between MoCA group and PTA or between MoCA group and the mTRF models were not significant. A significant interaction was observed between PTA and the segmentation word-level model, whereas no other interactions between PTA and the mTRF models were significant, indicating that hearing impairment affects the encoding accuracy for this specific model. Additionally, no significant three-way interactions were found between MoCA group, PTA, and the mTRF models, indicating that the combined effect of cognitive decline and hearing impairment did not differentially affect the encoding accuracy across the different mTRF models.

To further explore the significant interaction between PTA and the segmentation word-level model, we conducted post-hoc tests. Specifically, we examined the estimated marginal means (EMMs) of the interaction at \(-1\) , 0, and \(+1\) standard deviations of PTA, revealing the following: At PTA \(z = -1\) , the estimated difference in encoding accuracy between the acoustic and segmentation word model was 0.019 ( \(SE = 0.002\) , \(t(160) = 9.5\) , \(p < 0.001\) ). At PTA \(z = 0\) , the difference was 0.024 ( \(SE = 0.001\) , \(t(160) = 16.5\) , \(p < 0.001\) ). At PTA \(z = 1\) , the difference was 0.028 ( \(SE = 0.002\) , \(t(160) = 13.8\) , \(p < 0.001\) ). The EMMs for each mTRF model are visualized in Fig.  3 B. These results indicate that, compared to the acoustic model, hearing impairment significantly affects encoding accuracy in the segmentation word-level model, with greater differences observed as hearing ability decreases.

Overall, our analysis suggests that while there is no overarching difference in encoding accuracy between participants with and without cognitive decline, hearing loss impacts encoding accuracy in the segmentation word-level model. Please note that the supplementary analysis treating MoCA as a continuous variable yielded similar conclusions (see Supplementary Analysis 1 and Table S1 ).

Encoding accuracy by multivariate temporal response function (mTRF) model and estimated marginal means (EMMs) of the interactions between four-frequency pure-tone average (PTA) and the mTRF models. ( A ) Encoding accuracies (Pearson’s r , averaged across all 32 electrodes) for each mTRF model. Violin plots show the distribution of the individual data points colored by Montreal Cognitive Assessment (MoCA) group, with the dashed line indicating the median and the dotted lines indicating the interquartile range. All mTRF models exhibited significantly lower encoding accuracies compared to the acoustic model. ( B ) EMMs of the interaction between PTA and the mTRF models. The acoustic model served as the reference level. The interaction was significant for the segmentation word-level model, with a greater difference in encoding accuracy observed as hearing ability decreased. The error bars represent the standard error of the mean. Significance levels are indicated as: \(p < 0.001\) (***). Seg., segmentation; Lin., linguistic.

mTRF-model based effects of MoCA group and PTA on response function signal power

The response functions to each speech feature modeled in the mTRF models are depicted in Fig.  4 . We evaluated the effects of cognitive decline on the signal power of these response functions by comparing the root mean square (RMS) values between participants with and without early signs of cognitive decline, using a LMM to account for individual differences and the nesting of RMS values within participants. Specifically, we ran the LMM separately for each mTRF model, with the RMS of the response functions to the different speech features nested within three electrode clusters (F, frontal; C, central; P, parietal) and participants. The results are summarized in Table  2 .

Overall, the results largely paralleled those of the encoding accuracy analysis. First, no significant main effect was found for the MoCA group across any of the mTRF models, indicating that the signal power of the response functions did not differ between participants with and without cognitive decline. Second, PTA did not significantly affect the signal power for any of the mTRF models. Additionally, no significant interactions were found between MoCA group and PTA across any of the mTRF models. Third, the cluster variable was significant in all five LMMs, as reflected in the topographical maps of the response functions in Fig.  4 . Parietal clusters exhibited lower RMS values compared to frontal clusters for all mTRF models, indicating that the signal power was more prominent in the frontal cluster. For the word- and phoneme-based models, central clusters showed significantly higher RMS values than frontal clusters, suggesting that in these higher-order linguistic models, the signal power was more distinct in central clusters. Finally, the speech feature variable also significantly influenced signal power, depending on the mTRF model. For the acoustic model, RMS for the envelope onsets was comparable to the envelope. In the segmentation model, signal power for phoneme onsets was significantly higher than for word onsets. In the word-based model, signal power for word frequency was significantly lower than for word surprisal. In the phoneme-based model, signal power for phoneme entropy was significantly lower than for phoneme onsets.

Taken together, these results suggest that the signal power of response functions to natural speech is not influenced by early signs of cognitive decline or hearing ability. However, the analysis indicates that the signal power of response functions to higher-order linguistic features is significantly influenced by electrode clusters and speech features. Also here it is noteworthy that the supplementary analysis treating MoCA as a continuous variable yielded similar conclusions (see Supplementary Analysis 2 and Table S2 ).

Mean weights of response functions for each speech feature in the multivariate temporal response function (mTRF) models by Montreal Cognitive Assessment (MoCA) group, with the low MoCA group indicating early signs of cognitive decline, and topographical maps. Response functions shown in the plot were globally z -transformed for visualization purposes. Topographical maps were derived from the largest average peak in the response functions, displaying the mean activity across all participants in a 50 ms window centered around the peak latency, as indicated by the vertical dashed line in the response functions. Displayed response functions are averaged from three key electrodes, marked in yellow on the topographical maps. The response functions showed distinct peaks that differed in amplitude and latency between the speech features. However, no significant differences in peak amplitudes or latencies were observed between the groups, with the exception of response peak latency in phoneme onsets in the parietal cluster during the early time window, as indicated by the shaded gray area. Significance levels are indicated as: \(p < 0.01\) (**).

Group comparisons of peak amplitudes and latencies

Our analysis of response functions to different speech features revealed distinct peaks that vary in amplitude and latency across responses, as illustrated in Fig.  4 . We performed a detailed analysis of these peaks to investigate possible differences in peak amplitudes and latencies between participants with and without cognitive decline, in addition to the RMS analysis, which focuses primarily on signal power and neglects variations in peaks and their latencies. Our focus was on responses where at least \(75\%\) of participants exhibited a peak (see summary in Table S4 ). The results of these comparisons, detailed in Table  3 , predominantly showed no significant differences in peak amplitudes or latencies between the two groups during an earlier time window. However, an exception was noted for phoneme onsets in this time window. Specifically, participants with early signs of cognitive decline exhibited earlier peak latencies compared to those without cognitive decline in the parietal cluster (two-tailed Mann–Whitney U test, \(U = 218.5\) , Holm-Bonferroni corrected \(p = 0.008\) , \(r = -0.41\) ). The descriptive statistics for all peak latencies are summarized in Table S5 . On average, the peak latency for phoneme onsets was \(43 \pm 40\,\hbox {ms}\) for the normal MoCA group and \(17 \pm 12\,\hbox {ms}\) for the low MoCA group.

In the later time window, our analysis also revealed no significant differences in peak amplitudes or latencies across the different clusters for both groups. Overall, these findings suggest that response functions peaks of neural encoding of natural speech do not significantly differ between participants with and without early signs of cognitive decline, except for phoneme onsets in the early time window.

The primary aim of our study was to explore how older adults, both with and without early signs of cognitive decline, encode acoustic, segmentation, and linguistic cues in natural continuous speech, while also considering the impact of hearing ability. We used the mTRF framework to analyze neural responses to various acoustic, lexical, and sublexical features in an audiobook’s speech stream. This analysis serves as a proxy for understanding how the brain processes these features, using EEG data from our previous study 14 . Our analysis focused on encoding accuracy, signal power, and response peak and latencies across five models: acoustic, segmentation at the word- and phoneme-level, and linguistic at the word- and phoneme-level.

Contrary to our hypothesis, our findings revealed no significant impact of cognitive decline on neural encoding of speech as measured by our metrics, despite existing literature suggesting a link between cognitive decline and deteriorating language performance 2 , 3 , 4 , 5 . Similarly, we found no significant main effect of hearing ability on neural encoding of speech features, though hearing loss—a common comorbidity in cognitively declining older adults—typically alters speech processing 8 , 9 , 27 , 28 , 30 . This is surprising, especially considering the common pool model of cognitive processing resources 31 , which suggests that cognitive decline and hearing loss may compete for limited cognitive and perceptual resources, thereby reducing neural encoding of speech features in affected individuals. This theory suggests that if participants with advanced cognitive decline or hearing loss were included, differences in neural encoding might be more pronounced, particularly in tasks requiring more cognitive resources, such as speech-in-noise perception or in individuals with diagnosed MCI or AD. For a related discussion on natural speech tracking with and without visual enhancement, which treats resources differently, see Frei et al. 32 . In our data, we did not observe such an association between cognitive decline and neural encoding, suggesting that early cognitive decline did not significantly affect the neural processing of speech in our participants.

Cognitive factors are known to influence neural tracking of speech. Studies have shown that neural speech tracking, particularly the phase-locking of the neural response to the amplitude envelope, is crucial for successful speech comprehension 33 , 34 , 35 . Studies also demonstrated that neural speech tracking underlies successful speech comprehension 29 , with a positive relationship observed between neural tracking and speech comprehension in older adults with both normal hearing and hearing impairment 27 , 36 . Furthermore, research has indicated that the older brain recruits additional higher-level auditory regions during the early stages of speech processing to maintain speech comprehension 19 , 37 . Thus, both cognitive decline and hearing loss are known to affect neural encoding of speech, with potential implications for speech comprehension. The lack of a significant effect of cognitive decline on neural encoding in our study may be due to the relatively early stage of cognitive decline in our participants, as indicated by the MoCA scores, see also the limitations discussed below.

What we did observe was a significant interaction between hearing ability and the segmentation word-level model, indicating that increasing hearing impairment led to a decrease in the brain’s ability to track word segmentation in natural speech. This is noteworthy since recognizing words in continuous speech is a complex task requiring substantial cognitive resources, which can become even more challenging in the presence of hearing loss 38 , 39 . It is intriguing that we did not see these interactions in the higher-order linguistic models, nor did we observe a significant main effect of PTA on neural encoding accuracy, which is commonly reported in other studies 27 , 28 , 30 . We also found that while investigating the word-level segmentation mTRF model at the response signal level, hearing ability did not affect the response signal power, contrasting with the encoding accuracy results.

Another noteworthy result was the significantly earlier peak latency for phoneme onsets in participants with early signs of cognitive decline compared to those without, observed in a parietal cluster during the early time window. Given that phoneme processing is more demanding than word processing 40 , it is possible that the earlier latencies reflect a compensatory mechanism or a heightened sensitivity to processing demands in individuals with cognitive decline. The emergence of this result in a parietal cluster is also consistent with the literature, as the parietal cortex is known to be involved in phonological processing 41 . Additionally, a previous study demonstrated a decrease in encoding accuracy in linguistic neural tracking with age, particularly in a comparable parietal region 21 .  This could suggest that cognitive decline impacts the timing of more demanding phonological processes, leading to these earlier neural responses.

Overall, our study suggests that while there is no overarching difference in speech encoding between participants with and without cognitive decline, hearing loss specifically impacts word segmentation in speech processing.

Implications for the study of cognitive decline and hearing loss

Drawing on our previous research 14 , we anticipated no significant differences in the acoustic encoding of speech in this dataset. However, we did expect to observe differences in the encoding of linguistic features, particularly in participants with early signs of cognitive decline, which we did not find. Our interest was in exploring potential variations in neural encoding of linguistic features, given the established link between language performance and cognitive functions, which may alter under cognitive decline. Previous results where we applied machine learning methods to voice parameters extracted from a semi-spontaneous speech task recorded with the same participants, we were able to classify the low MoCA group with a cross-validated accuracy of \(71\%\) 42 . In our current study, in contrast, we found no significant differences in neural encoding of linguistic features between participants with and without early signs of cognitive decline. The neural responses appeared relatively homogeneous across participants, regardless of their cognitive status, suggesting that neural encoding of speech in natural continuous settings may not effectively indicate early cognitive decline in older adults. Regarding hearing ability, our findings diverged from existing studies, where hearing loss often correlated with enhanced cortical speech tracking measures in older adults 27 , 28 , 30 . The relatively good hearing ability in our sample, with only seven individuals exhibiting a PTA exceeding \(34\,\hbox {dB HL}\) , which can be considered as moderate hearing loss 43 , might explain the lack of a significant association between hearing ability and neural encoding measures. We hypothesize that a sample with a higher prevalence of hearing loss might have yielded different results. The lack of a significant main effect of cognitive decline on neural encoding suggests that early cognitive decline might not uniformly disrupt neural processing of speech at the acoustic and linguistic levels. In addition, the specific impact of hearing loss on word-level segmentation underscores the importance of auditory cues in successful word recognition and highlights the additional cognitive load imposed by hearing impairment 39 .

Dual role of encoding accuracies in mTRF modeling

Our study found differences in encoding accuracies across the five mTRF models: acoustic, segmentation at the word- and phoneme-level, and linguistic at the word- and phoneme-level. The acoustic model consistently showed higher encoding accuracy compared to the other models. In contrast, the segmentation models, particularly at the phoneme-level, exhibited lower encoding accuracies. There is a high correlation between speech features like the ones we used 25 , which we accounted for as detailed in the Methods section. Specifically, the envelope of the speech signal, which is the basis for the acoustic model, contains information about the speech rate, rhythm, and prosody 44 , while the segmentation and linguistic models are more isolated to boundaries and feature-engineered linguistic cues. Furthermore, the speech envelope is a continuous signal, while the segmentation and linguistic models are based on discrete features and are directly derived from the speech wave heard by participants. This distinction likely accounts for the differences in encoding accuracies, as the acoustic signal contains more direct, continuous auditory information compared to the derived linguistic features.

Initially, encoding accuracy in mTRF models was introduced as a measure to assess the quality of the model, involving statistical testing 18 . Specifically, one approach is to establish a null distribution using a permutation test procedure to define a null distribution against which the accuracy score is tested 18 . Similarly, some researchers establish a noise floor by phase scrambling the target regressors and then comparing the encoding accuracies from the original data to the noise floor 28 , 45 . However, other researchers have started using encoding accuracy as a measure of neural tracking, reflecting how well the brain follows specific features of the speech signal 22 , 25 , using it as a proxy for how well the speech representation is reflected in the EEG signal. The review by Gillis et al. 25 discusses the use of encoding models as a diagnostic tool to assess the auditory pathway, highlighting this dual role. This dual role of encoding accuracy is a crucial methodological consideration when using the mTRF framework to study neural speech processing. In our data, we found that encoding accuracy is highly correlated with the signal power of the response signal (see Figure S2 for the correlation between encoding accuracy and signal power). This observation aligns with the notion that both the quality of the model and the neural tracking of speech can be inferred from the encoding accuracy.

The methods used to establish encoding accuracies also vary. Some scholars use a nested cross-validation approach to estimate encoding accuracies, as we did in the current manuscript, where the test set is also rotated (e.g., 28 , 46 ), while others use a leave-one-trial-out cross-validation approach (e.g., 47 ), as recommended in the sample procedure outlined by Crosse et al. 18 and which we did in our previous study 14 . Depending on this procedure, the encoding accuracies can vary, which is an important consideration when comparing results across studies. It is noteworthy that there are no established benchmark values for expected encoding accuracies when comparing different acoustic, linguistic, and segmentation models.

Given the strengths of the mTRF framework, which provides both a signal response that allows for neurophysiological interpretation 15 and a general encoding accuracy 25 , it is important to have clear research recommendations. Future studies should aim to delineate when to interpret response signals in terms of their power and shape, including time lags, and when it is more appropriate to focus on encoding accuracies. Establishing guidelines and normative values for these interpretations would enhance the reliability and consistency of findings in neural speech processing research.

Limitations of the study and future directions

Neuropsychological assessment : A major limitation of our study was that we relied on the MoCA as a proxy for detecting putative MCI, i.e., early signs of cognitive decline. We chose this approach for two main reasons: First, ideally, we would have conducted this study with patients diagnosed with MCI or AD, but it proved difficult to recruit participants with a clear neurocognitive profile—a common issue in early cognitive decline studies. Therefore, we used the MoCA, a screening instrument for MCI, to provide a rough classification of our participants. Second, we preferred using the MoCA over a neurocognitive test battery, as the MoCA was specifically developed for screening evaluations. Although the MoCA is suitable for initial screening, it may not be sensitive enough to detect subtle cognitive changes in our participants. Furthermore, the stage of cognitive decline detected with the MoCA may be too early to significantly affect neural speech encoding. Therefore, in replicating the study, we would rely on more comprehensive neuropsychological assessments that allow for a more accurate classification of cognitive status and a deeper understanding of the relationship between cognitive decline and neural speech encoding. In addition to the MoCA, it would have been beneficial to administer a more comprehensive, age-appropriate IQ test. An example for our case would be the LPS \(50+\) , which assesses cognitive status and intellectual profiles in individuals aged 50–90 years and aids in diagnosing brain function disorders (e.g., early detection of degenerative diseases) 48 . This would allow us to relate the MoCA cut-off scores to the IQ test results, providing a more comprehensive picture of the cognitive status of our participants. We suggest that future studies incorporate such assessments to more accurately describe these relationships.

Stimulus complexity and cognitive demand : The complexity of the auditory stimulus in our study, involving the presentation of an audiobook in silence, might not have been sufficiently challenging to reveal differences in neural encoding between participants with and without early signs of cognitive decline. Prior research from our lab has shown that neural tracking of speech in older adults is significantly affected by cognitive load, particularly in speech-in-noise conditions 29 . Thus, embedding natural speech stimuli in more demanding listening conditions could provide a more robust assessment of the impacts of cognitive decline on speech processing, representing a promising direction for future research.

Methodological considerations in neural encoding : The methodology we used to investigate neural speech processing, particularly the use of the mTRF framework, faced limitations worth noting. The interdependence of the speech features used in our study posed a challenge in accurately isolating the effects of individual features on neural encoding. We chose to address this by regressing out the shared variance between features, which is one of several approaches, see review by Gillis et al. 25 . While this method helps mitigate some confounding effects, it may still have influenced the outcomes of our models. Furthermore, the linguistic models used—specifically, the word- and phoneme-based models—were constrained by the available features. Unlike previous studies that used 5-gram language models to estimate word surprisal, such as, e.g., Kries et al. 22 or Gillis et al. 49 , we adopted a different approach due to our resource constraints. We used German BERT 59 , a pretrained large language model, to estimate word surprisal, potentially yielding different estimates compared to those derived from 5-gram language models. For phoneme surprisal, we designed a custom phonetic lexicon based on the Montreal Forced Aligner’s (MFA) pronunciation dictionary, and used the DeReKoGram frequency dataset 50 , which is an important source for behavioral or neurophysiological studies that require a large-scale corpus, for word frequencies. Additionally, see Weissbart et al. 20 for a customized approach to n -gram models for estimating word surprisal. While our method allowed for bespoke estimations, it may have introduced inaccuracies in word- and phoneme-based speech feature calculations, potentially affecting the results of our mTRF models. Future research should strive for a unified methodological approach in constructing auditory encoding models. This would facilitate direct comparisons between studies and ensure more consistent estimations of linguistic surprisals, enhancing the reliability and interpretability of findings in cognitive neuroscience.

Conclusion and outlook

Our study revealed a significant interaction between hearing ability and the segmentation word-level model. Participants with reduced hearing ability showed lower encoding accuracy for the word segmentation model, suggesting that hearing impairment specifically affects the neural encoding of word boundaries in natural speech. This finding underscores the importance of auditory cues in successful word recognition and highlights the additional cognitive load imposed by hearing impairment. While we did not observe significant differences in the neural encoding of linguistic features between older adults with and without early signs of cognitive decline, our findings emphasize the need to consider the interplay between cognitive and auditory functions, particularly in the context of hearing loss. Future studies should incorporate more comprehensive neuropsychological assessments and introduce more challenging listening conditions to better understand these relationships. These enhancements would improve our understanding of the relationship between cognitive decline, hearing impairment, and neural encoding of speech in older adults. Additionally, recruiting individuals with more severe cognitive decline in future research could provide insights into the effects at different stages of cognitive impairment. Our work contributes to the expanding literature on the intersection of cognitive decline and hearing loss, highlighting the complex interactions between auditory and cognitive functions in older adulthood.

Participants and cognitive grouping

This reanalysis included 44 native Swiss-German speakers from the first study 14 . Participants were monolingual up to the age of seven, right-handed and retired. Exclusion criteria included a professional music career, dyslexia, significant neurological disorders, severe or asymmetrical hearing loss and the use of hearing aids. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Canton of Zurich (BASEC No. 2019-01400), with all research methods conducted in adherence to the relevant guidelines and regulations. The sessions were conducted at the Linguistic Research Infrastructure (LiRI, liri.uzh.ch ). Written informed consent was obtained from all participants and they received compensation for their participation.

Participants were divided into groups based on the MoCA 12 , 24 . MoCA scores range from 0 to 30, with a cutoff of 26 for normal cognitive function. Participants who scored 26 or more were assigned to the normal MoCA group, participants below 26 to the low MoCA group. The normal MoCA group included 25 participants (14 women) with an average age of \(68.6 \pm 5.3\) years (range: 60–83 years). The low MoCA group included 19 participants (12 women) with a mean age of \(71.7 \pm 6.3\) (range: 60–82 years). The age differences between the groups were not statistically significant (two-tailed Mann–Whitney U test: \(U =\) 311.5, \(p = .081\) , \(r = 0.31\) ). The distribution of MoCA scores is shown in Fig.  1 A.

We measured the hearing thresholds for pure tones at frequencies of 125, 250, 500, 1000, 2000, 4000, and \(8000\,\hbox {Hz}\) in both ears, using the Affinity Compact audiometer (Interacoustics, Middelfart, Denmark), equipped with a DD450 circumaural headset (Radioear, New Eagle, PA, USA). Individual frequency thresholds are shown in Fig.  1 B. Overall hearing ability was determined using the four-frequency PTA, the average of the thresholds at 500, 1000, 2000 and \(4000\,\hbox {Hz}\) 26 . The average interaural difference was \(4.5 \pm 4.4\,\hbox {dB HL}\) (range: 0– \(17.5\,\hbox {dB HL}\) ), indicating symmetrical hearing ability. The PTA, averaged over both ears, was \(19.6 \pm 11.8\,\hbox {dB HL}\) for the normal MoCA group and \(20.4 \pm 11.5\,\hbox {dB HL}\) for the low MoCA group. Most participants showed no to mild hearing impairment, while seven showed moderate impairment ( \(\hbox {PTA} > 34\,\hbox {dB HL}\) ): two in the low MoCA group and five in the normal MoCA group. Furthermore, PTA was correlated with age ( \(\rho = 0.5\) , \(p = .0003\) , Fisher’s \(z = 0.57\) , \(n = 44\) ), see Fig.  1 C. No significant group differences in hearing ability were found (two-tailed Mann–Whitney U test: U = 262.5, p = .561, r = 0.11). However, as hearing ability is relevant and has an influence on auditory encoding 21 , 27 , 28 , we included PTA as a control variable in the analyses.

EEG recording

We recorded the EEG using the Biosemi ActiveTwo system (Biosemi, Amsterdam, The Netherlands) with 32 electrodes (10–20 system), four external electrodes and a sampling rate of \(16.384\,\hbox {kHz}\) . We positioned two external electrodes above and below the right eye to measure the electrooculogram (EOG), and two on the left and right mastoids. Participants sat in a soundproof, electromagnetically shielded booth during the recording. During the listening tasks, we instructed participants to focus on a fixation cross displayed on a screen and to minimize movement, especially when the cross was visible. Throughout the experiment, we monitored and maintained electrode offsets below \(20\,\upmu \hbox {V}\) .

Participants listened to 25 segments from the German audiobook version of Sylvia Plath’s novel, “The Bell Jar,” read by a professional female speaker 36 . Segments lasted on average \(45.7 \pm 2.7\,\hbox {s}\) , totalling a listening time of approximately \(20\,\hbox {min}\) . Silent gaps were limited to \(500\,\hbox {ms}\) and the average speech wave intensity was scaled to \(70\,\hbox {dB}\) SPL. Each segment commenced with a short silence of approximately \(467\,\hbox {ms}\) , which we retained to introduce stimulus onset jitter. We calibrated the sound level such that segments were consistently played at \(70\,\hbox {dB}\) peSPL. Audiobook segments were presented bilaterally through electromagnetically shielded insert ER3 earphones (Etymotic Research, Elk Grove Village, IL, USA)

Signal processing

We performed all data processing in Python 3.11.6 and used the MNE-Python package 51 for all signal preprocessing steps. Unless otherwise specified, all filters applied to the EEG and speech wave signals were non-causal Infinite Impulse Response (IIR) Butterworth filters with an effective order twice the specified filter order, which was always set to 3. Anti-alias filters were consistently applied at \(\frac{1}{3}\) of the target rate.

First, we removed bad electrodes—on average \(1.5 \pm 1.6\) electrodes per participant—and then referenced the EEG signals to the mean of the two mastoid channels. For six participants with at least one noisy mastoid channels, we used cap electrodes T7 and T8 as reference. We then segmented the continuous EEG from \(-4\) to \(54\,\hbox {s}\) relative to audiobook onset and downsampled the epochs to \(512\,\hbox {Hz}\) after applying an anti-alias filter at \(170.7\,\hbox {Hz}\) . We used Independent Component Analysis (ICA) to remove artifacts from the EEG signals. We created a copy of the epoch instance for ICA and filtered the epoch copy with a high-pass filter at \(1\,\hbox {Hz}\) (zero-phase, non-causal Hamming window Finite Impulse Response (FIR) filter, transition bandwidth: \(1\,\hbox {Hz}\) , filter length: 1691 samples), a process reported to facilitate ICA decomposition 52 . We performed ICA using the Picard algorithm 53 with 1000 iterations, aiming to obtain \(99.9\%\) of the variance of the signal. Furthermore, we improved the ICA performance by using five iterations within the FastICA algorithm 54 . After ICA fitting, the components associated with eye-related artifacts were automatically labeled using the external EOG electrodes as references, and the components associated with muscle activity or singular artifacts were manually labeled based on topography, temporal occurrence, and frequency spectrum. On average, we excluded \(2.5 \pm 1.1\) components per participant. We zeroed out the components in the original epoch instance and then performed electrode interpolation for the epochs. The EEG was then downsampled to \(128\,\hbox {Hz}\) after applying an anti-alias filter at \(42.7\,\hbox {Hz}\) . Finally, we band-pass filtered the EEG between 0.5 and \(25\,\hbox {Hz}\) . To facilitate matrix storage, the epochs were cut to 0 to \(45\,\hbox {s}\) relative to audiobook onset.

Speech wave

The speech waves from each segment were processed to extract acoustic features. We first downsampled the speech waves to \(15\,\hbox {kHz}\) using an anti-alias filter at \(5000\,\hbox {Hz}\) . Speech waves were then passed through a Gammatone filterbank 55 with 28 channels with center frequencies from 50 to \(5000\,\hbox {Hz}\) spaced equally on the equivalent rectangular bandwidth (ERB) scale. Each Gammatone frequency channel output was half-wave rectified and raised to the power of 0.3, before we avaraged the filter outputs across channels to obtain a univariate temporal envelope. In line with the EEG preprocessing, we downsampled the envelopes to \(128\,\hbox {Hz}\) after applying an anti-alias filter at \(42.7\,\hbox {Hz}\) , and eventually band-pass filtered them between 0.5 and \(25\,\hbox {Hz}\) . We then truncated the envelopes to a uniform length of \(45\,\hbox {s}\) or padded them with zeros (depending on the segment length).

Acoustic and linguistic speech features extraction

Our goal was to model neural responses not only to acoustic features of the speech signal, but also to a range of lexical (word-based) and sublexical (phoneme-based) representations in the signal. To this end, we generated a range of time-aligned linguistic speech representations as impulse features from the audiobook transcription. Drawing inspiration from Kries et al. 22 , we constructed five models, each with distinct speech features, to quantify the tracking of different levels of linguistic information in the speech signal. These models included (1) an acoustic model, (2) a segmentation model at the word-level, (3) a segmentation model at the phoneme-level, (4) a linguistic model at the word-level, and (5) a linguistic model at the phoneme-level. We generated the speech features for each segmentation and linguistic mTRF model as vectorized time series of zeros, with a sampling rate of \(128\,\hbox {Hz}\) and a length of \(45\,\hbox {s}\) , corresponding to the length and rate of the EEG epochs. Features were modeled as impulses at word and phoneme onsets, respectively. An example of the speech features is shown in Fig.  2 .

Acoustic features

The speech feature pair for the acoustic model were the envelope and the envelope onsets , both of which were extracted from the speech wave. The extraction of the envelope is described in the previous section. Since the brain is sensitive to contrast and changes and information is often encoded in acoustic onsets in particular 56 , the model also included the envelope onsets. We constructed the envelope onsets as the half-wave rectified derivative of the envelope.

Segmentation features at word- and phoneme-level

Speech features of segmentation consisted of word onsets and phoneme onsets . For extracting the onsets, we determined their boundaries using the MFA (version 2.2.14) 57 . We created a transcript in Praat and then used the pre-trained German MFA acoustic model and the German MFA pronunciation dictionary 58 to first, create a phonetic transcription of the audio file and second, to determine the timing of word and phoneme boundaries for each segment. The accuracy of word and phoneme boundaries, along with their time-aligned transcriptions, was manually verified and corrected as necessary. Word and phoneme onsets timepoints were then extracted from the MFA output and modeled as impulses of the value one in the vectorized time series of zeros.

Linguistic word-level features

The linguistic word-level model included word surprisal and word frequency as speech features. Word surprisal is an approximation of how unexpected a word is in a given context. We used the pre-trained German BERT model 59 for this calculation. BERT, short for Bidirectional Encoder Representations from Transformers, is a state-of-the-art language model designed for contextual language analysis. We adapted BERT to simulate unidirectional (left-to-right) context processing, reflecting natural listening comprehension. For each word in the audiobook segments, we created a sequence with the target word masked. The model then predicted the probability of the masked word based on its preceding context. We calculated word surprisal as the negative logarithm of the probability predicted by BERT for each segment.

Word frequency describes the frequency of a word out of context. We used DeReKoGram, a novel frequency dataset for 1-, 2- and 3-grams from the German Reference Corpus 50 , using the unigram frequencies to calculate the frequency of each word 20 . Relative unigram frequencies can be viewed as an estimate of the unconditional probability of occurrence of a word. First, we determined the relative frequency of each word in the audiobook segment-based transcript, and then we used the negative logarithm of this value as the word frequency. This procedure results in words with a high frequency producing a low value and vice versa 22 , 49 .

Linguistic phoneme-level features

The speech features in the linguistic phoneme-level model were phoneme surprisal and phoneme entropy . Phoneme surprisal reflects how surprising a phoneme is given the preceding phonemes. We calculated phoneme surprisal as the negative logarithm of the phoneme probability given an activated cohort of phonemes, in line with prior work 22 , 49 , 56 . We generated a phonetic lexicon with lexical statistics by combining pronunciations from the MFA pronunciation dictionary and word frequency derived as absolute unigram frequencies from the DeReKoGram dataset, in which missing pronunciations were manually added, and words occurring in the stimuli but missing from DeReKoGram were assigned a frequency of one 56 . We then used the lexicon to calculate the probability of each phoneme given the preceding phonemes in the activated cohort and thus derived surprisal values for each phoneme in the audiobook segments.

Phoneme entropy is an indicator for the degree of competition between words congruent with the current phonemic input 49 , 56 . At the beginning of a word utterance, a large number of potential words form the activated cohort, leading to a high level of competition. This competition decreases as the utterance progresses and the cohort becomes smaller. We calculated the phoneme entropy using the Shannon entropy formula applied to the words within the activated cohort. Specifically, the initial phoneme of each word included all words in the active cohort. We used the same phonetic lexicon as for phoneme surprisal to calculate the phoneme entropy.

Regressing out speech features not of interest

Following the methodology of Kries et al. 22 , we addressed the problem of collinearity between features before fitting the mTRF models. Given the interdependence of features—e.g., envelope onsets include information about word onsets and word onsets reflect phoneme onsets (and vice versa)—it was crucial to isolate the specific feature of interest for each model 25 . Before fitting the mTRF models, we regressed out features not of interest for the current model from the EEG signal. The reason for this is the collinearity between the features, for example, the envelope onsets contain information about word onsets, and word onsets contain information about phoneme onsets, but also vice versa, sublexical features can reveal information about lexical features 25 . We therefore regressed out the features of no interest from the EEG signal and then used the EEG residuals for the mTRF models. We did this using a linear regression model with the EEG signal as the dependent variable and the non-interesting features as independent variables. We then used the residuals, which were the difference between the EEG signal and the predicted EEG by the linear model, as target for the mTRF models. For the acoustic models, we regressed out the features for word- and phoneme-level segmentation and linguistic models. Regarding the segmentation models at both the word and phoneme levels, we regressed out the features for the acoustic model and the word- and phoneme-level linguistic models, but not for each other 22 . When addressing the word-level linguistic models, we regressed out the regressors for the acoustic, segmentation, and phoneme-level linguistic models. Similarly, in the phoneme-level linguistic models, we regressed out the regressors for the acoustic, segmentation, and word-level linguistic models.

mTRF modeling

We quantified the cortical encoding of the speech features using mTRF models computed with the Eelbrain toolbox 60 . Eelbrain applies the Boosting algorithm to fit the mTRF models while mitigating the overfitting that is present in correlated features as in our data 61 , 62 . Boosting is a coordinate descent algorithm that iteratively updates a sparse multi-temporal resolution filter (the mTRF model) by changing a single filter weight at a time based on training data, as opposed to the uniform filter weight adjustment of ridge regression, which is also a conventional method in TRF modeling 15 . After each update of the weights, the model is evaluated against validation data and training stops when error reduction ceases, preventing overfitting and ensuring that irrelevant filter weights remain at zero, thereby increasing the parsimony of the model. A detailed explanation of the Boosting algorithm in the Eelbrain toolbox can be found in Brodbeck et al. 60 .

For each participant, we fitted five mTRF models. To prevent overfitting the models to the onset effects of the speech wave, we truncated the first second of each speech feature and EEG time series before fitting the mTRF models 18 . Upon fitting the models using the Boosting algorithm, we adjusted the basis function in Eelbrain to \(1 \times 10^{-3}\) and normalized the feature-target pairs by z -transformation, setting the scale_data parameter to True . The models were fitted for time lags ranging from \(-200\) to 600 ms. We used a six-fold cross-validation approach in which the feature-target pairs based on 25 audiobook segments were systematically rotated through the training, validation, and testing phases. Each segment was used four times for training, once for validation, and once for testing across all folds. The models were calibrated using the training segments, optimized using the validation segments, and evaluated using the test segments, with encoding accuracy assessed using the Pearson correlation coefficient between the observed EEG residuals y and the predicted EEG residuals \(\hat{y}\) . The mean correlation coefficient across all validation folds was calculated for each electrode, resulting in a single metric for encoding accuracy per electrode. Consequently, each mTRF model yielded one correlation coefficient per electrode and two response functions (for acoustic and linguistic models) or one response function (for segmentation models) per speech feature. Note that only the acoustic and linguistic models are actual mTRF models, while the segmentation models are not multivariate, thus TRF models only, but for the sake of consistency, we refer to all models as mTRF models. We averaged the encoding accuracies across all electrodes to obtain a single, comprehensive value of encoding accuray, and stored it together with the response functions for further analysis.

Spatial and temporal clustering of response functions

Electrode clusters.

We selected nine a priori midline electrodes for further analysis: F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4, which were categorized into three cluster regions: frontal (F), central (C), and parietal (P). This approach allows to include the activity of individual electrodes in the analysis and at the same time to perform hierarchical modeling at the cluster level. The electrodes were selected based on their proximity to the auditory cortex and their relevance to neural speech processing, while covering a broad area of the scalp without making prior assumptions about the exact location of the neural generators of the response functions.

Temporal clusters

Previous studies have shown that peaks in the response functions occur in different time windows depending on the speech feature of interest 21 , 22 . To determine the time windows, we took a data-driven approach as follows: First, for each participant and each of the nine a priori selected electrodes, we identified the two largest peaks in each response function. We extracted peaks within time lags of \(-50\) to 600 ms, omitting most of the negative lags but accounting for potential peaks occurring close to time lag zero. We used the find_peaks function from SciPy to identify the peaks, setting the prominence parameter to 0.5, meaning that the peak must be at least 0.5 times higher than the surrounding data points. Finally, we excluded peaks with latencies below 0 ms, since neurophysiological responses are not expected to occur before the stimulus. Second, using the latencies of the two largest peaks across all electrodes, we performed K-Means clustering to group the time lags into two clusters. Specifically, we used the function KMeans from SciKit-Learn 63 with 100 initializations and two clusters. We then calculated the mean of the two cluster centers to determine the boundary between the two peaks. This process was repeated for each speech feature for later peak amplitude and latency analysis. Eventually, the boundaries were used to define an “early” and a “late” time window for each speech feature. Figure S1 shows the peaks identified and the time boundaries estimated by the K-Means clustering for all response functions and time windows are reported in Table S4 .

Statistical analyses

We ran all statistical analyses reported in this study in R (version 4.3.2). To run the LMM described in this section, we used the lme4 package 64 for statistical model fitting and reported the \(\beta\) estimates with \(95\%\) confidence intervals (CI) calculated through \(5\,000\) bootstrap resamples. The reports included CI, their lower and upper limits, t -values, and p -values. We normalized the continuous predictors (PTA) prior to model fitting, so the coefficients for these predictors are standardized \(\beta\) coefficients. For the categorical predictors (mTRF models), we used sum contrast coding. These coefficients represent deviations from the overall mean and are not standardized in the same way as the continuous predictors.

We used a LMM to examine the encoding accuracies across mTRF models, MoCA groups, PTA, and their interactions. The LMM model was formulated as:

In this model, encoding accuracy, averaged across all electrodes per participant and mTRF model, served as the dependent variable. MoCA group was coded as a binary factor (0: normal, 1: low), PTA values were normalized using z -transformation, and the mTRF model variable was classified into five levels (acoustic, word-level segmentation, phoneme-level segmentation, word-level linguistic, and phoneme-level linguistic). We included the interaction between MoCA group and PTA to examine the effect of cognitive group on encoding accuracy while controlling for hearing ability. Additionally, we included the interaction between MoCA group and mTRF model to investigate the effect of cognitive group on encoding accuracy across different models. To examine the effect of hearing ability on encoding accuracy across different models, we included the interaction between PTA and mTRF model. Furthermore, we incorporated the three-way interaction between MoCA group, PTA, and mTRF model to investigate the combined effect of cognitive group and hearing ability on encoding accuracy across different models. By including this three-way interaction term, we aimed to assess how cognitive decline influences speech processing at various levels. We modified the default contrast coding scheme from treatment contrasts to an orthogonal sum-to-zero coding system to account for potential interactions 65 . This adjustment allowed us to estimate the main effects at the level of the grand mean, ensuring more accurate interpretation of these effects. The model also included a random intercept for the factor participant ID to account for the nested data structure.

In addition to encoding accuracy, we examined the signal power of the response functions for each mTRF model. We calculated the signal power using the RMS over delays from 0 to 500 ms for each electrode and each speech feature-based response function separately. The RMS value was chosen for two reasons: First, because of its efficiency in quantifying the total energy of the response function. RMS is a reliable measure that is robust to signal fluctuations and sensitive to the magnitude and consistency of neuronal responses by capturing potential peaks regardless of their polarity. Second, while acoustic encoding typically exhibits a P1–N1–P2 pattern, mTRF response functions in neuronal responses are less well-defined. For example, previous studies 21 , 49 have identified a response to word surprisals similar to the N400 effect, characterized by a negative deflection around 400 ms after the stimulus. Using the RMS within the 0–500 ms window therefore allows us to comprehensively measure the total energy of the response function regardless of its specific shape.

A LMM with the following formula was used to examine the RMS values across mTRF models, MoCA groups, and their interaction with PTA:

We implemented the LMM separately for each of the mTRF models. RMS was the dependent continuous variable. Again, MoCA group was coded as a binary factor (0: normal, 1: low) and PTA values were normalized using z -transformation. Cluster was a factor with three levels (F, C, P, with F as reference). In line with the previous statistical model, we included the interaction between MoCA group and PTA and a random intercept for the factor participant ID to account for the nested structure of the data. In the acoustic, linguistic word- and phoneme-level models, speech feature was a factor with two levels, thus two speech features per mTRF model, with the first level serving as reference level, i.e., envelope for the acoustic model, word surprisal for the linguistic word-level model, and phoneme surprisal for the linguistic phoneme-level model. Again, we used the sum-to-zero coding system to estimate the main effects at the level of the grand mean.

For the word-level and phoneme-level segmentation models, we used a slightly different formula:

In these models, only one speech feature was included, i.e., word onsets for the word-level model and phoneme onsets for the phoneme-level model, thus, there was no factor of speech feature.

We conducted post-hoc comparisons to examine the interaction between PTA and the mTRF model at word-level segmentation on the encoding accuracy using the emmeans package, with the significance level adjusted using Tukey’s method.

Table S4 shows the percentage frequency of occurrence of peaks in three electrode clusters during early and late time windows. To examine group differences in these peak amplitudes and latencies, we focused on windows and clusters in which peaks occurred in at least \(75\%\) of participants (an arbitrary threshold). Consequently, our analyses included peaks in the early window for the speech features envelope, envelope onset, word onset, and word surprisal responses, and in the late window for word onset, phoneme onset, and word surprisal responses, with different clusters for each speech feature-based response. We used two-tailed Mann–Whitney U tests to assess the differences in peak amplitudes and latencies between the normal and low MoCA groups in these time windows. Since we performed multiple comparisons over the same peaks within different clusters, we applied the Holm-Bonferroni correction 66 to the p -values. This method involves adjusting the significance level of each test based on its rank among the other tests within the same response, thereby controlling the familywise error rate. Our report includes the U -statistic, corrected p -value, and effect size r for each comparison.

Data availability

The data used in this study are available upon request from the corresponding author.

Code availability

The code for signal processing, including the extraction of acoustic and linguistic speech features, the Boosting pipeline, and the statistical analysis described in this paper, are available at github.com/elbolt/acuLin-speech .

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Acknowledgements

We would like to express our gratitude to Elainne Vibal and Katarina Kliestenec for their tremendous assistance with data collection, and to Andrew Clark from LiRI for his support in setting up the EEG experiment. In addition, we are very grateful to Eleanor Chodroff for providing the MFA tutorial, which greatly aided our understanding of how to apply the MFA for forced alignment to our audiobook segments. Finally, we would like to thank the two reviewers for their insightful comments and suggestions, which helped us to improve the quality of this paper.

This study was funded by the Swiss National Science Foundation (SNSF, snf.ch , grant number PR00P1_185715 to NG). EB is a pre-doctoral fellow at International Max Planck Research School on the Life Course (IMPRS LIFE).

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EB and NG designed research, EB collected data, analyzed data and wrote the paper, NG revised the manuscript.

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Bolt, E., Giroud, N. Neural encoding of linguistic speech cues is unaffected by cognitive decline, but decreases with increasing hearing impairment. Sci Rep 14 , 19105 (2024). https://doi.org/10.1038/s41598-024-69602-1

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• Find Your Calm: Managing Stress & Anxiety

Stress Symptoms

What Is Stress?

Stress is your body's response to a challenging or demanding situation. When you feel stressed, your body releases certain hormones. Your hormones are chemical signals your body uses to tell your body systems what to do. The hormones your body releases when you're stressed get you ready to meet the challenge or demand in your environment. During the stress response, your body gets ready to flee or fight by increasing your heart rate, breathing rate, and blood pressure.

Not all stress is bad. In small doses, stress can help you accomplish tasks or prevent you from getting hurt. For example, stress is what makes you slam on the brakes to avoid hitting a suddenly stopped car in front of you. That's a good thing.

But people handle stressful situations differently. What stresses you out may be of little concern to someone else. 

Stress can be a short-term response to something that happens once or only a few times or a long-term response to something that keeps happening. Our bodies can usually handle short-term stress without long-term effects. But long-term or chronic stress can make you sick, both mentally and physically.

The first step to managing your stress is to know the symptoms. But recognizing stress symptoms may be harder than you think. Many of us are so used to feeling stressed that we may not know it until we get sick. Read on to learn more about the various symptoms you may have when you're stressed.

Difference between stress and distress

Stress is a normal reaction to challenges in your physical environment or in your perceptions of what's happening around you. Experts consider distress to be stress that is severe, prolonged, or both. Distress is when you feel you’re under more stress than you can handle.

Emotional Stress Symptoms

Mental symptoms of emotional stress include:

• Feeling more emotional than usual, especially feeling grumpy, teary, or angry
• Feeling anxious, overwhelmed, nervous, or on edge
• Feeling sad or depressed
• Feeling restless
• Trouble keeping track of or remembering things
• Trouble getting your work done, solving problems, making decisions, or concentrating 

Physical Stress Symptoms

Symptoms of stress that you might feel in your body include:

• Clenching your jaw and grinding your teeth
• Shoulder, neck, or back pain; general body aches, pains, and tense muscles
• Chest pain, increased heart rate, heaviness in your chest
• Shortness of breath
• Feeling more tired than usual (fatigue)
• Sleeping more or less than usual
• Upset stomach , including diarrhea , constipation , and nausea
• Loss of sexual desire and/or ability
• Getting sick more easily, such as getting colds and infections often

Respiratory distress

This is when you aren't getting enough oxygen or are having to work really hard to breathe. If you or a loved one has symptoms of respiratory distress, you need to call 911 and get to the ER as soon as possible. Signs include:

• Breathing faster than usual
• Color changes of your skin, mouth, lips, or fingernails. A blue color around your mouth, lips, or fingernails usually shows you aren't getting enough oxygen. Your skin may also look pale or gray.
• Grunting when you breath out
• A whistling with each breath (wheezing)
• Nose flaring
• Chest sinking below your neck or under your breastbone with each breath (retractions)
• Increased sweating, especially cold, clammy skin on your forehead
• Leaning forward while sitting to help take deeper breaths

Cognitive Stress Symptoms

Symptoms of stress that affect your mental performance include:

• Trouble getting your work done, solving problems, making decisions, or concentrating
• Feeling less commitment to your work
• Lack of motivation
• Negative thinking

Behavioral Stress Symptoms

Symptoms of behavioral stress include:

• Changes in your eating habits; losing or gaining weight
• Procrastinating and avoiding responsibilities
• Using alcohol, tobacco, or drugs to feel better
• Avoiding your friends and family; isolating yourself from others
• Failing to meet your deadlines
• Increased absences at school or work
• Doing your work more slowly
• Exercising less often

Symptoms of Chronic Stress

Chronic stress is when you experience stress over an extended time. This can have negative effects on your body and your mental state, and it can increase your risk of cardiovascular disease, anxiety, and depression.

In general, the symptoms of chronic stress are the same as those for shorter-term stress. You may not have all these symptoms, but if you have more than three symptoms and they last for a few weeks, you may have chronic stress. Potential symptoms to look for include:

• Aches and pains
• Changes in your sleeping patterns, such as insomnia or sleepiness
• Changes in your social behavior, such as avoiding other people
• Changes in your emotional response to others
• Emotional withdrawal
• Low energy, fatigue
• Unfocused or cloudy thinking
• Changes in your appetite
• Increased alcohol or drug use
• Getting sick more often than usual

Is It Stress or Something Else?

You may be dealing with something more serious than day-to-day stress if you have symptoms over a period of time even though you've tried to cope using healthy mechanisms. Long-term stress is linked to number of mental health disorders, such as:

• Chronic stress
• Substance use disorder
• Disordered eating

It may be time to visit your doctor if you're struggling to cope with the stress in your life or you have mental health problems from long-term stress. They can help you figure out ways of coping in a healthy way or refer you to a mental health professional who can help you.

Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is mental health condition that you may have after you have or witness a traumatic event, such as a natural disaster, accident, or violence. PTSD overwhelms your ability to cope with new stress. PTSD can lead to symptoms such as intrusive memories, avoidance behaviors, and hyperarousal. 

These symptoms can cause significant problems in your work or relationships. T alk to your doctor or a mental health professional if you've had or witnessed a traumatic event and have disturbing thoughts and feelings about it for more than a month, if your thoughts and feelings are severe, or if you feel like you're having trouble getting your life back on track.

What Are the Consequences of Long-Term Stress?

Ongoing, chronic stress can trigger or worsen many serious health problems, including:

• Mental health problems, such as depression, anxiety, and personality disorders
• Cardiovascular disease, including heart disease , high blood pressure, abnormal heart rhythms, heart attacks, and strokes
• Obesity and other eating disorders
• Menstrual problems
• Sexual dysfunction, such as impotence and premature ejaculation in men and loss of sexual desire in men and women
• Skin and hair problems , such as acne, psoriasis, and eczema, and permanent hair loss
• Gastrointestinal problems, such as GERD, gastritis , ulcerative colitis, and irritable bowel syndrome

Help Is Available for Stress

Stress is a part of life. What matters most is how you handle it. The best thing you can do to prevent stress overload and the health consequences that come with it is to know your stress symptoms.

If you or a loved one is feeling overwhelmed by stress, talk to your doctor. Many symptoms of stress can also be signs of other health problems. Your doctor can evaluate your symptoms and rule out other conditions. If stress is to blame, your doctor can recommend a therapist or counselor to help you better handle your stress.

Stress Takeaways

Stress is your body's response to a challenging or demanding situation. It can affect you physically, mentally, and behaviorally, especially when you have chronic stress. Chronic stress is when you are stressed for an extended time. Chronic stress can make it more likely for you to develop other mental health disorders, such as anxiety or depression. It can also affect your heart health and digestive health. If you're stressed and having trouble coping, it may be time for you to see your doctor or a mental health professional.

Stress FAQs

What can extreme stress cause?

Extreme stress, especially if it's prolonged, can cause emotional distress. And stress from a traumatic event, which is usually extreme, can cause posttraumatic stress disorder (PTSD). These are more serious cases of stress that overwhelm your ability to manage on your own. You may need to get a professional's help to get back on track. If you feel like you're having trouble managing your emotions, talk to your doctor. They can help you or direct you to someone who can help you.

Can stress make you throw up?

Yes, stress can make you throw up. Your digestive system is one of the many systems that stress can affect. In fact, you may have a whole range of other digestive symptoms, such as nausea, pain, and constipation or diarrhea. Not everyone has stress nausea or vomiting, but you may be more prone to it if you have a gastrointestinal condition, such as irritable bowel syndrome (IBS), or you have anxiety or depression.

You may be able to tell if you're stress vomiting if your episode passes when the stress goes away. If it doesn't, then your episode may be caused by something else. It's time to get checked out by your doctor if you have more than a couple of episodes or you can't figure out what's causing them.

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