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Infinitive or -ing verb? Avoiding common mistakes with verb patterns (1)

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task word use in sentence

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TASK in a Sentence Examples: 21 Ways to Use Task

Sentence with Task

Have you ever struggled to find the right words to explain a task or project to someone else? An example sentence with “task” is a sentence that demonstrates how to perform or approach a specific assignment or duty.

In writing, these example sentences with “task” serve as clear illustrations of instructions, methods, or expectations. By including these examples, writers can help others understand complex tasks more easily.

Table of Contents

7 Examples Of Task Used In a Sentence For Kids

  • Task : Draw a picture of your favorite animal.
  • Let’s complete the task of counting the fruits in the basket.
  • Can you identify the colors in this task ?
  • Your task is to write your name on the paper.
  • We have a fun task of playing a game together.
  • Task : Find all the circles in this picture.
  • Complete the task of matching the shapes with their shadows.

14 Sentences with Task Examples

  • Task for the upcoming week: Complete the research paper on Indian economy.
  • Don’t forget about the task of submitting all assignment drafts before the deadline.
  • Group meetings are essential to divide the task of preparing for the upcoming exams.
  • The task of organizing a college event requires efficient planning and coordination.
  • Ensure you schedule time for each task in your daily study planner.
  • The task of improving presentation skills can be achieved through practice and feedback.
  • Remember to prioritize the task of revising key concepts before the final exams.
  • Engaging in extracurricular activities can provide a refreshing break from academic tasks .
  • Collaborating with classmates can make complex tasks like group projects more manageable.
  • It’s important to break down a large task into smaller, achievable steps to avoid feeling overwhelmed.
  • Seeking feedback from professors can help you improve your performance on academic tasks .
  • Take breaks in between study tasks to maintain focus and productivity.
  • Don’t procrastinate on the task of conducting research for your term paper.
  • Practicing time management skills can help you efficiently tackle multiple tasks within a limited timeframe.

How To Use Task in Sentences?

Task is a noun that refers to a piece of work that needs to be done or an activity that one must undertake. When using “Task” in a sentence, it is important to remember that it can be singular or plural depending on the context. To use Task correctly, start by identifying the specific work or activity you want to talk about.

For example, “My boss assigned me a challenging task to complete by the end of the week.” In this sentence, Task is referring to a single piece of work that the speaker was given.

When talking about multiple tasks , you can say, “I have several tasks to finish before the deadline.” Here, Task is used in its plural form to convey that there is more than one work to be completed.

It is important to note that Task can also be used as a verb, meaning to assign a piece of work to someone. For instance, “I will task you with organizing the team meeting.” In this sentence, Task is used as a verb indicating that the speaker will assign the responsibility of organizing the team meeting to the listener.

By understanding the different ways Task can be used in sentences, you can effectively communicate about the work or activities that need to be done in any situation.

In conclusion, sentences with the keyword “task” are varied in nature and can communicate a range of instructions, responsibilities, or reminders. These sentences are often used to designate specific assignments or duties that need to be completed within a certain timeframe. They play a crucial role in clearly outlining the objectives or actions required to achieve a specific goal or outcome.

Whether it’s at work, school, or in personal settings, sentences with the word “task” serve as effective tools for organizing, prioritizing, and managing activities. By breaking down complex objectives into manageable steps, these sentences help individuals stay focused, track progress, and successfully achieve their goals.

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Task in a Sentence  🔊

Definition of Task

work to be done or completed

Examples of Task in a sentence

My task is to organize all of these papers before noon, but after that I can take a short break.  🔊

My mother gave me the task of mopping the floors before she got home from the grocery store, something I really hate doing.  🔊

Homework is a task that students are expected to complete on their own time, but sometimes they do it while still at school.  🔊

My teacher gave me the important task of delivering the class attendance sheet to the office for her.  🔊

The general gave his scout an important task to investigate enemy positions so he could make plans for the assault on the enemy position.  🔊

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Examples of 'task' in a sentence

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“Task” in a Sentence (with Audio)

Examples of how to use the word “task” in a sentence. How to connect “task” with other words to make correct English sentences.

task (n): a piece of work to be done, especially one done regularly, unwillingly, or with difficulty

Use “task” in a sentence

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English sentences focusing on words and their word families the word "task" in example sentences each page has up to 50 sentences. sentences with audio are listed first. (total: 58), the sentences.

Copyright © 2014 by Charles Kelly

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a definite piece of work assigned to, falling to, or expected of a person; duty.

any piece of work.

a matter of considerable labor or difficulty.

Obsolete . a tax or impost.

to subject to severe or excessive labor or exertion; put a strain upon (powers, resources, etc.).

to impose a task on.

Obsolete . to tax.

of or relating to a task or tasks: A task chart will help organize the department's work.

Idioms about task

take to task , to call to account; blame; censure: The teacher took them to task for not doing their homework.

Origin of task

Synonym study for task, other words for task, other words from task.

  • taskless, adjective
  • subtask, noun
  • un·tasked, adjective

Dictionary.com Unabridged Based on the Random House Unabridged Dictionary, © Random House, Inc. 2024

How to use task in a sentence

As more people come online, the most basic tasks—such as going out to the market to sell produce—will become more efficient.

He describes these tasks as “more work than most of the subscribers think”.

My tasks vary from marketing to writing a page for a magazine.

Here is a title that, in its prologue, tasks players with fighting a horde of angels on top of a moving jet.

Do some kids struggle more than others to attend to tasks, both academic and household?

Teachers often complain that they can never induce some of their pupils to ask questions on their tasks.

Indeed, 'we have laid upon him various arduous tasks touching the state of the country, and especially its tranquillity.'

All who were standing hurried to their tasks at this word of command, and all who were sitting as promptly rose.

In his class, which then consisted of three male students and a host of women, Rubinstein would often set the most comical tasks.

One of the first immediate tasks to be done was the training of soldiers in Virginia and the acquiring of cannon and fire-arms.

British Dictionary definitions for task

/ ( tɑːsk ) /

a specific piece of work required to be done as a duty or chore

an unpleasant or difficult job or duty

any piece of work

take to task to criticize or reprove

to assign a task to

to subject to severe strain; tax

Derived forms of task

  • tasker , noun
  • taskless , adjective

Collins English Dictionary - Complete & Unabridged 2012 Digital Edition © William Collins Sons & Co. Ltd. 1979, 1986 © HarperCollins Publishers 1998, 2000, 2003, 2005, 2006, 2007, 2009, 2012

Other Idioms and Phrases with task

see take to task.

The American Heritage® Idioms Dictionary Copyright © 2002, 2001, 1995 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company.

Task in a Sentence

36 examples of task in a sentence, definition of task.

task (verb) - assign a task to

View other definitions

How can task be used in a sentence?

And that's the process that I call task allocation.

C oncentration or commitment to a task is short lived.

She stopped to turn a trick before she called the task force number.

Evening out this responsibility is a vital longterm task for all Americans.

Now Holtz-Eakin's main task is defending the indefensible, McCain's tax policy.

On top of this urgent task, though, we have an important long-term task as well.

"Now Holtz-Eakin's main task is defending the indefensible, McCain's tax policy."

Their main task is to protect their citizens from evil-doers and exercise justice.

Really the question that I think about with these ants is what I call task allocation.

This task is a huge one, seeing as an average of 32 issues are released per academic year.

I called the task force myself and said, "Tell me this man was trapped from underneath rubble."

The Pakistan Government now faces the long-term task of holding the ground the Army has cleared.

Our short term task is to try to prevent the loss of millions of jobs and get our economy moving.

Of course, there's another, longer-term task ahead: regaining lost market share in the U.S., its No. 1 market.

I know that maybe in this country, we have different things that come to mind when we hear the word task force.

At present the main task is still essentially to discover and scientifically to establish the various cases of catalysis.

Of course, that's "not a short-term task," as Hillier says, and the U.S. government does accept it as a long-term strategy.

Unfortunately, putting out these fires will also postpone the long-term task of improving the quality of Guatemalan education.

The Fed's key short-term task now will be converting Goldman and Morgan, two highly leveraged investment banks, into less-risky institutions.

But the task is a massive one, and, as of 2008, the two nations face nuclear problems that scarcely registered during the upheaval of the 1990s.

This time, they had all the volunteers -- believers and atheists alike -- complete a word task designed to unconsciously prime religious thinking.

The project, on the drawing boards since 2005, has split the close-knit community and generated a long-term task force and hours of public hearings.

They will want Congress and its members to aggressively pursue policies that reduce India's grinding poverty -- a daunting, long-term task, to say the least.

Also I wrapped the $task like this: addslashes$task in insertTask.php because otherwise double quotes are not accepted into a new task, and the DHTML corrupts.

This issue should be left to an experienced and skilled lobbyist, whose long-term task is to bring about national and governmental unity on the future development of a metric America.

Not only has the government failed to take any precautionary measures with regard to global warming, it has also postponed the long-term task of restoration of the sheltering wetlands.

Exceptions are two near-term task forces to develop high priority, essential standards or generally acceptable principles needed for rapid progress in creating an electronic government.

If you haven't used macrodef yet: It allows you to define kind of a general template for a custom task and substitute certain attributes or elements when you call the task (very useful!).

I now have an achievable task on my list and the satisfaction of completing the short term task regularly while moving towards achieving my long-term goal - a vast improvement on the do-nothing-because-it's-all-too-difficult situation I had before.

March 21/07: Columbia Research Corp. in Washington, DC received a $6.3 million term task order (M67854-04-A-5167 Task Order 0003) for acquisition, logistics, and administrative support services for the Program Manager Optics & Non-Lethal Systems (ONS),

"Today I am able to say that we have concluded all questions with the U.S. about Russia's entry into the WTO, and now the main task is wrapping up talks with the working group in Geneva," Mr. Kudrin said in Yalta, Ukraine, Russian news agencies reported.

"Banks ought to fully realize that dealing with the impact of the crisis is a long-term task, and should pay close attention to risks accumulated from a burst of lending," the head of China's banking regulator, Liu Mingkang, said at the agency's quarterly meeting last week.

So, there's a long-term task force that will involve MySpace, acknowledging the importance of age and identity authentication, and also a commitment to explore and develop that technology, but also more than 70 specific measures right now that MySpace has implemented or will work to implement.

U.S. ENVOY TO AFGHANIS.AN: It's what you said, Nic, it's maintaining unity in this fraction coalition that's already been created with the help of the U.S. and international community and beginning to introduce security in different parts of the country and starting the long term task of reconstruction.

Now, the ethologist's main task is to understand the perceptual world of the animal and that requires a tricky kind of concentration, especially with a creature like the octopus, which is basically a snail that's as smart as a cat, and from the human perspective the most alien intelligence on the planet.

One knows this problem, especilly as developers/betatester: When a program freezes, than you call the task manager CTRL+ALT+DEL, and find that frozen program then you have to find and select the option "End Process", and finally you have to confirm the message, "Are you sure you want to terminate the process" with "yes".

Tips for Using task in a Sentence

You may have an easier time writing sentences with task if you know what words are likely to come before or after it, or simply what words are often found in the same sentence.

Frequent Predecessors

Words that often come before task in sentences. For example: " the task" or " a task"

Frequent Successors

Words that often come after task in sentences. For example: "task of " or "task . "

Associated Words

Words that aren't necessarily predecessors or successors, but are often found in the same sentence.

  • recommendations

Alternate Definitions

  • task (noun) - a tax; an assessment; an impost
  • task (noun) - labor imposed; especially, a definite quantity or amount of labor; work to be done; one's stint; that which duty or necessity imposes; duty, or duties collectively
  • task (noun) - specifically a lesson to be learned; a portion of study imposed by a teacher
  • task (noun) - work undertaken; an undertaking
  • task (noun) - burdensome employment; toil
  • task (transitive verb) - to oppress with severe or excessive burdens; to tax
  • task (transitive verb) - to charge; to tax, as with a fault
  • task (noun) - labor or study imposed by another, often in a definite quantity or amount
  • task (noun) - business; employment; undertaking; labor
  • task (noun) - see under <er>take</er>

Can 'Task' Be Used as a Verb?

No one likes tasks . This is unsurprising, given that the definitions we give for this noun include “a usually assigned piece of work often to be finished within a certain time,” “subjection to adverse criticism,” and “something hard or unpleasant that has to be done.” You will rarely hear someone speak of the “lovely task” they’ve just been given. But some are not content with disliking this word based on its semantic content, and have taken additional umbrage with its use as a verb.

can task be a verb

Could have sworn there was something else we had to do...

It is uncertain exactly why this distaste for the transitive verb form of task has come about. Some possibilities are that it is seen as being new, that it is business jargon, or that it frequently is found used in the passive voice. Prohibitions against the verb are rarely found in usage and style guides, and when they are encountered few seem to take them very seriously.

task is not a verb. — Telegraph Style Book; The official guide to house style for The Daily Telegraph , (https://www.telegraph.co.uk/style-book/t/) Ronald Koeman tasked with resuscitating mediocre Dutch national team and own management career — (headline) The Daily Telegraph (London, Eng.), 22 Mar. 2018 A unit of US special forces tasked with carrying out “decapitation” operations may be aboard a nuclear-powered submarine docked in the South Korean port of Busan, the nation’s newswire reported on Monday, citing a defence source. — The Daily Telegraph (London, Eng.), 17 Oct. 2017 Pippa Grange, 47, is tasked with changing the culture and mindset of England teams, and increasing their “psychological resilience” to the pressure of winning critical matches.… — The Daily Telegraph (London, Eng.), 4 Jan. 2018

Task is not a new verb. In fact, it has been verbing along since the 14th century, used with the meaning of “to assign a task to.” It also has an obsolete sense of “to impose a tax on,” and an additional current meaning of “to oppress with great labor.” The word has shown an increase in use of late, particularly in business writing (a form of English many people take great dislike to), but getting tasked with something is commonly found in military use from at least the 1960s.

One consolidated handbook covering the subjects of welfare organizations, pay and allowances, allotments, travel, shipment of household effects, overseas duty stations, disabilitv separation, retirement, promotion, reenlistment benefits, medical care, survivors' benefits-and others-would answer the prayers of those tasked with these additional duties. — Leatherneck (Quantico, VA), Oct. 1963 The platoon is tasked to conduct a two-week platoon sweep between Hue and Danang with the objective of eliminating all enemy forces within the Hue-Danang axis, the bush country as well as Route One. — Marine Corps Gazette , May 1970 Responsibility for delivering the food to Yuma was tasked to Marine Aerial Refueler-Transport Squadron 252. — Leatherneck (Quantico, VA), Jul. 1971

This particular turn of phrase did not originate with the military, and may be seen on occasion in much earlier examples.

The establishment involved immense expenses, and responsibilities, and was tasked with the transmission not only of intelligence, but of immense amounts of exchanges. — Niles’ National Register (Baltimore, MD), 19 Dec. 1846 It was impossible for me to write a line all this week, as I was on every committee which was tasked with receiving and entertaining Benjamin Harrison, President of the United States. — The American Israelite (Cincinnati, OH), 7 May 1891

It is fine with us if you wish to avoid using the passive voice in your writing, and we also have no problem with you eschewing the use of task as a verb. But any decent peeve should have a solid foundation, and saying that task is not a verb is deficient in that regard. Perhaps you could go with the old standby of “I just hate that word.”

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Task in a sentence

task word use in sentence

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folderol \ ˈfɒldəˌrɒl \ noun

1. nonsensical talk or writing 2. something of little value or significance

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The word folderol has appeared in one article on NYTimes.com in the past year, including on Oct. 28 in “ The Real End of David Chang’s Momofuku Ko Happened Years Ago ” by Pete Wells:

But the biggest and bravest thing Ko did was to go head-to-head with the most expensive and highly regarded places in the city, the Le Bernardins and Daniels and Jean-Georges, without benefit of a reservationist, or a maître d’hôtel, or a deep wine list, or a printed menu, or chairs with lumbar support, or coffee or tea. Ko would stand or fall on the strength of its cooking alone. … Ko was a guerrilla rebellion against fancy-pants restaurants and everything they stood for. It was food’s punk-rock moment. At least, that’s how it felt in the spring of 2008. It didn’t seem crazy to believe that high-performing restaurants could change, too, by dropping all the folderol that drove up prices and made people uncomfortable.

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David Nield

17 Tips to Take Your ChatGPT Prompts to the Next Level

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ChatGPT, Google Gemini, and other tools like them are making artificial intelligence available to the masses. We can now get all sorts of responses back on almost any topic imaginable. These chatbots can compose sonnets, write code, get philosophical, and automate tasks.

However, while you can just type anything you like into ChatGPT and get it to understand you. There are ways of getting more interesting and useful results out of the bot. This "prompt engineering" is becoming a specialized skill of its own.

Sometimes all it takes is the addition of a few more words or an extra line of instruction and you can get ChatGPT responses that are a level above what everyone else is seeing—and we've included several examples below.

While there's lots you can do with the free version of ChatGPT, a few of these prompts require a paid ChatGPT Plus subscription —where that's the case, we've noted it in the tip.

ChatGPT can give you responses in the form of a table if you ask. This is particularly helpful for getting information or creative ideas. For example, you could tabulate meal ideas and ingredients, or game ideas and equipment, or the days of the week and how they're said in a few different languages.

Using follow-up prompts and natural language, you can have ChatGPT make changes to the tables it has drawn and even produce the tables in a standard format that can be understood by another program (such as Microsoft Excel).

If you provide ChatGPT with a typed list of information, it can respond in a variety of ways. Maybe you want it to create anagrams from a list of names, or sort a list of products into alphabetical order, or turn all the items in a list into upper case. If needed, you can then click the copy icon (the small clipboard) at the end of an answer to have the processed text sent to the system clipboard.

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Get ChatGPT to respond as your favorite author.

With some careful prompting, you can get ChatGPT out of its rather dull, matter-of-fact, default tone and into something much more interesting—such as the style of your favorite author, perhaps.

You could go for the searing simplicity of an Ernest Hemingway or Raymond Carver story, the lyrical rhythm of a Shakespearean play, or the density of a Dickens novel. The resulting prose won't come close to the genius of the actual authors themselves, but it's another way of getting more creative with the output you generate.

ChatGPT can really impress when it's given restrictions to work within, so don't be shy when it comes to telling the bot to limit its responses to a certain number of words or a certain number of paragraphs.

It could be everything from condensing the information in four paragraphs down into one, or even asking for answers with words of seven characters or fewer (just to keep it simple). If ChatGPT doesn't follow your responses properly, you can correct it, and it'll try again.

Another way of tweaking the way ChatGPT responds is to tell it who the intended audience is for its output. You might have seen WIRED's videos in which complex subjects are explained to people with different levels of understanding. This works in a similar way.

For example, you can tell ChatGPT that you are speaking to a bunch of 10-year-olds or to an audience of business entrepreneurs and it will respond accordingly. It works well for generating multiple outputs along the same theme.

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Tell ChatGPT the audience it's writing for.

ChatGPT is a very capable prompt engineer itself. If you ask it to come up with creative and effective inputs for artificial intelligence engines such as Dall-E and Midjourney , you'll get text you can then input into other AI tools you're playing around with. You're even able to ask for tips with prompts for ChatGPT itself.

When it comes to generating prompts, the more detailed and specific you are about what you're looking for the better: You can get the chatbot to extend and add more detail to your sentences, you can get it to role-play as a prompt generator for a specific AI tool, and you can tell it to refine its answers as you add more and more information.

While ChatGPT is based around text, you can get it to produce pictures of a sort by asking for ASCII art. That's the art made up of characters and symbols rather than colors. The results won't win you any prizes, but it's pretty fun to play around with.

The usual ChatGPT rules apply, in that the more specific you are in your prompt the better, and you can get the bot to add new elements and take elements away as you go. Remember the limitations of the ASCII art format though—this isn't a full-blown image editor.

Screenshot of ChatGPT

A ChatGPT Plus subscription comes with image generation.

If you use ChatGPT Plus , it's got the DALL-E image generator right inside it, so you can ask for any kind of photo, drawing, or illustration you like. As with text, try to be as explicit as possible about what it is you want to see, and how it's shown; do you want something that looks like a watercolor painting, or like it was taken by a DSLR camera? You can have some real fun with this: Put Columbo in a cyberpunk setting, or see how Jurassic Park would look in the Victorian era. The possibilities are almost endless.

You don't have to do all the typing yourself when it comes to ChatGPT. Copy and paste is your friend, and there's no problem with pasting in text from other sources. While the input limit tops out at around 4,000 words, you can easily split the text you're sending the bot into several sections and get it to remember what you've previously sent.

Perhaps one of the best ways of using this approach is to get ChatGPT to simplify text that you don't understand—the explanation of a difficult scientific concept, for instance. You can also get it to translate text into different languages, write it in a more engaging or fluid style, and so on.

If you want to go exploring, ask ChatGPT to create a text-based choose-your-own adventure game. You can specify the theme and the setting of the adventure, as well as any other ground rules to put in place. When we tried this out, we found ourselves wandering through a spooky castle, with something sinister apparently hiding in the shadows.

Screenshot of ChatGPT

ChatGPT is able to create text-based games for you to play.

Another way to improve the responses you get from ChatGPT is to give it some data to work with before you ask your question. For instance, you could give it a list of book summaries together with their genre, then ask it to apply the correct genre label to a new summary. Another option would be to tell ChatGPT about activities you enjoy and then get a new suggestion.

There's no magic combination of words you have to use here. Just use natural language as always, and ChatGPT will understand what you're getting at. Specify that you're providing examples at the start of your prompt, then tell the bot that you want a response with those examples in mind.

You can ask ChatGPT for feedback on any of your own writing, from the emails you're sending to friends, to the short story you're submitting to a competition, to the prompts you're typing into the AI bot. Ask for pointers on spelling, grammar, tone, readability, or anything else you want to scrutinize.

ChatGPT cleared the above paragraph as being clear and effective, but said it could use a call to action at the end. Try this prompt today!

Screenshot of ChatGPT

Get ChatGPT to give you feedback on your own writing.

In the same way that ChatGPT can mimic the style of certain authors that it knows about, it can also play a role: a frustrated salesman, an excitable teenager (you'll most likely get a lot of emoji and abbreviations back), or the iconic western film star John Wayne.

There are countless roles you can play around with. These prompts might not score highly in terms of practical applications, but they're definitely a useful insight into the potential of these AI chatbots.

You can type queries into ChatGPT that you might otherwise type into Google, looking for answers: Think "how much should I budget for a day of sightseeing in London?" or "what are the best ways to prepare for a job interview?" for example. Almost anything will get a response of some sort—though as always, don't take AI responses as being 100 percent accurate 100 percent of the time.

If you're using the paid ChatGPT Plus tool, it will actually search the web (with Bing) and provide link references for the answers it gives. If you're using the free version of ChatGPT, it'll mine the data its been trained on for answers, so they might be a little out of date or less reliable.

Your answers can be seriously improved if you give ChatGPT some ingredients to work with before asking for a response. They could be literal ingredients—suggest a dish from what's left in the fridge—or they could be anything else.

So don't just ask for a murder mystery scenario. Also list out the characters who are going to appear. Don't just ask for ideas of where to go in a city; specify the city you're going to, the types of places you want to see, and the people you'll have with you.

Your prompts don't always have to get ChatGPT to generate something from scratch: You can start it off with something, and then let the AI finish it off. The model will take clues from what you've already written and build on it.

This can come in handy for everything from coding a website to composing a poem—and you can then get ChatGPT to go back and refine its answer as well.

You've no doubt noticed how online arguments have tended toward the binary in recent years, so get ChatGPT to help add some gray between the black and the white. It's able to argue both sides of an argument if you ask it to, including both pros and cons.

From politics and philosophy to sports and the arts, ChatGPT is able to sit on the fence quite impressively—not in a vague way, but in a way that can help you understand tricky issues from multiple perspectives.

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The Processing of Lexical Ambiguity: Evidence from Child and Adult Greek

  • Original Research
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  • Published: 22 February 2024
  • Volume 53 , article number  16 , ( 2024 )

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  • Maria Kaltsa   ORCID: orcid.org/0000-0002-2422-7889 1 &
  • Despina Papadopoulou   ORCID: orcid.org/0000-0002-3599-2842 2  

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The aim of the study is to examine the effect of sentential context on lexical ambiguity resolution in Greek adults and typically developing children. Context and word frequency are factors that can affect lexical processing, however, the role of them has not been thoroughly examined in Greek. To this aim, we assessed sentence context effects in homonym meaning activation in monolingual speakers of Greek, children and adults, using a cross-modal priming paradigm. Additionally, all participants conducted a verbal working memory task and an inhibition task so as to examine whether the use of sentential context for lexical ambiguity resolution relates to age and/or cognitive processing capacity. The data analysis showed (a) major processing differences between adults and children due to ambiguity and sentential context, (b) children’s processing times affected by cognitive skills while adults’ processing unaffected, and (c) visual word recognition intact for all participants.

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Introduction

The linguistic cues in lexical ambiguity resolution.

The current study aims at examining the contribution of meaning frequency and contextual information in child and adult lexical ambiguity resolution via the exploitation of homonyms. Homonyms are lexical items with the same phonological and orthographic sequence but with two or more semantically unrelated meanings; for example, the word bank may refer either to land alongside a river or lake or a financial establishment (Lyons, 1977 ). Homonymy is a useful tool for the examination of meaning activation and selection and due to the fact that phonological and orthographic cues are constant and the frequency of each meaning measurable, it allows us to examine the true contribution of sentential context in ambiguity resolution. Previous work has indicated that word forms associated with multiple distinct meanings have been linked to increased processing load compared to unambiguous words on the basis of behavioral (Rodd et al., 2002 ), electrophysiological (Hagoort & Brown, 1994 ) and hemodynamic (Gennari et al., 2007 ) data. Ambiguity in written language in particular requires increased cognitive resources (Gadsby et al., 2008 ; Miyake, Just, & Carpenter, 1994 ). Event-related potential (ERP) studies show that noun/verb homographs in contexts providing syntactic, but not semantic, cues for ambiguity resolution show a sustained negativity over frontal–central scalp regions but when disambiguating semantic information is available, the frontal negativity is not evident (Federmeier et al., 2000 ; Lee & Federmeier, 2008 , 2009 ) Footnote 1 . Earlier studies on lexical ambiguity resolution have identified sentential context, meaning frequency and word iconicity as some of the features that can affect lexical processing (Chen & Boland, 2008 ; Vu et al., 2000 ).

During language processing, the representation and maintenance of context information is critical for the successful comprehension of the stimuli, as the interpretation of words can vary significantly depending on their context of occurrence (Rodd et al., 2002 , 2004 ). Data on lexical ambiguity resolution from adults show that during meaning competition for selection, sentential context influences the timing and/or degree to which the alternative meanings are activated. Andreou et al. ( 2009 ) Footnote 2 tested 14 adult healthy native speakers of Greek to explore these parameters and found that both meanings of the ambiguous words were activated when the sentential context was not biased towards either meaning. Additionally, Andreou et al.’s findings revealed contextual effects, as an appropriately biasing sentential context facilitated the recognition of the meaning that was supported by it but not the non-favored meaning. Interestingly, the contextual effects were observed very early, when the inter-stimulus interval Footnote 3 (ISI) was set at 0 ms and also later (ISI 750 ms).

It is important to note that children’s sensitivity to contextual cues during language processing can be traced even to children as young as 19 months old; Friedrich and Friederici ( 2005 ) identified a N400 effect for audio stimuli with a semantically anomalous word in an otherwise normal sentence. However, data on lexical ambiguity resolution from young school-aged children show that children do not exploit context similarly to adults (Booth et al., 2006 ). Booth et al. ( 2006 ) identified two patterns in their data: (a) sentential context effects increased from age 9 to 12, and (b) lexical-level effects decreased. In addition, earlier studies report facilitatory effects of congruent Footnote 4 , single-word context for children aged from 7 to 10 but inhibitory, incongruent single-word context effects for children aged 11 to 12 (Simpson & Foster, 1986 ; Simpson & Lorsbach, 1983 ). The difficulty to quickly and effectively integrate sentential context in lexical ambiguity resolution is also attested in monolingual children with developmental disorders (Henderson, Clarke & Snowling, 2011), even though there is no consensus in the literature regarding the locus of those difficulties (Brock et al., 2008; Hahn et al., 2015 ; Norbury, 2005).

A key factor in lexical ambiguity appears to be the relative frequency of the word’s alternative meanings. There are balanced ambiguous words, in that the two meanings of these words have approximately equal association strengths to the ambiguous word form. An example of a balanced ambiguous word in English would be the word mold ; referring either to a container used to give shape to a liquid material or fungus with similar frequency for both meanings (example from Klepousniotou et al., 2012 ). Some ambiguous words, on the other hand, are biased, since one meaning is much more strongly associated with the word form and is referred to as dominant meaning, while the alternative meaning is the subordinate one. For example, the word ball in English has a dominant meaning referring to a spherical object that is kicked or thrown in a game and a subordinate meaning referring to dancing (example from Klepousniotou et al., 2012 ). Several studies have indicated that the more frequent, dominant meaning of an ambiguous word is activated more quickly than less frequent, subordinate meanings in neutral sentence contexts (Dopkins et al., 1992 ; Lucas, 1999 ; Sereno, Brewer, & O’Donnell, 2003 ; Simpson & Krueger, 1991 ). In the case of the ball paradigm, a neutral sentential context would not be related to either meaning as in the doctor held the ball which does not bias to either direction as the football player kicked the ball would. Duffy, Morris and Rayner (1988) report significant processing cost in reading times (RTs) on a biased ambiguous word when the sentential context biases towards the word’s subordinate meaning, but no such effects for balanced ambiguous word (see also the discussion in Vuong & Martin, 2011 ).

The subordinate bias effect has been demonstrated in a number of studies, which showed that RTs for ambiguous words are longer compared to those for unambiguous control words matched in frequency (Binder, 2003 ; Kambe et al., 2001 ; Rayner et al., 2006 ; Sereno, O’Donnell, & Rayner 2006 ). For the subordinate bias effect, however, to be evident two conditions need to be met: (a) the homonym must be strongly polarized, meaning that there is a more pronounced bias ratio with subordinate meanings retrieved only about 10% of the time in word association tasks, and (b) the frequency of the control unambiguous word needs to be matched to the homograph’s form frequency, that is the sum of all meaning frequencies (Binder, 2003 ; Binder & Rayner, 1998 ; Kambe et al., 2001 ; Pacht & Rayner, 1993 ; Rayner et al., 2006 ; Sereno, O’Donnell, & Rayner, 2006 ; Sereno et al., 1992 ). Meanwhile, research in younger speakers suggests that children are not sensitive to the relative frequency of each meaning of ambiguous lexical items (Booth et al., 2006 ; Simpson & Foster, 1986 ).

Lexical Access Models for Ambiguity Resolution

To accommodate the variation in findings with regard to child and adult monolingual data a number of lexical access models have been put forward. According to the reordered access model both meanings of ambiguous words are activated and remain in competition till ambiguity is resolved (Duffy et al., 2001 ). In light of this model, initial access of word meaning is not immune to contextual effects; for the biased ambiguous words even in the presence of disambiguating context that biases towards the subordinate meaning, the dominant meaning will be activated. Meanwhile the context-sensitive/selective access account proposes that the dominant meaning is not activated if the context is sufficiently constraining towards a subordinate meaning; possible constraints for this processing model can be frequency, type of context and strength of context (Kellas et al., 1995 ; Vu et al., 2000 , 2003 ).

Another lexical access model for ambiguity resolution is the dual mechanism account by Gernsbacher and St. John ( 2001 ), according to which there is a first mechanism described as the bottom-up, frequency-weighted activation of all homonym meanings and a second mechanism as the top-down Footnote 5 suppression of contextually irrelevant meanings. Gernsbacher’s and St. John’s ( 2001 ) proposal also makes a reference to variability due to reading skills; with less skilled readers having less efficient mechanisms for suppression of the unintended meaning. Yet children’s data from linguistic ambiguity resolution show some disparity in the use of bottom-up and top-down cues depending on their age and it remains an open question which type of lexical access account could have higher explanatory value for their course of processing (see Rabagliati et al., 2013 ; Snedeker, 2013 ; Snedeker & Trueswell, 2004 ; Snedeker & Yuan, 2008 ; Trueswell & Gleitman, 2007).

The role of Cognitive Capacity

As already presented above, the adult lexical ambiguity literature emphasizes the relative frequency of alternative meanings as well as the interaction between contextual bias and frequency (Binder & Rayner, 1998 ; Chen & Boland, 2008 ; Vu et al., 1998 ); however, even among healthy adults there is some variability in their performance that appear to relate to working memory (WM) and reading skills. Miyake, Just and Carpenter ( 1994 ) point that an individual’s working memory capacity relates to the attention and activation a person can assign to maintain lexical information activated during processing; specifically, their data suggest that, while individuals with low WM skills activate the most frequently accessed dominant meaning, individuals with high WM skills could maintain both meanings activated even after an eight-word delay. Gunter et al. ( 2003 ) also found that high WM capacity leads to higher efficiency in inhibiting alternative meanings of ambiguous words. Moreover, Gadsby et al. ( 2008 ) used a priming task with stimulus words arranged into word triplets with each triplet including two prime words followed by a target to examine congruent relationships (both the context and the target related to the same meaning of the homograph) and incongruent relationships (context and target related to different meanings of the homograph). They found evidence that individuals with high working memory spans exhibited a pattern of priming for congruent conditions and a lack of positive priming for incongruent conditions when processing homographs. Apart from cognitive resources, literacy skills also appear to affect performance. Gernsbacher and colleagues (Gernsbacher, 1993 ; Gernsbacher et al., 1990 ) showed that skilled readers can quickly suppress the contextually inappropriate meaning of an ambiguous word but less skilled readers are less effective.

Given that WM and reading skills appear to affect steady state grammars, when looking into developing grammars, it is essential to weigh the contribution of the cognitive processing capacity since it is itself under development. Literature points to the bidirectional relationship of language comprehension and working memory (see Payne, Kalibatseva, & Jugers, 2009 ; Seigneuric et al., 2000 ; Oakhill et al., 2003 ). Diamond ( 2006 ) cites the years from 5 to 11 as marking substantial improvements in cognitive flexibility, working memory, and processing speed. The failure to engage in top-down processes, which is often reported in child data, may be related to the current level of cognitive resources that need to be allocated to process language. Khanna and Boland ( 2010 ) Examined lexical ambiguity resolution in children aged 7 to 10 years and adults testing the impact of sentential and single-word contexts in cross-modal naming paradigms. Their data showed that when a sentential context was provided only older children and adults showed priming for context-appropriate targets, whereas when a single-word context was provided all groups showed contextual sensitivity. Additionally, their analysis highlighted that mature executive function abilities as measured via working memory span and inhibition skills were found to be associated with greater contextual sensitivity. Similarly, Coch and Holcomb ( 2003 ) advocate that younger and less skilled readers exhaust their cognitive resources while reading, leaving inadequate resources for the context integration process; consequently, it is necessary to factor in the role of cognitive skills such as working memory and inhibition which are necessary for top-down processing (Bunge et al., 2002 ; Casey et al., 2005 ; Protopapas et al., 2007 ).

Research Questions

The present study employs homonyms to explore the factors that affect lexical ambiguity resolution in Greek, an area largely understudied for typically developing, but also developed Greek speakers (for English see Khanna & Boland, 2010 ). More specifically, we address the following research questions:

whether meaning frequency and context affect Greek adult and child lexical ambiguity resolution,

whether language external factors such as age and cognitive skills (inhibition and verbal working memory) play a role in Greek adult and child lexical ambiguity resolution.

To do so, we assessed sentence context effects in homonym meaning activation in monolingual speakers of Greek, children and adults, using a cross-modal priming paradigm, similar to the one employed by Andreou et al. ( 2009 ); that is with a sentential context provided in audio and a visual target that related to either dominant or subordinate meaning presented in detail in the following section. Note that factoring in the different results attested by the Khanna and Boland ( 2010 ) study on sentence and single-word contexts, we opted for sentential contexts. To tease apart maturational differences in sentential context integration from cognitive load differences related to reading skills, we have selected an experimental paradigm that includes little reading; namely a cross-modal priming task with the prime being an audio stimulus of a sentence and the visual target a single short word.

The main experiment of the current study is a cross-modal priming task that examines lexical ambiguity resolution using homonyms in neutral and biasing contexts. Homonyms and control items that were included in the experimental design were selected with the use of three pre-tasks. The participant recruitment process across those pre-tasks is stratified random sampling; the stratified variables in the population were age, sex and educational level. First pre-task A was administered; on the basis of the results of pre-task A, the second pre-task was developed and then pre-task C followed. Specifically, to select the appropriate homonym items, we employed the data of pre-task A, to select the target items for the visual word recognition we employed pre-task B and to select the appropriate control items we employed the data of pre-task C. These pre-tasks were necessary, as there are no standardized lists of ambiguous words in Greek, as there are in English. The minimum requirement in terms of sample size for the pre-tasks was set to 50 participants and was satisfied in all cases.

Pre-task A tested the competing meaning frequencies of ambiguous items. Participants were asked to provide all possible meanings for 142 ambiguous noun words in Greek. 50 adult native speakers participated in the task (F: 27; M: 23 | Age M: 30; Age Range: 21–62). On the basis of the analysis 30 homonyms were selected for the main experiment with a high frequency dominant meaning (M: 74.8%; Range: 56–86%) and a low frequency subordinate meaning (M: 38.4%; Range: 32–46%); the exclusion criteria for the ambiguous words were (a) both meanings with the same frequency, and (b) frequency above 86% or below 32% so as to avoid highly polarized items.

Pre-task B aimed at identifying the target items for the visual word recognition that are strongly related to the homonyms for each of the two meanings. 84 Footnote 6 adult native speakers of Greek participated in the task (F: 48; M: 36 | Age M: 24; Age Range: 18–39). On the basis of the analysis, the most frequently first associated target words to each meaning were selected. All visual targets have the same word length and frequency across conditions; N letters M: 6.5 (SD: 1.7); N syllables M: 2.9 (SD: 0.9) and similar frequency according to Hellenic National Corpus (HNC: http://corpus.ilsp.gr ).

Pre-task C is a word familiarity task that was used to verify that homonyms and control words were of similar frequency. Participants were asked to rate items on a 5-point scale with 1 allocated to least frequent items and 5 to most frequent items. 52 adult native speakers of Greek participated in the task (F: 28; M: 24 | Age M: 26; Age Range: 19–45). The analysis confirmed that homonyms (M: 3.8, SD: 0.4) and control items (M: 3.9, SD: 0.6) selected are of the same frequency.

Experimental Design of Cross Modal Priming Task

For the main component of the study a cross-modal priming task was developed in order to examine the access of multiple meanings of ambiguous lexical items in neutral and biasing contexts. We opted for relatively equibiased homonyms for ambiguity resolution so as to minimize frequency-driven meaning selection that is found in highly polarized ambiguous words and thus avoid any subordinate bias effect in our data. The types of sentential context that we examined are dominant meaning biased, subordinate meaning biased and not related to either meaning. The cross-modal priming experiment was a speeded lexical decision task with the prime being an audio stimulus (homonym or control word) that appeared at the end of sentences biasing the dominant, the subordinate or neither meaning and the visual target words related to either the dominant or the subordinate meaning of the homonym examined. Participants were instructed that they would listen to a sentence and subsequently a visual target would appear on the screen; their task was to indicate whether the visual target was a word or not in Greek by pressing one of the two pre-specified buttons on the keyboard. The visual targets that appeared in the experimental conditions were selected on the basis of the outcomes of pre-task B. Table  1 exemplifies the experimental paradigm:

In the paradigm, the homonym a ˈ gio bears the dominant meaning ‘pot’ and the subordinate meaning ‘vessel’ and appeared in three different contexts; one that biases towards the ‘pot’ meaning (ˈ Evale ˈ liɣo ne ˈ ro sto a ˈ gio. He poured some water into the pot.); one that biases towards the ‘vessel’ meaning ( O ɣia ˈ tros ˈ tripise to a ˈ gio. The doctor cut through the blood vessel.); and an unrelated, neutral context (ˈ Iθele na ði to a ˈ gio . She/he wanted to see the pot/vessel.). The audio was followed by the visual targets ˈvazo ‘vase’ and ˈema ‘blood’. The same set of sentential contexts and visual targets were included in the control conditions with the homonym word being replaced by the control word xarˈti ‘paper’.

In this cross-modal priming task 30 homonyms were examined along with the same number of control items for all sentential contexts. Each homonym and control item appeared in 6 conditions that led to the development of 360 experimental items and 360 fillers. The length of all sentences that included the primes was 5–7 words (M: 5.6; SD: 0.6) and it did not statistically differ among conditions tested. For the filler items, visual targets were either illegal non-words with a phonotactic violation in the consonantal cluster of the first syllable (e.g. ˈzlota ) or pseudo-words that do not violate Greek phonotactic rules (e.g. ˈkreza ) so as to support the lexical decision component of the task. Given Andreou et al. ( 2009 ) findings on the absence of ISI effects, ISI was set to 0ms in the task. Stimuli were presented using E-Prime 2.0 software (Psychology Software Tools, 2012) and items were equally divided and randomized in six experimental versions, so that each version contained all types of primes and visual targets, while the same visual target and the same audio prime were never encountered more than once in each version. We administered the six experimental sessions to every participant in the study. The task was administered in a quiet room; 5 practice items were also included at the beginning of each session so that participants familiarize themselves with the experimental procedure. The duration of each session was approximately 15 min.

Research Hypotheses

Based on previous research on adult lexical ambiguity resolution (see Binder, 2003 ; Rayner et al., 2006 ; Rodd et al., 2004 ; Sereno, O’Donnell, & Rayner, 2006 ; Vu et al., 2000 ; Vuong & Martin, 2011 ), we expect the meaning frequency of the ambiguous words and the sentential context to affect word recognition. Lexical processing in children, on the other hand, is expected to be slowed down by contextual integration, since previous studies indicated difficulties with context integration in word recognition and limited sensitivity to the frequency of competing meanings (see Booth et al., 2006 ).

Regarding the relation between lexical processing and cognitive skills and considering that adults are skilled readers, we expect correlations between working memory capacity and lexical decision times and also between inhibition and effective integration of the sentential context cues in word recognition to be more pronounced in the child dataset (for skilled reading effects see Gernsbacher, 1993 ; Gernsbacher et al., 1990 ; for children see Bunge et al., 2002 ; Casey et al., 2005 ; Gunter et al., 2003 ; Khanna & Boland, 2010 ; Protopapas et al., 2007 ).

Participants

20 children (F:10, M:10) aged 10;4 to 11;3 years old (M:10;8, SD:0;3) participated in the main study Footnote 7 . Their cognitive screening included (a) the Raven’s Progressive Matrices Test (Raven, 2003 ) as a well-validated measure of basic cognitive functioning for non-verbal intelligence (M: 30.65; SD: 2.27); no child participant was eliminated on the basis of their Raven’s Matrices performance, (b) the Digit Span Backwards Recall of the Wechsler Adult Intelligence Scale (WAIS-IV, 2008 ) as a measure of verbal working memory (M: 14.85; SD: 4.61), and (c) the Nonverbal Stroop Card Sorting Test (Roid & Koch, 2017 ) as an inhibitory control measure (M: 47.85; SD: 2.11). The adult participants’ age (N: 20; F:11, M:9) ranged from 23 to 38 years old (M:29, SD:5), all of them were university graduates (Μ: 19.1 years of education) and their cognitive screening included the Digit Span Backwards Recall of the Wechsler Adult Intelligence Scale (WAIS-IV, 2008 ) as a measure of verbal working memory (M: 23.6; SD: 4.55) and the Nonverbal Stroop Card Sorting Test (Roid & Koch, 2017 ) as an inhibitory control measure (M: 39.05; SD: 14.78). None of the participants had a hearing or visual impairment issue or any other language related difficulty; relevant information was obtained when consent form was signed in a questionnaire format and school records were also advised during the recruiting process of child participants.

Data Analysis

Our design involved two dependent variables, accuracy and Lexical Decision Times (LDTs). To analyze the data Footnote 8 , we performed two separate analyses for each dependent variable. More specifically, we ran repeated measures analyses of variance (ANOVA), with Bonferroni correction in the Post Hoc analysis, with Ambiguity (homonyms vs. controls), Frequency (dominant meaning high frequency vs. subordinate meaning low frequency) and Context (dominant meaning bias vs. subordinate meaning bias vs. unrelated) as the within subjects variables and Age (children vs. adults) as the between subjects variable. When the interaction among all factors was significant, we performed follow-up t -tests in each group in order to further explore the locus of the interaction. In the follow-up t -tests we firstly explored in which conditions we get a facilitation (positive priming, faster LDTs for visual targets following homonyms than control words) or an inhibition (negative priming, faster LDTs for visual targets following control words over homonyms) effect. We further explored (a) the effect of meaning frequency by comparing the priming effect for dominant with that for subordinate meaning visual targets in each context condition and (b) the effect of context by comparing the priming effect for the unrelated with the one for the dominant/subordinate context in each frequency meaning.

Lexical Decision Times (LDTs)

Firstly, we present the online data, lexical decision times (LDTs), on the visual targets. LDTs were screened for extreme values and outliers. Outliers were defined as LDTs above or below 2 standard deviations from the mean LDT in each condition separately per subject and item. Outliers were replaced with the mean LDT for each condition per subject and item once this value was removed. This procedure affected 2.75% of the data. To analyze the processing data, we excluded incorrect responses and we performed a repeated measures analysis of variance (ANOVA) with Bonferroni correction in the Post Hoc analysis, with Ambiguity (homonyms vs. controls), Frequency (dominant meaning high frequency vs. subordinate meaning low frequency) and Context (dominant meaning bias vs. subordinate meaning bias vs. unrelated) as the within subjects variables and Age (children vs. adults) as the between subjects variable; follow-up t -test comparisons were conducted where necessary. Table  2 shows the LDTs in visual word recognition per condition for both groups.

The analysis showed a main effect of Age [ F 1 (1, 38)  = 64.590, p  < .001, η 2 p  = 0.630; F 2 (1, 54)  = 2.456, p  < .001, η 2 p  = 0.978] with adults processing times being considerably faster than those of children (adults: 584 < children: 1103), a main effect of Ambiguity [ F 1 (1, 38)  = 15.349, p  < .001, η 2 p  = 0.288; F 2 (1, 54)  = 193.649, p  < .001, η 2 p  = 0.782] with homonyms processing being highly costly (homonyms: 889 > controls: 797), a main effect of Context [ F 1 (2, 76)  = 23.054, p  = < 0.001, η 2 p  = 0.378; F 2 (2, 108)  = 115.492, p  < .001, η 2 p  = 0.681] with faster LDTs for unrelated context and slower LDTs for subordinate context bias (unrelated: 806 < dominant context bias: 842 < subordinate context bias: 882; all pair comparisons: p  < .001) and an interaction among all factors [ F 1 (2, 76)  = 11.514, p  < .001, η 2 p  = 0.233; F 2 (2, 108)  = 69.031, p  < .001, η 2 p  = 0.561].

The analysis of the adult data revealed only a few significant comparisons. Specifically, with regard to ambiguity, a facilitation effect with control words being slower than homonyms was attested only for dominant meaning visual targets in unrelated contexts [ t 2 (27) = -3.207, p  = .003]. An inhibition effect with control words processed faster than homonyms was found for dominant meaning visual targets in the dominant meaning bias contexts [ t 2 (27) = 4.919, p  < .001] and for subordinate meaning visual targets in subordinate contexts [ t 2 (27) = 7.480, p  < .001]. With reference to frequency effects, the analysis shows that the dominant meaning is facilitated over the subordinate meaning in unrelated [ t 2 (27) = -2.473, p  = .020] and in subordinate meaning bias contexts [ t 2 (27) = -2.822, p  = .009]. Interestingly, the opposite effect is obtained in dominant meaning bias contexts, as facilitation was found for the subordinate over the dominant meaning [ t 2 (27) = 4.717, p  < .001]. Turning to the effects of context, we found that the dominant or the subordinate bias context did not facilitate the recognition of the homonym in either meaning bias. On the contrary, the unrelated context enhanced the facilitation (a) of the dominant meaning in dominant and subordinate bias contexts [dominant bias context: t 2 (27) = -5.848, p  < .001; subordinate bias context: t 1 (19) = -2.048, p  = .055; t 2 (27) = -3.474, p  = .002] and (b) of the subordinate meaning in the subordinate bias context [ t 2 (27) = -5.017, p  < .001].

Further analysis of the child dataset revealed that ambiguity adds processing cost across conditions [dominant meaning visual target – dominant context bias: t 1 (19) 3.452, p  = .003; t 2 (27) = 7.073, p  < .001; dominant meaning visual target – subordinate context bias: t 1 (19) = 4.365, p  < .001, t 2 (27) = 14.625, p  < .001; dominant meaning visual target – unrelated context: t 2 (27) = 2.320, p  = .028; subordinate meaning visual target – subordinate context bias context: t 1 (19) = 3.599, p  = .002, t 2 (27) = 8.689, p  < .001; subordinate meaning visual target – unrelated context: t 1 (19) = 4.005, p  = .001, t 2 (27) = 12.312, p  < .001] with one exception; a facilitation effect for homonyms over control words was obtained only with subordinate meaning visual targets in dominant bias contexts [ t 1 (19) = -3.564, p  = .002, t 2 (27) = -9.172, p  < .001]. Regarding frequency, the dominant meaning visual targets were significantly more primed than the subordinate meaning ones in unrelated contexts [ t 1 (19) = -2.994, p  = .007, t 2 (27) = -6.647, p  < .001]. The subordinate meaning visual targets, on the other hand, were facilitated over the dominant meaning ones in subordinate bias contexts [ t 2 (27) = 2.051, p  = .050] but also in dominant bias contexts [ t 1 (19) = 5.113, p  < .001, t 2 (27) = 10.354, p  < .001]. Finally, the biasing contexts were not found to facilitate the relevant meaning of the ambiguous word. The unrelated context facilitated the recognition of the dominant meaning as compared to both the dominant bias context [ t 2 (27) = -6.899, p  < .001] and subordinate bias context [ t 1 (19) = -3.320, p  = .004, t 2 (27) = -8.995, p  < .001], while the dominant bias context facilitated the recognition of the subordinate meaning visual targets as compared to the unrelated context condition [ t 1 (19) = 4.644, p  < .001, t 2 (27) = 15.532, p  < .001].

Interim Summary of LDT data

The across groups analysis revealed ambiguity and context main effects with increased processing time for homonyms and faster response times for the unrelated sentential context followed by the dominant context bias and lastly by the subordinate context bias. A closer look at the adult dataset showed that their performance while unaffected by ambiguity, was driven by frequency and context. The child dataset, on the other hand, exhibited a significant ambiguity effect, since homonymy added processing cost across conditions; frequency appeared to facilitate processing, while no context effects were attested in their performance.

We turn to the accuracy scores on the recognition of the visual targets per participant group (Table  3 ).

The analysis showed a main effect of Age [ F 1 (1, 38)  = 7.830, p  = .008, η 2 p  = 0.171; F 2 (1, 54)  = 36.416, p  < .001, η 2 p  = 0.403] with adults outperforming children (adults: 99.1% > children: 91.8%), a main effect of Frequency [ F 1 (1, 38)  = 7.868, p  = .008, η 2 p  = 0.172] with higher accuracy for dominant visual targets (dominant meaning visual target: 95.9% > subordinate meaning visual target: 95.1%), no main effects of Ambiguity or Context and an interaction among all factors [ F 1 (2, 76)  = 3.500, p  = .035, η 2 p  = 0.084; F 2 (2, 108)  = 9.580, p  < .001, η 2 p  = 0.151].

Additional analysis of the adult data showed a positive priming effect of ambiguity only for dominant meaning visual targets following a sentential context biasing towards the dominant meaning [ t 1 (19) = 2.854, p  = .010]. As far as frequency is concerned, accuracy on the dominant meaning visual targets was enhanced over the subordinate meaning visual targets only in dominant bias contexts [ t 1 (19) = 3.297, p  = .004, t 2 (27) = 2.287, p  = .030]. No context effects were observed in the adult data.

Further analysis of the child dataset revealed that the ambiguity negatively affected children’s performance since higher accuracy scores were found in control conditions compared to homonym ones [dominant meaning visual target – subordinate context bias: t 1 (19) = -2.370, p  = .029, t 2 (27) = -8.360, p  < .001; dominant meaning visual target – unrelated context: t 2 (27) = -2.581, p  = .016; subordinate meaning visual target – unrelated context: t 1 (19) = -2.645, p  = .016, t 2 (27) = -3.224, p  = .003]. Turning to frequency effects, the subordinate meaning visual targets were facilitated compared to the dominant meaning ones in subordinate [ t 1 (19) = -2.057, p  = .054, t 2 (27) = -6.793, p  < .001] but also in dominant biasing contexts [ t 2 (27) = -6.084, p  < .001]. On the other hand, context facilitated child visual word recognition. We found that appropriately biasing contexts facilitated accuracy on the favored meaning. More specifically, dominant bias contexts facilitated accuracy on dominant meaning visual targets compared to the unrelated contexts [ t 2 (27) = -2.738, p  = .011] and also subordinate bias contexts enhanced accuracy on the subordinate meaning visual targets compared to unrelated contexts [ t 2 (27) = -2.761, p  = .010]. Additionally, the subordinate bias contexts negatively affected accuracy on the dominant meaning visual targets compared to the unrelated contexts [ t 1 (19) = 2.209, p  = .040; t 2 (27) = 3.618, p  = .001; t 2 (27) = -3.261, p  = .003]. Finally, the dominant bias contexts also facilitated accuracy on the subordinate meaning visual targets as compared to unrelated contexts [ t 1 (19) = 2.121, p  = .047, t 2 (27) = -9.929, p  < .001].

Interim Summary of Accuracy data

The across groups analysis revealed that a main effect of frequency with higher accuracy for dominant meaning visual targets. The adult dataset analysis showed a positive effect of ambiguity and some facilitation of frequency. Meanwhile, the child dataset analysis showed the opposite pattern with regard to ambiguity, since they performed more accurately in control items as opposed to homonyms. Lastly, there was evidence that biasing contexts increased accuracy on the favored meaning.

Correlation Analyses

We performed correlation analyses in order to explore whether participants’ LDTs and accuracy associate with cognitive screening scores, namely working memory and inhibition. Firstly, we present the outcome of the correlation analyses regarding LDTs. The adult dataset showed no significant correlations, suggesting that adult lexical processing is unaffected by their cognitive resources; note, however, that the adult participants were all highly educated and there was no significant variance among them with regard to their cognitive screening.

The correlation analysis of the LDT child data, nevertheless, showed that their verbal working memory scores related to their processing performance for ambiguity resolution with longer LDTs correlating with lower Digit Backwards Recall scores but only when the primes included homonyms [dominant meaning visual target – dominant context bias: r (20) = − 0.624, p  = .003; dominant meaning visual target – subordinate context bias: r (20) = − 0.484, p  = .031; dominant meaning visual target – unrelated context: r (20) = − 0.480, p  = .032; subordinate meaning visual target – subordinate context bias: r (20) = − 0.633, p  = .003; subordinate meaning visual target – unrelated context: r (20) = − 0.512, p  = .021]. The correlation analysis of inhibitory control and processing lexical ambiguity did not show any significant association between the two but significant correlations were identified for the processing of control items with shorter processing times associated to better inhibitory control performance in some conditions [dominant meaning visual target – dominant context bias: r (20) = 0.433, p  = .056; dominant meaning visual target – unrelated context: r (20) = 0.512, p  = .020; subordinate meaning visual target – subordinate context bias: r (20) = 0.473, p  = .035].

The correlation analysis was followed up by a regression analysis so as to explore the predictive power of the cognitive measures in lexical processing. Verbal working memory scores significantly predicted LDTs for dominant meaning visual targets in the homonym condition (dominant context bias: ß = -30.503, t (19) = -3.392, p  = .003, Tolerance = 1.000, VIF = 1.000, sr  = − 0.624; subordinate context bias: ß = -37.773, t (19) = -2.347, p  = .031, Tolerance = 1.000, VIF = 1.000, sr  = − 0.484; unrelated context: ß = -34.963, t (19) = -2.324, p  = .032, Tolerance = 1.000, VIF = 1.000, sr  = − 0.480) and explained a significant proportion of variance in processing times (dominant context bias: R 2  = 0.390, F (1, 18) = 11.507, p  = .003; subordinate context bias: R 2  = 0.234, F (1, 18) = 5.509, p  = .031; unrelated context: R 2  = 0.231, F (1, 18) = 5.402, p  = .032). Inhibitory control scores significantly predicted LDTs in the control condition (dominant meaning visual target – dominant context bias: ß = 66.456, t (19) = 2.039, p  = .056, Tolerance = 1.000, VIF = 1.000, sr  = 0.433; dominant meaning visual target – unrelated context: ß = 65.853, t (19) = 2.549, p  = .020, Tolerance = 1.000, VIF = 1.000, sr  = 0.515; subordinate meaning visual target – subordinate context bias: ß = 69.444, t (19) = 2.278, p  = .035, Tolerance = 1.000, VIF = 1.000, sr  = 0.473) and explained a significant proportion of variance in processing times (dominant meaning visual target – dominant context bias: R 2  = 0.188, F (1, 18) = 4.156, p  = .056; dominant meaning visual target – unrelated context: R 2  = 0.265, F (1, 18) = 6.499, p  = .020; subordinate meaning visual target – subordinate context bias: R 2  = 0.224, F (1, 18) = 5.188, p  = .035).

We performed a correlation analysis of participants’ accuracy scores across conditions to the cognitive screening scores of working memory and inhibition. The analysis showed no significant correlations suggesting that lexical access itself is not affected by cognitive skills.

Interim Summary of Correlation Analyses

The correlation and regression analyses showed that adults’ lexical processing and accuracy was unaffected by their cognitive skills. Note however, they all had very similar cognitive skills. Similarly, the child correlation of accuracy scores did not reveal any relationship to cognitive capacity but the correlation analyses of their processing times did show a close relationship between (a) verbal working memory skills and the processing of homonyms and (b) inhibition and the processing of control items. These findings were also verified by the respective regression models. Overall, cognitive capacity resources appear to contribute to lexical ambiguity resolution when looking at developing language data but not when examining a stable language system in relation with cognitive skills verifying earlier research.

Visual Non-Word Recognition & Processing

With regard to the non-word items of the cross-modal priming task that support the lexical decision component of the task, we analyze the performance of participants in relation to the status of non-words as illegal or pseudo-words due to the fact that it informs us on how the mental lexicon functions providing evidence on the lexical identification skills of the participants and the lexical access process when no lexical entry is available. Table  4 shows the accuracy and the LDTs in the visual non-word recognition per condition for both groups.

The data analysis suggests that both children and adults are better and faster at identifying illegal items as non-words in Greek [Accuracy – children: t 1 (19) = 4.197, p  < .001, t 2 (59) = 7.001, p  < .001; Accuracy – adults: t 1 (19) = 10.018, p  < .001, t 2 (59) = 3.082, p  = .003; LDTs – children: t 1 (19) = -9.936, p  < .001, t 2 (59) = -14.677, p  < .001; LDTs – adults: t 1 (19) = -7.626, p  < .001, t 2 (59) = -17.936, p  < .001].

Next, the correlation analysis of visual non-word recognition and the cognitive screening measures showed there is a correlation only of processing time to verbal working memory scores in the child dataset with shorter LDTs relating to higher scores in the Digit backwards task [illegal: r (20) = − 0.550, p  = .012; pseudo: r (20) = − 0.531, p  = .016]. The follow-up regression analysis showed that verbal working memory scores significantly predicted LDTs for both non-word types (illegal: ß = -31.935, t (19) = -2.797, p  = .012, Tolerance = 1.000, VIF = 1.000, sr  = − 0.550; pseudo: ß = -31.088, t (19) = -2.656, p  = .016, Tolerance = 1.000, VIF = 1.000, sr  = − 0.531) and explained a significant proportion of variance in LDTs (illegal: R 2  = 0.303, F (1, 18) = 7.821, p  = .012; pseudo: R 2  = 0.281, F (1, 18) = 7.052, p  = .016). Similarly to the lexical ambiguity resolution data, cognition appears to contribute to non-word recognition when examining developmental data.

Interim Summary of non-word data

The non-word data analysis showed that illegal non-words are processed faster and more accurately compared to pseudo-words both by adult speakers and children. The correlation analysis of the non-word data and cognitive skills revealed that in the case of child data alone processing time of non-words and verbal working memory skills were closely related with better cognition skills boosting the performance of children.

The study set out to examine lexical access in Greek ambiguous words so as to identify the contribution of frequency and context in ambiguity resolution (Research Question (a) for developing and stable grammars (Research Question (b). To follow the course of meaning activation and selection, we opted for homonyms which due to their constant phonological and orthographic cues can help us disentangle the role of meaning frequency and context in ambiguity resolution. Earlier studies on lexical ambiguity resolution have identified sentential context and word frequency as key features that can affect lexical processing (Chen & Boland, 2008 ; Vu et al., 2000 ). Studies focusing on the performance of adults emphasize the frequency of alternative meanings as well as the interaction between contextual bias and frequency (Binder & Rayner, 1998 ; Chen & Boland, 2008 ; Vu et al., 1998 ); in particular, while processing unbiasing sentential contexts the most frequent, dominant meaning of an ambiguous word was found to be activated more quickly than less frequent, subordinate meanings (Dopkins et al., 1992 ; Lucas, 1999 ; Sereno, Brewer, & O’Donnell 2003 ; Simpson & Krueger, 1991 ). For younger speakers, on the other hand, sentential context appears to have an incremental effect in performance in late childhood while lexical-level effects such as frequency seem to decrease with age (Booth et al., 2006 ; Gernsbacher et al., 1990 ). Overall developmental data suggest that there are difficulties with regard to top-down processing with the difficulty to integrate linguistic context still being evident at the age of 12.

To accommodate this evidence, the current study tested both children and adults so as to provide a clearer outline of the monolingual mental lexicon. In addition, word–meaning association strengths in ambiguous words may vary significantly, and consequently processing can be costlier for linguistic context integration tasks often reported as a strong subordinate bias effect (Balota et al., 2001 ; Binder, 2003 ; Novick et al., 2005 ; Rayner et al., 2006 ; Sereno, O’Donnell, & Rayner 2006 ). In order to avoid this type of effect, the methodological design cross-checked the frequency of all relevant meanings of the homonyms tested and selected relatively equibiased items with a high frequency dominant meaning (74.8%) and a low frequency subordinate meaning (38.4%) excluding polarized items and ambiguous items with the same distribution of both meanings.

The data analysis of accuracy scores in visual word recognition showed a main effect of frequency in the dataset with dominant meaning visual targets being more accurately identified than subordinate meaning visual targets which is in line with earlier literature on lexical ambiguity resolution (Chen & Boland, 2008 ; Vu et al., 2000 ). Adults’ performance did not appear to be much regulated by ambiguity or context since their visual word recognition was rather intact with accuracy scores higher than 98% across experimental conditions. Further exploration of the child dataset showed that context interplayed with frequency during visual word recognition. Firstly, the accuracy on visual targets whose meaning was favored in the preceding context was facilitated compared to the unrelated context. Secondly, we found evidence that the context inhibited the non-favored meaning, as the subordinate bias context negatively affected accuracy of dominant meaning visual targets compared to the unrelated context. Notice, however, that performance on subordinate meaning visual targets was enhanced by both dominant and subordinate meaning biasing contexts as compared to unrelated contexts. This finding indicates that the children encounter some difficulties in effectively incorporating the sentential context cues during lexical ambiguity resolution, as reported in previous studies (Booth et al., 2006 ), particularly when word recognition involves the less frequent meaning of the homonym. The evidence with regard to accuracy of lexical access appears to verify our research hypothesis with regard to the child data and only partly with regard to the adult dataset who we anticipated would be more sensitive to frequency and context variables.

Turning to the adult processing data, the analysis did not show strong lexical priming effects. The adults’ performance on visual targets appears to be highly controlled and overall unaffected from ambiguity, with priming emerging only in two conditions; a facilitation effect for dominant meaning visual targets in unrelated contexts and an inhibition effect for dominant meaning visual targets in the dominant meaning bias contexts and for subordinate meaning visual targets in subordinate contexts. The former result suggests that when sentential integration is not demanding, as in unrelated contexts, homonyms can facilitate high frequency meaning targets, whereas when sentential context and targets match both meanings of homonyms remain active and have an inhibition effect. This evidence provides support to our hypothesis that the interaction of frequency and contextual bias can be critical in the processing of ambiguity by adult native speakers and not the independent contribution of context per se. Andreou et al. ( 2009 ), who tested adult native speakers of Greek, also reported limited priming effects with regard to context. In the case of our dataset, the highly automated processing of our adult participants can be attributed to the fact that they were highly qualified speakers with an average of 19.1 years of education; their skilled reading could cover the processing effects attested in earlier studies (see Duffy et al., 2001 ; Kellas et al., 1995 among others). However, considering the role of literacy skills in processing it is not surprising that the adult participants could outweigh linguistic information integration effects (see Gernsbacher & St. John, 2001 ; Rabagliati et al., 2013 ; Snedeker, 2013 ; Snedeker & Yuan, 2008 ; Trueswell & Gleitman, 2007). Nevertheless, this assumption requires confirmation with further lexical processing data to be obtained from speakers of different literacy profiles so as to be able to explain fully the impact of language external factors.

The analysis of the adult processing times showed an advantage for the dominant meaning in unrelated contexts, which is in line with previous work (Andreou et al., 2009 ). However, we found advantages for the dominant and the subordinate meaning visual targets in the contexts that are not biased towards them; the dominant is favored over the subordinate one in dominant bias contexts and the subordinate meaning in dominant bias contexts. It seems that the biasing contexts enhanced facilitation of the non-favored meaning. Notice, also, that the adult LDT data do not show evidence for context effects, in that dominant and subordinate meaning visual targets are facilitated in unrelated rather than in biasing contexts. These findings are difficult to accommodate within current lexical ambiguity resolution literature. One possible explanation that we would like to put forward relates to the high cognitive capacities and reading skills of our adult participants, which may have contributed to the suppression of the contextually favored meaning. A second explanation is a methodological one. More specifically, the ISI in this study was set at 0ms, due to previous findings in Greek (see Andreou et al., 2009 ). This short ISI interval, may, however, have impeded the emergence of contextual effects. We leave this issue open for future investigation.

The children’s processing times for ambiguity resolution showed a strong ambiguity effect with homonyms processing being highly costly along with a main effect of context with faster LDTs for unrelated context and slower LDTs for subordinate context bias (unrelated context < dominant context bias < subordinate context bias). Frequency was also a significant factor in ambiguity resolution; specifically, for unrelated contextual primes faster response times were found for dominant meaning visual targets as opposed to subordinate meaning visual targets. Moreover, as attested in the adult data, the biasing contexts did not facilitate the processing of the ambiguous words, since on one hand the unrelated context primed the dominant meaning more effectively than the dominant and the subordinate bias contexts and on the other hand the dominant bias context enhanced the subordinate meaning compared to the unrelated context. These two findings corroborate the child accuracy data of our study and provide further support for the integration difficulties that children even in late childhood are still facing verifying our research hypothesis (Booth et al., 2006 ; Gernsbacher et al., 1990 ).

Lastly, both for children and adults’ cognitive resources are critical in lexical processing. Prior research has shown that the children’s difficulties to engage in top-down processes is related to the availability of cognitive resources required to process language (Coch & Holcomb, 2003 ; Khanna & Boland, 2010 ). In view of the working memory requirements for lexical access, we included screening of working memory and inhibition which have been identified as critical for top-down processing (for detailed discussion see Bunge et al., 2002 ; Casey et al., 2005 ; Protopapas et al., 2007 ). The analysis revealed that decision making in lexical access is not related to the cognitive resources available either in the child or adult dataset. There were though significant correlations when we examined the LDTs of children. In the case of homonyms and non-words better verbal working memory skills were correlated to faster processing and in the case of control unambiguous items better inhibition skills facilitated language processing. An intriguing finding is that working memory and inhibitory control differentially affect lexical processing. Working memory is involved in the recognition of visual targets whose meaning has been primed by an ambiguous word but also by preceding contextual information. It seems, thus, that the capacity of working memory has a significant impact on word recognition when the retainment of various information sources, such as contextual meaning, contributes to faster lexical processing. Τhe processing of control words, by contrast, demands inhibitory control skills, since preceding lexical and contextual cues need to be suppressed for the recognition of the control words to be fast and efficient. Overall, the correlations attested in our child data between lexical processing and cognitive skills suggest that for developing grammars of 10;4 to 11;3 yrs old children the automatic spreading of activation within the semantic network is supported by cognitive skills such as working memory thus verifying our research hypothesis. The adult data, though, did not exemplify this effect due to the high educational background of the participants and the homogeneity in performance in their cognitive screening.

To conclude, with regard to Research Question (a) our adult data indicate that meaning frequency has a significant impact on lexical ambiguity resolution, while the context effects were limited, and, thus argue in favor of accounts in lexical processing which prioritize frequency over contextual cues (Kellas et al., 1995 ; Vu et al., 2000 , 2003 ). The child data, on the hand, display a somewhat different picture, since contextual information does not facilitate word recognition. The child lexical processing system appears to be more affected by frequency than by sentential context and, thus, displays a bottom-up pattern. Finally, in relation to Research Question (b), the processing of ambiguous words is costly for children and affected by working memory and inhibitory skills.

With regard to the modality of the tasks, most studies present the ambiguous word in written form apart from a few studies (e.g. Chen & Bolland, 2008; Friedrich & Friederici, 2005 ; Henderson, Clarke & Snowling, 2011).

Note that the primary focus of the Andreou et al. ( 2009 ) study is to examine the effects of sentential context on lexical ambiguity resolution in patients with schizophrenia; hence, only the data of the non-clinical population is referred to with this section.

ISI in experimental design refers to the temporal interval between the offset of one stimulus to the onset of another.

In a congruent relationship both the context and the target related to the same meaning, while in an incongruent relationship context and target related to different meanings.

Top-down parsing models prioritize context and background knowledge in explaining language processing, while bottom-up parsing models rely on semantic, syntactical and phonological properties to describe the process (see detailed discussion in Rabagliati et al., 2013 ).

Note that the sample size in this pre-task was increased to ensure that we have identified the appropriate target items with word length and frequency conditions also satisfied.

Ethical consideration was given to all aspects of experimental study and written informed consent from legal guardians of minors was obtained. Ethics approval was granted by the AUTH Ethics Committee No. 212201/2021.

Out of the 30 homonyms, 2 words were excluded from the analysis due to an audio tech issue.

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Kaltsa, M., Papadopoulou, D. The Processing of Lexical Ambiguity: Evidence from Child and Adult Greek. J Psycholinguist Res 53 , 16 (2024). https://doi.org/10.1007/s10936-024-10063-y

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