#abstractreasoning — Public Fediverse posts
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DATE: August 26, 2026 at 10:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: General intelligence predicts actual cultural problem solving across three countries
Navigating diverse cultural environments requires a specific set of skills known as cultural intelligence. A recent study of adults in the United States, China, and Greece reveals that tests measuring how people actually solve cross-cultural problems capture different mental abilities than surveys asking people to rate their own intercultural skills. The research, published in Intelligence, suggests that these two approaches function similarly across entirely different cultures while assessing distinct psychological traits.
As globalization increasingly brings people of varying backgrounds into close contact, individuals often find themselves in unfamiliar social terrain. A lack of awareness in these situations can lead to misunderstandings or interpersonal friction. Cultural intelligence involves the informal, learned knowledge required to act appropriately around people who hold different beliefs, customs, or ideologies.
Educational researcher Rui Wang of Nanyang Technological University and psychologist Robert J. Sternberg of Cornell University led an investigation to understand how to best measure this capability. For years, researchers have evaluated cultural intelligence using self-report questionnaires. These surveys ask individuals to rate their own attitudes and motivations during cultural interactions. Such tests measure “typical performance,” which refers to how people believe they usually act on a daily basis.
Self-reports can be subject to self-perception biases. People might overestimate their abilities, a phenomenon psychologists refer to as the Dunning-Kruger effect. They might also provide answers they think the researchers want to hear. To address this gap, Sternberg previously developed a “maximum performance” test for cultural intelligence.
Instead of asking people how they might act, a maximum performance test presents them with hypothetical challenges and scores their actual problem-solving responses. The assessment asks individuals to navigate imagined situations, such as figuring out local norms while traveling for leisure or negotiating a business deal in an unfamiliar country.
Until now, this scenario-based test had only been evaluated in Western, industrialized nations. Wang, Sternberg, and their team wanted to see if the test worked reliably in other parts of the world. They also wanted to determine if the two types of tests consistently measured different aspects of the mind.
The research team recruited 454 university students from three distinct regions. The sample included 157 students from the United States, 140 from China, and 157 from Greece. Each participant completed an online assessment that included both types of cultural intelligence measures. All materials were translated into the local languages by native speakers to ensure accuracy.
For the self-report measure, participants rated 20 statements about their own cross-cultural awareness and behavior. For the scenario-based test, they typed out open-ended solutions to 24 hypothetical challenges. Independent raters from each respective country then scored these written answers on a scale from zero to five, looking for thoughtful, unique, and culturally appropriate solutions.
In addition to the cultural assessments, the participants took standardized tests of abstract reasoning and fluid intelligence. Fluid intelligence is the ability to think logically and solve novel problems without relying on prior knowledge. To measure this, participants completed a number series test and a matrix reasoning task, which required them to identify underlying patterns in geometric shapes.
The students also completed a personality inventory. This survey measured traits like extraversion, openness to experience, and conscientiousness. Finally, the participants answered questions about their personal views on the value of learning foreign languages, living abroad, and encountering people with vastly different opinions.
When the researchers analyzed the results, they found that the scenario-based test and the self-report survey were largely unrelated to each other. This separation occurred consistently in all three countries. The two tests appeared to tap into completely different psychological domains.
Scores on the scenario-based test were closely linked to abstract reasoning abilities and positive views on cultural exchange. Participants who performed well on the pattern-recognition tasks also tended to give more nuanced, culturally sensitive answers to the hypothetical scenarios. This pattern suggests that actual cross-cultural problem solving relies heavily on general cognitive skills.
Conversely, scores on the self-report survey were closely tied to personality traits. Participants who described themselves as highly extraverted, open-minded, and conscientious also rated their own cultural intelligence highly. Their actual problem-solving abilities on the abstract tasks did not predict their self-reported cultural scores.
The underlying psychological structure of these tests was nearly identical in the American and Chinese samples. In both groups, cultural problem-solving grouped together with general intelligence, while self-reported cultural skills grouped together with personality traits.
The Greek sample displayed a slightly altered pattern. Among the Greek participants, high scores on the scenario-based test were linked to both general intelligence and certain personality traits, such as high extraversion.
The researchers noted that national college entrance exams in Greece emphasize argumentative essay writing. This educational background might have influenced how the Greek students approached the open-ended scenarios, blending their cognitive skills with their expressive personality traits.
These results do not imply that one test is fundamentally superior to the other. Rather, the two assessments capture complementary halves of a broader psychological picture. Self-reports measure a person’s willingness and attitude toward diverse interactions. Scenario tests measure their cognitive capacity to handle the actual logistics of those interactions.
The study relied exclusively on university-aged adults. The results might not reflect the developmental arc of older individuals who have spent decades living and working abroad. Cultural intelligence is shaped through extended, real-world experience. A younger demographic might approach the open-ended questions differently than seasoned professionals who possess extensive practical knowledge.
Additionally, taking a time-limited, open-ended written test requires specific academic skills. Participants who are more willing to write lengthy, elaborate answers might achieve higher scores on the scenario test simply because of their test-taking stamina. Future investigations will need to examine how varied educational practices and language processing demands influence how people demonstrate their cultural adaptability.
The study, “Cultural intelligence in context: Examining the generalizability of a cultural intelligence measure across three countries,” was authored by Rui Wang, Arezoo Soleimani Dashtaki, Xueni Zhang, Konstantinos G. Tsigaridis, and Robert J. Sternberg.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #CulturalIntelligence #CrossCulturalProblemSolving #ScenarioBasedTesting #SelfReportVsPerformance #GlobalEducation #AbstractReasoning #FluidIntelligence #DunningKrugerAwareness #CognitionAndCulture #IntlStudyFindings
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DATE: August 14, 2026 at 09:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Neuroscientists uncover a universal mechanism for human decision making
When humans learn new rules to make decisions, our brains appear to use the exact same information-gathering process that they use to process basic physical sensations. A recent study shows that a specific brain wave associated with collecting evidence readily adapts to track arbitrary, newly learned categories. The research was published as a preprint in The Journal of Neuroscience.
To understand how we make choices, neuroscientists often rely on a concept called evidence accumulation. This theory suggests that the brain acts like a bucket collecting drops of water. As sensory information comes in, the brain continuously gathers this evidence until it reaches a specific threshold, triggering a final decision.
Researchers can observe this accumulation process in real time using an electroencephalogram, or EEG. By placing sensors on a person’s scalp, scientists track electrical patterns known as brain waves. One specific pattern, called the centro-parietal positivity, reliably mirrors the evidence accumulation process.
The centro-parietal positivity presents as a gradual buildup of positive electrical voltage in the brain. This voltage climbs steadily while a person weighs their options. The electrical signal peaks just before the individual executes a response, like pressing a button.
Prior studies have shown that this brain wave tracks evidence from physical stimuli, such as a group of dots moving across a screen. As more dots move in the same direction, the electrical signal climbs faster. It also tracks information pulled from memory, like recalling trivia facts, or basic semantic knowledge.
Other research has found that the brain can accumulate evidence based on fixed, universally shared visual concepts. For instance, people naturally distinguish between vertical lines and diagonal lines. The brain uses these permanent visual frameworks to sort out incoming information.
Scientists did not know if the brain could apply this accumulation mechanism to entirely arbitrary rules. If a rule is newly invented and highly specific to one person, the evidence does not actually exist in the visual environment. Instead, the brain must compute the evidence internally by comparing what it sees against a newly learned, imaginary standard.
University of Nevada, Reno researchers Arianna Thoksakis and Edward F. Ester designed a study to test this question. They wanted to see if the brain’s decision-making machinery is truly flexible across different types of information. If so, the centro-parietal positivity should respond to abstract, newly learned rules just as it responds to direct sensory input.
The researchers recruited volunteers to complete a visual categorization task while hooked up to an EEG machine. Data from 38 participants was ultimately included in the analysis. The participants viewed circular images filled with hundreds of parallel lines.
During a training phase, participants had to categorize these images into two distinct groups by pressing specific keys on a keyboard. The researchers assigned a hidden, arbitrary dividing line for each participant. For example, a boundary might be set at exactly 73 degrees, completely invisible to the individual.
Any lines tilted counterclockwise to this specific angle belonged to the first category, while clockwise lines belonged to the second category. Through trial and error, guided by correct or incorrect feedback after every choice, the participants had to figure out their unique boundary. Most volunteers learned the invisible rule within a few short rounds.
Once the participants understood the rule, they moved on to the main task. The researchers presented lines tilted at specific angles relative to the participant’s hidden boundary. Some images featured lines tilted 45 degrees away from the boundary, making them easy to categorize. Other images were much harder, featuring lines tilted just two degrees away from the dividing line.
Behavioral results showed that participants were faster and more accurate when the lines were rotated further from their learned boundary. To connect this performance to the brain’s internal processes, the researchers used a mathematical framework called a drift-diffusion model. This approach separates the raw speed of a physical reaction from the cognitive process of weighing options.
The mathematical model estimates a specific metric known as the drift rate, which represents the speed of information gathering. The calculations confirmed that drift rates increased steadily as the lines moved further from the boundary. Essentially, the larger the angular distance from the hidden rule, the stronger the evidence became. This stronger evidence allowed the brain to accumulate information at a much faster pace, leading to quicker choices.
Next, the researchers examined the EEG data to see if the brain’s electrical signals matched this behavioral pattern. They measured the centro-parietal positivity buildup during the moments leading up to each participant’s button press. The electrical slope grew much steeper for images that were further from the category boundary.
The team then compared the behavioral math to the electrical brain recordings. They found a strong correlation across the participants. Individuals who showed a high behavioral sensitivity to the visual categories also displayed a highly sensitive electrical buildup in their brain waves.
This correlation suggests that the electrical signal is a direct reflection of the underlying decision variable. The brain’s machinery for accumulating physical sensations and its machinery for accumulating computed, abstract evidence are not separate systems. They appear to be a single, highly adaptable mechanism.
To verify that this electrical buildup was truly about decision-making, the researchers checked another region of the brain entirely. They analyzed beta waves over the motor cortex, which specifically control the physical movement of the hands and fingers. Because the right side of the brain controls the left hand and vice versa, researchers can track exactly when the brain prepares to push a button.
They needed to ensure the decision signals were not just the result of a participant flexing their muscles to press a key. While the motor cortex did show the expected activation as participants prepared to respond, this activity did not change based on the difficulty of the image. The motor preparation remained identical whether the lines were two degrees or 45 degrees from the boundary. This confirms that the centro-parietal positivity reflects the mental act of deciding, rather than the physical act of moving.
There are a few methodological details to consider regarding the study design. When an image suddenly appears on a screen, it causes a rapid burst of visual processing in the brain. Because participants responded relatively quickly, this initial visual response could overlap temporally with the decision-making brain waves being measured.
While this visual overlap is present, it is unlikely to fully explain the strong correlation seen between individual brain waves and computational drift rates. Future studies could separate the visual onset from the decision-making period to completely rule out any sensory interference.
Additionally, this experiment relied on a very specific type of visual feature. Participants evaluated a single dimension, which was the orientation of straight lines. The rule separating the categories was also absolute, relying on a hard dividing line.
In natural environments, categories are rarely this simple. Objects belong to categories based on a mixture of shapes, colors, and textures, and the boundaries are often probabilistic rather than absolute. Testing whether the brain’s evidence accumulation mechanism works the same way for these messier, real-world categories will require additional research.
The study, “Neural Measures of Human Decision Making Track Evidence Accumulation in Learned Space,” was authored by Arianna Thoksakis and Edward F. Ester.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #Neuroscience #DecisionMaking #EvidenceAccumulation #CentroParietalPositivity #EEG #DriftDiffusionModel #AbstractReasoning #VisualCategorization #NeuralMarkers #BrainWaves
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CW: Solution
The correct answer: E
Rule 1️⃣ : The black circle moves one place clockwise in each turn along the vertices of the octagon.
Rule 2️⃣ : The black triangle moves 3 places anticlock-wise in each turn along the vertices of the octagon.
Rule 3️⃣ : Every time the black circle and the black triangle overlap, a black ring is added inside the grey octagon.
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Look at the first line of images.
Which one among A, B, C, D, E is the next in sequence?
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CW: Solution
Correct answer: D
Rule 1: The vertical lines shift one place to the right each time. The right-most line reappears on the left.
Rule 2: The four shapes inside the rectangles and the circle rotate one place clockwise in each step.
[I selected the correct answer through a partial reasoning. I didn't guess rule 2 but I spotted from the sequence that the shape in the circle should be a ⭐ (C, D, E) and that the sticks should be low, high, medium (D)]
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Look at the first line of images.
Which one among A, B, C, D, E is the next in sequence?
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CW: Solution
Correct answer: D
Rule 1: The bottom-left half of the background is always black. The other half is randomly white or grey.
Rule 2: The double-line arrow (with the big arrow-head) spins anticlockwise, one place at a time.
Rule 3: The single-line arrow spins clockwise, two places at a time.
#EPSOPrep
#EPSO
#AbstractReasoning
#EUrope[Source: The Ultimate Test Book Administrators 2025, Andràs Baneth]
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Look at the first line of images.
Which one among A, B, C, D, E is the next in sequence?
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"Yesterday OpenAI announced some very impressive results from their not-yet-released o3 model. According to the announcement, o3 has made enormous progress over its predecessors on several “reasoning” benchmarks, in particular, two quite difficult ones: Frontier Math, a benchmark containing hundreds of unpublished math problems that are known to be hard even for human math whizzes, and the Abstraction and Reasoning Corpus (ARC), a collection of concept-induction tasks which I’ve written about here, here, and here.
In this post I’ll discuss the o3 results on ARC. If you’re interested in AI and active on social media, you’ve likely already heard about these results, but I’ll try to add more context and my own thoughts here."
https://aiguide.substack.com/p/did-openai-just-solve-abstract-reasoning
#AI #GenerativeAI #OpenAI #o3 #ArcPrize #AbstractReasoning #LLMs