A series of five studies found that conspiracy beliefs were higher in societies with lower freedom of speech and lower population support for freedom of speech. Additionally, the experimental reduction of perceived freedom of speech increased conspiracy beliefs. The research was published in the European Journal of Social Psychology.

Conspiracy beliefs are explanations for events that assume powerful groups are secretly manipulating outcomes behind the scenes. They tend to arise when people feel uncertainty, lack control, or distrust official sources of information. Such beliefs simplify complex realities by offering a clear villain and a seemingly coherent narrative.

They can spread quickly because they appeal to emotions more than evidence and rely on dramatic, memorable claims. People who hold them tend to selectively seek information that confirms their views while dismissing contradictory facts.

Conspiracy beliefs can create strong group identities, giving believers a sense of belonging and special insight. At the same time, they can undermine trust in institutions, science, and democratic processes. Persistent conspiracy thinking can affect decision-making in areas like health, politics, and personal relationships. These beliefs are difficult to change because the individuals holding them tend to interpret counterarguments as part of the supposed conspiracy.

Study author Paul Bertin and his colleagues investigated the relationship between freedom of speech and conspiracy beliefs, both at the country level and using experimental designs. In their experimental studies, they also focused on isolating the specific effects of freedom of speech from the potential effects of overall perceptions of democracy.

In their first study, the authors calculated the associations between several measures of freedom of speech (using data ranging from 26 to 68 countries), conspiracy beliefs about COVID-19 (measured during the 2020 pandemic in 69 countries), support for freedom of speech, corruption perceptions, and perceptions of how democratic a society is.

In experiments 2a and 2b, participants—who were 190 English-speaking Americans recruited via Prolific (2a) and 90 Belgian students (2b)—imagined themselves as citizens of a fictional country. They were randomly divided into two groups.

One group read an article describing the freedom of speech in that fictional country as low (online opinion filters, persecution of journalists, surveillance of citizens), while the other read an article describing the freedom of speech as high (citizens can freely share their opinions, independent media exist, peaceful protests are allowed).

After this, they read a fictional article about a car crash in which an opposition leader, who was a favorite in upcoming elections, died. The text stated that the government had officially declared the event an accident, but that a fellow party member suspected the government was involved.

Participants rated how much they believed this claim, and the authors took this rating as an indicator of the participants’ endorsement of conspiracy beliefs. In Study 2b, the authors also included an assessment of generic conspiracy beliefs, rather than just the rating of the specific situation in the story.

Studies 3a and 3b aimed to replicate Studies 2a and 2b in a real-world context and with a control condition. Participants in Study 3a were 436 French-speaking individuals recruited via Foule Factory, while Study 3b included 498 French-speaking participants recruited via Prolific.

In these studies, participants were told that they would participate in a task aimed at improving an infographic showing the level of freedom of speech in different countries. Across different conditions, the authors manipulated the indicators of freedom of speech in France, the participants’ country.

The researchers then checked that participants had registered the presented information about freedom of speech in France and had them complete assessments of conspiracy theory beliefs adapted to the French context. Study 3b differed from this procedure in that information about freedom of speech in France was presented as “projections into the future,” not as current data.

Results of the first study showed that conspiracy beliefs tended to be higher in countries with lower freedom of speech and lower support for freedom of speech. Conspiracy beliefs also tended to be higher in countries perceived as less democratic and more corrupt. However, the association with freedom of speech became non-significant when the authors controlled for perceptions of corruption and democracy.

Results of Studies 2a and 2b showed that conspiracy beliefs were higher when participants perceived freedom of speech as low. This effect remained even after the authors controlled for the perceived level of democracy.

Study 3a did not find an association between how freedom of speech in France was presented and the endorsement of conspiracy beliefs; the authors suspect that participants might not have accepted the information about freedom of speech in France (their own country) as credible. In Study 3b, when freedom of speech information was presented as projections for the future, higher levels of conspiracy beliefs were again registered in participants who were shown projections that freedom of speech in France would become low.

“We showed that conspiracy beliefs about the COVID-19 were higher in countries with lower levels of expert-estimated and support for freedom of speech. However, the significance of these effects faded after accounting for two confounders, namely, corruption and electoral democracy. Switching to experimental designs, we observed that freedom of speech causally impacted conspiracy beliefs in two studies conducted in a fictional context. This effect was robust to the control of perception of democracy,” the study authors concluded.

The study contributes to the scientific understanding of the links between freedom of speech and conspiracy beliefs. However, it should be noted that the number of countries used in Study 1 was quite low for the statistical procedures the authors applied, resulting in low statistical power.

Additionally, in Studies 2a and 2b, participants could reasonably have expected that a government already persecuting people for independent opinions might also resort to killing an opposition figure, as such events do happen in real-world conditions. If that occurred, their ratings might not have reflected the effects of freedom of speech per se on conspiracy beliefs, but rather rational assessments of the personal safety of public figures in totalitarian regimes.

The paper, “The Impact of Freedom of Speech on Conspiracy Beliefs,” was authored by Paul Bertin, Elisa Tognon, Kenzo Nera, Rrita Bajraktari, Pit Klein, Vincent Yzerbyt, and Olivier Klein.

A study using electroencephalography found that the extent of memory retention after sleep is associated with time spent in slow wave sleep. The location where the brain shows neural activity associated with successful memory recall shifted from the parietal areas to the anterior temporal lobe after sleep. The paper was published in Neuropsychologia.

Slow wave sleep is the deepest stage of non-REM sleep, characterized by large, slow brain waves and a highly reduced level of consciousness. During this stage, the brain significantly alters its activity, allowing neurons to rest and recover from daytime demands. It plays a central role in consolidating memories, especially facts and skills learned throughout the day.

Slow wave sleep also supports physical restoration by promoting the release of growth hormone and facilitating tissue repair. The immune system becomes more active during this phase, helping the body fight infections and maintain long-term health. Because the brain is less responsive to external stimuli, slow wave sleep provides the most uninterrupted and restorative rest.

This stage helps clear metabolic waste from the brain, contributing to long-term cognitive health. People who get too little slow wave sleep experience reduced attention, poorer memory, and diminished emotional regulation the following day. Slow wave sleep also helps stabilize neural circuits, making future learning easier.

Study author Simon Faghel-Soubeyrand and his colleagues wanted to assess how sleep may support the reorganization of memory recall networks in the brain. They note that our memories are not static, but that they change in both strength and quality over time. Previous studies indicate that, as these changes happen, the location of brain activity during recall changes. The optimal time for this consolidation to occur is during sleep, when the brain is sheltered from external stimulation. They conducted a study to examine this using high-density electroencephalography (EEG).

Study participants were 24 university students. Their average age was roughly 23 years. Five of them were male.

The participants completed two separate sessions of word-image memory tasks. Word-image memory tasks require people to learn and later recall or recognize associations between written words and corresponding pictures. The two sessions were 7–8 days apart. One set of sessions was “object sessions,” where the images participants learned to associate with words were either a car or a guitar. The other set of sessions was “scene sessions,” where participants associated words with either a house or a corridor. Within a session, images were paired with 100 unique verbs.

Each of these sessions was followed by two recall sessions. One recall session happened before sleep, while the second happened after sleep. In general, the first recall session happened in the evening. The participants then slept through the night. The experimenter would wake them at 7:30 the next morning, and after a few minutes to fully awaken, they would again try to recall what they learned. During these activities, study authors took EEG recordings of the participants’ brain activity.

Results showed that the location in the brain where the strongest signs of recall activity occurred shifted from before to after sleep. While the primary activity during memory recall was in the parietal area of the brain before sleep, it moved towards the anterior temporal lobe after sleep. Individuals who spent more time in slow wave sleep tended to recall more of what they learned after sleep, and the shift in the location of brain activity during recall tended to be greater in these individuals.

Overall, results indicated that the profiles of brain activity during successful recall of memories changed overnight and that the extent of these changes was higher in individuals who spent more time in slow wave sleep.

“Together, these findings suggest a link between SWS [slow wave sleep] and the consolidation and functional reorganisation of episodic memory networks,” study authors concluded.

The study contributes to the scientific understanding of the way memory retention and recall function. However, the study was conducted on a small sample of students. Results on other demographic groups and older individuals might differ.

The paper, “Slow wave sleep is associated with a reorganisation of episodic memory networks,” was authored by Simon Faghel-Soubeyrand, Polina Perzich, and Bernhard P. Staresina.

A study comparing musicians with non-musicians across different age groups in India found that the ability to perceive speech in a noisy environment is lower in older participants, indicating that it declines with age. However, these differences between groups of different ages were more pronounced in non-musicians, indicating that age-related cognitive decline in this ability might be slower in musicians. The research was published in the Journal of Otology.

Age-related hearing loss, also called presbycusis, is a gradual decline in the ability to hear high-frequency sounds as people get older. It typically results from cumulative damage to the delicate hair cells in the inner ear that are responsible for converting sound waves into neural signals. Genetic predisposition, lifetime noise exposure, cardiovascular health, and metabolic conditions such as diabetes all influence how quickly it develops.

The decline often begins subtly, making it harder to understand speech in noisy environments or to distinguish similar consonants. Over time, people may feel that others are mumbling, need to increase the volume on devices, or struggle with group conversations. Because the change is slow, many individuals do not recognize the extent of their hearing loss until it becomes functionally limiting.

Study authors Kruthika S. and Ajith Kumar Uppunda wanted to explore how the ability to perceive speech in a noisy environment—i.e., speech perception in noise (SPiN) abilities—changes with age. They noted that musicians with lifelong musical training often exhibit a noticeable advantage in comprehending speech in noise.

However, prior research has been inconsistent; while some studies show clear benefits, others (such as a 2014 study by Ruggles et al.) found no differences when comparing young musicians to non-musicians. With this in mind, the study authors set out to explore how musicians and non-musicians of different ages differ in their speech perception in noise abilities.

Study participants were 75 musicians and 75 non-musicians. They were divided into five age groups: 10–19 years, 20–29 years, 30–39 years, 40–49 years, and 50–59 years, with each age group consisting of 15 musicians and 15 non-musicians.

First, study participants were screened to ensure they had normal hearing thresholds and outer hair cell function, ruling out standard clinical hearing loss. After that, they underwent an assessment of speech perception in noise using the Kannada Sentence Identification Test.

Results showed no differences between musicians and non-musicians in their basic abilities to hear tones or in the functioning of the cochlea’s outer hair cells. However, musicians performed better than non-musicians on the speech-in-noise tasks across all age groups. As expected, the ability to perceive speech in noise was lower in older participants, but this decline was faster in non-musicians than in musicians. Non-musicians began to show significant deterioration in the 40–49 age range, while musicians maintained their performance levels until the 50–59 age range.

“Music training can significantly delay or lessen the degenerative consequences of the aging process on SPiN [speech perception in noise]. Furthermore, the current study found that music training increases SPiN capacities in people of different ages. Thus, musical activities, if incorporated into a comprehensive rehabilitation strategy in aging individuals, may promote healthy aging,” the study authors concluded.

The study contributes to the scientific understanding of age-related changes in auditory processing. However, it should be noted that this was not a longitudinal study, but a study examining individuals of different ages at the same time. Because of this, it is not possible to know for sure whether the observed effects are truly effects of aging or differences between generations of people.

The paper, “Non-Musicians Experience Early Aging in Speech Perception in Noise Abilities Compared to Musicians,” was authored by Kruthika S. and Ajith Kumar Uppunda.

An analysis of policy documents from 116 R1 U.S. universities found that 63% of these institutions encourage the use of generative AI, with 41% offering detailed guidance for its use in the classroom. More than half of the institutions discussed the ethics of generative AI use, while the majority of guidance focused on using generative AI for writing activities. The research was published in Computers in Human Behavior: Artificial Humans.

Generative AI is a type of artificial intelligence that creates new content such as text, images, audio, code, or video based on patterns learned from large datasets. It works by using models like neural networks to predict and generate outputs that resemble human-created content.

People use generative AI to write documents, summarize information, create artwork, design products, and automate routine tasks. It also supports scientific research by analyzing data, generating hypotheses, and assisting in code or experiment design. Businesses use it for customer support, marketing, prototyping, and improving productivity across many workflows.

In education, generative AI helps students learn by providing explanations, tutoring, and personalized feedback. In medicine, it assists with interpreting data, drafting reports, and even exploring molecular designs for new drugs. Artists and designers use it to explore creative variations and accelerate their creative process. However, generative AI also raises concerns about misinformation, copyright issues, and ethical use.

Study author Nora McDonald and her colleagues wanted to explore what guidance higher education institutions were providing to their constituents about the use of generative AI, what the overall sentiment was regarding its use, and how that sentiment was manifested in actual guidelines.

They were also interested in ethical and privacy considerations, if represented in the guidelines. These authors note that, although the use of generative AI—primarily ChatGPT—became very popular very quickly after its release, there are voices in education that remain staunchly opposed to the use of such applications.

The study authors collected policy documents and guidelines that were publicly available on the internet from 116 R1 institutions, utilizing the Carnegie Classification framework for classifying colleges and universities in the United States. According to this classification, R1 institutions are universities with the highest level of research activity.

The researchers downloaded documents that specifically dealt with generative AI, resulting in a total of 141 documents. Four researchers reviewed 20 of these documents to create a codebook (a coding system for classifying the documents according to their contents). They then used this system to categorize all the other documents.

Results showed that 56% of institutions provided sample syllabi for faculty that included policies on generative AI use, while 55% gave examples of statements regarding usage permissions, such as “embrace,” “limit,” or “prohibit.” Fifty percent provided activities that would help instructors integrate and leverage generative AI in their classrooms, while 44% discouraged the use of detection tools meant to catch AI-generated work. Fifty-four percent provided guidance for designing assignments in ways that discourage the use of generative AI by students, and 23% gave guidance on how to use AI detection tools.

Overall, 63% of universities encouraged the use of generative AI, and 41% offered detailed guidance for its use in the classroom. The majority of guidance focused on writing activities; references to code and STEM-related activities were infrequent and often vague, even when mentioned. Fifty-two percent of institutions discussed the ethics of generative AI regarding a broad range of topics.

“Based on our findings we caution that guidance for faculty can become burdensome as policies suggest or imply substantial revisions to existing pedagogical practices,” the study authors concluded.

The study contributes to the scientific understanding of the stances U.S. universities take on generative AI use. However, the results of the study are based on an analysis of policy documents, not on the study of real classroom practices, which might not fully reflect the provisions specified in the policies.

The paper, “Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,” was authored by Nora McDonald, Aditya Johri, Areej Ali, and Aayushi Hingle Collier.

A study of individuals with autism and their siblings and parents found that autistic individuals and their siblings used fewer causal explanations to connect story elements when asked to tell a story based on a series of pictures. They also used fewer descriptions of the thoughts and feelings of protagonists. The research was published in the Journal of Autism and Developmental Disorders.

Autism is a neurodevelopmental condition characterized by differences in social communication, sensory processing, and patterns of behavior or interests. People on the autism spectrum tend to perceive and organize information in distinctive ways that can be strengths in some contexts and challenges in others. Among other things, they seem to differ from their neurotypical peers in the way they tell stories—specifically regarding their narrative patterns and abilities.

Research shows that many autistic individuals produce narratives that are shorter or less elaborated compared to neurotypical peers, focusing more on concrete details than on social or emotional aspects. Difficulties may appear in organizing stories into a clear beginning, middle, and end, or in emphasizing the motives, thoughts, and feelings of characters. At the same time, many autistic people display strong memory for facts and may provide narratives rich in precise and specific information.

Study author Kritika Nayar and her colleagues wanted to explore and compare the narrative skills of individuals with autism and their first-degree relatives. They wanted to see whether their narrative skills and styles showed similarities with their relatives who do not have autism.

Study participants were 56 autistic individuals, 42 of their siblings who do not have autism, 49 control participants without autism (who were not related to the autistic participants), 161 parents of autistic individuals, and 61 parents who do not have autistic children.

Overall, there were 58 parent-child pairs in the autism group, and 20 parent-child pairs in the control group. The average age of participants with autism and their siblings and peers was approximately 17–19 years. The average age of parents of participants with autism was roughly 50 years, and the average age of parents of non-autistic participants was roughly 46 years.

Study participants were given a 24-page wordless picture book called “Frog, Where Are You?” depicting the adventures of a boy and his dog while searching for a missing pet frog. The story is comprised of five main search episodes in addition to the introduction, plot establishment, and resolution. Participants were asked to narrate the story page-by-page while viewing it on a device that tracked their eye movements.

All audio files of their narration were transcribed and then hand-coded by researchers. Study authors looked for descriptions of affective states and behaviors of protagonists, and protagonists’ cognitive states and behaviors. They also looked for causal explanations of story protagonists’ behaviors and for causal explanations of protagonists’ feelings and cognitions.

The study authors differentiated between explicit causal language, marked by the use of the term “because,” and more subtle use of causal language indicated by words such as “so,” “since,” “as a result,” “in order to,” and “therefore.” They also looked for the presence of excessive detail and for topic perseveration (whether a participant got stuck on a specific topic) throughout the story. Study authors analyzed participants’ eye movements while telling the story.

Results showed that participants with autism and their siblings used fewer descriptions of affect and cognition, and fewer causal explanations than control participants. They were also more likely to omit story components.

Parent groups did not differ in their overall use of causal language or in how often they described feelings and thoughts (cognition) of story protagonists. However, parents of participants with autism used more causal explanations of story protagonists’ thoughts and feelings (affect), but fewer causal descriptions of characters’ behavior compared to control parents. Results also showed some differences in gaze patterns between participants with autism and their siblings on one side, and control participants on the other.

“Findings implicate causal language as a critical narrative skill that is impacted in ASD [autism spectrum disorder] and may be reflective of ASD genetic influence in relatives. Gaze patterns during narration suggest similar attentional mechanisms associated with narrative among ASD families,” study authors concluded.

The study contributes to the scientific understanding of the cognitive characteristics of individuals with autism. However, authors note that the eye-tracking metrics used, which focused on the entirety of the book, might have masked certain important patterns of gaze that could unfold over the course of time.

The paper, “Narrative Ability in Autism and First-Degree Relatives,” was authored by Kritika Nayar, Emily Landau, Gary E. Martin, Cassandra J. Stevens, Jiayin Xing, Sophia Pirog, Janna Guilfoyle, Peter C. Gordon, and Molly Losh.

A biochemical analysis of brains of deceased individuals with Alzheimer’s disease found markers of impaired insulin signaling and impaired mitochondrial function. Analyses also indicated altered neuroinflammation in these brains. The paper was published in Alzheimer’s & Dementia.

Alzheimer’s disease is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior. It is the most common cause of dementia. Alzheimer’s disease typically begins with subtle problems in forming new memories. Over time, the disease disrupts language, reasoning, orientation, and the ability to carry out everyday tasks.

At the biological level, Alzheimer’s is characterized by the accumulation of amyloid-β plaques (abnormal clusters of protein fragments) outside neurons and tau protein tangles (twisted fibers of the tau protein) inside them.

These accumulations make neurons gradually lose their ability to communicate and eventually die, causing widespread brain atrophy. Early symptoms may appear years before diagnosis. There is currently no cure, though some medications and lifestyle interventions might be able to modestly slow symptom progression.

Study author Alex J. T. Yang and his colleagues note that metabolic dysregulation might contribute to the development of Alzheimer’s disease. They conducted a study in which they explored the differences in various metabolic and biochemical indicators between post mortem (after death) brains of individuals who suffered from Alzheimer’s disease and those who did not suffer from dementia. They focused on metabolic signaling, synaptic protein content, morphology of microglia cells in the brain, and markers of inflammation.

These researchers obtained samples from Brodmann area 10 of the brains of 40 individuals from the Douglas Bell Canada Brain Bank (Montreal, Quebec, Canada). Of these individuals, 20 were diagnosed with Alzheimer’s disease, and 20 were not. The number of males and females was equal in both groups (10 men – 10 women). At the time of death, the average age of these individuals ranged between 79 and 82 years, depending on the group.

Study authors used mitochondrial respirometry, Western blotting, cytokine quantification via microfluidic immunoassays, and immunohistochemistry/immunofluorescence to examine metabolic, signaling, and inflammatory markers in the studied brain tissues.

Mitochondrial respirometry is a technique that measures how effectively mitochondria (a type of cell organelle) consume oxygen to produce cellular energy (ATP). Western blotting is a method that separates proteins by size and uses antibodies to detect and quantify specific proteins in a sample.

Cytokine quantification via microfluidic immunoassays is a technique that uses antibodies to measure concentrations of inflammatory signaling molecules. Immunohistochemistry/immunofluorescence is a tissue-staining method that uses antibodies linked to enzymes or fluorescent dyes to visualize the location and amount of specific proteins in cells or tissue sections.

The results showed that brains of individuals with Alzheimer’s disease had markers of impaired insulin signaling and impaired mitochondrial function. They also had greater neuroinflammation. Differences in metabolic signaling markers were higher in female than in male brains, and this dysregulation was worse in women with Alzheimer’s disease.

“This study found that AD [Alzheimer’s disease] brains have distinct metabolic and neuroinflammatory environments compared to controls wherein AD brains present with worse metabolic dysregulation and greater neuroinflammation. Importantly, we also provide evidence that female AD brains are more metabolically dysregulated than males but that female brains may also possess a greater compensatory response to AD progression that likely occurs through a separate mechanism from males,” the study authors concluded.

The study sheds light on biochemical specificities of brains of individuals with Alzheimer’s disease. However, the study was conducted on post mortem human brains. Protein expression in these brains may differ from live ones due to factors such as age, medical history, and the time between death and tissue preservation or analysis.

The paper, “Differences in inflammatory markers, mitochondrial function, and synaptic proteins in male and female Alzheimer’s disease post mortem brains,” was authored by Alex J. T. Yang, Ahmad Mohammad, Robert W. E. Crozier, Lucas Maddalena, Evangelia Tsiani, Adam J. MacNeil, Gaynor E. Spencer, Aleksandar Necakov, Paula Duarte-Guterman, Jeffery Stuart, and Rebecca E. K. MacPherson.