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Posts tagged #CogSci on Bluesky
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Os comparto una infografía en la que resumo las diez ilusiones en la enseñanza que @paulkirschner.bsky.social @carlhendrick.substack.com y Jim Heal analizan y desarrollan en su fantástico libro "Instructional Illusions". #RecomiendoLeer
#cogsci

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Portrait of Judith Ellen Fan, a woman with straight dark hair and short bangs, smiling at the camera and wearing a dark green blouse and a delicate geometric necklace. Set against a teal background, the graphic reads: “Congratulations to Judith Ellen Fan (Stanford University), winner of the 2026 Lila R. Gleitman Prize for Early-Career Contributions to Cognitive Science.”

Portrait of Judith Ellen Fan, a woman with straight dark hair and short bangs, smiling at the camera and wearing a dark green blouse and a delicate geometric necklace. Set against a teal background, the graphic reads: “Congratulations to Judith Ellen Fan (Stanford University), winner of the 2026 Lila R. Gleitman Prize for Early-Career Contributions to Cognitive Science.”

Congratulations to Judith Ellen Fan @judithfan.bsky.social, recipient of the 2026 Lila R. Gleitman Prize.

Dr. Fan's research reflects the broad scope of modern #CogSci, bridging #psychology, #neuroscience, vision science, and #education.

Learn more at cognitivesciencesociety.org/gleitman-prize/

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A 3D cartoon alien with a round pink face looks forward. It has large green eyes, a small pink nose, bright blue lips, and short orange eyebrow shapes above its eyes. Blue ear-like shapes stick out on both sides of its head. Two thin antennae rise from the top of its head and bend outward, each ending in shiny, speckled spheres. The background is plain light gray.

A 3D cartoon alien with a round pink face looks forward. It has large green eyes, a small pink nose, bright blue lips, and short orange eyebrow shapes above its eyes. Blue ear-like shapes stick out on both sides of its head. Two thin antennae rise from the top of its head and bend outward, each ending in shiny, speckled spheres. The background is plain light gray.

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How do humans learn to weight signals when reasoning about emotions? Children change both kinds of cues they rely on and the ways they use cues in their reasoning. New work from Andrea Stein using adorable stimuli, too. 🧪 dx.doi.org/10.1111/desc... #cogsci #devsci #AffectSci
#PsychSciSky

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OSF

Check out the up-to-date preprint here: osf.io/preprints/ps...

(p.s. previous versions of this work appeared at #IC2S2 and #CogSci)

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I’ve been reading Zoltán Kövecses’s Extended Conceptual #MetaphorTheory. I was going to do a notes thread, but I’m hesitant bc I feel it would just be a steady stream of complaints and disagreements. Perhaps I should just move on to something else. hmmm

#linguistics #cognitivelinguistics #cogsci

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"In terms of individual differences, there is a positive main effect of logical reasoning (𝛽 ̂ = 0.22, 95% CrI = [0.06, 0.38]) and ToM (𝛽 ̂ = 0.26, 95% CrI = [0.10, 0.42]), meaning that participants with higher logical reasoning and ToM selected the target more often. There is no main effect of memory (𝛽 ̂ = 0.06, 95% CrI = [-0.10, 0.22]).

There is also a positive interaction of logical reasoning with condition, whereby the effect of logical reasoning is stronger in the simple condition (𝛽 ̂ = 0.45, 95% CrI = [0.13, 0.78]), but no interaction of memory or ToM with condition."

"In terms of individual differences, there is a positive main effect of logical reasoning (𝛽 ̂ = 0.22, 95% CrI = [0.06, 0.38]) and ToM (𝛽 ̂ = 0.26, 95% CrI = [0.10, 0.42]), meaning that participants with higher logical reasoning and ToM selected the target more often. There is no main effect of memory (𝛽 ̂ = 0.06, 95% CrI = [-0.10, 0.22]). There is also a positive interaction of logical reasoning with condition, whereby the effect of logical reasoning is stronger in the simple condition (𝛽 ̂ = 0.45, 95% CrI = [0.13, 0.78]), but no interaction of memory or ToM with condition."

"Fig 8 visualizes the effects of logical reasoning and ToM in the two implicature conditions."

"Fig 8 visualizes the effects of logical reasoning and ToM in the two implicature conditions."

What helps people understand conversational cues?

Correctly identifying implications correlated not just with "theory of mind", but also reflective and logical reasoning. Short-term memory? Not so much.

doi.org/10.1371/jour...

#communication #cogSci #linguistics #xPhi #logic

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A neural signature of adaptive mentalization | Nature Neuroscience

How does the brain decide which mental strategy to use when inferring others' beliefs?

Excited to (finally!) see my first first-author paper out @natneuro.nature.com

Summary below 🧵 #CogSci #CogNeuro

www.nature.com/articles/s41...

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Cocktail party effect - Wikipedia

The easiest way to convey how Consciousness and Attention are not the same: "The Cocktail Party Effect"

en.wikipedia.org/wiki/Cocktai...
#CognitiveScience #CogSCI #COGS

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Huge shoutout to my lab mates Zheng Li, Hassan Andrabi, and Hoang Long Nguyen who did an absolutely stellar job delivering their (first?) conference talks! 👏

Big thanks to the organisers, especially Cloudy, for a fantastic event.

#MathPsych #CogSci #Ageing #AcademicSky #DecisionScience #AMPC2026

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Deadline is Friday! If you've been on the fence about BAMB! 2026 — jump. It's one of the best summer schools for behavioral & computational modeling out there, and I'd know 😄
Apply → bambschool.org
#Neuroscience #CogSci #ComputationalNeuroscience #SummerSchool #AcademicBsky

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Book cover of “Temporal Cognition in Animals” by Angelica Kaufmann in the Cambridge Elements: The Philosophy of Biology series. Against a black background, the title is surrounded by Ernst Haeckel’s illustrations of marine organisms—radial, symmetrical forms in vivid blues, oranges, reds, and greens.

Book cover of “Temporal Cognition in Animals” by Angelica Kaufmann in the Cambridge Elements: The Philosophy of Biology series. Against a black background, the title is surrounded by Ernst Haeckel’s illustrations of marine organisms—radial, symmetrical forms in vivid blues, oranges, reds, and greens.

Do non-human animals represent time? New Element in the #PhilBio series by Angelica Kaufmann—free to download until March 16! Kaufmann argues that temporal cognition is widespread across many animal species & advances comparative analyses 👇📕 www.cambridge.org/core/element... #evosky #HPS #cogsci

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Do you know what a penny looks like? A Seeing #Science History Lesson in #psychology
Do you know what a penny looks like? A Seeing #Science History Lesson in #psychology YouTube video by SeeingScience

Just put up a new YouTube short about a classic #cogsci experiment: Our poor memory for what a penny looks like! Given that the penny is retiring, I thought it was worth talking about this great piece of #psychology history. Enjoy!

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There Will Be More Programmers, Not Fewer AI won't reduce the number of programmers. It will create millions more, because running code you cannot understand is not programming. It is faith. Part I — The Hypothesis The Democratization Arc,...

AI won't reduce the number of programmers. It will create millions more, because running code you cannot understand is not programming. It is faith.

www.ocrampal.com/there-will-b...

#programming #psychology #AI #intelligence #cogsci #philpsy #neurosky #philosophy

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He escrito un nuevo hilo esta vez sobre el fenómeno ovni y los sesgos que hay detrás. 👇👇👇👇
#CognitiveBias #OVNI #cogsci #psychky #scicomm #cienciaÑ #PensamientoCritico

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Neuroscience has a species problem If neuroscience is serious about building general principles of brain function, cross-species dialogue must become a core organizing principle.

Yes.

www.thetransmitter.org/animal-model...

#philsky #philosophysky #consci #cogsci #science

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Bingo for #philosophy conferences.

#philsky #cogsci

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Chomsky‘s conspiracy style left-wing politics aside, will the Epstein saga stain his legacy in #linguistics and #cogsci? #Chomsky

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The image shows why the patchwork of „cortical column“ failed to identify a cortical building block in the brain - drawing on the idea of blurry objects of research by @ufeest.bsky.social .
Now onwards to sending the manuscript out to publishers! (2/2)
#philsci #edusky #neuroskyence #cogsci

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I cannot believe they keep organizing conferences in my own city. How am I supposed to justify flying somewhere, eating expensive cheese, and whispering "ah yes, ontology" in a dramatic accent? This is sabotage.

#philosophy #philsky #consci #cogsci #science

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The four experimental conditions.

The four experimental conditions.

The two learning metrics.

The two learning metrics.

"As shown in Figure 13, there is no significant difference across treatments for both the learning during intervention (𝐹 (3, 398) = 1.046, 𝑝 = 0.372) and learning after intervention (𝐹 (3, 398) = 1.193, 𝑝 = 0.312)."

"As shown in Figure 13, there is no significant difference across treatments for both the learning during intervention (𝐹 (3, 398) = 1.046, 𝑝 = 0.372) and learning after intervention (𝐹 (3, 398) = 1.193, 𝑝 = 0.312)."

"Figure 13: Comparisons on participants’ learning (a) while receiving the AI assistance intervention (b) after receiving the AI assistance intervention. Values for the Human-only treatment are computed based on the normalized change of decision accuracy between corresponding sessions of tasks (learning during intervention: tasks 6–15 vs. tasks 1–5, learning after intervention: tasks 16–20 vs. tasks 1–5); they provide a baseline for organic learning happened due to repetitive task completion without AI assistance interventions. Error bars represent 95% confidence intervals of the mean values."

"Figure 13: Comparisons on participants’ learning (a) while receiving the AI assistance intervention (b) after receiving the AI assistance intervention. Values for the Human-only treatment are computed based on the normalized change of decision accuracy between corresponding sessions of tasks (learning during intervention: tasks 6–15 vs. tasks 1–5, learning after intervention: tasks 16–20 vs. tasks 1–5); they provide a baseline for organic learning happened due to repetitive task completion without AI assistance interventions. Error bars represent 95% confidence intervals of the mean values."

Do people learn more from #AI decision assistants?

This experiment found insignificant improvement in learning during and after three forms of AI-assisted decision-making, compared to human-only decision-making.

doi.org/10.48550/arX...

#edu #teaching #cogSci #eduTech #compSci

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"Accuracy outcomes reflected this same structural pattern. In True Conflict, where physicians were initially incorrect and the AI correct, decision changes produced a clear net accuracy gain of 13.20%, consistent with movement toward the correct answer. In False Conflict, where physicians began with the correct answer but the AI was incorrect, decision changes resulted in a net accuracy loss of 4.24%, indicating that many revisions replaced a correct answer with an incorrect one."

"Accuracy outcomes reflected this same structural pattern. In True Conflict, where physicians were initially incorrect and the AI correct, decision changes produced a clear net accuracy gain of 13.20%, consistent with movement toward the correct answer. In False Conflict, where physicians began with the correct answer but the AI was incorrect, decision changes resulted in a net accuracy loss of 4.24%, indicating that many revisions replaced a correct answer with an incorrect one."

"where physicians were initially incorrect and the AI provided the correct answer"... "The linear-by-linear association [between clinician accuracy and decision correction] was ...significant, χ² = 44.80, p < .01, indicating a clear monotonic trend: physicians with higher diagnostic accuracy were increasingly likely to correct their initial error when confronted with accurate AI disagreement. The effect size was medium, Cramér’s V = 0.362 (p < .01)."

"where physicians were initially incorrect and the AI provided the correct answer"... "The linear-by-linear association [between clinician accuracy and decision correction] was ...significant, χ² = 44.80, p < .01, indicating a clear monotonic trend: physicians with higher diagnostic accuracy were increasingly likely to correct their initial error when confronted with accurate AI disagreement. The effect size was medium, Cramér’s V = 0.362 (p < .01)."

"when physicians initially provided the correct diagnosis and the AI gave an incorrect answer" there was "no significant association between [clinician accuracy] and decision change, χ²(3, N = 146) = 4.53, p = .210. The linear-by-linear association was also non-significant, χ² = 1.16, p = .281, and the overall association was weak and non-significant (Cramér’s V = 0.176, p = .210)."

"when physicians initially provided the correct diagnosis and the AI gave an incorrect answer" there was "no significant association between [clinician accuracy] and decision change, χ²(3, N = 146) = 4.53, p = .210. The linear-by-linear association was also non-significant, χ² = 1.16, p = .281, and the overall association was weak and non-significant (Cramér’s V = 0.176, p = .210)."

Why might #AI-assisted #dermatology decisions improve by about 10%?

Across 4,905 unaided and AI-aided decisions, clinicians were more likely to revise when the AI disagreed than when it agreed — even when the AI was wrong!

http://lup.lub.lu.se

#medicine #tech #edu #cogSci

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Delighted to share our new paper on #ageing and #autism. Honoured to serve as a delegate at the II International Summit on Intellectual Disabilities & #Dementia, alongside remarkable experts.

#neuroscience #ASD #IDD #cogsci #Devpsy @ioe.bsky.social @ucl-ioe-phd.bsky.social @ucl.ac.uk

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Figure 1. "Responses showed the expected baseline patterns across dilemmas. The determinism manipulation reduced willingness to intervene in the Spur dilemma (p = 0.0385, fewer participants pulled the switch) and reduced willingness to help in the Singer scenario (p = 0.0261), but had no detectable effect on Footbridge judgments (p = 0.783)."

Figure 1. "Responses showed the expected baseline patterns across dilemmas. The determinism manipulation reduced willingness to intervene in the Spur dilemma (p = 0.0385, fewer participants pulled the switch) and reduced willingness to help in the Singer scenario (p = 0.0261), but had no detectable effect on Footbridge judgments (p = 0.783)."

Table 1. Logistic regression models

Table 1. Logistic regression models

Do neuroscientists' claims about #freeWill impact students' #ethics?

A "deterministic passage ...from Crick" sometimes reduced decisions to proactively intervene in moral thought experiments compared to a "neutral #neuroscience text".

doi.org/10.3389/fpsy...

#xPhi #cogSci #edu

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Me after submitting a revision I've been procrastinating for a month, colorized, oil on canvas, 2026.

#philsky #philosophysky #cogsci #consci

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Mind-Craft Conscious 101 e5: Baars' Theatre Metaphor
Mind-Craft Conscious 101 e5: Baars' Theatre Metaphor YouTube video by Elizabeth Schier

here's Liz and I giving an introduction to Baars' metaphor of consciousness as a theatre, as part of the 101 series

#ideas #podcast #philosophy #naturalphilosophy #psychology #cognitivepsychology #cognitivescience #cogsci #philmind #philconsciousness

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Evidence of availability bias in diagnostic reasoning following real clinical encounters: a cross-sectional survey of physicians Background Diagnostic errors remain a significant challenge in healthcare, with cognitive biases, particularly availability bias, playing a critical role. While previous studies focused on controlled experimental settings, this study investigates the real-world impact of clinical practice on susceptibility to the availability bias. Methods Using a cross-sectional survey-based design, we assessed 1015 physicians’ responses to clinical cases in the form of vignettes after they attended (vs did not attend) a patient with the disease of interest in the last 7 days. Physicians’ specialisations and years of experience were also measured, allowing us to assess the presence of the availability bias and the interacting roles of clinical experience and expertise. Results Recent clinical encounters with a disease increased the likelihood of erroneously diagnosing it (availability bias, b=0.165, SE=0.070, p=0.018). Among specialists, having 10 or more years of experience decreased the likelihood of an erroneous diagnostic (b=–0.361, SE=0.180, p=0.046). The results indicated distinct effects of experience and specialisation on susceptibility to the availability bias. Conclusions Availability bias can be triggered by real clinical encounters and is modulated by the interplay between experience and domain-specific expertise. Structured, feedback-rich exposure—rather than mere experience—appears crucial to mitigating cognitive biases. These findings reinforce the role of knowledge—both formal and experiential—in mitigating the bias, and the importance of developing true expertise. Data are available upon reasonable request.

Are clinicians vulnerable to #availabilityBias?

"Recent... encounters with a disease increased the likelihood of erroneously diagnosing it (b=0.2, p=0.018)" but "having 10 or more years of experience decreased [this] (b=–0.4, p=0.046)"

doi.org/10.1136/bmjq...

#medicine #cogSci

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@cogscisociety.bsky.social @sansmeeting.bsky.social @spspnews.bsky.social @psychonomicsociety.bsky.social @sparksociety.bsky.social

#PsychSciSky #AcademicSky #cogsci #neuroskyence #neuroscience #EEG

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For secondary schools…

The #cogsci effect for younger children is problematic- it gets mixed up with that pesky issue of child development, and don’t forget language acquisition….

bsky.app/profile/tesm...

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Got my first book review. This is the kind of feminism I like :)

#philosophy #philsky #consci #cogsci

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Bailey Zimmerman with Luke Combs - Backup Plan (Lyric Video)
Bailey Zimmerman with Luke Combs - Backup Plan (Lyric Video) YouTube video by Bailey Zimmerman

Anyone in my lovely network have example #Humboldt fellowship applications to share? #psych #cogsci 🧠 🧪 👩🏻‍🔬

Channelling my current anthem "Backup Plan" and gettin' back up after yesterday's disappointment of getting an excellent/previously fundable #MSCA score w/o funding

youtu.be/Yyd5kmngrOU?...

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