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Altıntas, T.

Publications and source records attributed to Altıntas, T..

3 recordsLinked to original sources

Anticipatory Threat of Shock Reduces the Number of Items Reported From a Brief Visual Display: Evidence for Capacity Loss Along the Scanning Order

Background: Anticipatory anxiety impairs working memory, but whether it also limits the earliest, high-capacity stage of visual storage is unclear. We asked whether the mere expectation of an aversive electrical stimulus reduces how many items people can report from a briefly flashed display, and whether any loss is spatially uniform. Methods: Eighty-eight healthy adults aged 18-25 years were randomly allocated to a threat-of-shock condition (surface electrodes on the wrist and the instruction that a painful stimulus might be delivered; none was) or a control condition (n = 44 each). Participants viewed a 4 x 3 letter matrix for 400 ms and performed untimed written whole report. Correct recall (0-12), intrusions and the position of each reported letter were scored. Groups were compared with Welch's t test, the Mann-Whitney U test, bootstrapped effect sizes, a binomial generalised linear model and a Bayes factor; spatial effects were tested with generalised estimating equations (GEE) accounting for letters nested within participants. Results: The threat group reported fewer correct letters (3.41 +/- 1.09) than controls (4.70 +/- 1.29); Welch t(83.6) = 5.11, p < .001, Hedges' g = 1.08, 95% CI [0.70, 1.53], BF10 ~ 7,549. Intrusions did not differ (p = .376). Recall declined more steeply from left to right under threat (group x column chi2(3) = 11.24, p = .011), with losses confined to the third and fourth letters of the top row (FDR-corrected q <= .003). Conclusions: Anticipatory threat reduced the amount of information transferred from a brief display into a reportable form without increasing guessing. Intact first items and depleted later items along the habitual reading order indicate a reduced number of items read out of a rapidly decaying visual store, rather than a hemifield-specific bias. The single-trial design and the absence of a manipulation check limit mechanistic inference.

neuroscience↗

Brief Peppermint Odour Exposure and Immediate Free Recall of a Visual Object Array: A Three-Arm Randomised Experiment

Background: Odours reach limbic and prefrontal targets through a uniquely direct pathway and are often claimed to enhance cognition, yet experimental evidence for specific essential oils is mixed and frequently confounded by expectancy. We tested whether a brief inhalation of peppermint or clove essential oil, compared with no odour, alters immediate free recall of a visual object array. Methods: Sixty healthy adults aged 18-50 years were randomly allocated to peppermint oil, clove oil or no odour (n = 20 each). After 30 s of inhalation (or none), participants studied a 4 x 4 array of 16 pictured items for 20 s and then wrote down every item they could recall, without time limit. Groups were compared with one-way and Welch ANOVA, Welch-corrected pairwise tests with Holm correction, Hedges' g with 95% confidence intervals and Bayes factors. Results: Recall differed across conditions (Welch F(2, 37.0) = 8.53, p < .001; omega2 = 0.14). Participants exposed to peppermint recalled more items (11.20 +/- 2.14) than those exposed to no odour (8.45 +/- 2.04; g = 1.29, 95% CI [0.61, 1.97], p(Holm) < .001, BF10 = 134). Clove (9.55 +/- 3.19) did not differ reliably from either no odour (g = 0.40, p(Holm) = .203) or peppermint (g = 0.60, p(Holm) = .127; BF10 = 1.29). Conclusions: A single brief exposure to peppermint oil was associated with substantially better immediate recall than no odour. Because the peppermint-clove contrast was inconclusive and no placebo odour was used, the data do not establish an odour-specific effect; general arousal and expectancy remain plausible contributors. Pre-registered, laboratory-based replications with placebo odours, blinding and arousal measures are required.

neuroscience↗

How Well Does Structural MRI Classify Autism? A Leakage-Free, Site-Aware and Externally Validated Machine-Learning Evaluation in Two ABIDE Cohorts

Background: Many studies report high accuracy for classifying autism spectrum disorder (ASD) from structural MRI, but small samples, single train-test splits, site effects and information leakage can inflate performance. We evaluated how well brain morphometry distinguishes autistic from typically developing (TD) individuals under rigorous, leakage-free validation. Methods: FreeSurfer-derived features (n = 280: regional thickness, area and volume; subcortical volumes; Yeo-network composites; asymmetry indices; global measures) from ABIDE II (804 participants; 341 ASD; 12 sites) were used to train logistic regression, linear support vector machine, random forest, XGBoost and multilayer perceptron classifiers. Confound regression (age, age2, sex, brain volume), site-wise centring and standardisation were learned within training folds only. Performance was assessed with nested 10-fold cross-validation, leave-one-site-out validation and external validation in ABIDE I (917 participants; 20 sites), and compared with full-pipeline permutation tests and a confound-only baseline. Results: Cross-validated discrimination was modest: the best model (regularised logistic regression on all features) reached an area under the ROC curve (AUC) of 0.60 [0.56, 0.64], significantly above chance (permutation p = .002) but far from clinically useful and below a confound-only baseline using age, sex, brain volume and site (AUC 0.63); non-linear models did not perform better (AUC 0.45-0.60 across models and feature sets). Pooled leave-one-site-out AUCs were 0.54-0.61, with large between-site variation, and AUCs in the external ABIDE I cohort were 0.52-0.58. Age-stratified models did not exceed AUC 0.60. Across 300 random 80/20 splits in young adults, test-set AUCs ranged from 0.21 to 0.90, showing that single-split estimates such as 0.74 can arise by chance. Conclusions: Under leakage-free, site-aware and externally validated evaluation, structural MRI morphometry classifies autism only slightly better than chance. Reports of high accuracy should be interpreted with caution, and the heterogeneity of autism argues for stratification and normative approaches rather than binary diagnostic classifiers.

neuroscience↗