# Computational decision-making phenotypes: latent profile analysis - URL: https://alisanburak.com/en/wcp-2026-posterler/so047 - Language: en - Last updated: not specified - Page title: Computational decision-making phenotypes: latent profile analysis | WCP 2026 Summary > Three latent profiles were identified in a nonclinical sample using computational decision-making parameters; these profiles differed in mania, behavioral acti… This page was translated automatically from Turkish. [Read the original](https://alisanburak.com/wcp-2026-posterler/so047) Bu sayfa, 26. Dünya Psikiyatri Kongresi'nde sunulan çalışmanın yapay zekâ desteğiyle hazırlanmış Türkçe özetidir. Çalışmanın sahibi sunan yazarlardır; atıf için orijinal kaynağa başvurun. SO047 Kısa Sözlü Poster SunumuNörobilim ve GörüntülemeKesitsel çalışma (gizil profil analizi) # Computational decision-making phenotypes: latent profile analysis COMPUTATIONAL DECISION-MAKING PHENOTYPES: A LATENT PROFILE ANALYSIS Sunan: Alessandro Ferrante Ülke: Italy Kurum: Karolinska Institutet, Department of Clinical Neuroscience Oturum: 2670 - PARALLEL SHORT ORAL POSTER SESSIONS: NEUROSCIENCE & NEUROIMAGING - Part I Tarih: Thu, 24.09.2026 Three latent profiles were identified in a nonclinical sample using computational decision-making parameters; these profiles differed in mania, behavioral activation, and social motivation. The aim was to identify latent profiles in a nonclinical sample that could inform risk stratification by decomposing impaired decision-making, a core feature of mood disorders, using parameters of dopamine-dependent processes: reinforcement learning (RL) and vigor. A total of 162 participants recruited from the general population via Prolific.com completed cognitive tasks measuring reinforcement learning, working memory, response inhibition, effort discounting, and foraging; participants were phenotyped using computational modeling, and latent profile analysis (LPA) was performed. Models with one to six profiles were estimated and selected using the Bayesian Information Criterion (BIC); the best-fitting model had three profiles with freely estimated variances and zero covariances (BIC = 2099 in the abstract). Class differences were examined using the Bolck-Croon-Hagenaars (BCH) method across the Altman Self-Rating Mania Scale, the CES-D depression screening measure, the Snaith-Hamilton Pleasure Scale, and the Apathy Motivation Index. In the poster, Profile 1 (n = 61) was characterized by high vigor and low RL, indicating impaired reward learning accompanied by increased motor drive; Profile 2 (n = 48) was characterized by low RL and low vigor, representing a poor-performance group with reduced reward sensitivity and motor engagement; and Profile 3 (n = 52) was characterized by greater reinforcement learning ability relative to the sample mean. In the BCH analysis, scores on the mania dimension were 0.42 for Profile 1, -0.02 for Profile 2, and -0.48 for Profile 3; behavioral activation scores were 0.34, -0.003, and 0.40, respectively; and social motivation scores were -0.19, -0.08, and 0.30, respectively. Significant differences were indicated among all three profiles for mania, between Profiles 1–3 and 2–3 for behavioral activation, and between Profiles 2–3 for social motivation. In conclusion, computational phenotyping may identify meaningful behavioral subgroups beyond traditional symptom-based measures, and further studies will determine whether these phenotypes are useful in differentiating mood disorders. [Orijinal postere git](https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/415) Bu çalışmaya nasıl atıf yapılır? APA 7: Ferrante, A., et al. (2026, September 24). Computational decision-making phenotypes: a latent profile analysis [Poster presentation]. 26th WPA World Congress of Psychiatry (WCP 2026), Stockholm, Sweden. https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/415 Vancouver: Ferrante A, et al. Computational decision-making phenotypes: a latent profile analysis [poster]. 26th WPA World Congress of Psychiatry (WCP 2026); 2026 Sep 23-26; Stockholm, Sweden. Available from: https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/415 Yazar adları ve başlık biçimi kongre sisteminden otomatik oluşturulmuştur; kullanmadan önce orijinal posterle karşılaştırın. ## Aynı konudan - [5-HTTLPR ve MAOA genotiplerinin Iowa kumar görevinde risk altında karar verme üzerindeki farklı duyarlılık etkileri](https://alisanburak.com/en/wcp-2026-posterler/ev0042) - [Duygusal düzensizliği olan erişkin DEHB hastalarında merkezi noradrenalin taşıyıcısı erişilebilirliğinin [11C]MRB PET/MRI ile in vivo görüntülenmesi ve nicelenmesi](https://alisanburak.com/en/wcp-2026-posterler/ev0065) - [Erişkin DEHB'de sapmış beyin kritikliği: uzun erimli zamansal korelasyonlar ve görüntüleme transkriptomiğinden çıkarımlar](https://alisanburak.com/en/wcp-2026-posterler/ev0067) - [Anksiyete biyobelirteci olarak fibroblast büyüme faktörü 2: tehdide yönelik dikkat yanlılığıyla ilişkileri](https://alisanburak.com/en/wcp-2026-posterler/ev0120) - [Trajedi paradoksu: hüzünlü müzikle olumlu duygudurum düzenlenmesinin psikolojik ve nörobiyolojik mekanizmaları](https://alisanburak.com/en/wcp-2026-posterler/ev0134) [Tüm WCP 2026 özetleri](https://alisanburak.com/en/wcp-2026-posterler) --- Kaynak / Source: https://alisanburak.com/en/wcp-2026-posterler/so047 İletişim / Contact: Bağdat Caddesi, Veli Güneysu Apt. 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