LEADER 02674nam0 22003613i 450 001 VAN0246417 005 20221201045504.798 010 $a978-88-7078-340-7 100 $a20220526d1995 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aNeuropsicologia cognitiva della schizofrenia$fChristopher D. Frith$gedizione italiana a cura di Lydia Miele e Sergio Bressi 210 $a Milano$cCortina$d1995 215 $aVI, 168 p.$cill.$d24 cm 330 $aLe anomalie cognitive che sottendono la schizofrenia suggeriscono una disfunzione nel sistema che genera e governa le rappresentazioni di alcuni eventi astratti (in particolare quelli mentali) nella coscienza. I pazienti schizofrenici, per esempio, non sono più in grado di costruire le rappresentazioni delle loro intenzioni di agire. In seguito, se compiono azioni, le sentiranno come ?venute dal cielo?, e quindi le vivranno come estranee. Il paziente che non ha coscienza dei propri obiettivi cesserà di agire spontaneamente e, di conseguenza, mostrerà una carenza di volontà. Studi di neuropsicologia umana e animale mostrano che, nella schizofrenia, i processi psicologici anomali possono essere riferiti a sottostanti sistemi cerebrali. Le interazioni tra la corteccia prefrontale e altre parti del cervello, in particolare la corteccia temporale, sembrano critiche per la costruzione dei contenuti della coscienza. Probabilmente, sono queste le interazioni alterate nella schizofrenia. 410 1$1001VAN0021426$12001 $aPsichiatria psicoterapia neuroscienze$1210 $aMilano$cRaffaello Cortina. 500 1$3VAN0246422$aˆThe ‰cognitive neuropsychology of schizophrenia$92967144 620 $dMilano$3VANL000284 700 1$aFrith$bChristopher Donald$3VANV201643$4070$01265206 702 1$aBressi$bsergio$3VANV201645$4340 702 1$aMiele$bLydia$3VANV201644$4340 712 02$aCortina, Raffaello$3VANV109008$4650 791 02$aR. Cortina$zCortina, Raffaello$3VANV109009 791 02$aCortina $zCortina, Raffaello$3VANV109010 801 $aIT$bSOL$c20221202$gRICA 856 4 $u/sebina/repository/catalogazione/documenti/207.pdf$z207.pdf 856 4 $u/sebina/repository/catalogazione/documenti/Neuropsicologia cognitiva della schizofrenia.pdf$zNeuropsicologia cognitiva della schizofrenia.pdf 899 $aBIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$1IT-CE0119$2VAN16 912 $aVAN0246417 950 $aBIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$d16CONS 4188 $e16NV 207 20220526 $sBuono 996 $aCognitive neuropsychology of schizophrenia$92967144 997 $aUNISOB LEADER 06022nam 22006975 450 001 9910502614603321 005 20250811104708.0 010 $a3-030-88942-4 024 7 $a10.1007/978-3-030-88942-5 035 $a(CKB)5140000000012943 035 $a(MiAaPQ)EBC6747799 035 $a(Au-PeEL)EBL6747799 035 $a(OCoLC)1275356908 035 $a(PPN)258296224 035 $a(DE-He213)978-3-030-88942-5 035 $a(EXLCZ)995140000000012943 100 $a20211008d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiscovery Science $e24th International Conference, DS 2021, Halifax, NS, Canada, October 11?13, 2021, Proceedings /$fedited by Carlos Soares, Luis Torgo 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (474 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12986 311 08$a3-030-88941-6 327 $aApplications -- Automated Grading of Exam Responses: An Extensive Classification Benchmark -- Automatic human-like detection of code smells -- HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM -- Predicting reach to find persuadable customers: improving uplift models for churn prevention -- Classification -- A Semi-Supervised Framework for Misinformation Detection -- An Analysis of Performance Metrics for Imbalanced Classification -- Combining Predictions under Uncertainty: The Case of Random Decision Trees -- Shapley-Value Data Valuation for Semi-Supervised Learning -- Data streams -- A Network Intrusion Detection System for Concept Drifting Network Traffic Data -- Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams -- Statistical Analysis of Pairwise Connectivity -- Graph and Network Mining -- FHA: Fast Heuristic Attack against Graph Convolutional Networks -- Ranking Structured Objects with Graph Neural Networks -- Machine Learning for COVID-19 -- Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models -- Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data -- Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning -- Sentiment Nowcasting during the COVID-19 Pandemic -- Neural Networks and Deep Learning -- A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data -- Calibrated Resampling for Imbalance and Long-Tails in Deep learning -- Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing -- Controlling BigGAN Image Generation with a Segmentation Network -- GANs for tabular healthcare data generation: a review on utility and privacy -- Preferences and Recommender Systems -- An Ensemble Hypergraph Learning framework for Recommendation -- KATRec: Knowledge Aware aTtentive Sequential Recommendations -- Representation Learning and Feature Selection -- Elliptical Ordinal Embedding -- Unsupervised Feature Ranking via Attribute Networks -- Responsible Artificial Intelligence -- Deriving a Single Interpretable Model by Merging Tree-based Classifiers -- Ensemble of Counterfactual Explainers. Riccardo Guidotti and Salvatore Ruggieri -- Learning Time Series Counterfactuals via Latent Space Representations -- Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems -- Local Interpretable Classifier Explanations with Self-generated Semantic Features -- Privacy risk assessment of individual psychometric profiles -- The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19 -- Spatial, Temporal and Spatiotemporal Data -- Local Exceptionality Detection in Time Series Using Subgroup Discovery -- Neural Additive Vector Autoregression Models for Causal Discovery in Time Series -- Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data. 330 $aThis book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021. The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data. . 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12986 606 $aArtificial intelligence 606 $aSocial sciences$xData processing 606 $aData mining 606 $aComputer networks 606 $aEducation$xData processing 606 $aArtificial Intelligence 606 $aComputer Application in Social and Behavioral Sciences 606 $aData Mining and Knowledge Discovery 606 $aComputer Communication Networks 606 $aComputers and Education 615 0$aArtificial intelligence. 615 0$aSocial sciences$xData processing. 615 0$aData mining. 615 0$aComputer networks. 615 0$aEducation$xData processing. 615 14$aArtificial Intelligence. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer Communication Networks. 615 24$aComputers and Education. 676 $a006.3 702 $aSoares$b Carlos A. Mota$f1945- 702 $aTorgo$b Lui?s 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910502614603321 996 $aDiscovery Science$92968615 997 $aUNINA