Predicting Known Sentences: Neural Basis of Proverb Reading Using Non-parametric Statistical Testing and Mixed-Effects Models

Predictions of future events play an important role in daily activities, such as visual search, listening, or reading. They allow us to plan future actions and to anticipate their outcomes. Reading, a natural, commonly studied behavior, could shed light over the brain processes that underlie those prediction mechanisms. We hypothesized that different mechanisms must lead predictions along common sentences and proverbs. The former ones are more based on semantic and syntactic cues, and the last ones are almost purely based on long-term memory. Here we show that the modulation of the N400 by Cloze-Task Predictability is strongly present in common sentences, but not in proverbs. Moreover, we present a novel combination of linear mixed models to account for multiple variables, and a cluster-based permutation procedure to control for multiple comparisons. Our results suggest that different prediction mechanisms are present during reading.Fil: Bianchi, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Shalóm, Diego Edgar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin

Rapid Access to Scalar Implicatures in Adjacency Pair Contexts: Experimental Evidence in Spanish

A conversational implicature arises when there is a gap between the syntactically and semantically encoded meaning of a sentence and the pragmatic meaning that is inferred in an actual communicative situation. Several experimental studies have approached the processing of implicatures and examined the extent to which the derivation of the pragmatic meaning is effortful, especially in the case of generalized implicatures, where the inferred meaning seems to be the most frequent one. In this study, we present two experiments that explore the processing of scalar implicatures with algunos ‘some’ in adjacency pair contexts through an acceptability judgment task and a self-paced reading task. Our results support the claim that the access to the meaning of some as only some is context sensitive. Moreover, they also indicate that adjacency pair structure contributes to making that meaning rapidly available.Fil: Loredo, Rodrigo. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Lingüística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kamienkowski, Juan Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Jaichenco, Virginia Irene. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Lingüística; Argentin

Unveiling Trail Making Test: Visual and manual trajectories indexing multiple executive processes

The Trail Making Test (TMT) is one of the most popular neuropsychological tests for executive functions (EFs) assessment. It presents several strengths: it is sensitive to executive dysfunction, it is easy to understand, and has a short administration. However, it has important limitations. First, the underlying EFs articulated during the task are not well discriminated, which makes it a test with low specificity. Second, the pen-and-paper version presents one trial per condition which introduces high variability. Third, only the total time is quantified, which does not allow for a detailed analysis. Fourth, it has a fixed spatial configuration per condition. We designed a computerised version of the TMT to overcome its main limitations and evaluated it in a group of neurotypical adults. Eye and hand positions are measured with high resolution over several trials, and spatial configuration is controlled. Our results showed a very similar performance profile compared to the traditional TMT. Moreover, it revealed differences in eye movements between parts A and B. Most importantly, based on hand and eye movements, we found an internal working memory measure that showed an association to a validated working memory task. Additionally, we proposed another internal measure as a potential marker of inhibitory control. Our results showed that EFs can be studied in more detail using traditional tests combined with powerful digital setups. The cTMT showed potential use in older adult populations and patients with EFs disorders.Fil: Linari, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Juantorena, Gustavo Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Ibáñez, Santiago Agustín. Trinity College Dublin; Irlanda. University of California; Estados Unidos. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Petroni, Agustín. University Goteborg; Suecia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentin

Modeling Human Visual Search in Natural Scenes: A Combined Bayesian Searcher and Saliency Map Approach

Finding objects is essential for almost any daily-life visual task. Saliency models have been useful to predict fixation locations in natural images during a free-exploring task. However, it is still challenging to predict the sequence of fixations during visual search. Bayesian observer models are particularly suited for this task because they represent visual search as an active sampling process. Nevertheless, how they adapt to natural images remains largely unexplored. Here, we propose a unified Bayesian model for visual search guided by saliency maps as prior information. We validated our model with a visual search experiment in natural scenes. We showed that, although state-of-the-art saliency models performed well in predicting the first two fixations in a visual search task (90% of the performance achieved by humans), their performance degraded to chance afterward. Therefore, saliency maps alone could model bottom-up first impressions but they were not enough to explain scanpaths when top-down task information was critical. In contrast, our model led to human-like performance and scanpaths as revealed by: first, the agreement between targets found by the model and the humans on a trial-by-trial basis; and second, the scanpath similarity between the model and the humans, that makes the behavior of the model indistinguishable from that of humans. Altogether, the combination of deep neural networks based saliency models for image processing and a Bayesian framework for scanpath integration probes to be a powerful and flexible approach to model human behavior in natural scenarios.Fil: Bujía, Gastón Elián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Sclar, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Vita, Sebastián Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Solovey, Guillermo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentin

Human and computer estimations of Predictability of words in written language

When we read printed text, we are continuously predicting upcoming words to integrate information and guide future eye movements. Thus, the Predictability of a given word has become one of the most important variables when explaining human behaviour and information processing during reading. In parallel, the Natural Language Processing (NLP) field evolved by developing a wide variety of applications. Here, we show that using different word embeddings techniques (like Latent Semantic Analysis, Word2Vec, and FastText) and N-gram-based language models we were able to estimate how humans predict words (cloze-task Predictability) and how to better understand eye movements in long Spanish texts. Both types of models partially captured aspects of predictability. On the one hand, our N-gram model performed well when added as a replacement for the cloze-task Predictability of the fixated word. On the other hand, word embeddings were useful to mimic Predictability of the following word. Our study joins efforts from neurolinguistic and NLP fields to understand human information processing during reading to potentially improve NLP algorithms.Fil: Bianchi, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Bengolea Monzón, Gastón. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Fernández Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Shalóm, Diego Edgar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentin

Looking for a face in the crowd: Fixation-related potentials in an eye-movement visual search task

Despite the compelling contribution of the study of event related potentials (ERPs) and eye movements to cognitive neuroscience, these two approaches have largely evolved independently. We designed an eye-movement visual search paradigm that allowed us to concurrently record EEG and eye movements while subjects were asked to find a hidden target face in a crowded scene with distractor faces. Fixation event-related potentials (fERPs) to target and distractor stimuli showed the emergence of robust sensory components associated with the perception of stimuli and cognitive components associated with the detection of target faces. We compared those components with the ones obtained in a control task at fixation: qualitative similarities as well as differences in terms of scalp topography and latency emerged between the two. By using single trial analyses, fixations to target and distractors could be decoded from the EEG signals above chance level in 11 out of 12 subjects. Our results show that EEG signatures related to cognitive behavior develop across spatially unconstrained exploration of natural scenes and provide a first step towards understanding the mechanisms of target detection during natural search.Fil: Kaunitz, Lisandro N.. University of Leicester; Reino UnidoFil: Kamienkowski, Juan Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Universidad Diego Portales; Chile;Fil: Varatharajah, Alexander. University of Leicester; Reino UnidoFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Física del Sur; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; ArgentinaFil: Quian Quiroga, Rodrigo. University of Leicester; Reino UnidoFil: Ison, Matias Julian. University of Leicester; Reino Unid

Intercepting the First Pass: Rapid Categorization is Suppressed for Unseen Stimuli

The operations and processes that the human brain employs to achieve fast visual categorization remain a matter of debate. A first issue concerns the timing and place of rapid visual categorization and to what extent it can be performed with an early feed-forward pass of information through the visual system. A second issue involves the categorization of stimuli that do not reach visual awareness. There is disagreement over the degree to which these stimuli activate the same early mechanisms as stimuli that are consciously perceived. We employed continuous flash suppression (CFS), EEG recordings, and machine learning techniques to study visual categorization of seen and unseen stimuli. Our classifiers were able to predict from the EEG recordings the category of stimuli on seen trials but not on unseen trials. Rapid categorization of conscious images could be detected around 100 ms on the occipital electrodes, consistent with a fast, feed-forward mechanism of target detection. For the invisible stimuli, however, CFS eliminated all traces of early processing. Our results support the idea of a fast mechanism of categorization and suggest that this early categorization process plays an important role in later, more subtle categorizations, and perceptual processes

Associação entre fatores individuais e contextuais e o desempenho cognitivo em pré-escolares com necessidades básicas insatisfeitas

En el marco de un proyecto de intervención, orientado a optimizar el desempeño cognitivo a través de actividades de juego para madres y sus hijos, este estudio presenta los resultados de un análisis de asociación entre factores (a) individuales (i.e. cortisol; actividad electroencefalográfica; lenguaje; y salud), y (b) contextuales (i.e. características del hogar; salud materna; y lenguaje materno), con la eficiencia en la solución de tareas con demandas cognitivas, en una muestra de 46 niños de 5 años de edad, sin historia del trastorno del desarrollo, y pertenecientes a hogares con NBI. Luego de aplicar análisis no paramétricos de tendencias entre grupos, los resultados indicaron a los siguientes como los factores de mayor asociación con el desempeño cognitivo: (a) conectividad y potencia neurales; y (b) lenguaje materno. El abordaje implementado contribuye con una mejora en la comprensión de las asociaciones entre factores individuales y contextuales del desempeño cognitivo, al considerar diferentes niveles de organización involucrados en su desarrollo.In the context of an experimental intervention aimed at optimizing cognitive development through play activities for mothers and their children, this study presents the results of an association analysis between (a) individual (i.e. cortisol, electroencephalographic activity, language, and health conditions), and (b) contextual factors (i.e. home characteristics, maternal health, and mother language) with the efficiency in task solution with cognitive demands, in a sample of 46 5-years-old children, with no history of developmental disorder, and from UBN homes. After applying non-parametric trend analyses between groups, the results indicated the following as the factors of greatest association with cognitive performance: (a) neural connectivity and power; and (b) mother language. The implemented approach contributes to the understanding of the associations between individual and contextual factors of cognitive performance, considering different levels of organization involved in its development.No contexto de uma intervenção experimental objetivando otimizar o desenvolvimento cognitivo através de atividades lúdicas para mães e seus filhos, este estudo apresenta os resultados de uma análise de associação entre (a) atividade individual (cortisol, atividade eletroencefalográfica, linguagem e condições de saúde) e (b) fatores contextuais (características domiciliares, saúde materna e língua materna), com a eficiência em solução de tarefas com demandas cognitivas, em uma amostra de 46 crianças de 5 anos de idade, sem história de transtorno de desenvolvimento e de domicílios com necessidades básicas insatisfeitas. Após a aplicação de análises de tendências não-paramétricas entre os grupos, os resultados indicaram os seguintes fatores de maior associação com o desempenho cognitivo: (a) conectividade neural e poder; E (b) a língua materna. A abordagem implementada contribui para a compreensão das associações entre fatores individuais e contextuais de desempenho cognitivo, considerando diferentes níveis de organização envolvidos em seu desenvolvimento.Fil: Prats, Lucía María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Segretin, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Fracchia, Carolina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Kamienkowski, Juan Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pietto, Marcos Luis. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Hermida, Maria Julia. Universidad Torcuato di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Giovannetti, Federico. Centro de Educación Médica e Investigaciones Clínicas “Norberto Quirno”; ArgentinaFil: Mancini, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Gravano, Agustin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sheese, Brad. Illinois Wesleyan University; Estados UnidosFil: Lipina, Sebastián Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; Argentin

Delays without Mistakes: Response Time and Error Distributions in Dual-Task

BACKGROUND: When two tasks are presented within a short interval, a delay in the execution of the second task has been systematically observed. Psychological theorizing has argued that while sensory and motor operations can proceed in parallel, the coordination between these modules establishes a processing bottleneck. This model predicts that the timing but not the characteristics (duration, precision, variability...) of each processing stage are affected by interference. Thus, a critical test to this hypothesis is to explore whether the quality of the decision is unaffected by a concurrent task. METHODOLOGY/PRINCIPAL FINDINGS: In number comparison--as in most decision comparison tasks with a scalar measure of the evidence--the extent to which two stimuli can be discriminated is determined by their ratio, referred as the Weber fraction. We investigated performance in a rapid succession of two non-symbolic comparison tasks (number comparison and tone discrimination) in which error rates in both tasks could be manipulated parametrically from chance to almost perfect. We observed that dual-task interference has a massive effect on RT but does not affect the error rates, or the distribution of errors as a function of the evidence. CONCLUSIONS/SIGNIFICANCE: Our results imply that while the decision process itself is delayed during multiple task execution, its workings are unaffected by task interference, providing strong evidence in favor of a sequential model of task execution

The Neural Basis of Decision-Making and Reward Processing in Adults with Euthymic Bipolar Disorder or Attention-Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) share DSM-IV criteria in adults and cause problems in decision-making. Nevertheless, no previous report has assessed a decision-making task that includes the examination of the neural correlates of reward and gambling in adults with ADHD and those with BD