Pronoun processing:computational, behavioral, and psychophysiological studies in children and adults

In de frase ‘Gisteren sprak James met Rob. Hij bekende de diefstal.’ kan zowel James als Rob de bekennende dief zijn. De oorzaak van deze ambiguïteit is dat persoonlijke voornaamwoorden zoals hij en hem geen vaste betekenis hebben, maar dat hun betekenis afhankelijk is van de context. Jacolien van Rij onderzocht waarom persoonlijke voornaamwoorden eenvoudig lijken voor volwassenen, maar moeilijk zijn voor kinderen. Verschillende factoren spelen een rol bij de interpretatie van voornaamwoorden, waaronder de structuur van de voorafgaande zin. Volwassenen weten meestal meteen naar wie het voornaamwoord verwijst, maar kinderen interpreteren een voornaamwoord vaak anders dan bedoeld door de spreker. Jacolien van Rij onderzocht waarom persoonlijke voornaamwoorden eenvoudig lijken voor volwassenen, maar moeilijk voor kinderen. Om deze vraag te beantwoorden ontwikkelde Van Rij computermodellen die het produceren en interpreteren van persoonlijke voornaamwoorden nabootsen. Deze modellen testte ze in verschillende experimenten met kinderen en volwassenen. Van Rij ontdekte dat kinderen zich meer gedroegen als volwassenen en minder fouten maakten met voornaamwoorden wanneer ze vertraagde spraak hoorden. Dit bevestigt het idee dat de verwerkingssnelheid van kinderen onvoldoende is om alle relevante informatie te kunnen gebruiken. Volwassenen gedroegen zich daarentegen meer als kinderen wanneer ze tijdens het interpreteren van een voornaamwoord een tweede taak moesten uitvoeren, zoals het onthouden van cijfers. Aanvullende studies, waarin hersenactiviteit werd gemeten met EEG en pupilgrootte met eyetracking, suggereren dat het volwassen gebruik van de context afhankelijk is van voldoende werkgeheugencapaciteit. Kortom, de schijnbaar eenvoudige interpretatie van persoonlijke voornaamwoorden is eigenlijk een ingewikkeld proces dat op veel manieren mis kan gaan

Cognitive Flexibility in Cognitive Architecture:Simulating using Contextual Learning in PRIMs

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns

Cognitive Flexibility in Cognitive Architecture:Simulating using Contextual Learning in PRIMs

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns

Cognitive Flexibility in Cognitive Architecture:Simulating using Contextual Learning in PRIMs

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns

Cognitive Flexibility in Cognitive Architecture:Simulating using Contextual Learning in PRIMs

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns

Cognitive Flexibility in Cognitive Architecture:Simulating using Contextual Learning in PRIMs

The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns