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Research in the past several decades has indicated that basic processes involved in perception, action, and cognition are highly interactive. Moreover, such processes co-evolve during learning and development. Our faculty examine this co-evolution across a variety of ecologically-relevant problems including location memory, word learning, object categorization, collision avoidance, and interceptive actions.
Language
The ICDLS offers an environment that is on the cutting edge of language research. Our research employs state of the art techniques such as eye-tracking, computational modeling, methodologies for working with infant, child, and adult populations, as well as access to populations with specific language impairment and cochlear implantation. This research is fundamentally cross-disciplinary and builds on the strengths of faculty in the Psychology and Speech Pathology departments.
Developmental Psychobiology and Comparative Cognition
Research using non-human animal species, including bonobos, rats, and pigeons, helps psychologists to broaden their perspectives and test the generalizability of their theories. At the University of Iowa, researchers explore the biological bases of behavioral development -- in fetuses, newborns, and infants -- to better understand the origins of motor skills, learning, sleep, and other biobehavioral processes. In addition, our faculty conduct research using a variety of species to reveal the cognitive processes that underlie the perception and learning of categorical relations among objects. Our research employs a diversity of methods, from digital video analysis of behavior to neurophysiology. Many of the methods used in our field have been devised and refined by faculty at Iowa.
Process Modeling
Recent theoretical approaches such as Connectionism, Dynamic Systems Theory, and Developmental Systems Theory have emphasized the emergent, step-by-step, and highly interactive nature of learning and development. Formal models of developmental and learning processes provide a central tool in efforts to understand how individuals change over both shorter and longer periods of time. Our faculty use a variety of modeling approaches from connectionist models, to dynamic field models, to hybrid neural network models that integrate the strengths of different theoretical frameworks. Importantly, we are committed to moving beyond demonstration proofs that models can capture individual phenomena. Rather, we seek to develop integrative theories that generalize across multiple domains of inquiry.
