My research investigates the cognitive and computational nature of human learning and exploration. My goals are to understand how new information and concepts are acquired, how this information guides decisions, and how people actively seek out information in their environments. I examine these questions using a translational approach utilizing computational modeling of behavior from various different populations. By investigating the limits and variety in these critical mental functions across a wide range of factors, my work seeks to reveal a more complete picture of how these processes work together to create intelligent behavior. Toward this endeavor my past research has investigated effects of normal aging, genetic factors, symptoms of depression and ADHD, and cognitive enhancement via photobiomodulation on learning and decision-making. The focus of my current research is understanding the mechanisms underlying these processes in young children, how they differ than in adults, and what these differences mean about how the brain and mind develop. In particular I am interested in the ways children’s immature cognitive processes can be adaptive, tuned to the particular needs of early childhood to enable broad information gathering—which is necessary to build a framework of general knowledge for use later in life. My current research can be divided into two main lines of research which investigate the development of category learning and the development of exploratory decision-making.

The development of exploratory behavior

Exploration is an essential human activity facilitating effective learning and decision-making. Organisms must constantly balance the competing demands of acquiring new information and using that information to achieve rewarding outcomes (also known as the exploration-exploitation tradeoff). Humans employ a wide range of exploration strategies to approach this complex problem. Which strategies they employ depend on many factors including the structure and dynamics of the environment, the goals of the learner, and the knowledge they have already acquired. Some strategies are more systematic than others, directing exploration at times and places for which uncertainty is the greatest. My past research has investigated this type of systematic exploration as a function of symptoms of depression (Blanco, Otto, Maddox, Beevers, & Love, 2013), symptoms of ADHD (Blanco & Medina, in prep), normal aging (Blanco, Love, Ramscar, Otto, Smayda, & Maddox, 2016; Cooper, Blanco, & Maddox, 2017), and genes influencing dopamine levels in prefrontal cortex (Blanco, Love, Cooper, McGeary, Knopik, & Maddox, 2015).

Exploration is most valuable when one knows little, and so it is especially useful for young children, who have limited knowledge of the world around them. Information acquisition is critical for young children to build a foundation of knowledge and experience on which to base their future learning and decisions. And yet, little is currently known about the ways young children explore, what strategies they employ, what processes guide their search for information, and how their exploration changes as their brains continue to develop and they acquire more knowledge. While their need for information is greater, there are also unique challenges facing young children. In addition to having less background knowledge and experience on which to base their exploration, many areas of the brain and cognitive processes involved in systematic exploration in adults are still immature in young children. In particular, recent research (including my own) suggests a critical role of processing in the prefrontal cortex—one of the slowest developing brain areas—during systematic exploration in adults.

With different needs and limited cognitive resources at their disposal, children likely rely on vastly different strategies than adults. My current studies investigate the ways that young children (e.g. 4-year-olds) explore while making decisions. My recent findings have uncovered surprisingly systematic behavior in young children’s exploration, but which differs critically from that of adults. Rather than being explicitly motivated to acquire information in a goal-directed manner, simpler mechanisms (such as novelty preference and immature attention allocation) seem to come together in children to produce behavior that facilitates broad information gathering. My ongoing studies combine computational modeling, eye-tracking, and longitudinal data collection across a wide range of age groups (infants through 8-year-olds) to understand what factors influence this behavior and how it develops into adult-like exploration over time.

The development of category learning

How do people acquire new conceptual knowledge, and how is this knowledge stored in the brain? Formal models of categorization posit that objects are composed of individual features which determine category membership. Different categories may be defined by different relationships between their features, and different theories or models propose different ways of processing and comparing those features in order to determine category membership.

An important aspect of this process is the way in which attention is allocated between different features during learning. Children and adults tend to allocate their attention differently. Adults have mature attentional control and often utilize selective attention to efficiently focus their processing on only the most relevant features of an object. Children on the other hand, tend to distribute their attention broadly, paying attention to everything. While selective attention is often effective and efficient, this efficiency also has potential costs (such as learned inattention). Distributed attention, conversely, is slower and less efficient, but avoids these costs. In one set of studies I investigate children’s and adults’ patterns of attention allocation while learning categories, the benefits and costs of each—including consequences for future learning, and why differing attentional patterns may be more effective at different points in life. Results of these studies suggest that while adults’ flexible control of attention may be optimized for quick and efficient performance in the moment, children’s broadly distributed attention may sacrifice immediate performance in favor of long-term learning.

These differences in attention allocation also have important implications for the ways in which categories are represented in the brain. Examining and comparing these differences provides a unique window in which to evaluate the representational assumptions of formal models of categorization. By leveraging developmental differences and predictions from successful categorization models, I evaluate learning and memory of categories with various internal structures in order to gain new insight into the structure of human conceptual representations.