Emeritus: Projects that are not currently active, but remain interests in the lab
Word learning, plasticity, and aphasia recovery
In our early research on word learning in neurologically intact young adults, we found that statistical segmentation facilitates word learning in behavioral experiments (Mirman, Magnuson, Graf Estes, & Dixon, 2008) and used a computational model to explain this effect and the trade-off between frequency and transitional probability statistics (Mirman, Graf Estes, & Magnuson, 2010). More recently, we have found that verbal short-term memory, but not word processing abilities, predicted ability to learn novel words in a group of individuals with aphasia (Peñaloza et al., 2016; 2017). We are still interested in behavioral, neurological, and biological predictors of recovery and the possibility that non-invasive brain stimulation can be used to measure or promote plasticity to enhance the effects of therapy.
Competition and cooperation among co-activated representations, and the role of cognitive control in spoken language
One of the most widely-held basic principles of cognitive processing is that during a task (recognition, categorization, memory retrieval, etc.) multiple related or similar representations are activated in parallel. But, once activated, do these representations compete or cooperate? Both facilitative and inhibitory effects have been shown, but researchers have tended to focus on just one kind of effect at a time. We both facilitative effects both facilitative and inhibitory effects within the same task (Mirman & Magnuson, 2008; Mirman, 2011), and developed a simple interactive activation and competition model, which revealed that these reversals arise from a single computational principle: strongly active neighbors have a net inhibitory effect and weakly active neighbors have a net facilitative effect (Chen & Mirman, 2012). More than just fitting the behavioral data, the model also made a novel and counter-intuitive prediction that preview duration should induce a U-shaped pattern of neighborhood effects by modulating degree of neighbor activation, which we subsequently tested and confirmed (Chen & Mirman, 2015).
Real-world language use depends on other cognitive functions, particularly memory and cognitive control. We have found that at least some deficits in spoken word recognition in aphasia may be attributable to deficits in response selection (Mirman, Yee, Blumstein, & Magnuson, 2011) and that participants with aphasia have particular difficulty categorizing objects based on a single dimension (Lupyan & Mirman, 2013). We have also examined effects of item repetition (Mirman, Britt, & Chen, 2013), distinguished lexical and semantic competition (Britt, Ferrara, & Mirman, 2016), and investigated the role of frontal regions in resolving competition (Mirman & Graziano, 2013; Nozari, Mirman, & Thompson-Schill, 2016). These issues are related to the broad distinction between "storage" deficits, in which the lexical-semantic representations are thought to be impaired, and "access" deficits, in which the representations are thought to be intact but access to them is impaired, possibly due to deficits in related functions of memory and cognitive control. We reviewed the behavioral phenomena associated with access deficits in aphasia, the main theoretical perspectives on these deficits, and identified important open questions and promising future directions (Mirman & Britt, 2014).
Our research has shown direct top-down effects of word knowledge on perception of speech sounds, including guiding perceptual learning (Mirman, McClelland, & Holt, 2006) and delaying recognition of context-inappropriate stimuli (Mirman, McClelland, & Holt, 2005). Top-down effects are also modulated by attention – the more you attend to the context, the stronger its effect (Mirman, McClelland, Holt, & Magnuson, 2008). Such effects of context are a general property of cognition: an ambiguous color looks more brown when presented in context of “chocolate” (Kubat, Mirman, & Roy, 2009). This work was part of a larger effort to solidify interactive processing as a core principle of cognition and we have reviewed the empirical and computational evidence for interactive processing in several articles (McClelland, Mirman, & Holt, 2006; Mirman, 2008; McClelland, Mirman, Bolger, & Khaitan, 2014). A truly interactive system is not well-described by components (such as separate processing levels for words and speech sounds), so instead of focusing on components, we should focus on the dynamics that describe the interactions. We have used tools from statistical physics, computational biology, and other domains of “complexity science” to study cognition, perception, and action (Dixon, Holden, Mirman, & Stephen, 2012), particularly in the domain of eye movements (Stephen, Mirman, Magnuson, & Dixon, 2009; Stephen & Mirman, 2010; Kelty-Stephen & Mirman, 2013) and with a special interest in individual differences (Mirman, Irwin, & Stephen, 2012).