
Andrew Mattarella-MickeStanford University | SU
Andrew Mattarella-Micke
PhD
About
20
Publications
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Introduction
Skills and Expertise
Additional affiliations
September 2012 - August 2014
August 2006 - August 2012
Publications
Publications (20)
Using functional magnetic resonance imaging, we tested the hypothesis that cinematic structure shapes variation in social-cognitive brain activity. Using our film, we completed an exploratory analysis of how activations in the temporal-parietal junction (TPJ), and the intraparietal sulcus (IPS) are shaped by variations in insert shots (e.g., shots...
Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much...
Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other than language modeling further improve performance? In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of...
Mapping numbers onto space is foundational to mathematical cognition. These cognitive operations are often conceptualized in the context of a “mental number line” and involve multiple brain regions in or near the intraparietal sulcus (IPS) that have been implicated both in numeral and spatial cognition. Here we examine possible differentiation of f...
Individual differences in working memory capacity (WMC) correlate with an ever-growing number of cognitive tasks. These individual differences are thought to predict performance because they tap into variation in executive attention. A little-explored question, however, is to what extent individual differences in WMC might account for performance i...
Although the age at which a skill is learned (age of acquisition [AoA]) is one of the most studied predictors of success in domains ranging from language to music, very little work has focused on this factor in sports. In order to uncover how the age at which a skill is learned relates to how athletes cognitively represent that skill, we asked a gr...
In the current study, we explored how a person's physiological arousal relates to their performance in a challenging math situation as a function of individual differences in working memory (WM) capacity and math-anxiety. Participants completed demanding math problems before and after which salivary cortisol, an index of arousal, was measured. The...
We investigated how auditory language processing is modified by a listener’s previous experience with the specific activities mentioned in the speech. In particular, we asked whether neural responses related to language processing depend on one’s experience with the action-based content of this language. Ice-hockey players and novices passively lis...
In two experiments, we explored how the situation model of a math story problem impacts math problem performance. Participants completed multiplication story problems in which a set of objects was associated with or dissociated from a protagonist, making them more or less accessible in memory during answer retrieval. On the basis of previous findin...
Experience alters behavior by producing enduring changes in the neural processes that support performance. For example, performing a specific action improves the execution of that action via changes in associated sensory and motor neural circuitry, and experience using language improves language comprehension by altering the anatomy and physiology...
Questions
Question (1)
Group ICA and intersubject correlations (e.g., Hasson et al. 2004) on fMRI data are estimated using very different processes, yet they share a similar interpretation: they identify and characterize regions with reproducible time-series across subjects. I am trying to put into words exactly how these approaches might differ in terms of their output, to help identify which theoretical questions each approach is most appropriate for.
Here are my current intuitions:
ICA is able to extract overlapping components, while a significant intersubject correlation in a particular region has only one characteristic time-series.
Intersubject correlations produce an output that carries significance at any level of description (usually voxelwise). Each voxel (or ROI, etc) has no necessary relationship with contiguous voxels, while ICA components are extracted at the component-level, across voxels.
(But, in practice, because voxels are likely to be correlated, contiguous intersubject voxels are going to be related. )
In short, I was hoping to get people's intuitions on the way these tools should be used in tandem vs. jointly, and what distinct information each approach provides, and correct any misconceptions I might have.
Thanks!