<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>human behavior | John Thorp</title><link>https://jnthorp.github.io/tag/human-behavior/</link><atom:link href="https://jnthorp.github.io/tag/human-behavior/index.xml" rel="self" type="application/rss+xml"/><description>human behavior</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 30 Jan 2024 13:00:00 +0000</lastBuildDate><image><url>https://jnthorp.github.io/media/icon_hu7032ec0ed3b220067d1822c52f98a335_44239_512x512_fill_lanczos_center_3.png</url><title>human behavior</title><link>https://jnthorp.github.io/tag/human-behavior/</link></image><item><title>prediction and inference across scales of granularity</title><link>https://jnthorp.github.io/talk/prediction-and-inference-across-scales-of-granularity/</link><pubDate>Tue, 30 Jan 2024 13:00:00 +0000</pubDate><guid>https://jnthorp.github.io/talk/prediction-and-inference-across-scales-of-granularity/</guid><description>&lt;div class="alert alert-note">
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Further event details, including [page elements](https://docs.hugoblox.com/reference/markdown/) such as image galleries, can be added to the body of this page. --></description></item><item><title>inference guides retroactive effects of arousal on memory</title><link>https://jnthorp.github.io/project/tgif/</link><pubDate>Wed, 31 May 2023 00:00:00 +0000</pubDate><guid>https://jnthorp.github.io/project/tgif/</guid><description>&lt;p>Aversive category conditioning has been shown to retroactively increase recognition of related items through arousal-mediated reactivation. Schemas and incongruent, one-shot associations have also been shown to mediate reactivation, though in separate paradigms and through disparate neural pathways. Here, we design two experiments to test the relationship between the contingency structure of aversive learning and retroactive memory enhancements. Both experiments consist of a preconditioning phase in which participants view items congruent and incongruent with scenes belonging to two separate real-world schemas. Experiment 1 then associates an entire schema with aversive shock, with participants showing improved recognition for previously-encountered congruent, but not incongruent, items. Experiment 2 associates a schema with shock while maintaining one scene as safe, which leads to participants showing improved high-confidence recognition for all previously-encountered items, both congruent and incongruent. Individual differences in how strongly participants generalize shock expectancy ratings across the entire schema – that is, whether they infer the individual scenes or the schema broadly to be the source of the aversion – were then correlated with the memory effects from Experiment 1.&lt;/p></description></item><item><title>contextual stability as a continuous moderator of event segmentation</title><link>https://jnthorp.github.io/project/timewarp/</link><pubDate>Sat, 31 Dec 2022 00:00:00 +0000</pubDate><guid>https://jnthorp.github.io/project/timewarp/</guid><description>&lt;p>Our senses receive a constant stream of information, with one moment leading continuously to the next. Afterwards, however, we remember our experiences as discrete events. These events are thought to be typically organized around stable contexts, with context often consisting of an unchanging goal or physical location. Studies of the individual differences in this segmentation process have contributed insights into basic cognition and mapped novel clinical markers. For instance, the ability to normatively detect the boundaries between these events is weakened across such wide-reaching disorders as Alzheimer’s disorder, schizophrenia, autism-spectrum disorder, and even during healthy aging. While the hippocampus is known to hold a critical role in event segmentation broadly, current functional models have yet to be extended to its role in event segmentation. A major limitation of the existing literature to parse these individual differences and neural functional models, however, is that it has treated event boundaries as binary occurrences. Theoretical accounts hold that more stable contexts should lead to stronger event boundaries, and that the ability to gradate these event boundaries ought to rely on mechanisms of cognitive control. Critically, these claims have yet be tested. This ommission leaves sources of variance that, in tandem with cognitive control functions, may provide meaningful clinical markers as well as provide theoretical insight.&lt;/p>
&lt;p>To test this, I built a paradigm that modulates the stability of a context as a continuous function rather than a binary occurence. This allowed me to test how individuals&amp;rsquo; event segmentation behaviors evolved over a continuous range of contextual stability, and particularly how these developed non-linearly and differently from each other. I found that the difference in participants&amp;rsquo; memory for boundary and non-boundary item-color memory (a standard measure of event segmentation) increased non-linearly as contexts became more stable and that participants did this very differently, with some increasing very smoothly and others increasing suddenly at an inflection point. Future work will tie these behaviors to existing clinical markers and cognitive control measures known to covary with clinical outcomes. fMRI studies can then examine how univariate signals evolve across the body of the hippocampus as well as how multivariate signals in the lateral entorhinal cortex process temporal context at different levels of contextual stability.&lt;/p></description></item><item><title>eeg markers of mind-wandering in the real-world classroom</title><link>https://jnthorp.github.io/project/spagna/</link><pubDate>Sun, 31 Jul 2022 00:00:00 +0000</pubDate><guid>https://jnthorp.github.io/project/spagna/</guid><description>&lt;p>Paper coming soon!&lt;/p></description></item><item><title>quantifying bias in assessments of medical students</title><link>https://jnthorp.github.io/project/bias/</link><pubDate>Mon, 31 Jan 2022 00:00:00 +0000</pubDate><guid>https://jnthorp.github.io/project/bias/</guid><description>&lt;p>Paper coming soon!&lt;/p></description></item></channel></rss>