Dr. Christopher B. Germann
I believe that every website should be a masterpiece, combining aesthetics and functionality seamlessly.
As a researcher with a PhD in Cognitive Psychology and Psychophysics, my work focuses on understanding the intricate processes that drive human thought, perception, memory, and decision-making. Through a blend of experimental research and cutting-edge neuroscientific techniques, I aim to uncover the mechanisms that shape how we learn, process information, and create mental models of reality.
# Install and load required packages
if (!require(tidyverse)) install.packages("tidyverse", dependencies=TRUE)
if (!require(officer)) install.packages("officer", dependencies=TRUE)
if (!require(rvg)) install.packages("rvg", dependencies=TRUE)
library(tidyverse)
library(officer)
library(rvg)
# Create a ggplot object
ggp <- diamonds %>%
mutate(carat = floor(carat)) %>%
group_by(carat, cut, clarity, color) %>%
summarise(price = mean(price)) %>%
ggplot(aes(x = carat, y = price, fill = color)) +
geom_bar(stat = 'identity') +
facet_grid(cut ~ clarity) +
theme_bw() +
guides(fill = FALSE) +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
# Create a PowerPoint document and add the plot
doc <- read_pptx()
doc <- add_slide(doc, layout = "Title and Content", master = "Office Theme")
doc <- ph_with(doc, dml(ggobj = ggp), location = ph_location_type(type = "body"))
# Save the PowerPoint document
print(doc, target = "editable_plot.pptx")
This code snippet creates a snowball igraph based on the OpenAlex IDs of two of my papers.
# Conditionally install necessary packages if not installed
if (!requireNamespace("igraph", quietly = TRUE)) install.packages("igraph")
if (!requireNamespace("ggraph", quietly = TRUE)) install.packages("ggraph")
if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
if (!requireNamespace("tidygraph", quietly = TRUE)) install.packages("tidygraph")
# Load libraries
library(tidygraph) # Important for as_tbl_graph
library(igraph)
library(ggraph)
library(ggplot2)
library(openalexR) # Replace with actual package name
# Snowballing method to collect documents citing the target papers
snowball_docs <- oa_snowball(
identifier = c("W2969932117", "W2975681496"),
verbose = TRUE
)
# Visualize using ggraph with a stress layout
ggraph(graph = as_tbl_graph(snowball_docs), layout = "stress") +
geom_edge_link(aes(alpha = after_stat(index)), show.legend = FALSE) +
geom_node_point(aes(fill = oa_input, size = cited_by_count), shape = 21, color = "white") +
geom_node_label(aes(filter = oa_input, label = id), nudge_y = 0.2, size = 3) +
scale_edge_width(range = c(0.1, 1.5), guide = "none") +
scale_size(range = c(3, 10), guide = "none") +
scale_fill_manual(values = c("#000000", "#3C8DBC"), na.value = "grey", name = "") +
theme_graph() +
theme(
plot.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.position = "bottom"
) +
guides(fill = "none")
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My research spans key areas of cognitive psychology and neuroscience, from the neural correlates of attention and memory to the cognitive strategies involved in problem-solving and decision-making. By integrating cognitive models with neuroimaging data, I seek to build a more comprehensive understanding of how the brain supports complex cognitive functions.
In addition to classical Fisherian frequentist methods, I utilize Bayesian modelling approaches for data analysis.
Markov Chain Monte Carlo (MCMC) simulations are a powerful tool that can be combined with Bayesian statistics to achieve statistically rich inferences.
⦿ Psychophysics: The interface between psyche & physis.
⦿ Cognitive Neuroscience: Investigating the brain's role in cognitive processes using neuroimaging data (e.g., EEG and fMRI).
⦿ Memory and Learning: Examining how information is perceived, encoded, stored, and retrieved, with a focus on both short-term and long-term memory systems.
⦿ Attention and Perception: Exploring how we selectively focus on certain stimuli and how sensory information is processed to form our perception of the world.
⦿ Decision-Making: Studying the cognitive and neural mechanisms behind how humans make choices, assess risks, and navigate uncertainty.
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The Game of Life is a cellular automaton devised by mathematician John Conway.
Iterative Rules:
1. Any live cell with fewer than two live neighbors dies, as if by underpopulation.
2. Any live cell with two or three live neighbors lives on to the next generation.
3. Any live cell with more than three live neighbors dies, as if by overpopulation.
4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.
This application is designed to administer the IPIP-NEO 50-Item Personality Inventory and analyze self-reported user responses. The IPIP is an open-source personality inventory that serves as an alternative to the proprietary NEO-PI. Results are visualized, and a custom report can be downloaded as a PDF.
See also: Johnson, J. A. (2014). Measuring thirty facets of the Five-Factor Model with a 120-item public domain inventory: Development of the IPIP-NEO-120. Journal of Research in Personality, 51, 78-89.
Behavioural economics, heuristics and biases, dual-process theories, top-down control, executive functions, ego-depletion, etc.
The ggseg R package provides tools for 3D and 2D brain visualizations, facilitating the mapping of neuroimaging data onto cortical regions in an intuitive, flexible way. It integrates with ggplot2 to produce customizable brain plots based on various brain parcellation schemes. The package also supports 3D visualizations with plotly, enabling interactive exploration of brain regions and data overlays.
The Big Five Personality Model describes five key traits: Openness (creativity and curiosity), Conscientiousness (organization and responsibility), Extraversion (sociability and assertiveness), Agreeableness (compassion and cooperation), and Neuroticism (emotional stability and stress levels). Mnemonic: OCEAN
Cross-language studies have found a sixth Honesty-Humility factor.
This app visualizes the Bitcoin price time series as an interactive candlestick chart and uses an auto-(S)ARIMA function in R to create a forecast based on historical data (i.e., 356 days; fetched in real-time via the CoinGecko API). The app allows users to compute predictions using Random Forest Machine Learning and Gradient Boosting Machines (n.trees = 1000), inter alia.
The web is evolving at a fast pace (i.e., the exponential progress of information technology). By utilizing HTML5 and CSS3 techniques, web developers can implement engaging animations, transitions, and transformations, inter alia.
Lorem ipsum dolor sit amet consectetur. Id faucibus non augue id tempor nibh. Turpis aenean vulputate tortor tempus ultrices.
In time series analysis, STL and ARIMA are both commonly used techniques. Both have their pros & cons. STL is primarily used to decompose a time series into its components (trend, seasonality, and residual), while ARIMA captures linear relationships in the data and rather implicitly handles trends and seasonality through differencing and the autoregressive and moving average components. Understanding the differences between these methods and knowing when to use each can significantly impact the analysis.
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Social psychology and Group Dynamics -> pendulum
Psychobiomarker
Ageing
Perceptual Interface Theory
Syntax vs. Semantics
Belief Bias
Confirmation Bias
Observer Bias
The 'move' function uses the GSAP JavaScript library to create a fluid, high-performance transition animation between states.
Syntax vs. Semantics
Belief Bias
Confirmation Bias
Observer Bias
