Publications by Andrei Tamas Foldes
motion production task
knitr::opts_chunk$set(echo = TRUE) library(afex) library(emmeans) library(sjPlot) library(tidyverse) library(ggstatsplot) data <- read.csv("data_clean2.csv", header = TRUE) data <- subset(data, Participant_ID != 4 & Participant_ID != 12) Quick look library(tidyverse) ## ── Attaching packages ──────────────�...
142 sym R (7669 sym/35 pcs) 8 img 4 tbl
interferon - ica-aroma
knitr::opts_chunk$set(echo = TRUE, cache = TRUE) library(R.matlab) ## R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help. ## ## Attaching package: 'R.matlab' ## The following objects are masked from 'package:base': ## ## getOption, isOpen library(RCurl) library(tidyverse) ## ── Attaching packages �...
1982 sym R (74314 sym/319 pcs) 45 img 30 tbl
Absolute weight transformation
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, fig.fullwidth=TRUE, out.width = "100%") library(R.matlab) ## R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help. ## ## Attaching package: 'R.matlab' ## The following objects are masked from 'package:base': ## ## getOption, isOpen library(RCurl) library(ti...
1909 sym R (64527 sym/290 pcs) 41 img 29 tbl
Premise pair performance, joint rank and distance
Version 1: Strength of memory encoding and rank of items affect delayed inference performance Dataset 1 (Within-subject Transitive Inference with 27h and 3h delayed testing) by Tamas n = 70 best fitting model is the one that includes interaction between Hierarchy ( time of encoding ) and Immediate premise pair performance ( strength of memory...
3744 sym Python (40222 sym/134 pcs) 28 img 14 tbl
Interferon
knitr::opts_chunk$set(echo = TRUE) library(R.matlab) ## R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help. ## ## Attaching package: 'R.matlab' ## The following objects are masked from 'package:base': ## ## getOption, isOpen library(RCurl) library(tidyverse) ## ── Attaching packages ────�...
1610 sym R (34082 sym/86 pcs) 9 img 13 tbl
Between subject Ellenbogen replication
Online Ellenbogen replication attempt (total n=110 after clearning) group learningCriteria subs sleep 66% 32 sleep 75% 24 wake 66% 31 wake 75% 23 No difference in Inference performance at 66% learning criteria No significant difference for 75% learning criteria, but there are promising signs (unfortunately this condition is still underpow...
557 sym R (24152 sym/75 pcs) 14 img 6 tbl
TI task (within-subject)
Description goal was to pilot a within-subject version of the TI task to check time-dependent consolidation effect on inference performance 73 subs collected via Pavlovia Setup: learning/feedback terminated 66% in all 3 versions of the pilot, what did vary was a) stimulus set b) number of blocks of trials shown to participant without feedback...
1607 sym R (12423 sym/57 pcs) 5 img 2 tbl
Ellrep - bw
Online Ellenbogen replication attempt (total n=110 after clearning) group learningCriteria subs sleep 66% 32 sleep 75% 24 wake 66% 31 wake 75% 23 No difference in Inference performance at 66% learning criteria No significant difference for 75% learning criteria, but there are promising signs (p<0.3,unfortunately this condition is still u...
626 sym R (37798 sym/95 pcs) 20 img 9 tbl
IAPS
Load data Whole dataset [S2,4] For simplicity lets see the best model we can get by modelling rating as a normally distributed interval scale outcome variable # lets find the maximal random effect structure # this is the classical within-subject anova in glm form r1 <- lmer(as.numeric(rating) ~ 1 + (1 | participant), data=arousal.extended.wb...
2547 sym R (41547 sym/137 pcs) 19 img 10 tbl
interferon
Node degree Grouped by scale, condition, subject data %>% pivot_longer( cols = starts_with("ROI"), names_to = "ROI", values_to = "degree") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% summarise(meanDegree = mean(degree)) -> data.aggr ## Joining, by = "fileIndex" ## `summarise()` has grouped output ...
254 sym 4 img