Word Reading

Platform: PsychoPy · French · verified 66d ago

Data saving is not built in

This task collects experimental data but does not save it automatically. If you deploy this task for your study, you must implement your own data persistence mechanism (e.g., server-side logging, database, or file export).

Description

Participants silently read isolated French words presented one at a time. On occasional catch trials a pseudoword appears and participants make a lexical decision (button press). The design contrasts ambiguous (colexified) and unambiguous words to investigate how the bilingual brain resolves lexico-semantic ambiguity. Each word is presented exactly twice across 4 runs, with the two presentations always in different runs. MEG triggers mark word onset with condition-specific codes (ambiguous, unambiguous, catch).

Contact & Lab

Lab

NoCe Lab

noce-lab.github.io

Principal investigator

Valentina Borghesani

Valentina.Borghesani@unige.ch

University of Geneva

Task contact

abigail.licata@unige.ch

Contributors

Valentina Borghesani

University of Geneva

Supervision

Abigail Licata

University of Geneva

Conceptualization

Software

Methodology

Hugo Farajallah

University of Geneva

Software

Citation

Haro, J., Demestre, J., Boada, R., & Ferré, P. (2017). ERP and behavioral effects of semantic ambiguity in a lexical decision task. Journal of Neurolinguistics, 44, 190–202. https://doi.org/10.1016/j.jneuroling.2017.06.001

Credits

No credits provided.

Domains

Psycholinguistics

Semantics

Part of suite:

CoLex

Target Population

Age Categories

young adults

Clinical Status

non-clinical

Additional Notes

Native and non-native French speakers

Languages

French

Run under

Engine

PsychoPy

Operating system

Any

Format

In-lab

Modalities

behavioral

meg

Task Outputs

What it measures

Investigate neural responses to ambiguous (colexified) vs unambiguous word reading in native and non-native French speakers using MEG. Paradigm akin to Haro et al. (2017): single-word reading with infrequent pseudoword lexical-decision catch trials, contrasting ambiguous and unambiguous words.

Output formats

csv

One CSV per participant with all trials across runs

Experimental design

Trial-based

256 trials

Within-subject: condition

Conditions

condition

AMB

UNAMB

CATCH

— Ambiguous word, unambiguous word, or pseudoword catch trial

Key measures for analysis

Catch trial accuracy (%)
Catch trial mean RT (ms)
MEG evoked responses by condition

Recorded variables

Variable	Role	Type	Description	Unit
`participant_id`	metadata	string	Participant identifier	-
`run`	metadata	number	Run number (1-4)	-
`trial_in_run`	metadata	number	Trial number within the run	-
`word`	independent	string	Word or pseudoword presented	-
`is_catch`	independent	boolean	Whether this is a catch (pseudoword) trial	-
`condition`	independent	string	Trial condition: AMB, UNAMB, or CATCH Values: AMB, UNAMB, CATCH	-
`trigger_code_word_onset`	metadata	number	MEG trigger code sent at word onset	-
`response`	dependent	string	Key pressed on catch trials (empty on STIM trials)	-
`rt_s`	dependent	number	Reaction time on catch trials	s
`fixation_duration_s`	metadata	number	Jittered fixation duration (0.2-0.7 s)	s