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Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

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A blind spot in correct naming latency analyses.

Gary M Oppenheim^1,2

¹a Bangor University , Bangor , Gwynedd , UK.

Cognitive Neuropsychology

|July 11, 2017

Summary

This summary is machine-generated.

Analyzing speech errors and naming latencies reveals how errors impact language production. Excluding incorrect word retrievals from analysis skews results, altering the observed distribution of correct naming times.

Keywords:

Language production computational modeling distributional analysis errors naming latencies

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Area of Science:

Cognitive Psychology
Psycholinguistics
Computational Linguistics

Background:

Speech errors and naming latencies are key behavioral data for language production research.
Current chronometric analyses often focus solely on correct response times.
Understanding error-prone situations necessitates considering both correct responses and errors.

Purpose of the Study:

To demonstrate how excluding incorrect word retrievals affects the analysis of correct picture naming latency distributions.
To illustrate the impact of speech errors on the statistical properties of naming times.

Main Methods:

Simulation studies were used to model the effects of excluding errors.
Analysis focused on the distribution of correct naming latencies.

Main Results:

Excluding incorrect word retrievals predictably alters the observed distributions of correct naming latencies.
Speech errors act as a stochastic deadline, censoring successful production.
Exclusion of errors reduces the mean, variance, and skew of correct response latencies compared to uncensored distributions.

Conclusions:

Accurate modeling of language production requires incorporating speech error data.
The censoring effect of errors must be accounted for when interpreting naming latency distributions.
Ignoring errors provides an incomplete picture of language production dynamics.