Time Perception with Dr Devin Terhune: Psychophysical experiments

The video discusses psychophysical timing experiments, focusing on training participants to discriminate between different time intervals using auditory tones. It explains the temporal bisection task and how data is plotted to fit psychometric functions. The video also covers extracting parameters like precision and bias, and their applications in clinical settings. Finally, it addresses the importance of ecological validity and suggests future research directions involving real-world stimuli.

What is the primary goal of training participants in psychophysical timing experiments?

• To improve their musical skills

• To help them recognize different colors

• To teach them to memorize numbers

• To enable them to distinguish between short and long intervals

In a temporal bisection task, what are participants asked to judge?

• The volume of the stimulus

• The color of the stimulus

• The duration of the stimulus

• The shape of the stimulus

What shape does the data form when plotting the proportion of long responses against stimulus intervals?

• A straight line

• A circular pattern

• A sigmoid curve

• A zigzag pattern

What does a steep slope in the sigmoid curve indicate about a participant's performance?

• They are very slow in responding

• They are very precise in their judgments

• They are biased towards longer intervals

• They are unable to distinguish intervals

How can temporal biases in participants be identified?

• By measuring their reaction time

• By analyzing the slope of the sigmoid curve

• By observing their body language

• By extracting parameters from the psychometric function

What is a major concern regarding the ecological validity of psychophysical experiments?

• They do not accurately reflect real-world timing

• They require too many participants

• They are too complex to analyze

• They are too expensive to conduct

What approach is being taken to improve the ecological validity of timing experiments?

• Focusing on shorter intervals

• Using more complex mathematical models

• Incorporating real-world stimuli and contexts

• Increasing the number of trials