Time Perception with Dr Devin Terhune: Psychophysical experiments

The video discusses psychophysical timing experiments conducted in a lab setting, focusing on training participants to discriminate between different time intervals using auditory signals. 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 issue of ecological validity in psychological research and suggests future directions for more realistic experimental paradigms.

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

• To improve their memory recall

• To enhance their auditory skills

• To help them distinguish between different time intervals

• To increase their reaction time

In a temporal bisection task, what is plotted on the X-axis?

• Accuracy percentage

• Stimulus intervals

• Proportion of long responses

• Participant's reaction time

What shape does the plot of a temporal bisection task typically exhibit?

• Parabolic

• Sigmoid

• Exponential

• Linear

What does a steep slope in the psychometric function indicate?

• Bias towards longer intervals

• Bias towards shorter intervals

• Low precision in interval judgment

• High precision in interval judgment

How can temporal biases be useful in psychophysical experiments?

• They show the participant's level of interest in the task

• They indicate the participant's speed of response

• They provide insights into participant's tendency to overestimate or underestimate stimuli

• They help in understanding participant's memory capacity

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

• The number of participants involved

• The duration of the experiments

• The simplicity of the stimuli used

• The complexity of the tasks

What approach is being suggested to improve ecological validity in psychophysical experiments?

• Incorporating real-world stimuli and contexts

• Increasing the number of participants

• Using more complex mathematical models

• Shortening the duration of experiments