Verbal responses are a convenient and naturalistic way for participants to provide data in psychological experiments (Salzinger, 1959). However, audio recordings of verbal responses typically require additional processing, such as transcribing the recordings into text, as compared with other behavioral response modalities (e.g. typed responses, button presses, etc.). Further, the transcription process is often tedious and time-intensive, requiring human listeners to manually examine each moment of recorded speech. Here we evaluate the performance of a state-of-the-art speech recognition algorithm (Halpern et al., 2016) in transcribing audio data into text during a list-learning experiment. We compare transcripts made by human annotators to the computer-generated transcripts. Both sets of transcripts matched to a high degree and exhibited similar statistical properties, in terms of the participants' recall performance and recall dynamics that the transcripts captured. This proof-of-concept study suggests that speech-to-text engines could provide a cheap, reliable, and rapid means of automatically transcribing speech data in psychological experiments. Further, our findings open the door for verbal response experiments that scale to thousands of participants (e.g. administered online), as well as a new generation of experiments that decode speech on-the-fly and adapt experimental parameters based on participants' prior responses.

Verbal responses are a convenient and naturalistic way for participants to provide data in psychological experiments (Salzinger, 1959). However, audio recordings of verbal responses typically require additional processing such as transcribing the recordings into text, as compared with other behavioral response modalities (e.g. typed responses, button presses, etc.). Further, the transcription process is often tedious and time-intensive, requiring human listeners to manually examine each moment of recorded speech. Here we evaluate the performance of a state-of-the-art speech recognition algorithm (Halpern et al., 2016) in transcribing audio data into text during a list-learning experiment. We compare the computer-generated transcripts to transcripts made by human annotators. Both sets of transcripts matched to a high degree and exhibited similar statistical properties, in terms of the participants' recall performance and recall dynamics that the transcripts captured. This proof-of-concept study suggests that speech-to-text engines could provide a cheap, reliable, and rapid means of automatically transcribing speech data in psychological experiments. Further, our findings open the door for verbal response experiments that scale to thousands of participants (e.g. administered online), as well as a new generation of experiments that decode speech on-the-fly and adapt experimental parameters based on participants' prior responses.

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We perceive, interpret, and remember ongoing experiences through the lens of our prior experiences. Inferring that we are in one type of situation versus another can lead us to interpret the same physical experience differently. In turn, this can affect how we focus our attention, form expectations about what will happen next, remember what is happening now, draw on our prior related experiences, and so on. To study these phenomena, we asked participants to perform simple word list-learning tasks. Across different experimental conditions, we held the set of to-be-learned words constant, but we manipulated how incidental visual features changed across words and lists, along with the orders in which the words were studied. We found that these manipulations affected not only how the participants recalled the manipulated lists, but also how they recalled later (randomly ordered) lists. Our work shows how structure in our ongoing experiences can influence how we remember both our current experiences and unrelated subsequent experiences.

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