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.

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.

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Feature-based and location-based volitional covert attention 1 are mediated by different mechanisms and affect memory at 2 different timescales 3 Abstract 6Our ongoing subjective experiences, and our memories of those experiences, are shaped by our prior 7 experiences, goals, and situational understanding. These factors shape how we allocate our attentional 8 resources over different aspects of our ongoing experiences. These attentional shifts may happen overtly 9 (e.g., when we change where we are looking) or covertly (e.g., without any explicit physical manifestation). 10Additionally, we may attend to what is happening at a specific spatial location (e.g., because we think 11 something important is happening there) or we may attend to particular features irrespective of their 12 locations (e.g., when we search for a friend's face in a crowd). We ran two covert attention experiments that 13 differed in how long they asked participants to maintain the focus of the features or locations they were 14 attending. Later, the participants performed a recognition memory task for attended, unattended, and 15 novel stimuli. Participants were able to shift the location of their covert attentional focus more rapidly than 16 they were able to shift their focus of covert attention to stimulus features, and the effects of location-based 17 attention on memory were longer-lasting than the effects of feature-based attention.