Quail: A Python toolbox for analyzing and plotting free recall data

Heusser, Andrew C.; Fitzpatrick, Paxton C.; Field, Campbell E.; Ziman, Kirsten; Manning, Jeremy R.

doi:10.21105/joss.00424

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…We further considered a number of more nuanced memory performance measures that are typically associated with list-learning studies. We also provide a software package, Quail, for carrying out these analyses (Heusser et al, 2017).…”

Section: Naturalistic Extensions Of Classic List-learning Analysesmentioning

confidence: 99%

Geometric models reveal behavioural and neural signatures of transforming experiences into memories

2021

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The mental contexts in which we interpret experiences are often person-specific, even when the experiences themselves are shared. We developed a geometric framework for mathematically characterizing the subjective conceptual content of dynamic naturalistic experiences. We model experiences and memories as trajectories through word embedding spaces whose coordinates reflect the universe of thoughts under consideration. Memory encoding can then be modeled as geometrically preserving or distorting the shape of the original experience. We applied our approach to data collected as participants watched and verbally recounted a television episode while undergoing functional neuroimaging. Participants' recountings all preserved coarse spatial properties (essential narrative elements), but not fine spatial scale (low-level) details, of the episode's trajectory. We also identified networks of brain structures sensitive to these trajectory shapes. Our work provides insights into how we preserve and distort our ongoing experiences when we encode them into episodic memories.

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Section: Naturalistic Extensions Of Classic List-learning Analysesmentioning

confidence: 99%

Geometric models reveal behavioural and neural signatures of transforming experiences into memories

2021

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“…Our experiment code and data may be downloaded here. We also created an open-source Python toolbox for analyzing and plotting free recall data, and for automatically transcribing audio data (Heusser et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Is automatic speech-to-text transcription ready for use in psychological experiments?

Ziman¹,

Heusser²,

Fitzpatrick³

et al. 2017

Preprint

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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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“…Following our prior work (Heusser et al, 2017), we used a permutation-based correction procedure to help isolate the behavioral aspects of clustering that we were most interested in. After computing the uncorrected clustering score (for the given list and observed recall sequence), we compute a "null" distribution of n additional clustering scores after randomly shuffling the order of the recalled words (we use n = 500 in the present study).…”

Section: Adaptive Conditionmentioning

confidence: 99%

“…Because our experiment incorporated a speech-to-text component, all of the behavioral data for each participant could be analyzed just a few seconds after the conclusion of the recall intervals for each list. We used the Quail Python package (Heusser et al, 2017) to apply speech-to-text algorithms to the just-collected audio data, aggregate the data for the given participant, and estimate the participant's memory fingerprint using all of their available data up to that point in the experiment. Two aspects of our implementation are worth noting.…”

Section: Figs 6 S8)mentioning

confidence: 99%

Feature and order manipulations in a free recall task affect memory for current and future lists

Manning¹,

Whitaker²,

Fitzpatrick³

et al. 2023

Preprint

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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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Quail: A Python toolbox for analyzing and plotting free recall data

Cited by 7 publications

References 8 publications

Geometric models reveal behavioural and neural signatures of transforming experiences into memories

Geometric models reveal behavioural and neural signatures of transforming experiences into memories

Is automatic speech-to-text transcription ready for use in psychological experiments?

Feature and order manipulations in a free recall task affect memory for current and future lists

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