Abstract
Semantic feature production norms have several desirable characteristics that have supported models of representation and processing in adults. However, several key challenges have limited the use of semantic feature norms in studies of early language acquisition. First, existing norms provide uneven and inconsistent coverage of early-acquired concepts that are typically produced and assessed in children under the age of three, which is a time of tremendous growth of early vocabulary skills. Second, it is difficult to assess the degree to which young children may be familiar with normed features derived from these adult-generated datasets. Third, it has been difficult to adopt standard methods to generate semantic network models of early noun learning. Here, we introduce Feats—a tool that was designed to make headway on these challenges by providing a database, the Language Learning and Meaning Acquisition (LLaMA) lab Noun Norms that extends a widely used set of feature norms McRae et al. Behavior Research Methods 37, 547–559, (2005) to include full coverage of noun concepts on a commonly used early vocabulary assessment. Feats includes several tools to facilitate exploration of features comprising early-acquired nouns, assess the developmental appropriateness of individual features using toddler-accessibility norms, and extract semantic network statistics for individual vocabulary profiles. We provide a tutorial overview of Feats. We additionally validate our approach by presenting an analysis of an overlapping set of concepts collected across prior and new data collection methods. Furthermore, using network graph analyses, we show that the extended set of norms provides novel, reliable results given their enhanced coverage.
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Acknowledgements
This work was supported by grants from the NIH (R03DC013638, R01DC018593 awarded to AB, and F31DC017089 to REP) and NSERC (Discovery Grant 05652 to KM). It would not have been possible without the research assistants (Emanuel Boutzoukas, Sofia Castillo, Ireney Duval, Stacey Hanson and Kathleen Hopkins) who contributed to this effort.
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Appendices
Appendix 1: Instructions for Feature Production Norms
Instructions (please read carefully)
This experiment is part of an investigation into how people read words for meaning. To help us conduct this work, we need information on what people know about different things in the world. On the following pages, there are words that each denote a concept, with each being followed by 14 blank lines. Please fill in as many of these lines as you can with properties of the concept to which the word refers. Examples of different types of properties would be: physical properties, such as internal and external parts, and how it looks, sounds, smells, feels, or tastes; functional properties, such as what it is used for; where, when and by whom it is used; things that the concept is related to, such as the category that it belongs in; and other facts, such as how it behaves, or where it comes from. Please note that even though many of the words can be thought of as something other than a noun (e.g., “camp” can refer to the place where your tent is pitched, or the action of camping), all words on the following pages are meant to be considered as nouns only (e.g., “camp,” the place). Below, we have provided 3 examples to give you an idea of what might be considered a property description of a concept.
duck | cucumber | balloon |
|---|---|---|
is a bird | is a vegetable | different colors |
is an animal | has green skin | bursts |
waddles | has a white inside | expands |
flies | has seeds inside | floats |
lays eggs | is cylindrical | produces a loud noise when popped |
quacks | is long | is colorful |
swims | grows in gardens | is large |
has wings | grows on vines | is lightweight |
has a beak | is edible | is round |
has webbed feet | is crunchy | made of plastic |
has feathers | used for making pickles | made of rubber |
lives in ponds | eaten in salads | requires air |
lives in water | used at parties | |
hunted by people | used by children | |
is edible | used by clowns |
You may be able to think of more and/or different types of properties for these concepts, but these examples should give you an idea of what is requested of you. Please do not languish an extraordinary amount of time on each word, but also please take a bit of time to consider the relevant properties of each entity or object. In other words, complete this questionnaire reasonably quickly, but keep the relevant types of properties in mind. Thank you very much for completing this questionnaire.
Appendix 2
Appendix 3: List of re-normed concepts
The following 31 concepts were re-normed as part of this study to determine whether our data collection procedures yielded similar data to that captured in the McRae et al., (2005) norms.
alligator | elephant | rocker |
|---|---|---|
ball | grape | shoes |
bike | house | sink |
bin_(waste) | key | slippers |
boat | lamb | socks |
book | motorcycle | stick |
car | orange | stove |
church | pajamas | tractor |
crayon | peas | truck |
doll | potato | |
dress | rock |
Appendix 4: Instructions for Feature Toddler-Accessibility Rating Norms
Instructions (please read carefully):
We are interested in understanding how people learn about features of real world objects. For example, how do children learn that something like a POLAR BEAR can be described by features like: <is white>, <has fur>, <an animal>, <is large>, and <lives in the Arctic>. The features that we learn depend on experiences built over a lifetime. Sometimes, the features that young children know about objects can be very different from adults' understanding of these same items.
The purpose of this experiment is to rate features as to how likely they are to be part of what a toddler understands about the world. For example, the feature <is white> is likely to be accessible to a toddler (even if they don't know the word "white"), while a feature like <lives in the Arctic> is a feature that a toddler probably has not come across and would not yet understand.
Your ratings of how likely a feature is understood by toddlers will be made on a 1 to 7 scale. A value of 1 will indicate that a feature is very unlikely to be part of a toddler’s knowledge, and a value of 7 will indicate that a feature is very likely to be part of a toddler’s knowledge. Values 2 to 6 will indicate intermediate ratings.
In the example above, <is white> would be given a score of very likely to be part of a toddler’s knowledge, but <lives in the Arctic> would be given a score of very unlikely to be part of a toddler’s knowledge.
We will also provide some examples of different words that go with each feature, to give you a concrete idea of how it is relevant. A toddler may not know all or even most of the examples, but do not take this into account when making your rating. Instead, simply focus on how likely the feature is to be a part of a toddler's knowledge, assuming they were familiar with the examples.
Please feel free to use the whole range of values provided when making your ratings. Mark the rating that is most appropriate for each feature. When making your ratings, try to be as accurate as possible, but do not spend too much time on any one feature.
Appendix 5
Appendix 6
Appendix 7
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Borovsky, A., Peters, R.E., Cox, J.I. et al. Feats: A database of semantic features for early produced noun concepts. Behav Res (2023). https://doi.org/10.3758/s13428-023-02242-x
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DOI: https://doi.org/10.3758/s13428-023-02242-x