We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as <i>Every kid climbed a tree</i> was investigated. Such sentences are ambiguous with respect to the number of trees inferred; either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with <i>tree</i>. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the <i>quantity</i> of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.
We can interpret “dog bite man” into a particular scene or context, and furthermore, this context would be easier to understand than “man bite dog”. Thus, in English, we can use a simple noun verb noun (n-v-n) heuristic [
In [
Whereas previous works examining on-line sentence interpretation [
issues regarding quantifier scope).
In this work, we examine the role that numerical cognition plays in people’s conceptual knowledge of events. We hypothesize that real-time sentence interpretation is not derived solely via algorithmic computation but instead from heuristic knowledge regarding events [
We build on our previous work here and take as our starting point an off-line norming task, reported in [
A follow-up items analysis in [
As such, it could be the case that people understand sentences by simply attending to the n-v-n sequence in a sentence, which would then activate conceptual world knowledge, assumed to be built on experience in the world, and is thus independent of grammatical considerations [
In the present work, we claim that conceptual world knowledge includes information regarding the quantity of participants in events. Moreover, this information is available immediately in sentence comprehension, and thus would be available in real-time language processing.
We build on recent claims that sentence comprehension can occur without any grammatical analysis; heuristic (word-based) mechanisms alone can be used [
In the current work, we build on the findings above by explicitly testing the assumption regarding the mental representation of conceptual events and number, using a novel sentence-picture verification task. Presently, instead of sentences, n-v-ns evoking a conceptual script (in the form of n1-v-n2) were presented. Sentences from [
We take this as a starting point, and in the present experiment, have participants choose a picture that best matched their interpretation of n-v-n stimuli. Participants had to respond to the final word (n2) in n-v-n triplet stimuli with respect to singular/plural number. That is, for the n1-v-n2 script, kid climb tree, derived from Every kid climbed a tree, participants had to choose a picture which had several trees or just one, in a scene with multiple kids (for details of stimuli, see Methods below).
Given this design, our predictions were straightforward. Judgments for QSA sentences in the previous experiment, regarding plural vs. singular interpretation (e.g., Every kid climbed a tree, Every jeweller appraised a diamond) should serve as significant predictors of plural vs. singular interpretations of corresponding n-v-n stimuli (e.g., kid climb tree, jeweller appraise diamond) in the current experiment.
If so, this work would show that conceptual knowledge of events not only includes information about the nature of protagonists, location, and instruments [
Forty-five Brock University undergraduate students (40 female, mean age 20 years, range 18 to 30 years) were recruited from February to June 2012. Participants were either paid for their participation or received partial course credit. All subjects were native speakers of English, had normal or corrected-to-normal vision and were right handed. None of the participants reported any neurological impairment, history of neurological trauma or use of neuroleptics. Also, none of them had participated in the norming task reported in [
This study received ethics approval from the Brock University Social Science Research Ethics Board (SREB) prior to the commencement of the experiment (REB 12-080). Written, informed consent was received from all participants prior to their participation in the experiment.
Simple n1-v-n2 word triplets (e.g., kid climb tree, jeweller appraise diamond) were constructed by stripping the quantifiers and inflection from the QSA sentences used in [
Also note that, in order to divide stimuli evenly into three lists, one of the 160 scenarios from [
Images used in the pictures were found using various image databases online.
Control conditions were such that n2 was preceded by a quantifier that unambiguously signaled either singular or plural number. The form of the Control Singular condition was n1-v-one-n2 (e.g., kid climb one tree, jeweller appraise one diamond) and the Control Plural condition was n1-v-several-n2 (e.g., kid climb several tree, jeweller appraise several diamond). These control linguistic stimuli were followed by exactly the same pictures as those in the Ambiguous condition (see
Experimental Condition | Formata | Example Stimuli |
---|---|---|
Ambiguous | n1-v-n2 | kid climb tree |
Control Singular | n1-v-“one”-n2 | kid climb one tree |
Control Plural | n1-v-“several”-n2 | kid climb several tree |
aThe column, Format, describes the structure of the “triplet” stimuli. n1―first noun; v―verb; n2―second noun.
There were 159 n-v-n scenarios for each of the three experimental conditions (Ambiguous, Control Singular, Control Plural) resulting in a total of 477 experimental stimuli. In order to reduce repetition effects, the stimuli were divided into three counterbalanced lists, such that each participant saw an equal number of conditions from each scenario. This resulted in 53 trials per experimental condition (Ambiguous, Control Singular, and Control Plural) per list, so that each participant saw 159 experimental items in total.
In addition to the experimental trials, there were 231 filler trials to reduce the predictability of the experimental stimuli and to reduce the chance of participants adopting meta-linguistic processing strategies (see
In total, each list viewed by a participant contained 390 stimuli: 159 target experimental stimuli and 231 filler trials as described above. As noted earlier, each participant saw one list only, with sentences presented in a pseudo-random fixed sequence using the program, Mix [
Informed consent was obtained from each participant before the experiment began. All participants completed a short demographics survey on handedness and reading preferences and a short computerized test of working memory
Filler Condition | Formata | Example |
---|---|---|
Filler, Singular Determiner (the, this, that) | det-n1-v-n2 or n1-v-det-n2 | this lumberjack chop log; nanny make that breakfast |
Filler, Plural Quantifier Determiner (all, many, two, four, six) | q-n1-v-n2 or n1-v-q-n2 | many beaver build dam; bandit rob all train |
Content Filler, Left Visual Field | n1-v-n2 | man anger wife |
Content Filler, Central Visual Field | q(numeral)-n-v | ten fax arrive |
aThe column, Format, describes the structure of the “triplet” stimuli. det―determiner; q―quantifier; q(numeral)―numeric quantifier; n1―first noun; v―verb; n2―second noun.
Repeated measures ANOVA were conducted for mean accuracy rates and response times, using IBM SPSS, version 20.0 [
A paired samples t-test was performed to examine apparent differences between word frequencies of singular and plural variations of n2 words.
Following study completion, it was recognized that items in the Filler Singular condition including the determiner the (e.g., the senior watch television) should not be included in analyses for singular interpretation, since this determiner does not unambiguously indicate singular number.
Binary Response DataBinary response data analyses were carried out using the statistical software R (version 3.1.0,[
We analyzed our data by modeling responses using a logit mixed-effect model [
We also analyzed the odds of plural number inference in Ambiguous vs Control Singular conditions. The analysis was a logistic regression with the following formula: glm(Number Judgment ~ Condition, data = data, family = “binomial”) and p-values were estimated using lmerTest package [
Finally, we analyzed the accuracy in Control Singular vs Control Plural conditions in a logistic regression with the following formula: glm(Number Judgment ~ Condition, data = data, family = “binomial”) and p-values were estimated using lmerTest package [
Given the novelty of the current paradigm, mean accuracy rates by participant for Control conditions and response times for all critical conditions (in ms) are first examined in order to establish that participants were able to perform the task correctly (see
3Note that the formula does not include word frequency as a random effect. Effectively, word frequency is a quantitative measure of the real-world experience with particular lexical items. Since the question we are asking is whether responses to n-v-ns can be predicted by sentences that contain those very same lexical items, if we control for word frequency, we would be taking out a fundamental component of the factor that we are interested in modeling.
The high accuracy rates for both Control Singular and Control Plural conditions indicate the success of this novel paradigm―participants were able to perform the task appropriately regarding number inference and picture matching. That is, while it could be argued that the plural picture scenario does not rule out the single-tree interpretation, the fact that participants were able to distinguish between these unambiguously marked number conditions shows that they were indeed responsive to the numerical contrast in the experiment (for further evidence of this, see complete filler results in Appendix D which also indicate high accuracy). In addition, participants were clearly sensitive to the ambiguity present in the Ambiguous conditions; RTs for this condition were 425 ms and 335ms longer than Control Singular and Control Plural conditions, respectively (F (2, 88) = 143.4, MSE = 18,058, p < 0.001, η p 2 = 0.765).
Next, we report results directly relevant to our hypothesis regarding plural picture
Condition | Accuracy (%) | RT (ms) | Proportion Plural Judgments (%) |
---|---|---|---|
Mean (MSE) | Mean (MSE) | Mean (MSE) | |
Ambiguous | --- | 1541 (63) | 7.5 (1.0) |
Control Singular | 97 (0.4) | 1112 (46) | 2.7 (0.4) |
Control Plural | 89 (1.4) | 1213 (47) | 89.0 (1.4) |
choices in the current experiment. Results revealed that responses to items from the previous quantifier norming study did serve as a significant predictor of plural judgments in the present experiment (b = 1.46, SE = 0.60, z = 2.41, p = 0.02). Thus, according to the present model, a greater proportion of plural responses made to sentences in the previous experiment predicts a greater likelihood of plural picture choice to a corresponding n-v-n in the current experiment. The odds of such a choice are 4.31 times (=e1.46) greater for a one-unit increase in plural response to a sentence in the previous experiment (odds ratio, OR = 4.31, 95% CI = [1.30, 14.64]). Thus, number interpretation to sentential stimuli does serve as a predictor of number interpretation to (conceptual event) n-v-n stimuli.
Next, we note that we had no other a priori hypotheses in the current experiment. We recognize that the plural picture choices for the Ambiguous n-v-n condition in the present experiment are in the opposite direction as compared to responses to quantifier ambiguous sentences. Given that the n-v-ns had no inflection, this is not surprising. Participants favoured singular interpretations in the current experiment, since in English, plural is overwhelmingly marked via -s inflection. Without it, nouns are likely interpreted as singular. Furthermore, plural pictures are necessarily visually more complex than the singular pictures. Thus, at the face of it, a complete lack of inflection (which would heavily bias towards a singular interpretation), along with the less visually complex choice of a singular picture, would explain the bias for singular choices found in the current experiment. That being said, it is worth pointing out that the plural picture choices for the Ambiguous condition were still significantly higher than those for the Control Singular condition (b = −1.07, SE = 0.15, z = −7.17, p < 0.001). This suggests, importantly, that participants performed a different number inference for Ambiguous vs. Control Singular conditions. Next, we examine differences in word frequencies between singular vs plural words as a way to understand the bias for singular found in the current experiment.4
Relative log word frequencies of singular n2 variations (M = 0.58, SD = 0.05) were found to be significantly greater than those of plural n2 variations (M = 0.42, SD = 0.05), resulting in a significant mean difference of 0.16 (t = 18.94, df = 157, p < 0.001, 95% CI = [0.15, 0.18]).5
Thus, given the fact that the singular form of the words was significantly higher in terms of word frequency, it is not surprising that, overall, a very strong singular bias was found in the current experiment, which used uninflected n-v-ns. Further evidence of the stark singular bias is revealed via analyses regarding the Control Singular vs. Control Plural conditions. Although accuracy rates for both Control conditions were close to ceiling (see
In the current study, a novel conception of the sentence-picture matching task was employed―instead of full sentences, n-v-n triplets were presented. People were asked to choose the picture that matched their interpretation regarding the number of entities inferred for the final item of the triplet. Participants’ near ceiling accuracy rates for unambiguous control conditions, as well as their increased response times for ambiguous conditions indicated that they were indeed sensitive to the numerical contrast in the experiment, and that they performed this novel task correctly [
Despite a strong bias for choosing a singular interpretation, binary logistic regression analyses revealed that plural interpretation for corresponding sentences did serve as significant predictors of plural picture choices to n-v-n stimuli. The strong singular bias is explained due to the nature of the linguistic stimuli (in English, nouns lacking inflection are interpreted as singular; in addition, singular words were found to be lexically more frequent than plural stimuli), and due to the nature of the current experimental task (choosing between pictures that contained more vs. fewer items).
At the face of it, the findings support the long-held assumption (however, yet to be empirically demonstrated) that variation in sentence interpretation can be explained via lexically based factors.
We interpret the cognitive significance of the predicted statistical effect as follows: these findings indicate that our mental representation of conceptual event knowledge must include numerical information regarding quantity of participants in the event. That is, initial comprehension of QSA sentences of the form Every kid climbed a tree consists of a fast-and-frugal interpretation regarding the likely number of participants in the (n1-v-n2) event, and does not rely exclusively on algorithmic rules for interpretation. These effects are consistent with our recent model that language processing proceeds along a “Heuristic first, algorithmic second mechanism”. We further discuss these issues below.
This study sheds light on the question of whether people have expectations regarding the likely number of entities in a conceptual event, to date an unexplored aspect of schematic knowledge. That is, it is well-known that understanding language relies on the mental representation of world knowledge [
The present results are of theoretical importance as they call into question the dominant psycholinguistic perspective that algorithmic syntactic processing drives semantic interpretation of these and other sentences. Instead, the present results show that―at least under certain circumstances―interpretive processes need not include syntactic algorithms at all. That is, for the constructions examined here, experience trumps grammar (c.f. [
It is important to note that we are not claiming that the determiners “every” and “a” play no role in the interpretation of QSA sentences. The stark differences found in interpretation of QSA sentences vs. their corresponding n1-v-n2 triplets clearly attests to the important contribution of these determiners. In addition, we note that the heuristic first mechanisms in use for sentences such as Every kid climbed a tree would not be in use for more complicated sentences of the form Every kid climbed at least five trees, which is logically equivalent to No kid climbed less than five trees6. These latter sentences would immediately invoke algorithmic mechanisms for comprehension (or System 2, in Kahneman’s terms), due to their complexity. It is our contention that many psycholinguistic experiments have in fact examined highly difficult sentence forms (such as centre-embedded relative clauses, for example). It would make sense if algorithmic processes immediately applied to those sentences, however, those are not the sorts of sentences encountered in day-to-day conversation. Thus, while it is true that language processing is automatic and occurs without effort―in day to day conversation, many sentences can indeed be understood in a shallow manner. The claim we make here (and previously) is that even certain QSA sentences may be understood in this way.
In sum, using logistic regression, this work empirically demonstrated that variation in sentence interpretation with respect to number, for identical syntactic constructions, is a function of lexical factors that contribute to event interpretation. Thus, our findings reveal that conceptual event interpretation must include information regarding the number of participants in a script.
Overall, the results support our recent model of language processing in that it proceeds along a “Heuristic first, algorithmic second mechanism”. That is, our findings confirm that the numerical interpretation of QSA sentences of the form Every kid climbed a tree can rely on heuristic processes dependent on event representations, rather than exclusively relying on algorithmic rules. Therefore, it challenges the dominant view that these sentences are primarily interpreted via logical algorithmic rules.
Thanks to Raechelle Gibson, Dilani Balasubramaniam, and Erin Murphy for stimuli preparation assistance, also to Raechelle Gibson for help in experimental set-up. We are grateful to Gary Libben, Harald Baayen, Joffre Mercier, Matthew Walenski and Louis Schmidt for comments. Thanks also to Victoria Witte for manuscript assistance.
Dwivedi, V.D., Goertz, K.E. and Selvanayagam, J. (2018) Heuristics in Language Comprehension. Journal of Behavioral and Brain Science, 8, 430-446. https://doi.org/10.4236/jbbs.2018.87027
Appendix A. Norming study stimuli and values.
Appendix B. Experimental stimuli lists. Lists of experimental stimuli and corresponding picture pairs shown to participants in the present study.
Appendix C. Experiment instructions. A detailed description of experiment instructions, including verbatim text, as they were shown to participants in the present study.
Appendix D. Filler data analysis. Mean accuracy (%), response times (RT, ms), and proportion plural responses (%) by filler condition along with detailed discussion. https://www.researchgate.net/project/the-neural-underpinnings-of-semantic-ambiguity