characterising kinds and instances of kinds: erp reflections

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Characterising kinds and instances ofkinds: ERP reflectionsSandeep Prasada a , Anna Salajegheh b , Anita Bowles b & DavidPoeppel ca Department of Psychology , Hunter College , New York, NY,USAb Cognitive Neuroscience of Language Laboratory , University ofMaryland , Baltimore, MD, USAc Cognitive Neuroscience of Language Laboratory; Departmentof Biology; and Department of Linguistics , University ofMaryland , Baltimore, MD, USAPublished online: 08 Feb 2008.

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Characterising kinds and instances of kinds:

ERP reflections

Sandeep PrasadaDepartment of Psychology, Hunter College, New York, NY, USA

Anna SalajeghehCognitive Neuroscience of Language Laboratory, University of Maryland,

Baltimore, MD, USA

Anita BowlesCognitive Neuroscience of Language Laboratory, University of Maryland,

Baltimore, MD, USA

David PoeppelCognitive Neuroscience of Language Laboratory; Department of Biology; and

Department of Linguistics, University of Maryland, Baltimore, MD, USA

Syntactic and semantic information are computed online in a manner such thatelectrophysiological methods can detect distinct processes within a few hundredmilliseconds of a word. The amplitude of the N400 response has been shown toreflect semantic integration of a word in the context of a preceding word,sentence, and discourse. We show, in a combined behavioural and ERP study,that the N400 amplitude to the same word, in nearly identical sententialcontexts, is modulated as a function of subtly different morphosyntacticenvironments that condition either a generic (grass is green) or nongeneric (thegrass is green) reading. The results suggest that N400 amplitude reflects not

Correspondence should be addressed to Sandeep Prasada, Department of Psychology,

Hunter College, CUNY, 695 Park Avenue, New York, NY 10021, USA.

E-mail: [email protected]

We thank Nina Kazanina for help with the script and Elaine Dillingham, Susannah Hoffman

and Maura Pilotti for help conducting the behavioural experiments. This work was supported by

an NIH grant to DP (R01DC 05660) and a startup grant from Hunter College to SP. SP also

received infrastructure support from RCMI grant RR03037 from the National Center for

Research Resources (NIH) to the Gene Center at Hunter College. During part of the

preparation of this manuscript, DP was a Fellow at the Wissenschaftskolleg zu Berlin.

LANGUAGE AND COGNITIVE PROCESSES

2008, 23 (2), 226�240

# 2007 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

http://www.psypress.com/lcp DOI: 10.1080/01690960701428292

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only the existence of a semantic computation but can reflect processes relevantto the type of semantic relation being computed. Specifically, it is sensitive towhether a word is interpreted as characterising a kind/type or an instance of akind/token of a type.

A central problem in language comprehension involves when and how

incoming information is incorporated into existing semantic representations.

Event-related potentials (ERP) provide one tool for investigating this

question. Findings indicate that the N400 response reflects the processing

of semantic information, and that its amplitude varies with the degree to

which a word fits the preceding word or sentential context (e.g., Federmeier

& Kutas, 1999; Friederici, Pfeiffer, & Hahne, 1993; Hagoort & Brown, 1994;

Holcomb, Kounios, Anderson, & West, 1999; Kutas, 1993; Kutas & Hillyard,

1980, 1984; Neville, Nicol, Barss, Forster, & Garrett, 1991; Osterhout &

Mobley, 1995; Van Petten, 1993, 1995). Research by van Berkum, Hagoort,

and Brown (1999) shows that this sensitivity to semantic fit extends to extra-

sentential discourse contexts.

In the present study, we investigate whether the type of semantic relation a

given word has to the preceding linguistic context influences the N400.

Specifically, we investigate how N400 amplitude varies when a critical word

characterises a kind (e.g., banana) or an instance of a kind (e.g., this banana).

Kutas and Hillyard (1980) demonstrated that N400 amplitude varied with

the plausibility and/or predictability of a word within a given context. Using

sentences such as (1) and (2), they found that words such as beard in (1)

elicited a reduced N400 in comparison to less plausible or predictable words

such as city (2).

(1) He shaved off his moustache and beard.

(2) He shaved off his moustache and city.

This result has been extended in a number of ways. Studies consistently find

that more plausible and more predictable words elicit smaller N400

amplitudes. These data suggest that N400 amplitude reflects processes

involved in the integration of semantic information into the existing

context.1

However, these studies do not indicate whether N400 amplitude also

reflects the type of semantic relation a word has to its preceding context. A

notable exception are studies which have investigated the is a relation in

categorisation (A banana is a fruit/A banana is not a fruit/A banana is a

1 Debate continues concerning the nature of the processes involved in semantic integration

and their contribution to the N400; however, there is consensus that ease of integration of

semantic information is reflected in the N400 signal.

GENERICITY AND THE N400 227

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vehicle/A banana is not a vehicle) (e.g. Fischler, Bloom, Childers, Roucos, &

Perry, 1983). These studies show that the N400 is sensitive to whether the

subject category is a member of the predicate category, providing evidence

that N400 amplitude is sensitive to category inclusion. The present study

investigated whether N400 amplitude is also sensitive to the difference

between the relation involved in characterising a kind (or type) and

characterising an instance of a kind (or token of a type).

We held the critical word constant across sentences and manipulated its

semantic relation to the preceding context. In one set of sentences, the

critical word characterised a kind (3), whereas in another set, the same

critical word characterised an instance of that kind (4). We chose such

sentences because they have drastically different interpretations despite

identical content words and virtually identical surface forms.2 The inter-

pretations of these sentences differ along a number of (related) dimensions.3

First, whereas the predicate in (3) characterises a kind, the predicate in (4)

characterises a specific instance of a kind. Second, whereas (3) has a

determinate truth-value, (4) does not. It may be true of some banana but not

others, or a banana only at a given time. Finally, generics have discourse

independent interpretations, whereas nongenerics do not.

(3) Bananas are yellow.

(4) This banana is yellow.

(5) Bananas are green.

(6) This banana is green.

These differences in interpretation indicate that the critical word (yellow)

must be integrated with representations constructed based on the preceding

sentential context in different ways for generics and nongenerics. Further-

more, the manner in which the critical word is integrated depends not only

on whether the subject refers to a kind or to instances of a kind, but also on

2 Instead of choosing sentences such as (4), it would have been possible to choose nongeneric

sentences such as The bananas are yellow, which are even more similar to the generic sentences in

surface form. Given our interest in the semantic interpretation of generic and nongeneric

sentences, we chose to manipulate whether a single kind or a single instance of a kind was being

characterised. Thus, we held the number of things being characterised constant but manipulated

the nature of the thing being characterised (1 kind/1 instance). Since kinds contain indefinitely

many instances, it is not possible to match the number of instances referred to in the generic and

nongeneric conditions. It should be noted that 25% of our generic sentences were about kinds

that are referred to by mass nouns. For these sentences, the generic and nongeneric sentences

differed only in the presence/absence of a determiner (Grass is green/The grass is green).3 For a review of the ways in which generic sentences can differ from nongeneric sentences,

see Krifka et al. (1995). Here we concentrate on only those differences that are relevant to the

sentences used in our experiment.

228 PRASADA ET AL.

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the characterising word. For example, abundant must be interpreted

differently than yellow with respect to the kind BANANA.4

In sum, the interpretations of generic and nongeneric sentences differ in a

number of ways. If N400 amplitude is sensitive to the type of semanticrelation into which a critical word enters, then characterising words in

generic and nongeneric sentences may elicit distinct N400 responses.

Comparing the processing of generic and nongeneric sentences also

provides a theoretical basis for distinguishing the effects of predictability and

genericity. In true generic sentences (3), the critical word must refer to a

characteristic property of the kind. No such constraint applies to the critical

word in the corresponding nongeneric sentence (4). In a nongeneric sentence,

the critical word could refer to a characteristic property, or an uncharacter-istic or temporary property (e.g., green, wet). Thus, the critical word in true

generic sentences (3) should be more predictable than the same word in

corresponding nongeneric sentences (4). In contrast, critical words cannot

refer to an uncharacteristic property if a sentence is generic, but may do so if

the sentence is nongeneric. Consequently, critical words that refer to an

uncharacteristic property should be more predictable in nongeneric sen-

tences (6) than in generic sentences (5). These predictions concerning

genericity and predictability were tested behaviourally in Experiments 1Aand 1B and the reflections of genericity and predictability were tested

electrophysiologically in Experiment 2.

EXPERIMENT 1A

In Experiment 1A we used the Cloze procedure to determine if the

predictability of characteristic and uncharacteristic properties varies in

accordance with constraints on the interpretation of generic and nongeneric

sentences.

Method

Participants. Eighty-nine native speakers of American English partici-

pated.

Stimuli. Forty sets of four sentences were constructed. Each set

contained (i) a generic sentence with a characteristic property of the kind

(GC) (Bananas are yellow); (ii) a corresponding nongeneric sentence with thesame kind and characteristic property (NC) (The banana is yellow); (iii) a

4 Context can also influence interpretation. In principle, one could interpret sentences such

as (4) and (6) generically, however, this interpretation is difficult in the absence of a supporting

context. The present experiment did not provide such a context.


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generic sentence with the same kind, but an uncharacteristic property of the

kind (GU) (Bananas are green); and (iv) a corresponding nongeneric

sentence with the same kind and uncharacteristic property (NU) (The

banana is green). All generic sentences were three words long, the

corresponding nongenerics four words long. The uncharacteristic properties

chosen were properties that were plausible (e.g., bananas can be green).

Finally, the generics used were sentences such as (3) in which we understand

there to be a principled connection between the kind and the characteristic

property over and above any statistical connection between the two (Prasada &

Dillingham, 2006). The full set of sentences is given in Appendix A. For the

Cloze procedure, the final word of each sentence was replaced with a blank.

Two lists were constructed so that each list contained either the generic or

nongeneric version of an item.

Procedure. Participants were asked to read the sentence fragments to

themselves and fill in the blank with the first word that came to mind.

Results and discussion

Cloze probabilities for the critical words are given in Table 1.5 Characteristic

properties were predictable and uncharacteristic properties highly unpredict-

able.

Because of the lack of variability, no statistical test was possible, but it is

clear that characteristic words were more predictable than uncharacteristic

words and, crucially, that there was no difference in the predictability of

uncharacteristic words in generic and nongeneric contexts. A t-test showed

no difference in the predictability of characteristic words in generic and

nongeneric contexts. Furthermore, their distributions had virtually identical

standard deviations, skewness, and kurtosis. In sum, the critical words in our

5 Due to clerical errors on 2 items, the results are based on 38 items.

TABLE 1Cloze probabilities of critical words as a function of sentence type

and property type. Ranges given in parentheses

Property type

Characteristic Uncharacteristic

Sentence type

Generic 0.4453 (0�1) 0

Nongeneric 0.4222 (0�0.8696) 0.0029 (0�0.0011)

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generic and nongeneric sentences do not differ in their Cloze probabilities. If

they elicit distinct N400 amplitudes, the differences could not be attributed

to differences in the predictability of the critical words. We were concerned,

however, that the Cloze procedure may not provide a sensitive enough

measure of predictability, and thus also measured predictability in a ratings

task (Experiment 1B).

EXPERIMENT 1B

Participants. Sixteen native speakers of American English participated.

Stimuli. Set of sentences described above.

Procedure. Participants were presented with all 40 sets of sentences in

random order and asked to rate how predictable the last (critical) word of

the sentence was on a 7-point scale. Sentences were presented in sets with the

generic and corresponding nongeneric sentences presented one after the

other (with order of presentation counterbalanced across sets) to highlight

predictability differences. This potentially overestimates the predictability of

these words when the sentences are presented in random order as in

Experiment 2. Because our purpose was to determine if there was an effect of

genericity that could not be attributed to predictability, it was desirable to

use as sensitive a measure of predictability as possible. The order of sentences

with characteristic and uncharacteristic properties was counterbalanced.

Results and discussion

Mean ratings for predictability of the critical word in the four conditions

are given in Table 2. A 2�2 within-subjects ANOVA was conducted

TABLE 2Mean predictability ratings of the characterising property as a function

of sentence type and property type

Property type

Characteristic Uncharacteristic

M (SD) M (SD)

Sentence type

Generic 6.24 (0.44) 1.85 (0.68)

Nongeneric 5.79 (0.56) 3.37 (0.95)


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with variables of sentence type (generic/nongeneric) and property type

(characteristic/uncharacteristic). Participants’ ratings were the dependent

measure. Main effects of property type, F(1, 15)�274.57, pB.001, and

sentence type, F(1, 15)�23.83, pB.001 were found. There was also asignificant interaction, F(1, 15)�76.00, pB.001, indicating that the critical

word was rated as more predictable in generic than in nongeneric sentences

when the word referred to a characteristic property, F(1, 15)�10.92,

pB.005, but was rated as less predictable when the word referred to an

uncharacteristic property, F(1, 15)�75.03, pB.001. Finally, as hypothesised,

the characteristic property was more predictable than the uncharacteristic

property in both generic, F(1, 15)�363.84, pB.001, and nongeneric,

F(1, 15)�102.41, pB.001 sentences. In sum, the predictability of character-istic and uncharacteristic words was in accordance with the constraints on

interpretation imposed by generic and nongeneric sentences. Item analyses

yielded parallel results.

EXPERIMENT 2

Experiment 2 investigated whether N400 amplitude is sensitive to the distinct

semantic processes involved in interpreting a word as characterising a kind

versus an instance of a kind and if the effect of genericity can be

distinguished from that of predictability.

Materials and Methods

Participants. Twenty-eight adults (11 females) participated. All partici-

pants (age range 19�34) were right-handed speakers of American English,

had normal or corrected-to-normal vision, and had no neurological

abnormalities. Data from 8 participants were excluded due to excessiveeye-movements.

Stimuli. The 40 sets of sentences from Experiment 1 were used. The

critical word, e.g., yellow or green, was marked in the EEG file for selective

averaging. The ratio of distracter to target sentences was �1.5:1. Distracters

were approximately matched in length to the target sentences and were

derived from an unrelated experiment on eventive/stative verbs.

Procedure. Sentences were presented in RSVP mode with an SOA of 300

ms and a duration of 300 ms/word. At the end of each trial, participants

initiated the next trial by button-press. Targets and distracters were

pseudorandomly interleaved. Participants’ task was to read the sentences.

Electrophysiological responses were recorded using Synamps Neuroscan

amplifiers and a 32-channel Electrocap fitted with sintered Ag/AgCl

electrodes (modified 10�20 electrode configuration). Data were recorded

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continuously, in AC mode, using a mastoid reference (sampling rate 1000 Hz,

recording bandwidth 0.15�100 Hz). Impedances were below 5 kOhm for each

electrode.

Analysis. After manual artifact rejection, data were epoched around the

critical words (100 ms pre- to 700 ms post-stimulus), baseline corrected

(using the pre-stimulus interval), and averaged by condition within each

subject. For statistical analysis, ERPs were quantified as mean amplitude

during the selected time bin (300�500 ms), and six channels were selected:

three for which the amplitude of the N400 is typically modulated by

semantic factors (Pz, P3, P4; Group 1), and three for which it is not (Fz,

F3, F4; Group 2). Because this experiment is a hypothesis-testing study and

not a topographic brain-mapping study, we restricted the statistical analysis

to those channels known both from the literature and from visual

inspection of our data to yield robust N400 effects. Given that the design

predicts only subtle N400 contrasts across conditions due to their close

matching and similarity, we selected centro-parietal channels to probe for

the effect of genericity and frontal channels as controls. The 300�500 ms

time window was chosen on the basis of visual inspection of our response

profile and because it is the most common interval used in the N400

literature. Mean amplitudes were entered into a repeated measures 2�2�2

ANOVA with the factors of genericity, characteristicness, and electrode

group. For all analyses, Greenhouse�Geisser corrected values are reported.

For visualisation purposes, electrophysiological data are shown low-passed

filtered at 7 Hz.

Results

Figure 1 shows the electrode layout (four conditions overlaid) and highlights

the robust N400 effect. The differentiation among conditions is most obvious

along centro-parietal midline electrodes, consistent with standard N400

findings. Figure 2 shows the canonical N400 channel (Pz) with all four

conditions. During the N400 time window, the two generic conditions (red,

black) show a greater negativity than the corresponding nongenerics (blue,

green). Moreover, uncharacteristic sentences generally displayed greater

negativity than characteristic sentences, consistent with standard assump-

tions about predictability.

The ANOVA revealed a significant effect of genericity, F(1, 19)�8.267,

pB.01, and an interaction between genericity and electrode group,

F(1, 19)�4.508, pB.05. This interaction was due to there being a

significantly higher N400 response to generic than nongeneric sentences in


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electrode group 1, F(1, 19)�13.982, pB.001, but no difference between the

two conditions in electrode group 2 (FB1).

Subsequent analyses showed that the difference between generic and

nongeneric sentences held for both uncharacteristic properties (grass is brown

versus the grass is brown, comparison1) and characteristic properties (grass is

green versus the grass is green, comparison2) at each of the electrodes in

group 1. Pz, comparison1, F(1, 19)�13.32, pB.001; comparison2, F(1,

19)�6.74, pB.014; P3, comparison1, F(1, 19)�10.31, pB.003; compar-

ison2, F(1, 19)�10.14, pB.003; P4, comparison1, F(1, 19)�7.28, pB.012;

comparison2, F(1, 19)�7.112, pB.013.

Planned contrasts were conducted comparing characteristic versus

uncharacteristic properties for both the generic and nongeneric conditions

at each of the electrodes in group 1. Whereas the mean N400 amplitude for

uncharacteristic properties was always numerically greater than that of

characteristic properties, the differences were not statistically significant or

were only marginally so.

Figure 1. Recording layout showing all channels, with the four critical conditions overlayed. A

robust N400 response is visible across the centro-parietal channels. A differentiation across

conditions is most clear along midline electrodes.

colo

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GENERAL DISCUSSION

The results demonstrate that N400 amplitude is sensitive to semantic

processes involved in interpreting a critical word as characterising a kind

rather than an instance of a kind. A significantly larger N400 was found

when a critical word characterised a kind than when that same word

characterised an instance of that kind. Furthermore, critical words in the

generic-characteristic condition (Bananas are yellow) elicited a larger N400

response than the same words in the nongeneric-characteristic condition

(The banana is yellow), even though the critical words were equally or more

predictable in the former condition, and were not anomalous in any way.

This result suggests that N400 amplitude is sensitive to the type of semantic

relation into which a critical word enters.

It is unlikely that the difference in sentence position can explain differences

between the N400 amplitude to the critical word in generic and nongeneric

conditions. Whereas it has been found that words later in a sentence generally

elicit smaller N400 responses, this result is usually interpreted as reflecting

additional constraints that are imposed on words later in the sentence, thus

making them more predictable (Kutas, Van Petten, & Besson, 1988; Van

Petten, 1995). However, as confirmed by Experiment 1B, when the critical

word refers to a characteristic property, it is less predictable in nongeneric

than in generic sentences, even though it appears later in nongeneric

sentences. Despite this, the characteristic critical words elicited greater

Figure 2. ERP waveforms at electrode Pz for the four conditions of interest. The critical word

appeared at 0 ms. There was an effect of condition in the N400 time window (300�500 ms). The

two generic conditions (red � uncharacteristic, black � characteristic) show a stronger N400

response than the two nongeneric conditions (green � uncharacteristic, blue � characteristic).

Moreover, the uncharacteristic conditions are associated with larger negativity.

colo


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N400 responses in the generic sentences, and thus this N400 effect is unlikely

to be due to predictability effects arising from sentence position.

The interpretation of sentences that characterise kinds differs from that of

sentences that characterise instances of kinds in a number of ways. It is

unclear which of these differences is reflected in the N400 amplitude. The

fact that both true and false generic sentences elicited an increased N400 in

comparison to their corresponding nongeneric sentences suggests that the

effect is not tied to a specific truth-value (see Fischler et al., 1983 for similar

findings).

It can be argued that generic characteristic sentences (3) express aspects of

the representation of concepts within semantic memory and that their truth

is verified by reference to those concepts (Prasada, 2000). In contrast,

nongeneric sentences must be evaluated with respect to discourse representa-

tions and extra-linguistic context. Previous research suggests that N400

amplitude is sensitive to the structure of semantic memory (Federmeier &

Kutas, 1999; Fischler et al., 1983; Kounis & Holcomb, 1992). Thus, the effect

of genericity found in the present experiment may reflect the differential

involvement of semantic memory in the interpretation of generic and

nongeneric sentences.

Finally, differences in N400 amplitudes to generic and nongeneric

sentences may reflect the fact that the generic sentences expressed a principled

connection between the subject and the characterising word, whereas the

nongeneric sentences expressed a factual connection. If so, one might expect

statistical generics such as barns are red, which express factual connections

(Prasada & Dillingham, 2006), to be distinguished in their N400 response

from principled generics such as (3) in the same manner as nongenerics such

as (4). We are currently investigating this possibility.

Given the large number of studies that have found both effects of

predictability and plausibility on N400 amplitude (e.g., Federmeier & Kutas,

1999; Friederici et al., 1993; Hagoort & Brown, 1994; Kutas & Hillyard,

1980, 1984; Osterhout & Mobley, 1995; van Berkum et al., 1999; Van Petten,

1993, 1995), the lack of a reliable N400 effect of predictability/plausibility

was unexpected.6 However, the critical words used in the current study differ

from previous stimuli in theoretically important ways. For example, in the

present study, the unexpected words named values on the same dimension as

the expected words, and thus were semantically related to the expected word.

Previous research shows that semantic relatedness diminishes N400 ampli-

tude (Federmeier & Kutas, 1999).

6 Because the present experiment did not explicitly distinguish predictability and plausibility

we refer to their effect ambiguously, though we recognise that the two effects can be

distinguished (e.g., Federmeier & Kutas, 1999).

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CONCLUSIONS

The capacity to talk about kinds as well as instances of kinds is a

fundamental aspect of semantic competence that develops at a very early

age (Gelman, 2003). The present study suggests that N400 amplitude is

sensitive to some of the semantic processes that distinguish interpreting a

word as characterising a kind as opposed to an instance of a kind, and thus

to the type of semantic relation into which a word enters. Given that the

generic and nongeneric sentences had identical content words, the resultssuggest that N400 amplitude is sensitive to interpretational differences that

are morphosyntactically signalled.

Although we do not know which difference between the interpretation of

generic and nongeneric sentences is reflected by N400 amplitude, there are

explicit proposals about the dimensions along which the interpretation of

such pairs of generic and nongeneric sentences differ (Carlson & Pelletier,

1995). Thus, it will be possible for future research to refine our under-

standing of this effect in a systematic and theoretically motivated manner.

Manuscript received September 2006

Revised manuscript received March 2007

First published online October 2007

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Appendix A

Generic sentences Nongeneric sentences

Grass is green. The grass is green.

Grass is brown. The grass is brown.

Snow is white. The snow is white.

Snow is yellow. The snow is yellow.

Jalapenos are hot. The jalapeno is hot.

Jalapenos are mild. The jalapeno is mild.

Fire is hot. The fire is hot.

Fire is cold. The fire is cold.

Whales are big. This whale is big.

Whales are small. This whale is small.

Lemons are sour. This lemon is sour.

Lemons are bland. This lemon is bland.

Diamonds are expensive. This diamond is expensive.

Diamonds are cheap. This diamond is cheap.

Sirens are loud. This siren is loud.

Sirens are quiet (soft). This siren is quiet (soft).

Rocks are hard. This rock is hard.

Rocks are soft. This rock is soft.

Monsters are scary. This monster is scary.

Monsters are friendly. This monster is friendly.

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APPENDIX (Continued)


Feathers are light. This feather is light.

Feathers are heavy. This feather is heavy.

Knives are sharp. This knife is sharp.

Knives are dull. This knife is dull.

Spinach is green. This spinach is green.

Spinach is brown. This spinach is brown.

Apples are healthy. This apple is healthy.

Apples are poisonous. This apple is poisonous.

Games are fun. This game is fun.

Games are dangerous. This game is dangerous.

Bananas are yellow. This banana is yellow.

Bananas are green. This banana is green.

Dirt is brown. This dirt is brown.

Dirt is red. This dirt is red.

Pretzels are salty. This pretzel is salty.

Pretzels are sour. This pretzel is sour.

Winters are cold. This winter was cold.

Winters are warm. This winter was warm.

Elephants are big. This elephant is big.

Elephants are small. This elephant is small.

Cookies are sweet. This cookie is sweet.

Cookies are salty. This cookie is salty.

Cats are furry. This cat is furry.

Cats are hairless. This cat is hairless.

Cheetahs are fast. This cheetah is fast.

Cheetahs are slow. This cheetah is slow.

Oatmeal is mushy. This oatmeal is mushy.

Oatmeal is hard. This oatmeal is hard.

Angels are good. This angel is good.

Angels are bad. This angel is bad.

Dancers are graceful. This dancer is graceful.

Dancers are clumsy. This dancer is clumsy.

Needles are sharp. This needle is sharp.

Needles are dull. This needle is dull.

Strawberries are red. This strawberry is red.

Strawberries are green. This strawberry is green.

Mothers are caring. This mother is caring.

Mothers are selfish. This mother is selfish.



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Balls are round. This ball is round.

Balls are oval. This ball is oval.

Carrots are crunchy. This carrot is crunchy.

Carrots are soggy. This carrot is soggy.

Champagne is bubbly. This champagne is bubbly.

Champagne is flat. This champagne is flat.

Clowns are funny. This clown is funny.

Clowns are sad. This clown is sad.

Lions are carnivorous. This lion is carnivorous.

Lions are vegetarian. This lion is vegetarian.

Vinegar is sour. This vinegar is sour.

Vinegar is sweet. This vinegar is sweet.

Athletes are fit. This athlete is fit.

Athletes are fat. This athlete is fat.

Birds can fly. This bird can fly.

Birds can swim. This bird can swim.

Cherries are red. This cherry is red.

Cherries are brown. This cherry is brown.

Silver is shiny. This silver is shiny.

Silver is dull. This silver is dull.

Sponges are soft. This sponge is soft.

Sponges are hard. This sponge is hard.


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characterising kinds and instances of kinds: erp reflections

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