Parent–toddler play talk: Toddler speech is differentially associated with paternal and maternal speech in interaction

Abstract

Children’s speech is influenced by the speech they hear, in particular by the parental speech addressed directly to them. The aim of this study was to analyse toddlers’ speech with their parents and to investigate the influence of specific characteristics of child-directed speech on child speech in real time during mother–child and father–child dyadic interactions. Eighty-four children (M = 24.11 months, SD = 1.37, 42 girls) and their parents were video-recorded in dyadic freeplay. Parent and child speech was compared on measures of quantity (number of words), quality (vocabulary diversity, proportion, and composition of nouns/verbs) and productivity or complexity. Overall findings reveal very similar speech produced by mothers and fathers with their toddlers and by toddlers with each parent, with the exception of vocabulary diversity patterns. Furthermore, the child-directed speech of mothers and fathers was differently associated with child speech in interaction. Findings suggest that children may be exposed to different lexical sets by mother and father and there may be added value for the child of engaging in separate dyadic interactions with mother and father. Implications for research include sampling child speech across contexts and conversational partners.

Keywords

Child-directed speech child speech mothers fathers dyadic interaction vocabulary diversity

In a bioecological model of development, children’s interactions with parents influence their development (Bronfenbrenner & Morris, 2006). Child-directed speech (CDS) has a proximal influence on child real-time language performance yet the dynamic effects of variations in linguistic input in this context have not been fully explored. The purpose of this study is to contribute to the limited research on toddlers’ speech performance in freeplay interaction against a backdrop of mixed findings that have accumulated when characterising paternal compared to maternal child-directed speech (CDS) and its association with child speech (CS).

Parents adapt their child-directed speech

A large literature has revealed the extent to which parents adjust and adapt the speech they address to children, although evidence has primarily been collected with mothers (e.g. Mimeau et al., 2020). Prosodic, phonological, morphological, syntactic and other modifications are systematically applied to adult speech (Cychosz et al., 2021; Moscoso del Prado et al., 2020; Roy et al., 2009; Snow, 1995) according to a range of child characteristics, primarily language ability (Dailey & Bergelson, 2023; Pancsofar, 2020) and age (Rowe & Snow, 2020). Overall parents engage in significant levels of fine-tuning to the child’s lexical knowledge even at the level of individual words (Huttenlocher et al., 2010; Leung et al., 2021; Odijk & Gillis, 2021), and specific inflectional and syntactical constructions (Moscoso del Prado et al., 2020; Odijk & Gillis, 2023). Importantly, parental fine-tuning efforts affect child language (Cychosz et al., 2021; Denby & Yurovsky, 2019) and shape children’s access to various forms and constructions (Casla et al., 2022).

Children adapt their speech in interaction

Children’s language and their early lexicon develop in interaction with the people and environment around them (Tardif et al., 2008). Misiek et al. (2020) report ‘robust evidence of children’s ability to engage in multi-level linguistic alignment to adult speech from two years old’ (p. 56). Corpora studies have found child–adult language matching on levels of complexity (Dale & Spivey, 2006; Kunert et al., 2011) and function words (Borelli et al., 2019). This coordination may not apply to all parts of language (e.g. syntax: Schwab & Lew-Williams, 2016) but these dynamic effects have yet to be fully explored.

We found only two observational studies that have directly investigated discourse effects or the real-time influence of the characteristics of CDS on child speech in play interactions with both mothers and fathers with our target age group. Tamis-LeMonda et al. (2012) found several unique associations between mothers’ and fathers’ language (word types, grammatical and communicative diversity) and child language. More recently, however, Røe-Indregård et al. (2022) with a Norwegian sample found no relationships between linguistic features of mother and father CDS and their child’s CS.

Maternal and paternal child-directed speech

It is suggested that mothers produce more diverse vocabulary (Bingham et al., 2013), interact more didactically, are focussed on developing child vocabulary by naming and labelling objects thereby producing a lot of nouns (Majorano et al., 2013) and engage in more instructive play with objects (MacDonald & Parke, 1984; Stevenson et al., 1988) compared to fathers. However, while O’Brien and Nagle (1987) found fathers referred to objects more than mothers, Majorano et al. (2013) found fathers of Italian 2-year-olds produced fewer names, had a lower noun frequency and produced fewer didactic utterances than mothers. Two other early studies reported that fathers spent less time in object play with older infants and toddlers (Roopnarine et al., 1990; Teti et al., 1988). Fathers have been found to engage in more functional and exploratory play (de Falco et al., 2010; Stevenson et al., 1988), to engage in more physical activity and play for its own sake (Parke & Cookston, 2019), to be more challenging communicative partners using more wh-questions (Rowe et al., 2017) and to use more action directives incorporating verbs than mothers (Tamis-LeMonda et al., 2012). However, many of these findings are two decades old and recent research reports more similarities than differences in mothers’ and fathers’ CDS (for reviews, see Ferjan Ramírez, 2022; Pancsofar, 2020) and findings of Sheth and Ferjan Ramirez (2022) suggest that differences between maternal and paternal CDS are more pronounced at ages younger than 2 years.

Mothers’ and fathers’ CDS is also influenced by the environment in which interaction takes place, for example, dyadic and triadic settings elicit different CDS patterns with fathers in particular reported to be affected by the presence of other speakers (Quigley & Nixon, 2022). The props or toys provided for the test session (O’Brien & Nagle, 1987) and the activity can also be relevant (see Holme et al., 2022, for a review). Studies across multiple languages report more nouns during book reading and more verbs during play (e.g. Altınkamış et al., 2014; Choi, 2000). However, Choi (2000) also found that English-speaking mothers favoured nouns in both reading and play contexts and Goldfield (1993) reported that nouns were more frequent in play without toys. Doering et al. (2020) report situational differences in maternal language to toddlers during book-reading compared to toy play but only for the United States and not the German mothers in their sample. There is little to no information on fathers’ CDS in these different contexts.

Longitudinal studies have also shown (e.g. Baker et al., 2015; Majorano et al., 2013; Pancsofar & Vernon-Feagans, 2006, 2010) that fathers’ speech is uniquely associated with child language. However, paternal CDS has rarely been studied in terms of its real-time salience for, and uptake by, the child. To date, very few studies have investigated discourse effects with typically developing toddlers in interaction with fathers. The inclusion of fathers allows us not just to determine if mothers and fathers expose their children to different CDS but in turn to explore child conversation with mothers compared to with fathers. Studying the dynamic interplay of both CDS and CS is crucial for understanding child language development.

The current study

This study examines CDS and CS in a group of 2-year-old toddlers and their parents. We analysed the speech produced in a freeplay setting as it has been found to elicit optimum levels and types of interaction and language, especially for father–child pairs (Kwon et al., 2013) and we predicted that toddler and parent speech would be positively associated on measures of complexity (mean length of utterance; MLU), diversity (type-token ratio; TTR) and lexical composition. The following three hypotheses were tested:

Hypothesis 1. Based on findings that fathers tend to be challenging communicative partners, to engage in a lot of physical play and to be action orientated, we predicted that fathers would use more complex language and more verbs than mothers (higher MLU and higher proportion of verbs) and that toddlers would have higher MLU and use more verbs in interaction with father.

Hypothesis 2. Based on findings that mothers tend to be didactic and directive in play contexts, and to use a lot of labelling and referential language, we expected that mothers’ CDS would be more lexically diverse than fathers and would contain more nouns (higher VOCD and higher proportion of nouns) and that toddler speech would be more lexically diverse (higher TTR) and contain more nouns in interaction with mother.

Hypothesis 3. Finally, based on longitudinal findings that fathers’ speech is uniquely associated with child language development, we predicted that elements of fathers’ and mothers’ speech would be differentially associated with elements of toddler speech in dyadic interaction.

Method

Participants

Eighty-four children (M_age = 24.1 months, SD = 1.37; 42 girls), their mothers (M_age = 34.6 years, SD = 5.64), and their fathers (M_age = 36.63 years, SD = 5.05) from Dublin, Ireland participated in the study. Children were all full term, had no reported health or developmental difficulties and were acquiring English as their first language in monolingual households. The sample was well-educated: 39.7% of mothers had a post-graduate degree, 57.7% had a Bachelor’s degree or had completed a non-degree course after high school and 2.6% had just completed high school. The highest education levels for fathers were as follows: 30.1% had a post-graduate degree, 63.3% had a Bachelor’s degree or had completed a non-degree course after high school, 6.3% had just completed high school and 6.3% had left high school before completion. All parents were White and monolingual Irish–English speakers.

Procedure

Ethical approval to conduct the study was obtained from the School of Psychology Ethics Committee within Trinity College Dublin, Ireland. The data used in this analysis are a subset of a larger ongoing study at the Infant and Child Research lab in Trinity College Dublin and were collected between 2016 and 2019. Families were recruited through mother–toddler groups on Facebook, notices placed in local crèches and supermarkets, and through snowballing. When potential participants made contact, they were asked to provide further details about their child to establish eligibility for participation. Exclusion criteria included a diagnosis of developmental disorder or delay, serious medical or health conditions, born preterm and bilingual or multilingual household. If parents consented to take part, an appointment was made for them to come into the laboratory on the university campus. Families were not compensated for their participation but were offered copies of recordings of parent–child interactions.

Upon arrival to the laboratory, mother, father and child were introduced to the observation room and were left alone to familiarise themselves with the setting. A research assistant then introduced a box of toys to the room and either the mother or father were asked to ‘play with their child as they normally would’, while the other parent left the room with the research assistant. Toys were selected to provide opportunities for different types of play and interactive experiences. They included a set of soft cars, a ball, a Mr. Potato Head^™ (a plastic model of a potato which can be decorated with a variety of plastic parts that attach to the main body), bricks for building towers and a selection of farm animals. After 10 minutes, the research assistant returned to the room and the language sub-scales of the Bayley Scales of Infant Development were administered in the presence of one parent. Following this, families were offered a break if required.

Next, a second dyadic interaction, following the same structure as the first, was set up, with the other parent who had left the room for the first interaction session. During each dyadic interaction, the Wechsler adult intelligence scale (WAIS) verbal sub-tests were completed with the other parent in a different room. The order of mother–child and father–child interactions was counterbalanced across different families.

Measures

The Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV)

Three verbal sub-tests of the WAIS-IV were administered to mothers and to fathers: Similarities, Vocabulary and Information, which together yield a standardised verbal comprehension index (VCI). The Verbal sub-tests have demonstrated excellent internal consistency and test–retest reliability (Wechsler, 2008). Scores range from 60–145; higher scores reflect higher ability where a score of 90–109 is average and falls within 25–73 percentile rank.

The Bayley Scales of Infant and Toddler Development – Third Edition (BSID-III)

Two sub-scales of the BSID-III were administered by a trained research assistant to assess the child’s receptive and expressive language ability. The receptive language scale assesses the child’s understanding of words, understanding of grammar and tenses, and knowledge of prepositions. The expressive scale assesses the child’s ability to label objects, use different tenses of verbs, and use prepositions. These scales are widely used and have demonstrated acceptable levels of internal consistency, test–retest reliability, and concurrent validity (Bayley, 2006). Raw scores for each sub-scale were converted to standard scores according to the scale manual. The child’s standard scores are used in the present analyses (with a normative mean of 10 and standard deviation of 3). Higher scores reflect higher ability.

Transcription protocol

All speech (excluding singing) in the play interactions was transcribed verbatim at the utterance level by trained research assistants according to the Codes for the Human Analysis of Transcripts (CHAT; MacWhinney, 2000). Utterances were defined as speech units which are separated by a pause, change in intonation, and/or grammatical structure.

To guarantee inter-transcriber reliability, research assistants were trained on the transcription and utterance segmentation protocols under the supervision of the first author. The trainees practised transcribing trial samples and, once these samples matched verified master files, they transcribed the samples included in the study. The first author then reviewed each transcript for the accuracy of the orthographic transcription and the segmentation into utterances of all transcripts using the video recordings. All parent and child language variables were extracted from the transcripts using the Computerised Language Analysis programme (CLAN; MacWhinney, 2000).

Parents’ and childrens’ speech

Quantity

Total number of utterances and number of word tokens were computed to indicate amount of speech.

Syntactic complexity

Mean length of utterance in morphemes (MLU-m) and number of verbs per utterance, two widely used indices of syntactic complexity, were computed. MLU-m is calculated by dividing the total number of morphemes by total number of utterances for each speaker. Morphemes were used in this calculation given that morphemes may be more developmentally sensitive than number of words. Longer utterances (i.e. higher MLU-m) imply more complex syntactic constructions (Brown, 1973). Utterances containing fewer verb phrases (lower verb per utterance ratio) are considered to be less syntactically complex.

Vocabulary diversity

Parent VOCD is calculated by summing the number of unique word roots spoken by parents during interaction. The calculation of VOCD takes into consideration the length of the interaction and uses repeated random sampling in its calculation. Higher values of VOCD indicate that speech is more lexically diverse.

Child vocabulary diversity was calculated as a proportion of the number of different word types over total number of word tokens produced (type-token ratio; TTR). Higher TTR implies greater vocabulary diversity. TTR was used as the VOCD measure requires a minimum speech sample of 50 words, which was not attained by all children in the current sample.

Nouns (excluding pronouns and vocatives) and all basic or lexical verbs (excluding auxiliary, copular, participle verbs and gerunds but including accompanying infinitive verbs which provide the main semantic content of the clause) were extracted using the command freq + t%MOR + t*MOT + u + s"v|n|*" from CLAN’s part of speech tagger MOR (Morphosyntactic Analysis) and hand checked for accuracy. Proportions of nouns and verbs with the total number of word tokens as denominator were calculated. In addition, the following variables were computed: the number of types, tokens and the TTR for both nouns and verbs, and the noun to verb ratio (total number of noun tokens ÷ total number of verb tokens).

Analysis

Data were analysed using SPSS version 26. Independent t-tests conducted to determine effects of the order of the interactions (child with mother or child with father first) revealed no systematic differences on any mother, father or child language variables. Independent and pairwise t-tests were conducted to investigate mean differences between mothers’ and fathers’ language input (CDS) during dyadic interaction with their children, and differences in child language (CS) in dyadic interaction with mother compared to with father. Pearson correlations and multiple linear regressions were conducted to assess relationships between parental CDS and CS. In addition, direct measures of child verbal ability were included as covariates to control for language ability.

Results

There was no support for any of the propositions in H1 (Table 1). Results show no significant differences between mothers’ and fathers’ MLU-m or proportion of verbs, nor between MLU-m or proportion of verbs in CS with mother compared to with father.

Table 1.

Descriptive statistics for standardised language assessment scores, lexical and syntactic measures for parents and children in interaction with each parent (n = 84 families).

Language measureParent	FatherM (SD)Range	MotherM (SD)Range	Independent t	Pearson’s r
# Utterances	176.26 (54.6) 74–334	188.75 (55.12) 82–319	1.76	0.30**
# Tokens	665.64 (238.57) 174–1634	728.09 (229.3) 235–1399	1.83	0.10
# Types	144.69 (29.16) 59–212	155.03 (27.39) 88–219	–2.52*	0.27
VOCD	42.95 (6.93) 28.07–58.64	46.09 (6.99) 28.02–58.64	2.86**	0.23**
MLU-m	4.16 (1.06) 2.4–7.36	4.21 (0.91) 2.62–8.39	–0.52	0.52**
Nouns (%)	13.58 (3.19) 7.99–25.55	13.47 (3.08) 7.79–24.56	0.31	0.42**
Noun tokens	72.63 (27.15) 6–144	81.97 (30.64) 22–166	–2.12*	0.81
Noun types	31.92 (10.53) 5–60	34.72 (10.01) 12–60	1.86	0.32
Noun TTR	0.46 (0.11) 0.23–0.83	0.45 (0.10) 0.19–0.72	–0.75	0.01*
Noun to verb ratio	0.67 (0.18) 0.39–1.46	0.68 (0.19) 0.34–1.16	–0.67	0.33**
Verbs (%)	24.25 (2.13) 17.76–29.34	24.24 (2.4) 17.74–30.09	0.03	0.11
Verb tokens	91 (39.1) 27–253	96.45 (39.8) 17–243	–1.48	0.36
Verb types	30.86 (8.89) 11–52	32.61 (7.96) 14–56	1.39	0.54
Verb TTR	0.36 (0.09) 0.17–0.65	0.37 (0.10) 0.22–0.82	0.17	0.38
Verbs per utterance	0.76 (0.20) 0.41–1.27	0.74 (0.17) 0.41–1.47	–0.61	0.38**
WAIS	111.57 (12.38) 78–148	109.98 (10.92) 86–134	–1.18
Language measureChild	With fatherM (SD)Range	With motherM (SD)Range	Paired t	Pearson’s r
# Utterances	74.89 (32.69)1–169	71.03 (32.78)11–145	–1.13	0.54***
# Tokens	103.79 (67.66)1–370	97.90 (58.83)2–283	0.97	0.62**
# Types	39.73 (21.55)1–99	40.75 (20.99)2–96	–0.51	0.64***
Language measureChild	With fatherM (SD)Range	With motherM (SD)Range	Paired t	Pearson’s r
TTR	0.43 (0.14)0.18–1	0.47 (0.15)0.15–1	2.34*	0.50**
MLU-m	1.71 (0.56)1–3.41	1.74 (0.55)1–4.07	0.60	0.74**
Nouns (%)	21.62 (13.28)0–70.18	20.77 (12.51)0–72.22	0.56	0.41
Noun tokens	14.25 (11.01)0–43	15.63 (12.46)0–65	1.06	0.49***
Noun types	7.53 (5.04)0–22	8.82 (6.36)0–26	2.08*	0.53***
Noun TTR	0.58 (0.25)0–1	0.59 (0.25)0–1	0.67	0.26*
Noun to verb ratio	3.04 (3.68)0–17	2.37 (3.16)0–29	1.24	0.08
Verbs (%)	10.88 (7.17)0–26.36	11.07 (6.97)0–25	–0.27	0.55
Verb tokens	9.78 (11.7)0–67	8.07 (7.23)0–26	–1.48	0.45***
Verb types	4.94 (4.65)0–21	4.72 (3.89)0–15	0.54	0.65***
Verb TTR	0.57 (0.34)0–1	0.57 (0.32)0–1	0.16	0.23*
Verbs per utterance	0.17 (0.16)0–0.59	0.17 (0.14)0–0.54	–0.06	0.61***
Bayley REC	12.40 (3.47)5–19
Bayley EXP	11.57 (2.84)4–18

MLU-m: mean length of utterance in morphemes; VOCD: vocabulary diversity; % verbs: proportion of total number of tokens that were verbs; % nouns: proportion of total number of tokens that were nouns; TTR: type-token ratio; BSID REC: Bayley receptive language score; BSID EXP: Bayley expressive language score; WAIS: Wechsler adult intelligence scale verbal comprehension index score.

p < 0.05; **p < 0.01; ***p < 0.001.

The second hypothesis was partially supported (Table 1). Mothers’ VOCD score was significantly higher than fathers’ VOCD, t(166) = 2.86, p = 0.005, d = 0.45, 95% CI = [40.41, 43.46], and child TTR score with mothers was significantly higher than child TTR in interaction with fathers, t(83) = 2.34, p = 0.022, d = 0.26, 95% CI = [0.01, 0.07]. There was no significant difference between maternal and paternal proportion of nouns nor between CS proportion of nouns with mother compared to with father. However, mothers produced significantly more noun tokens than fathers, t(166) = 2.12, p = 0.03, d = 0.23, 95% CI = [0.57, 18.11], and children produced more noun types with mothers than with fathers, t(166) = –2.08, p = 0.04, d = –0.22, 95% CI = [–2.51, –0.05].

To address the final hypothesis that fathers’ and mothers’ speech would be differentially associated with child language in interaction, first, zero-order correlations were conducted to identify candidate predictor variables of child language from the set of parent language variables (Tables 2 and 3).

Table 2.

Bivariate associations between paternal language and child language (n = 84).

FatherChild	# Words	MLU-m	VOCD	% nouns	Noun tokens	Noun types	Noun TTR	% verbs	Verb tokens	Verb types	Verb TTR	Noun-verb ratio	Verbs/utt	WAIS
# Words	0.06	0.22*	0.01	–0.16	–0.11	–0.01	0.11	–0.07	–0.14	–0.12	0.05	–0.11	0.17	0.14
MLU-m	–0.04	0.38**	0.06	–0.00	–0.03	0.02	0.08	–0.09	–0.03	–0.05	–0.04	0.04	0.33**	0.20
TTR	–0.25*	–0.17	–0.00	0.33**	0.10	0.05	–0.08	–0.06	0.21	0.05	–0.16	0.28**	–0.16	–0.08
% nouns	0.00	–0.02	–0.07	0.45**	0.06	–0.00	–0.07	–0.31**	0.09	0.09	0.02	0.47**	–0.09	–0.04
Noun tokens	–0.21	–0.03	–0.00	–0.17	0.18	0.25*	0.04	–0.01	0.02	0.01	–0.03	–0.01	0.00	0.84
Noun types	–0.22	0.00	–0.04	0.10	0.23*	0.34*	0.04	–0.01	0.12	0.19	–0.03	–0.02	0.08	0.06
Noun TTR	0.04	–0.03	0.03	0.05	–0.05	–0.02	–0.06	–0.01	0.05	0.14	0.03	0.02	0.09	–0.18
% verbs	0.05	0.17	0.05	–0.03	0.06	0.42	0.06	0.05	0.05	0.05	–0.01	–0.06	0.18	0.12
Verb tokens	–0.07	0.00	–0.13	–0.07	–0.04	0.06	0.24*	0.04	0.07	0.02	0.00	0.09	–0.03	0.01
Verb types	–0.03	0.11	–0.13	–0.03	–0.08	0.08	0.23*	0.03	0.09	0.16	0.02	0.05	–0.04	0.01
Verb TTR	–0.02	–0.03	0.02	0.03	0.06	0.03	–0.14	–0.02	0.12	0.14	–0.09	–0.03	–0.01	–0.04
N–V ratio	0.00	–0.12	–0.16	0.18	–0.02	0.08	0.06	–0.06	–0.08	–0.04	0.03	0.16	–0.07	–0.03
Verbs/utt	0.00	0.25*	0.06	0.08	–0.02	0.02	–0.05	–0.00	0.03	0.12	–0.04	–0.13	0.23*	0.15
Bayley REC	0.05	0.21	0.24*	–0.04	0.02	0.00	0.04	–0.06	0.11	0.01	–0.17	–0.02	0.20	0.02
Bayley EXP	0.13	0.24*	0.02	–0.01	–0.00	0.08	0.07	–0.02	–0.04	0.01	0.05	–0.02	0.23*	0.13
Father WAIS	0.03	0.25*	–0.05	–0.03	0.00	0.08	0.07	0.11	–0.04	0.01	0.05	–0.02	0.23*	–

MLU-m: mean length of utterance in morphemes; VOCD: vocabulary diversity; TTR: type-token ratio; % nouns: proportion of total number of words that were nouns; % verbs: proportion of total number of words that were verbs; N/V ratio: noun to verb ratio; Verbs/utt: verbs per utterance; Age: child age in months; BSID REC: Bayley receptive language score; BSID EXP: Bayley expressive language score; WAIS: Wechsler Adult Intelligence Scale verbal comprehension index score.

p < 0.05; **p < 0.01.

Table 3.

Bivariate associations between maternal language and child language (n = 84).

MotherChild	# Words	MLU-m	VOCD	% nouns	Noun tokens	Noun types	Noun TTR	% verbs	Verb tokens	Verb types	Verb TTR	Noun–verb ratio	Verbs/utt	WAIS
# Words	0.13	–0.01	0.02	–0.06	0.01	–0.08	–0.07	–0.04	0.07	–0.13	0.05	–0.04	0.02	0.02
MLU-m	–0.00	0.15	0.16	0.02	0.18	0.02	–0.19	–0.05	0.03	0.05	0.01	0.03	0.16	–0.01
TTR	–0.03	0.06	0.02	0.19	0.11	0.26*	0.04	–0.14	–0.01	0.12	0.08	0.16	–0.01	–0.12
% nouns	–0.22	–0.01	0.16	0.40**	0.07	0.02	–0.11	–0.11	0.16	0.13	–0.15	0.33**	–0.05	0.01
Noun tokens	–0.02	0.08	–0.03	0.08	0.12	0.29	0.09	0.0.03	0.00	0.10	0.070.	0.05	0.10	0.09
Noun types	0.02	0.05	–0.01	0.01	0.14	0.33**	0.10	0.02	0.08	0.17	0.05	0.05	0.07	0.10
Noun TTR	0.10	–0.05	0.18	–0.03	0.01	0.14	0.06	–0.130.	0.06	0.12	0.06	0.01	–0.09	0.06
% verbs	0.05	0.00	0.08	–0.08	0.05	0.06	0.04	0.13	0.06	0.06	0.05	–0.06	0.07	0.00
Verb tokens	0.05	0.07	0.09	–0.02	–0.01	0.04	0.03	–0.09	0.21	0.23*	–0.10	0.04	0.02	0.22*
Verb types	0.09	0.16	0.11	0.04	0.07	0.09	–0.01	–0.03	0.20	0.25*	–0.06	0.07	0.11	0.13
Verb TTR	–0.10	0.17	–0.02	–0.00	0.18	0.05	–0.17	0.05	0.02	0.02	0.03	–0.06	0.19	–0.03
N/V ratio	0.01	0.05	0.14	0.01	–0.17	–0.22*	0.06	0.06	–0.10	–0.12	0.03	–0.01	0.09	0.00
Verbs/utt	0.02	0.05	0.16	0.04	0.05	0.02	0.00	–0.22*	0.03	0.06	0.09	0.11	–0.04	0.06
BSID REC	0.27*	0.15	0.20	–0.11	0.14	0.10	–0.05	0.09	0.07	–0.00	–0.05	–0.08	0.19	0.22*
BSID EXP	0.29**	0.19	0.21*	–0.02	0.03	–0.02	0.02	0.05	–0.03	–0.06	0.05	–0.02	0.22*	0.14
Mother WAIS	0.10	0.06	–0.02	–0.00	–0.11	–0.06	0.06	–0.14	–0.14	–0.20	0.02	0.06	0.00	–

MLU-m: Mean length of utterance in morphemes; VOCD: vocabulary diversity; TTR: type-token ratio; % nouns: proportion of total number of words that were nouns; % verbs: proportion of total number of words that were verbs; N/V ratio: noun to verb ratio; Verbs/utt: verbs per utterance; Age: child age in months; BSID REC: Bayley receptive language score; BSID EXP: Bayley expressive language score; WAIS: Wechsler Adult Intelligence Scale verbal comprehension index score.

p < 0.05; **p < 0.01.

Next, regression models were fit to examine associations between parental speech characteristics and child speech characteristics in real time, controlling for child language ability. Child age was not independently associated with any of the interaction measures and thus was not included in the regression models. Child Expressive and Receptive language scores were included in regression models as predictors of child language performance.

Only parent speech variables that were independently correlated with child speech characteristics (significance level set at 0.01 to mitigate effects of multiple correlations) were included in regression models, and in each model control variables were child Receptive and Expressive language scores. Five child speech characteristics with mothers and fathers were identified from the correlational analyses as being related to parental speech characteristics: MLU-m with fathers, TTR with father, proportion of nouns with fathers, proportion of nouns with mothers, and noun types with mother.

The final hypothesis, that, controlling for child language ability, fathers’ and mothers’ CDS is differentially associated with child speech in interaction, was supported (Table 4). Child MLU with father was significantly related to fathers’ MLU and their expressive language score but only 4% of variance was explained by fathers’ MLU (model 1). Child TTR with father was not related to any of the mother or father variables (model not shown). Child proportion of nouns with father was significantly associated with proportion of nouns in father speech (22% of variance explained; model 2), and child proportion of nouns with mother was significantly associated with proportion of nouns in mother speech and their expressive language score (14% of variance explained; model 3), but not proportion of verbs for either mother or father. Child noun types with mother were significantly associated only with mothers’ noun types (model 4).

Table 4.

Hierarchical regressions testing the prediction of child speech by parental CDS, controlling for child language scores.

Dependent measure	Predictors	R ²	Adj R²	Standardised β
1. Child MLU-m with father	Step 1
	Bayley REC score			0.19
	Bayley EXP score			0.48***
		0.36	0.35
F (6, 75) = 9.90, p < 0.001	Step 2
	Bayley REC score			0.17
	Bayley EXP score			0.43***
	Father MLU-m			0.60*
	Mother MLU-m			0.07
	Father verbs/utterance			–0.40
	Mother verbs/utterance			–0.03
		0.44	0.39
2. Child % nouns with father	Step 1
	Bayley REC score			0.05
	Bayley EXP score			–0.10
		0.00	–0.01
F (6, 75) = 4.88, p < 0.01	Step 2
	Bayley REC score			0.04
	Bayley EXP score			–0.11
	Father % nouns			0.40**
	Mother % nouns			0.02
	Father % verbs			–0.19
	Mother % verbs			0.21
		0.28	0.22
3. Child % nouns with mother	Step 1
	Bayley REC score			–0.13
	Bayley EXP score			0.27*
		0.05	0.03
F (6, 75) = 3.76, p < 0.01	Step 2
	Bayley REC score			–0.08
	Bayley EXP score			0.25*
	Father % nouns			0.11
	Mother % nouns			0.40**
	Father % verbs			–0.01
	Mother % verbs			–0.11
		0.23	0.17
4. Child noun types with mother	Step 1
	Bayley REC score			0.01
	Bayley EXP score			0.03
		0.00	–0.02
F (6, 75) = 2.25, p < 0.05	Step 2
	Bayley REC score			–0.03
	Bayley EXP score			0.06
	Father noun types			0.04
	Mother noun types			0.33**
		0.11	0.07

CDS: child-directed speech; BSID REC: Bayley receptive language score; BSID EXP: Bayley expressive language score.

p < 0.05; **p < 0.01; ***p < 0.001.

Discussion

We tested three hypotheses relating to parental child-directed speech and child speech produced in dyadic freeplay interaction. The first hypothesis was not supported; we found no effect of parent gender on CDS or CS complexity or verb production. Fathers did not produce more verbs than mothers but there was a verb bias overall in both maternal and paternal CDS. This may be a function of the context and the toys provided for the play session, for example, parents could point to objects rather than name them (Fieldsteel et al., 2020), nonetheless it is instructive that both mothers’ and fathers’ CDS displays this bias. The reverse was true for CS with toddlers showing a pronounced noun bias, in keeping with the child language acquisition literature (Taverna & Waxman, 2020). An interesting aspect to investigate would be whether parents are using a lot of questions, specifically wh-questions, that preferentially elicit nouns over verbs, and are suggested to be linked (at least for English-speaking mothers) to an early noun bias (Goldfield, 2000; Oshima-Takane & Titova, 2021 ).

The second hypothesis received some support as the speech of mother–child pairs was more lexically diverse than the speech of father–child pairs, despite the contextual restrictions imposed by the activity and setting. Furthermore, mothers produced more noun tokens than fathers and children produced more noun types with their mothers. Children exposed to mothers’ significantly more diverse lexical input used more diverse vocabulary themselves, presumably at least prompted by, if not even directly copying her vocabulary. One study found that the influence of mothers’ lexical diversity on child lexical diversity/vocabulary is most evident at the age of 24 months (Pan et al., 2005), the age of the children in this study. Furthermore, fathers’ less diverse vocabulary was matched by lower diversity in the child’s speech with him compared to with mother. An analysis of child–parent repetition could be conducted to investigate this pattern, as Casla et al. (2022) suggest 24 months may represent a transitional phase in relation to spontaneous verbal repetitions. It would be important to establish if this pattern persists at later ages.

Overall we found high levels of concordance in children’s speech across dyadic interaction with mother and with father. Children spoke as much and used equally complex speech with their father as with their mother. The sole difference related to child vocabulary, where toddlers matched the diversity of their speech to that of the parent with whom they were interacting, using less diverse vocabulary, specifically fewer different noun types, with father than with mother. Tamis-LeMonda et al. (2012) and Rowe et al. (2004), with low socio-economic status (SES) families, found an opposite effect, namely, that children produced more word types with father than with mother, even though, also in contrast to our findings, mother and father produced a similar amount of word types.

These different findings likely reflect both time and sample characteristic effects. Rowe et al.’s (2004) study as a test of the bridge hypothesis took place at a time when fathers were typically less involved in childcare and with their children. Fathers’ speech to their children was reported to be more challenging and to take less account of what the child knew or their vocabulary. If child speech is influenced by the speech of their conversational partner, we might expect child speech with father to reflect that. Furthermore, in Rowe et al.’s (2004) low-income sample (n = 33), the children were older (mean age = 27.53 months) when observed interacting with their father compared to their mother (mean age = 24.77 months) which may have affected on that finding. In this study, all play interactions took place on the same day. Rowe et al. (2004) also suggested that ‘low-income fathers and mothers may have more similar goals for communicative interaction with their toddlers than do middle-class parents’ (p. 289). The mothers and fathers in this study had high levels of education and above average verbal ability. We know a lot about the language practices of mothers with high compared to low levels of education in one-on-one interaction with their children (e.g. more intentionally didactic with a focus on promoting vocabulary, more labelling, more expansions of child utterances). Less is known about the practices of their male counterparts but our finding of lower vocabulary diversity in father CDS suggests there may be different language goals in interaction.

In contrast, our finding of children using a more diverse vocabulary with mother compared to with father does accord with other studies, for example, Hoff (2010) reported that children aged 1;9–3;0 used a richer vocabulary in conversation with mother compared to with their older siblings. This suggests that child speech is dynamically influenced by their conversational partner and by the composition of the language experienced in interaction.

It would be useful to analyse the actual vocabulary set produced by mother and father to establish whether child vocabulary aligns in content as well as level of diversity. Although mothers’ vocabulary was more diverse than fathers’ overall, mothers may be using vocabulary that the child is familiar with from routine interactions (Pancsofar & Vernon-Feagans, 2010). Fathers’ smaller vocabulary set might nonetheless be made up of more uncommon or unfamiliar words that can enrich the child’s vocabulary beyond that produced in communication with mother. Ratner (1988) found that paternal speech, although less diverse than maternal speech, contained more rare and significantly less common vocabulary items than maternal speech. However, these findings originate from a different socio-cultural context and may not reflect contemporary interactional styles (e.g. Steenhoff et al., 2019).

An analysis of the social and contextual cues accompanying maternal and paternal labelling events during the interactions could also shed some light on this aspect of CDS (Chen et al., 2021; Custode & Tamis-LeMonda, 2020; Schroer & Yu, 2022). Mothers and fathers may have attended to different aspects of the environment and selected different objects for play and for talk. Toddlers may also interact differently with father compared to mother and analysis of dyadic behaviours such as joint attention and levels of responsiveness would expand our understanding of the dynamics of parent–child conversations.

Finally, based on findings that fathers’, but not mothers’, lexical diversity was associated with later child language development (Pancsofar & Vernon-Feagans, 2006, 2010), we asked whether fathers’ and mothers’ CDS is differentially associated with CS in real-time interaction. Having accounted for child’s own language ability, the proportion of nouns and noun types in mother–child speech were found to be associated. Child proportion of nouns was also significantly associated with fathers’ proportion of nouns during father–child interaction. Language complexity, measured by MLU, was also significantly associated in father–child dyads.

Overall, the strongest associations were observed on noun usage between the dyads. Fieldsteel et al. (2020) found that maternal nouns mostly labelled the toys with which they were playing. The mothers and fathers in this study could be choosing different toys from the set provided thereby eliciting a different noun set that was particularly salient in the interaction. Fathers may do less labelling of present objects and more asking of questions which may elicit different nouns. It remains to be seen if this association will be influential for child language outcomes longitudinally. Majorano et al. (2013), for example, found that fathers’, but not mothers’, noun frequency at 15 months predicted child language at 30 months, even though mothers spoke more, produced longer utterances and displayed greater vocabulary diversity.

This study has several strengths. Both parents were included and tested at the same time point to acknowledge fathers as a source of critical developmental influence and observational methods as the gold standard are adopted. We compared mothers’ and fathers’ verbal ability using a standardised instrument to ensure any difference observed between the CDS of mothers and fathers was not the result of different levels of ability. We measured child language through standardised instrument rather than parental report. Hypothesis driven tests with theoretically driven variables were conducted and only data derived covariates were selected for analyses.

Some limitations should also be acknowledged. The sample size is relatively small and homogeneous, as evidenced in the low levels of variance observed across several key variables, and the observed effects of the input variables were small and should be interpreted with caution. There may have been an element of self-selection bias where only families with children, boys in particular, with good language skills volunteered to participate (Yu et al., 2020). Further all associations investigated are concurrent. The study aim was to explore the real-time online dynamics of parent–child interaction and the immediate effect of the linguistic environment provided by parental input on the child’s speech. However, it is also important to investigate if, and how, this parental speech is related to later language development. These findings are applicable only in the context of the English language as we know lexical composition, proportion of nouns and verbs, and noun-to-verb ratio vary across languages. Furthermore, several studies have shown wide cross-linguistic differences in parent CDS patterns (e.g. Bornstein et al., 1992; Fernald & Morikawa, 1993). Equally, these findings may not readily transfer to other socio-demographic cohorts (MacLeod & Demers, 2023), and Rosemberg et al.’s (2020) study of CDS of lower and mid-to-high SES parents reveals that its effects should not be taken as homogeneous across groupings.

Finally, the semi-naturalistic context of the laboratory may not reflect the linguistic environment of the home setting (Bergelson et al., 2019; Rosemberg et al., 2020; Tamis-LeMonda et al., 2017) although there is research attesting to the validity of this approach and a vast literature examining language in toy play interaction. Although Tamis-LeMonda et al. (2017) found a strong correlation between the distribution of types and tokens across different types of data collection methods, caution is still required when extrapolating from the input patterns observed in this context to more general daily language environments.

Conclusion

This study investigated fathers’ and mothers’ child-directed speech and child speech in freeplay interaction. We found that the CDS of mothers and fathers in play interaction with their 2-year-olds is very similar, with the exception of greater maternal vocabulary diversity, and that 2-year-olds produce very similar speech with their mother and with their father, except for greater vocabulary diversity with their mothers. We also found that the proportion of child nouns in interaction is associated with the corresponding proportion of nouns produced by the parent with whom they are interacting.

Parental CDS differences in interaction may be a function of gender-based styles, of context, activity, toys, and/or a host of other factors. But arguably, for a toddler in a one-on-one interaction with a parent, the most salient factor of that context is the parent’s CDS. Our findings show that child speech mirrors the patterns of the CDS that they hear. The important finding here is that CDS seems to be the factor that is directly influencing the child’s real-time language performance patterns.

Future research should analyse the noun types used by mothers and fathers as different types of learning have been associated with frequency and context effects (Potter & Lew-Williams, 2022; Rosa et al., 2022; Sandhofer et al., 2000). Furthermore, we found that toddlers’ and fathers’ language complexity was associated in interaction. These findings are important as they suggest that during dyadic turn-taking episodes, toddlers actively adjust their speech to the speech of their partner. This finding, if replicated, could have implications for measuring child vocabulary or for characterising child real-time language. If child grammatical complexity and lexical diversity, for example, is locally influenced by the conversational partner, research should sample child speech not only across contexts but also with different partners.

Despite the circumscribed transferability of these findings, this study adds to the limited body of research on fathers’ speech in interaction with toddlers and provides evidence that parent and child speech is dynamically linked in terms of its composition. Fathers remain underrepresented in this field and many of the small number of studies have been in low-SES contexts. Our findings, with mothers and fathers of high verbal ability and education levels, add to the small set of studies showing differences in the same setting between mother’s and fathers’ vocabulary diversity, and further that toddlers’ speech is influenced by parents’ speech in real time.

Early vocabulary is a robust predictor of later academic and social success (Bleses et al., 2016) and as linguistic input plays a key role in vocabulary acquisition (Jones et al., 2023), it is important to identify any source of influence in the input. The words children produce are influenced by the words spoken directly to them (Jones et al., 2023) thus with the increased availability of fathers to their children, at least in this socio-demographic context, it is important to profile fathers’ CDS as resource for the child. Our findings suggest that children may be exposed to different lexical sets by different conversational partners, namely mother and father, and points to the added value for the child of engaging in separate dyadic interactions with both mother and father.

Supplemental Material

sj-docx-1-fla-10.1177_01427237231200436 – Supplemental material for Parent–toddler play talk: Toddler speech is differentially associated with paternal and maternal speech in interaction

Supplemental material, sj-docx-1-fla-10.1177_01427237231200436 for Parent–toddler play talk: Toddler speech is differentially associated with paternal and maternal speech in interaction by Jean Quigley and Elizabeth Nixon in First Language

Footnotes

Author contributions

Jean Quigley: Conceptualisation; Data curation; Formal analysis; Investigation; Methodology; Project administration; Writing – original draft; Writing – review & editing.

Elizabeth Nixon: Conceptualisation; Investigation; Methodology; Project administration; Writing – review & editing.

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Jean Quigley

Elizabeth Nixon

Supplemental material

Supplemental material for this article is available online.

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