The speaker’s lexical-semantic network in the tip of the tongue state

The speaker’s lexical-semantic network in the tip of the tongue state | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report The speaker’s lexical-semantic network in the tip of the tongue state Marie Couvreu, Marina Laganaro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970418/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract All speakers experience the Tip-of-the-Tongue (ToT) phenomenon when trying to retrieve specific words. ToTs have been explained by a temporary reduction of lexical-phonological transmission or inhibition of competitors in the mental lexicon. An alternative way to approach ToTs is to consider the state of the lexical-semantic network of the speaker. Here we investigate how the organisation of the lexical-semantic network of the speaker differs between accessible words and ToTs. 40 neurotypical young adults performed a picture-naming task with low frequency items to induce ToT states, followed by a continuous free word association task. Accessible words were characterised by richer lexical-semantic networks with more superordinate associates, whereas ToT states were marked by more heterogeneous networks dominated by coordinate associates. The organisation of the lexical-semantic network differs depending on the ability to access the word at a given time at the individual level, providing further hints to understand ToT states. Psychology Cognitive Neuroscience Tip of the Tongue lexical-semantic network Lexical access Figures Figure 1 Figure 2 Figure 3 Introduction “Leo : a little thing, a (pause) with... peach... (pause), what's the dish or the name again? It's in the garden” (Stern, 2023–2027). Such phenomenon is more than occasional in real life and has been studied experimentally to understand failure to access words that are in the speaker’s lexicon. Two main hypotheses have been proposed to specifically explain ToT states. The first hypothesis, transmission deficit , postulates that speech production is organized top-down into the hierarchical systems of semantic, lexical and phonological nodes and a ToT occurs because of a deficit in activation transfer from lexical to the phonological nodes (Burke et al., 1991 ; Cohen & Faulkner, 1986 ; Rastle & Burke, 1996 ; Yaniv & Meyer, 1987 ). The second hypothesis, inhibition deficit , postulates that ToT is caused by other words that are temporarily activate and/or cannot be inhibited, blocking the access to the target word (Brown, 1979 ; Jones, 1989 ; Roediger, 1974 ). Although not developed specifically for ToT, the spreading-activation theory (Collins & Loftus, 1975 ), with the conception that the mental lexicon is organised in network (Roelofs, 1992 ), postulates that the activation spreads into the mental lexicon to reach the target word by activating all related words. However, sometimes this activation fails to propagate sufficiently leading to word retrieval difficulty, as in anomia. To our knowledge, it has not yet been used to investigate the ToT state, despite its potential to improve our understanding of how the lexical-semantic network is organised in this state. The network’s perspective in ToT states In this way, the network perspective (Baronchelli et al., 2013 ; De Deyne & Storms, 2008a ; Siew et al., 2019 ; Vitevitch et al., 2014 ) constitutes the main approach to study the relationships between words at the lexical-semantic level (Collins & Loftus, 1975 ; Steyvers & Tenenbaum, 2005 ). Some lexical-semantic factors such as the semantic richness and the prototypicality of the network of the word may influence the speed of access of the target word. The semantic richness of a word is measured by the number of other words that are associated with it (Chan & Vitevitch, 2010 ; Goldstein & Vitevitch, 2017 ). In word production studies, the semantic richness facilitates word production (in terms of reaction time and accuracy) (De Deyne et al., 2013 ; Pexman et al., 2002 , 2003 ; Krethlow et al., 2020 ; Rabovsky et al., 2016 , 2021 ; Robert & Rico Duarte, 2016 ; Steyvers & Tenenbaum, 2005 ). This is explained by the associates increasing the activation at the conceptual level, which then spreads to the correct lexical representation, thus facilitating the response. The types of links reflect the nature of semantic relations between words — either taxonomic (superordinate, coordinate, subordinate) or thematic —indicate how these relations influence word production. Indeed, it has been shown that naming a picture is interfered when coordinate words are presented simultaneously (e.g. cat - dog), but it is facilitated with associates (e.g., honey – bee) (Bloem et al., 2004 ; McDonagh et al., 2020 ; Rabovsky et al., 2016 ; Roelofs, 1992 ; Alario et al., 2000 ) although different results have been observed with other paradigms (Abdel Rahman & Melinger, 2007 , 2011 ; de Zubicaray et al., 2014 ). Despite some contradictory results, studies highlight the fact that different semantic relations affect word production. In addition to richness and type of associates, the network prototypicality, - measured by entropy, which characterises network homogeneity — also influences word access (Krethlow et al., 2020 ). However, and crucially for our purpose here, the properties of the lexical-semantic system have usually been considered as speaker-independent, meaning that the same system – usually based on external databases - is used to explain the behaviour of all the speakers. As speakers do not experience ToTs for the same words at the same time, and the lexical-semantic network is dynamic (Dubossarsky et al., 2017 ), meaning that there must be speaker-specific organisation of the network as considered in the present study. Present study The organisation of the lexical-semantic network influences word retrieval and thus most likely also has an impact on ToTs. The network system is dynamic and changes between individuals (Dubossarsky et al., 2017 ; Wulff et al., 2022 ). Unlike other studies on the mental lexicon that rely on existing databases representing an 'average' lexical-semantic network for a given word, this study considers the organisation of each participant's lexical-semantic network for each word. It adopts the same paradigm as database-based approaches: the continuous free word association task (De Deyne et al., 2008b). This task provides information about the number of associates (semantic richness) given per word, the prototypicality and the semantic relation between the associates and the target word (De Deyne et al., 2013 ; Dubossarsky et al., 2017 ; Krethlow et al., 2020 ). De Deyne & Storms ( 2008a ) showed that the first associate was similar across participants, whereas the second and third were more heterogeneous. Studies (Dubossarsky et al., 2017 ; Krethlow et al., 2020 ) added a measure of entropy and found that words with greater heterogeneity in their associates (i.e. high entropy) were more difficult to access. To have a more precise understanding of the lexical-semantic network, the categorisation of these associates using existing classifications allows us to have a clearer window on the impact of the link between these associates on word retrieval. We expect that a rich associative network in the continuous free word association task should be associated with correctly retrieved and produced words, whereas a poor associate network should be observed in correspondence to ToTs. On the other hand, the nature of the semantic relationship between the target word and the associates may also influence word retrieval. Method Participants 46 native French participants (36 females, age 18 to 34, mean: 22) young adults were recruited in a psychology course with informed consent, and the procedure was approved by the Ethics Committee of the University of Geneva. Materials The paradigm used in this study involves two main tasks: a 110 items picture-naming task and a continuous free word association task on the same words. The 110 colour pictures were selected from a pre-test (65 other participants, 200 low frequency words). The 200 words in the pre-test were selected from databases (Brodeur et al., 2010 , 2012 , 2014 ) and previous ToT studies (Alario & Ferrand, 1999 ; Brown & Nix, 1996 ; Gollan & Brown, 2006 ; James & Burke, 2000 ; Moreno-Martínez & Montoro, 2012 ). The final 110 stimuli have 85% name agreement and a mean frequency of 1.82 per million from Lexique.org (New et al., 2005). Of the 110 stimuli, 80 induced the most ToTs in the pre-test, and the 30 others, which are fillers in the present study are more frequent words and were always produced correctly in the pre-test. All stimuli are concrete and imageable words (see Appendix A). Procedure Each trial started with the picture-naming task, immediately followed by questions on the speaker’s state and the continuous free word association task (see Fig. 1). The procedure starts over for each stimulus. Stimuli were presented on a 17-inch screen (60 Hz, 50 cm viewing distance), with 300 x 300 pixels color images on a white background. The experiment was run using-Prime software (E-Studio 3). Each trial began with a 500 ms fixation cross, then the picture appeared for 2,500 ms, during which the participant had to name it orally as quickly and accurately as possible and the response was audio-recorded. Then participants indicated their state relative to the picture they had to name by pressing a color-coded key: word accessed (state A), ToT state (state T), unknown word (state O). If the word was unknown (state O), the next trial began with the following picture to be named, and the same procedure starts over. If the word was accessed (state A), participants rated their confidence (1–5) and then made the continuous free word association task. In a ToT state (T), they indicated whether they had retrieved the word in between using the keyword (yes/no) and then immediately moved to the continuous free word association task. For the free word association task, the participants had 10 seconds to say aloud all words that came to mind related to the target word displayed on screen. To prevent participants from anticipating the planning of their associates, 30 filler items which are more frequent words, were randomly inserted into the paradigm. These items did not trigger the continuous free-word association task and were therefore not analysed. Instructions clearly defined ToT as the frustrating feeling of knowing a word without being able to retrieve it. All verbal responses were recorded with a micro-headset, allowing us to check whether the response “state A” was coherent with the participant’s production. The experiment took place in a standard room, unsupervised, with two counterbalanced item orders. A break occurred after 55 trials and stimuli were pseudo-randomized to avoid semantic or phonological clustering. 5 sample trials were used at the beginning of the experiment (see Fig. 1 for an illustration). Figure 1. Illustration of the paradigm Data analyses Picture-naming task The recorded productions from the picture-naming task were analysed using CheckVocal software (v4.4.0, Protopapas, 2007) to categorise them as correct, incorrect or absence of response and to extract production latencies. Trials without responses in the picture-naming task were categorised as ToT or “don’t know” according to the answer given to the 3 choices. Trials with mismatches between the participant's verbal responses and their answers to the 3 choices, were removed. One participant was excluded due to high inconsistencies (≥ 1 SD above the mean) and 5 others due technical issues, meaning that 40 participants were included in the final analyses. One word (walrus) was excluded due to many “don’t know” responses (50%). Other stimuli were removed if they elicited responses exclusively in one condition, with no or only one response in the other (10 words), meaning that 69 words were included in the final analyses. Continuous free word association task All associates recorded within 10 seconds were transcribed manually to compute the number and order of the associates given by each participant for each target word. All fist associates were classified into eight categories, combining taxonomic classification and an adaptation of the semantic features analysis (SFA) (Balthazar et al., 2008 ; Boyle, 2010 ; Miller, 1995 ; Raposo et al., 2012 ) (see Appendix B for examples). Inter-rater reliability was examined on 174 trials, indicating a substantial agreement between raters for the categorisation (k = 0.87, z = 46.1). For network richness, the number of associates per target word given within the 10 seconds was the dependent variable in a generalised mixed-effect model (R Core Team, 2022), including participants as a random effect and states (T or A) as a fixed effect. To investigate how first associate categories varied between the two states a Bayesian framework was used in R (R Core Team, 2022). Bayesian analysis provides a distribution of plausible effect values; we report 95% credible intervals, indicating where the true effect likely lies. The model has converged well (R̂ ≈ 1.00). Categories representing less than 5% of responses across all participants were excluded. To evaluate the network prototypicality, and the entropy (H) was computed based on the first associate given by participants for each cue using to the following equation: $$H=\sum_{i=1}^{k}{p}_{i}{log}_{2}\left(\frac{1}{{p}_{i}}\right)$$ K is the number of distinct first associates given per participant; pi is the proportion of participants (per state) producing each associate word and the sum is computed over all types with non-zero response probability. Higher H values indicate greater variability in responses across participants for a given word, reflecting lower network prototypicality. Exploratory comparison to existing databases Given the novelty of collecting and using associates at the level of individual speakers rather than relying on general databases, the results may be limited in interpretation on the organisation of the lexical-semantic network. We therefore conducted an exploratory comparison of the distributions between the categories of strongest associates in our study and those from the English SWOW database (De Deyne et al., 2019 ), as no such database exists for infrequent French words. In SWOW, participants are asked to write three words related to a written target cue, without being timed. As the target order was unavailable, we used the associate most frequently produced across participants and categorized each associate from SWOW according to our classification. The same procedure was applied to our own database for the 'ToT' and 'Accessible' states. For each category, we summed the strengths of all associates and normalized this sum by the total strength of all associates across categories in the three databases (ToT, Accessible and SWOW). This enabled us to calculate a ratio for each category in a sample of 52 words that overlapped with those in the SWOW database. Due to differences in how the data was collected, exploratory analyses were used. Results Picture-naming task Participants reported ToT states in 31.28% trials, produced words correctly in 49.11% of trials with a mean response time of 1227.06 ms and reported that they did not know the word in 19.61% trials. Considering only the relevant conditions (ToT and Accessible), a total of 716 TOTs (39.27%) and 1124 correct responses (60.72%) were analysed. Continuous free word association task The average number of associates given depending on the response is presented in Fig. 3. On average, 5.72 associates were produced in the Accessible state and 5.04 in the ToT state. The result of the statistical model (X2(1) = 16.32, p < 0.001) showed that participants produced significantly more associates when they had access to the word than when they were in a ToT state (see detailed model in Appendix C1). Bayesian analysis showed that the probability of the functional category is higher regardless of the state, though it is more likely in a ToT state to have coordinate associates (β = 0.85, SE = 0.27, 95% CI = [0.33, 1.39]). Compared to other categories, coordinate associates are significantly more likely to be in a ToT state compared to superordinate (β =- 1.23, SE = 0.27, 95% CI = [-1.78, -0.69]) except for the perceptive category, which shows no significant difference (β = -0.35, SE = 0.27, 95% CI = [-0.98, 0.29]). Superordinate associates are more likely in an Accessible state than coordinates (β = 1.19, SE = 0.29, 95% CI = [0.61, 1.73]) and perceptive (β = 0.78, SE = 0.29, 95% CI = [0.23, 1.36]) but there was no significant difference with functional and spatiotemporal associates (see Appendix D for detailed analyses). Subordinate and other categories were not analysed because of the limited rate (< 5% in total). Figure 2 C represents the distribution of the categories between states. The model on the network prototypicality measured with the entropy of the first associate (H) revealed higher scores for the ToT state and lower scores for the accessible condition (X2(1) = 8.13, p = 0.004) (see Fig. 2 B). An illustration of the lexical-semantic network is in the appendix (Appendix G). Exploratory comparison to the SWOW database The overall distribution of categories of associates from SWOW is very similar to those obtained in the present study but seems to differ for coordinate and superordinate associates (see Fig. 3). Coordinates appear to be more prevalent in the SWOW database, while superordinate associates tend to be more prevalent in the present data (across ToT and Accessible). Figure 3. Distribution of the type of the strongest associate among databases Discussion The aim of the present study was to investigate the organisation of the speaker’s lexical-semantic network in a ToT state compared to when the word is accessed and produced correctly using a picture-naming task followed immediately by a continuous free word association task performed by the participants. The analyses were run on three factors that have been shown to influence word production in previous studies based on associative databases, i.e. the semantic richness, the prototypicality and the semantic relation of the lexical-semantic network. Results showed a more heterogeneous network with few associates and predominantly more coordinates in a ToT state relative to accessible words, while the superordinates appeared more frequently when the word was accessible. The supplementary analyses revealed that the distribution of these two categories differed between the present data and the SWOW database for the subset of analysed items, whereas the distribution of all other categories remained similar. In the following we will discuss the results related to the richness/accessibility of the associative network in ToT states and in light of the organisation of the lexical-semantic network. The lexical-semantic network in a ToT state VS. “Accessible” state The present findings demonstrate that the number of associates given by the speaker is significantly related to the success of word production, with fewer associates generated for ToT words than for accessible ones. As the participants had 10 seconds to provide associated words in all conditions, and with the interim questions between the two tasks, it is unlikely that the difference in performance between the states is due to a lack of time in one condition. It is also noteworthy that the present results at the individual level are consistent with studies showing that a richer associative network of a word measured with a population sample database facilitates lexical access in terms of accuracy (Krethlow et al., 2020 ; Pexman et al., 2003 , 2008 ; Rabovsky et al., 2016 , 2021 ; Yap et al., 2012 ). Such facilitative effect of semantic richness on word production is generally attributed to stronger activation at the conceptual-level, enabling faster and more accurate retrieval (Krethlow et al., 2020 ; Pexman et al., 2008 ; Rabovsky et al., 2016 ). This interpretation aligns with the spreading-activation theory (Collins & Loftus, 1975 ), which proposes that successful word retrieval depends on sufficient activation between the cue word and other words. Beyond the impact of the individual semantic richness on the speaker’s performance, our findings suggest that both the prototypicality and semantic relations within the lexical-semantic network also play a significant role in determining word retrieval success during ToT states. Indeed, words with an heterogenous network are more likely to elicit ToT states than an Accessible state. Similarly, Krethlow et al. ( 2020 ) found that network connections were stronger when the network was homogeneous across age groups, suggesting that greater semantic dispersion may hinder access. The nature of the semantic relation of the first associate also differs between ToT state and Accessible states, with a higher probability of having a coordinate in ToT and superordinate in Accessible states. The greater proportion of coordinates observed in relation to ToT states can be explained by the inhibition deficit framework, in which coordinates that share many semantic features act as strong competitors, blocking access to the target word. We can hypothesis that these co-activated competitors likely remain accessible and are easily retrieved during the word association task in a ToT state. According to the Swinging Lexical Network Hypothesis (Abdel Rahman & Melinger, 2009 , 2019 ), while a word’s semantic richness may facilitate retrieval, this effect can be overridden by the inhibitory impact of strongly co-activated lexical competitors—as may occur for ToT words with fewer accessible and more coordinate associates. These results are in line both, with the lexical selection by competition theory (Levelt et al., 1999 ; Roleofs, 1992), as well as with the inhibition deficit hypothesis in the ToT framework, in which related words received sufficient activation to prevent access to the target word (Brown, 1979 , 1991 ). Our results also highlight a greater likelihood of perceptive features in ToT states in opposition to spatiotemporal associates and that the functional category is the most prominent in all states. Participants provided perceptive associations at the same level as coordinates in ToT states, calling into question the initial hypothesis that thematic relations generally facilitate word access and the opposition of the role between taxonomic and thematic relation on word production (Nozari & Pinet, 2020). When participants have direct access to the word, they produce more superordinate than coordinate associates. Although few studies have examined superordinate relations in word production, evidence from the picture-word interference paradigm suggests that superordinate distractors do not cause lexical competition (Costa et al., 2003 ; Hantsch et al., 2005 ), increasing even the accessibility of to the cue word. A MEG study (Dirani & Pylkkänen, 2023 ) suggests that superordinate categories are activated early during picture naming task, prior to lexical access. This could explain why more superordinate words were produced when the target word was accessible – its activation may automatically trigger related higher-level categories. With regards to the distribution of associates, the exploratory comparison between the SWOW database and the present study showed that coordinates appear to be stronger in SWOW, while superordinates tend to be stronger in the present data. This may seem counterintuitive at first, but it is consistent with the interpretation of our results presented above. Indeed, in the SWOW database, participants have an immediate written access to the cue (i.e. do not have to retrieve it from the concept), therefore, we can assume that the coordinate associates do not have to be inhibited and can be activated more easily than in the present task. By contrast, in the present study, participants had to inhibit competitors during the picture-naming task to retrieve/select the correct noun as admitted in theories of lexical selection by competition (Abdel Rahman & Melinger, 2009 , 2019 ; Belke et al., 2005 ; Roelofs, 2018 ). The distribution across the present data and SWOW is quite similar for the other categories, even when a picture is presented instead of a written word. This suggests that the organisation of the lexical-semantic network as indexed with the present task appears to align with a larger, more general database, except for the types of associates that are likely inhibited during lexical selection in a picture naming task. To note that the present study has not the intention to exclude the lexical-phonological mechanisms of the Transmission Deficit Hypothesis but may provide a complementary perspective of ToTs in considering lexical-semantic variables. Future research should consider grouping lexical and semantic variables alongside phonological variables in order to gain a better understanding of how these layers interact within the framework of the multiplex structure of the mental lexicon (Stella, 2020 ). In conclusion, this study provides further evidence that lexical-semantic variables shape the mental lexicon and influence word access. To the best of our knowledge, this is the first research that studies the impact of the lexical-semantic network of the speaker directly on ToT states. Results showed that reduced richness, low entropy and coordinate associates tend to elicit more retrieve deficits. By combining picture naming and continuous free word association, the paradigm offers insight into how individual network organisation affects ToT state. This paradigm also holds potential for studying individuals from different age-groups (Krethlow et al., 2020 ) and individuals from clinical populations, particularly in understanding anomia in post-stroke or primary progressive aphasia. Declarations Acknowledgments This research received funding from the National Center of Competence in Research Evolving Language, Swiss National Science Foundation Agreement #51NF40_180888 Disclosure statement The authors report there are no competing interests to declare. Data availability statement The data that support the findings of this study are openly available in the “Yareta institutional open science archive repository” at https://doi.org/10.26037/yareta:qow6muni7vaydl75r3ogo55pcm. 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Memory Cognition 7(6):485–495. https://doi.org/10.3758/BF03198265 Mahon BZ, Costa A, Peterson R, Vargas KA, Caramazza A (2007) Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. J Experimental Psychology: Learn Memory Cognition, 33 (3), Article 3. Massaro M, Tompkins CA (1994) Feature analysis for treatment of communication disorders in traumatically brain-injured patients: An efficacy study. Clin aphasiology 22:245–256 McDonagh DC, Fisher AV, Nozari N (2020) Do Taxonomic and Associative Relations Affect Word Production in the Same Way? Miller GA (1995) WordNet: A lexical database for English. Commun ACM 38(11):39–41. https://doi.org/10.1145/219717.219748 Moreno-Martínez FJ, Montoro PR (2012) An Ecological Alternative to Snodgrass & Vanderwart: 360 High Quality Colour Images with Norms for Seven Psycholinguistic Variables. PLoS ONE 7(5):e37527. https://doi.org/10.1371/journal.pone.0037527 Nozari N (2019) The dual origin of semantic errors in access deficit: Activation vs. inhibition deficit. Cognit Neuropsychol 36(1–2):31–53 Pexman PM, Hargreaves IS, Siakaluk PD, Bodner GE, Pope J (2008) There are many ways to be rich: Effects of three measures of semantic richness on visual word recognition. Psychonomic Bulletin Review 15(1):161–167. https://doi.org/10.3758/PBR.15.1.161 Pexman PM, Holyk GG, Monfils M-H (2003) Number-of-features effects and semantic processing. Memory Cognition 31(6):842–855. https://doi.org/10.3758/BF03196439 Pexman PM, Lupker SJ, Hino Y (2002) The impact of feedback semantics in visual word recognition: Number-of-features effects in lexical decision and naming tasks. Psychonomic Bulletin Review 9(3):542–549. https://doi.org/10.3758/BF03196311 Rabovsky M, Schad DJ, Abdel Rahman R (2016) Language production is facilitated by semantic richness but inhibited by semantic density: Evidence from picture naming. Cognition 146:240–244. https://doi.org/10.1016/j.cognition.2015.09.016 Rabovsky M, Schad DJ, Rahman RA (2021) Semantic Richness and Density Effects on Language Production: Electrophysiological and Behavioral Evidence Raposo A, Mendes M, Marques JF (2012) The hierarchical organization of semantic memory: Executive function in the processing of superordinate concepts. NeuroImage 59(2). Article 2. https://doi.org/10.1016/j.neuroimage.2011.08.072 Rastle KG, Burke DM (1996) Priming the Tip of the Tongue: Effects of Prior Processing on Word Retrieval in Young and Older Adults. J Mem Lang 35(4):586–605. https://doi.org/10.1006/jmla.1996.0031 Robert C, Rico Duarte L (2016) Semantic Richness and Aging: The Effect of Number of Features in the Lexical Decision Task. J Psycholinguist Res 45(2):359–365. https://doi.org/10.1007/s10936-015-9352-8 Roediger HL (1974) Inhibiting effects of recall. Memory Cognition 2(2):261–269. https://doi.org/10.3758/BF03208993 Roelofs A (1992) A spreading-activation theory of lemma retrieval in speaking. Cognition 42(1):107–142. https://doi.org/10.1016/0010-0277(92)90041-F Roelofs A (2018) A unified computational account of cumulative semantic, semantic blocking, and semantic distractor effects in picture naming. Cognition 172:59–72. https://doi.org/10.1016/j.cognition.2017.12.007 Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P (1976) Basic objects in natural categories. Cogn Psychol 8(3):382–439. https://doi.org/10.1016/0010-0285(76)90013-X Rose SB, Aristei S, Melinger A, Abdel Rahman R (2019) The closer they are, the more they interfere: Semantic similarity of word distractors increases competition in language production. J Experimental Psychology: Learn Memory Cognition 45(4):753–763. https://doi.org/10.1037/xlm0000592 Sailor K, Brooks PJ, Bruening PR, Seiger-Gardner L, Guterman M (2009) Exploring the time course of semantic interference and associative priming in the picture–word interference task. Q J Experimental Psychol 62(4):789–801. https://doi.org/10.1080/17470210802254383 Schwartz BL, Metcalfe J (2011) Tip-of-the-tongue (ToT) states: Retrieval, behavior, and experience. Memory Cognition 39(5). Article 5. https://doi.org/10.3758/s13421-010-0066-8 Siew CSQ, Wulff DU, Beckage NM, Kenett YN, Meštrović A (2019) Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics. Complexity , 2019 , 1–24. https://doi.org/10.1155/2019/2108423 Stella M (2020) Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing. Physica A 554:124382. https://doi.org/10.1016/j.physa.2020.124382 Stern G (2023–2027) Corpus SOIR (Set of Ordinary Interactions Recordings). Lausanne: Université de Lausanne Steyvers M, Tenenbaum JB (2005) The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cogn Sci 29(1):41–78. https://doi.org/10.1207/s15516709cog2901_3 Vitevitch MS, Goldstein R, Siew CSQ, Castro N (2014) Using complex networks to understand the mental lexicon. Yearbook Poznan Linguistic Meeting 1(1):119–138. https://doi.org/10.1515/yplm-2015-0007 Wulff DU, Hills TT, Mata R (2022) Structural differences in the semantic networks of younger and older adults. Sci Rep 12(1). Article 1. https://doi.org/10.1038/s41598-022-11698-4 Alario X-F, Segui J, Ferrand L (2000) Semantic and associative priming in picture naming. Q J Experimental Psychol Sect A 53(3):741–764. https://doi.org/10.1080/713755907 Yaniv I, Meyer DE (1987) Activation and metacognition of inaccessible stored information: Potential bases for incubation effects in problem solving. J Experimental Psychology: Learn Memory Cognition 13(2):187–205. https://doi.org/10.1037/0278-7393.13.2.187 Yap M, Pexman P, Wellsby M, Hargreaves I, Huff M (2012) An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition. Frontiers in Human Neuroscience , 6 . https://www.frontiersin.org/articles/ 10.3389/fnhum.2012.00072 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8970418","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":597101920,"identity":"3be67c97-6a12-4654-8439-efaa2d508c6e","order_by":0,"name":"Marie Couvreu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-1302-5895","institution":"University of Geneva","correspondingAuthor":true,"prefix":"","firstName":"Marie","middleName":"","lastName":"Couvreu","suffix":""},{"id":597101935,"identity":"91861709-5c29-43d7-b3c8-9bc0661d93a4","order_by":1,"name":"Marina Laganaro","email":"","orcid":"https://orcid.org/0000-0002-4054-0939","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Laganaro","suffix":""}],"badges":[],"createdAt":"2026-02-25 18:06:27","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8970418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8970418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103482023,"identity":"6846f440-e1db-40ee-939f-17a0b9d74987","added_by":"auto","created_at":"2026-02-26 08:18:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":215525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIllustration of the paradigm\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8970418/v1/8ba609cbedc7f6bbe867a980.png"},{"id":103507482,"identity":"69a2040f-9ce6-4e7b-b523-58560395f0a0","added_by":"auto","created_at":"2026-02-26 13:41:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(A) Mean association between the two states. (B) Entropy values between the two states. (C) Distribution of categories between the two states\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8970418/v1/82f1e2f5298f586eb9d83941.png"},{"id":103482024,"identity":"c6c2c7c3-59ca-4d53-a43d-d67b4a31bfa5","added_by":"auto","created_at":"2026-02-26 08:18:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of the type of the strongest associate among databases\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8970418/v1/7f42492ed45410349b6c8ed6.png"},{"id":103509991,"identity":"ea422788-a53a-4cd4-9d77-4dec6b02012b","added_by":"auto","created_at":"2026-02-26 14:02:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8970418/v1/40d080bb-6137-4476-80ca-fb6e1e412288.pdf"},{"id":103482022,"identity":"7b050608-4ea0-4749-9680-fc62c51f8306","added_by":"auto","created_at":"2026-02-26 08:18:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":524003,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-8970418/v1/a5006306af197bef5f22baa1.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe speaker’s lexical-semantic network in the tip of the tongue state\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003e\u0026ldquo;Leo : a little thing, a (pause) with... peach... (pause), what's the dish or the name again? It's in the garden\u0026rdquo;\u003c/em\u003e (Stern, 2023\u0026ndash;2027). Such phenomenon is more than occasional in real life and has been studied experimentally to understand failure to access words that are in the speaker\u0026rsquo;s lexicon.\u003c/p\u003e \u003cp\u003eTwo main hypotheses have been proposed to specifically explain ToT states. The first hypothesis, \u003cem\u003etransmission deficit\u003c/em\u003e, postulates that speech production is organized top-down into the hierarchical systems of semantic, lexical and phonological nodes and a ToT occurs because of a deficit in activation transfer from lexical to the phonological nodes (Burke et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Cohen \u0026amp; Faulkner, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Rastle \u0026amp; Burke, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Yaniv \u0026amp; Meyer, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The second hypothesis, \u003cem\u003einhibition deficit\u003c/em\u003e, postulates that ToT is caused by other words that are temporarily activate and/or cannot be inhibited, blocking the access to the target word (Brown, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Jones, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Roediger, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough not developed specifically for ToT, the spreading-activation theory (Collins \u0026amp; Loftus, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), with the conception that the mental lexicon is organised in network (Roelofs, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), postulates that the activation spreads into the mental lexicon to reach the target word by activating all related words. However, sometimes this activation fails to propagate sufficiently leading to word retrieval difficulty, as in anomia. To our knowledge, it has not yet been used to investigate the ToT state, despite its potential to improve our understanding of how the lexical-semantic network is organised in this state.\u003c/p\u003e\n\u003ch3\u003eThe network’s perspective in ToT states\u003c/h3\u003e\n\u003cp\u003eIn this way, the network perspective (Baronchelli et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; De Deyne \u0026amp; Storms, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e; Siew et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vitevitch et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) constitutes the main approach to study the relationships between words at the lexical-semantic level (Collins \u0026amp; Loftus, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Steyvers \u0026amp; Tenenbaum, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Some lexical-semantic factors such as the semantic richness and the prototypicality of the network of the word may influence the speed of access of the target word. The \u003cem\u003esemantic richness\u003c/em\u003e of a word is measured by the number of other words that are associated with it (Chan \u0026amp; Vitevitch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Goldstein \u0026amp; Vitevitch, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In word production studies, the semantic richness facilitates word production (in terms of reaction time and accuracy) (De Deyne et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pexman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rabovsky et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Robert \u0026amp; Rico Duarte, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Steyvers \u0026amp; Tenenbaum, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This is explained by the associates increasing the activation at the conceptual level, which then spreads to the correct lexical representation, thus facilitating the response. The types of links reflect the nature of semantic relations between words \u0026mdash; either taxonomic (superordinate, coordinate, subordinate) or thematic \u0026mdash;indicate how these relations influence word production. Indeed, it has been shown that naming a picture is interfered when coordinate words are presented simultaneously (e.g. cat - dog), but it is facilitated with associates (e.g., honey \u0026ndash; bee) (Bloem et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; McDonagh et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rabovsky et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roelofs, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Alario et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) although different results have been observed with other paradigms (Abdel Rahman \u0026amp; Melinger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; de Zubicaray et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite some contradictory results, studies highlight the fact that different semantic relations affect word production. In addition to richness and type of associates, the network prototypicality, - measured by entropy, which characterises network homogeneity \u0026mdash; also influences word access (Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, and crucially for our purpose here, the properties of the lexical-semantic system have usually been considered as speaker-independent, meaning that the same system \u0026ndash; usually based on external databases - is used to explain the behaviour of all the speakers. As speakers do not experience ToTs for the same words at the same time, and the lexical-semantic network is dynamic (Dubossarsky et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), meaning that there must be speaker-specific organisation of the network as considered in the present study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePresent study\u003c/h2\u003e \u003cp\u003eThe organisation of the lexical-semantic network influences word retrieval and thus most likely also has an impact on ToTs. The network system is dynamic and changes between individuals (Dubossarsky et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wulff et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Unlike other studies on the mental lexicon that rely on existing databases representing an 'average' lexical-semantic network for a given word, this study considers the organisation of each participant's lexical-semantic network for each word. It adopts the same paradigm as database-based approaches: the continuous free word association task (De Deyne et al., 2008b). This task provides information about the number of associates (semantic richness) given per word, the prototypicality and the semantic relation between the associates and the target word (De Deyne et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dubossarsky et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). De Deyne \u0026amp; Storms (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e) showed that the first associate was similar across participants, whereas the second and third were more heterogeneous. Studies (Dubossarsky et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) added a measure of entropy and found that words with greater heterogeneity in their associates (i.e. high entropy) were more difficult to access. To have a more \u003cb\u003eprecise\u003c/b\u003e understanding of the lexical-semantic network, the categorisation of these associates using existing classifications allows us to have a clearer window on the impact of the link between these associates on word retrieval.\u003c/p\u003e \u003cp\u003eWe expect that a rich associative network in the continuous free word association task should be associated with correctly retrieved and produced words, whereas a poor associate network should be observed in correspondence to ToTs. On the other hand, the nature of the semantic relationship between the target word and the associates may also influence word retrieval.\u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e 46 native French participants (36 females, age 18 to 34, mean: 22) young adults were recruited in a psychology course with informed consent, and the procedure was approved by the Ethics Committee of the University of Geneva.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaterials\u003c/h3\u003e\n\u003cp\u003eThe paradigm used in this study involves two main tasks: a 110 items picture-naming task and a continuous free word association task on the same words.\u003c/p\u003e \u003cp\u003eThe 110 colour pictures were selected from a pre-test (65 other participants, 200 low frequency words). The 200 words in the pre-test were selected from databases (Brodeur et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and previous ToT studies (Alario \u0026amp; Ferrand, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Brown \u0026amp; Nix, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Gollan \u0026amp; Brown, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; James \u0026amp; Burke, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Moreno-Mart\u0026iacute;nez \u0026amp; Montoro, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The final 110 stimuli have 85% name agreement and a mean frequency of 1.82 per million from Lexique.org (New et al., 2005). Of the 110 stimuli, 80 induced the most ToTs in the pre-test, and the 30 others, which are fillers in the present study are more frequent words and were always produced correctly in the pre-test. All stimuli are concrete and imageable words (see Appendix A).\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e Each trial started with the picture-naming task, immediately followed by questions on the speaker\u0026rsquo;s state and the continuous free word association task (see Fig.\u0026nbsp;1). The procedure starts over for each stimulus. Stimuli were presented on a 17-inch screen (60 Hz, 50 cm viewing distance), with 300 x 300 pixels color images on a white background. The experiment was run using-Prime software (E-Studio 3). Each trial began with a 500 ms fixation cross, then the picture appeared for 2,500 ms, during which the participant had to name it orally as quickly and accurately as possible and the response was audio-recorded. Then participants indicated their state relative to the picture they had to name by pressing a color-coded key: word accessed (state A), ToT state (state T), unknown word (state O). If the word was unknown (state O), the next trial began with the following picture to be named, and the same procedure starts over. If the word was accessed (state A), participants rated their confidence (1\u0026ndash;5) and then made the continuous free word association task. In a ToT state (T), they indicated whether they had retrieved the word in between using the keyword (yes/no) and then immediately moved to the continuous free word association task. For the free word association task, the participants had 10 seconds to say aloud all words that came to mind related to the target word displayed on screen. To prevent participants from anticipating the planning of their associates, 30 filler items which are more frequent words, were randomly inserted into the paradigm. These items did not trigger the continuous free-word association task and were therefore not analysed. Instructions clearly defined ToT as the frustrating feeling of knowing a word without being able to retrieve it. All verbal responses were recorded with a micro-headset, allowing us to check whether the response \u0026ldquo;state A\u0026rdquo; was coherent with the participant\u0026rsquo;s production. The experiment took place in a standard room, unsupervised, with two counterbalanced item orders. A break occurred after 55 trials and stimuli were pseudo-randomized to avoid semantic or phonological clustering. 5 sample trials were used at the beginning of the experiment (see Fig.\u0026nbsp;1 for an illustration).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIllustration of the paradigm\u003c/h2\u003e \u003cp\u003e \u003cdiv description=\"Une image contenant texte, diagramme, capture d\u0026rsquo;\u0026eacute;cran, PlanLe contenu g\u0026eacute;n\u0026eacute;r\u0026eacute; par l\u0026rsquo;IA peut \u0026ecirc;tre incorrect.\" class=\"Drawing\" id=\"787183012\" name=\"Image 6\"\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePicture-naming task\u003c/h2\u003e \u003cp\u003eThe recorded productions from the picture-naming task were analysed using CheckVocal software (v4.4.0, Protopapas, 2007) to categorise them as correct, incorrect or absence of response and to extract production latencies.\u003c/p\u003e \u003cp\u003eTrials without responses in the picture-naming task were categorised as ToT or \u0026ldquo;don\u0026rsquo;t know\u0026rdquo; according to the answer given to the 3 choices. Trials with mismatches between the participant's verbal responses and their answers to the 3 choices, were removed. One participant was excluded due to high inconsistencies (\u0026ge;\u0026thinsp;1 SD above the mean) and 5 others due technical issues, meaning that 40 participants were included in the final analyses. One word (walrus) was excluded due to many \u0026ldquo;don\u0026rsquo;t know\u0026rdquo; responses (50%). Other stimuli were removed if they elicited responses exclusively in one condition, with no or only one response in the other (10 words), meaning that 69 words were included in the final analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eContinuous free word association task\u003c/h2\u003e \u003cp\u003e All associates recorded within 10 seconds were transcribed manually to compute the number and order of the associates given by each participant for each target word. All fist associates were classified into eight categories, combining taxonomic classification and an adaptation of the semantic features analysis (SFA) (Balthazar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Boyle, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Miller, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Raposo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (see Appendix B for examples). Inter-rater reliability was examined on 174 trials, indicating a substantial agreement between raters for the categorisation (k\u0026thinsp;=\u0026thinsp;0.87, z\u0026thinsp;=\u0026thinsp;46.1).\u003c/p\u003e \u003cp\u003eFor network richness, the number of associates per target word given within the 10 seconds was the dependent variable in a generalised mixed-effect model (R Core Team, 2022), including participants as a random effect and states (T or A) as a fixed effect.\u003c/p\u003e \u003cp\u003eTo investigate how first associate categories varied between the two states a Bayesian framework was used in R (R Core Team, 2022). Bayesian analysis provides a distribution of plausible effect values; we report 95% credible intervals, indicating where the true effect likely lies. The model has converged well (R̂ \u0026asymp; 1.00). Categories representing less than 5% of responses across all participants were excluded.\u003c/p\u003e \u003cp\u003eTo evaluate the network prototypicality, and the entropy (H) was computed based on the first associate given by participants for each cue using to the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$H=\\sum_{i=1}^{k}{p}_{i}{log}_{2}\\left(\\frac{1}{{p}_{i}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eK\u003c/em\u003e is the number of distinct first associates given per participant; \u003cem\u003epi\u003c/em\u003e is the proportion of participants (per state) producing each associate word and the sum is computed over all types with non-zero response probability.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e Higher H values indicate greater variability in responses across participants for a given word, reflecting lower network prototypicality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExploratory comparison to existing databases\u003c/h2\u003e \u003cp\u003eGiven the novelty of collecting and using associates at the level of individual speakers rather than relying on general databases, the results may be limited in interpretation on the organisation of the lexical-semantic network. We therefore conducted an exploratory comparison of the distributions between the categories of strongest associates in our study and those from the English SWOW database (De Deyne et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as no such database exists for infrequent French words. In SWOW, participants are asked to write three words related to a written target cue, without being timed. As the target order was unavailable, we used the associate most frequently produced across participants and categorized each associate from SWOW according to our classification. The same procedure was applied to our own database for the 'ToT' and 'Accessible' states. For each category, we summed the strengths of all associates and normalized this sum by the total strength of all associates across categories in the three databases (ToT, Accessible and SWOW). This enabled us to calculate a ratio for each category in a sample of 52 words that overlapped with those in the SWOW database. Due to differences in how the data was collected, exploratory analyses were used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePicture-naming task\u003c/h2\u003e \u003cp\u003e Participants reported ToT states in 31.28% trials, produced words correctly in 49.11% of trials with a mean response time of 1227.06 ms and reported that they did not know the word in 19.61% trials. Considering only the relevant conditions (ToT and Accessible), a total of 716 TOTs (39.27%) and 1124 correct responses (60.72%) were analysed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eContinuous free word association task\u003c/h2\u003e \u003cp\u003eThe average number of associates given depending on the response is presented in Fig.\u0026nbsp;3. On average, 5.72 associates were produced in the Accessible state and 5.04 in the ToT state. The result of the statistical model (X2(1)\u0026thinsp;=\u0026thinsp;16.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed that participants produced significantly more associates when they had access to the word than when they were in a ToT state (see detailed model in Appendix C1).\u003c/p\u003e \u003cp\u003eBayesian analysis showed that the probability of the functional category is higher regardless of the state, though it is more likely in a ToT state to have coordinate associates (β\u0026thinsp;=\u0026thinsp;0.85, SE\u0026thinsp;=\u0026thinsp;0.27, 95% CI = [0.33, 1.39]). Compared to other categories, coordinate associates are significantly more likely to be in a ToT state compared to superordinate (β =- 1.23, SE\u0026thinsp;=\u0026thinsp;0.27, 95% CI = [-1.78, -0.69]) except for the perceptive category, which shows no significant difference (β = -0.35, SE\u0026thinsp;=\u0026thinsp;0.27, 95% CI = [-0.98, 0.29]). Superordinate associates are more likely in an Accessible state than coordinates (β\u0026thinsp;=\u0026thinsp;1.19, SE\u0026thinsp;=\u0026thinsp;0.29, 95% CI = [0.61, 1.73]) and perceptive (β\u0026thinsp;=\u0026thinsp;0.78, SE\u0026thinsp;=\u0026thinsp;0.29, 95% CI = [0.23, 1.36]) but there was no significant difference with functional and spatiotemporal associates (see Appendix D for detailed analyses). Subordinate and other categories were not analysed because of the limited rate (\u0026lt;\u0026thinsp;5% in total). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC represents the distribution of the categories between states.\u003c/p\u003e \u003cp\u003eThe model on the network prototypicality measured with the entropy of the first associate (H) revealed higher scores for the ToT state and lower scores for the accessible condition (X2(1)\u0026thinsp;=\u0026thinsp;8.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). An illustration of the lexical-semantic network is in the appendix (Appendix G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv description=\"Une image contenant texte, diagramme, capture d\u0026rsquo;\u0026eacute;cran, Trac\u0026eacute;Le contenu g\u0026eacute;n\u0026eacute;r\u0026eacute; par l\u0026rsquo;IA peut \u0026ecirc;tre incorrect.\" class=\"Drawing\" id=\"2093045941\" name=\"Image 4\"\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExploratory comparison to the SWOW database\u003c/h2\u003e \u003cp\u003eThe overall distribution of categories of associates from SWOW is very similar to those obtained in the present study but seems to differ for coordinate and superordinate associates (see Fig.\u0026nbsp;3). Coordinates\u003c/p\u003e \u003cp\u003eappear to be more prevalent in the SWOW database, while superordinate associates tend to be more prevalent in the present data (across ToT and Accessible).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of the type of the strongest associate among databases\u003c/h2\u003e \u003cp\u003e \u003cdiv description=\"Une image contenant texte, capture d\u0026rsquo;\u0026eacute;cran, diagramme, Trac\u0026eacute;Le contenu g\u0026eacute;n\u0026eacute;r\u0026eacute; par l\u0026rsquo;IA peut \u0026ecirc;tre incorrect.\" class=\"Drawing\" id=\"1833073779\" name=\"Image 10\"\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e The aim of the present study was to investigate the organisation of the speaker\u0026rsquo;s lexical-semantic network in a ToT state compared to when the word is accessed and produced correctly using a picture-naming task followed immediately by a continuous free word association task performed by the participants. The analyses were run on three factors that have been shown to influence word production in previous studies based on associative databases, i.e. the semantic richness, the prototypicality and the semantic relation of the lexical-semantic network. Results showed a more heterogeneous network with few associates and predominantly more coordinates in a ToT state relative to accessible words, while the superordinates appeared more frequently when the word was accessible. The supplementary analyses revealed that the distribution of these two categories differed between the present data and the SWOW database for the subset of analysed items, whereas the distribution of all other categories remained similar.\u003c/p\u003e \u003cp\u003eIn the following we will discuss the results related to the richness/accessibility of the associative network in ToT states and in light of the organisation of the lexical-semantic network.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe lexical-semantic network in a ToT state VS. \u0026ldquo;Accessible\u0026rdquo; state\u003c/h2\u003e \u003cp\u003eThe present findings demonstrate that the number of associates given by the speaker is significantly related to the success of word production, with fewer associates generated for ToT words than for accessible ones. As the participants had 10 seconds to provide associated words in all conditions, and with the interim questions between the two tasks, it is unlikely that the difference in performance between the states is due to a lack of time in one condition. It is also noteworthy that the present results at the individual level are consistent with studies showing that a richer associative network of a word measured with a population sample database facilitates lexical access in terms of accuracy (Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pexman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rabovsky et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yap et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Such facilitative effect of semantic richness on word production is generally attributed to stronger activation at the conceptual-level, enabling faster and more accurate retrieval (Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pexman et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rabovsky et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This interpretation aligns with the spreading-activation theory (Collins \u0026amp; Loftus, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), which proposes that successful word retrieval depends on sufficient activation between the cue word and other words.\u003c/p\u003e \u003cp\u003eBeyond the impact of the individual semantic richness on the speaker\u0026rsquo;s performance, our findings suggest that both the prototypicality and semantic relations within the lexical-semantic network also play a significant role in determining word retrieval success during ToT states. Indeed, words with an heterogenous network are more likely to elicit ToT states than an Accessible state. Similarly, Krethlow et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that network connections were stronger when the network was homogeneous across age groups, suggesting that greater semantic dispersion may hinder access.\u003c/p\u003e \u003cp\u003eThe nature of the semantic relation of the first associate also differs between ToT state and Accessible states, with a higher probability of having a coordinate in ToT and superordinate in Accessible states. The greater proportion of coordinates observed in relation to ToT states can be explained by the inhibition deficit framework, in which coordinates that share many semantic features act as strong competitors, blocking access to the target word. We can hypothesis that these co-activated competitors likely remain accessible and are easily retrieved during the word association task in a ToT state. According to the Swinging Lexical Network Hypothesis (Abdel Rahman \u0026amp; Melinger, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while a word\u0026rsquo;s semantic richness may facilitate retrieval, this effect can be overridden by the inhibitory impact of strongly co-activated lexical competitors\u0026mdash;as may occur for ToT words with fewer accessible and more coordinate associates. These results are in line both, with the lexical selection by competition theory (Levelt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Roleofs, 1992), as well as with the inhibition deficit hypothesis in the ToT framework, in which related words received sufficient activation to prevent access to the target word (Brown, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Our results also highlight a greater likelihood of perceptive features in ToT states in opposition to spatiotemporal associates and that the functional category is the most prominent in all states. Participants provided perceptive associations at the same level as coordinates in ToT states, calling into question the initial hypothesis that thematic relations generally facilitate word access and the opposition of the role between taxonomic and thematic relation on word production (Nozari \u0026amp; Pinet, 2020).\u003c/p\u003e \u003cp\u003e When participants have direct access to the word, they produce more superordinate than coordinate associates. Although few studies have examined superordinate relations in word production, evidence from the picture-word interference paradigm suggests that superordinate distractors do not cause lexical competition (Costa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hantsch et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), increasing even the accessibility of to the cue word. A MEG study (Dirani \u0026amp; Pylkk\u0026auml;nen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggests that superordinate categories are activated early during picture naming task, prior to lexical access. This could explain why more superordinate words were produced when the target word was accessible \u0026ndash; its activation may automatically trigger related higher-level categories.\u003c/p\u003e \u003cp\u003eWith regards to the distribution of associates, the exploratory comparison between the SWOW database and the present study showed that coordinates appear to be stronger in SWOW, while superordinates tend to be stronger in the present data. This may seem counterintuitive at first, but it is consistent with the interpretation of our results presented above. Indeed, in the SWOW database, participants have an immediate written access to the cue (i.e. do not have to retrieve it from the concept), therefore, we can assume that the coordinate associates do not have to be inhibited and can be activated more easily than in the present task. By contrast, in the present study, participants had to inhibit competitors during the picture-naming task to retrieve/select the correct noun as admitted in theories of lexical selection by competition (Abdel Rahman \u0026amp; Melinger, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Belke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Roelofs, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The distribution across the present data and SWOW is quite similar for the other categories, even when a picture is presented instead of a written word. This suggests that the organisation of the lexical-semantic network as indexed with the present task appears to align with a larger, more general database, except for the types of associates that are likely inhibited during lexical selection in a picture naming task.\u003c/p\u003e \u003cp\u003eTo note that the present study has not the intention to exclude the lexical-phonological mechanisms of the Transmission Deficit Hypothesis but may provide a complementary perspective of ToTs in considering lexical-semantic variables.\u003c/p\u003e \u003cp\u003eFuture research should consider grouping lexical and semantic variables alongside phonological variables in order to gain a better understanding of how these layers interact within the framework of the multiplex structure of the mental lexicon (Stella, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides further evidence that lexical-semantic variables shape the mental lexicon and influence word access. To the best of our knowledge, this is the first research that studies the impact of the lexical-semantic network of the speaker directly on ToT states. Results showed that reduced richness, low entropy and coordinate associates tend to elicit more retrieve deficits. By combining picture naming and continuous free word association, the paradigm offers insight into how \u003cem\u003eindividual\u003c/em\u003e network organisation affects ToT state. This paradigm also holds potential for studying individuals from different age-groups (Krethlow et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and individuals from clinical populations, particularly in understanding anomia in post-stroke or primary progressive aphasia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received funding from the National Center of Competence in Research Evolving Language, Swiss National Science Foundation Agreement #51NF40_180888\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in the \u0026ldquo;Yareta institutional open science archive repository\u0026rdquo; at https://doi.org/10.26037/yareta:qow6muni7vaydl75r3ogo55pcm.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel Rahman R, Melinger A (2007) When bees hamper the production of honey: Lexical interference from associates in speech production. 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ToTs have been explained by a temporary reduction of lexical-phonological transmission or inhibition of competitors in the mental lexicon. An alternative way to approach ToTs is to consider the state of the lexical-semantic network of the speaker.\u003c/p\u003e \u003cp\u003eHere we investigate how the organisation of the lexical-semantic network of the speaker differs between accessible words and ToTs. 40 neurotypical young adults performed a picture-naming task with low frequency items to induce ToT states, followed by a continuous free word association task.\u003c/p\u003e \u003cp\u003eAccessible words were characterised by richer lexical-semantic networks with more superordinate associates, whereas ToT states were marked by more heterogeneous networks dominated by coordinate associates. The organisation of the lexical-semantic network differs depending on the ability to access the word at a given time at the individual level, providing further hints to understand ToT states.\u003c/p\u003e","manuscriptTitle":"The speaker’s lexical-semantic network in the tip of the tongue state","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 08:18:29","doi":"10.21203/rs.3.rs-8970418/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7dfe91ec-8c5d-464c-97d3-664d3b4c66d4","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63540015,"name":"Psychology"},{"id":63540016,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2026-02-26T08:18:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 08:18:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8970418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8970418","identity":"rs-8970418","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}