When we learn and use language, we deal with two main types of concepts. Concrete concepts, which refer to things we directly experience (like a chair, running or the colour blue), and abstract concepts, which refer to ideas that we are unable to sense directly (like truth, democracy or love).

Most studies have looked at how people process these concepts in isolation, such as by reading single words on a screen. This revealed that the human brain processes each concept differently, with concrete concepts typically activating brain regions involved in sensory and motor experiences, and abstract concepts activating regions involved in emotion and complex thinking.

However, the experiments conducted in these studies do not represent real life situations, where humans often encounter and process both concepts simultaneously. For instance, at the same time as processing language, someone may also be seeing, hearing, and experiencing other things in their environment.

Kewenig et al. wanted to understand whether the brain processes abstract and concrete concepts differently depending on what a person may be visualizing at the same time. To achieve this, they used a technique known as functional MRI to record which regions of the brain are activated as participants watched different movies.

The team found that when abstract concepts (such as love) appeared with related visual information (such as people kissing), the brain processed them more like concrete concepts, engaging sensory and motor regions. Conversely, when concrete concepts (like a chair) appeared without related visual information, the brain processed them more like abstract concepts, engaging regions involved in complex thinking. This suggests that the way the human brain processes meaning is very dynamic and constantly adapting to available contextual information.

These findings could help improve artificial intelligence systems that process language and visual information together, making them better at understanding context-dependent meaning. They might also benefit people with language disorders by informing the development of more effective therapies that consider how context affects understanding. However, more research is needed to confirm these findings and develop practical applications, particularly studies testing whether similar brain patterns occur in other natural situations.

Humans acquire, and process language in situated multimodal contexts, through dynamic interactions with their environment. For example, children may learn what the word ‘tiger’ means primarily via sensory-motor experience: they see one on TV, or they are told that a tiger looks like a big cat. Conversely, the experience required for understanding the more abstract concept of ‘good’ will likely include an evaluation of the rational and emotional motives underscoring intentional actions. Consequently, while more concrete concepts have external physical references (they refer to objects or actions that are easily perceived in the world), more abstract concepts do not necessarily have such references (they generally refer more to cultural and societal constructs or peoples' inner states of mind) (Villani et al., 2019). Is this difference reflected in concrete and abstract representations in the brain during naturalistic processing? And are these static or can they change as a function of the multimodal contexts in which processing occurs?

Most studies of concrete and abstract processing are not naturalistic in that they present words or sentences isolated from the rich contexts in which we usually process them. Collectively, these studies suggest that concrete and abstract concepts engage separate brain regions involved in processing different types of information. (Bedny and Thompson-Schill, 2006; Binder et al., 2009; Sabsevitz et al., 2005; Wang et al., 2010; Vigliocco et al., 2014). Concrete words engage regions involved in experiential processing (Barsalou et al., 2003). For example, motor-related cortices activate during the processing of action verbs like ‘throw’ (Hauk et al., 2004), or action-related nouns like ‘hammer’ (Vigliocco et al., 2006; Kiefer and Pulvermüller, 2012), auditory cortices for sound-related words like ‘telephone’ (Goldberg et al., 2006; Kiefer et al., 2008), and visual cortices for color-related words like ‘yellow’ (Hsu et al., 2011; Simmons et al., 2007). These results are consistent with the view that we learn and neurobiologically encode concrete concepts in terms of the sensory and motor experiences associated with their referents.

In contrast, some studies of abstract concepts have found greater activation in brain regions associated with general linguistic processing (Binder et al., 2005; Mellet et al., 1998; Noppeney et al., 2004; Sabsevitz et al., 2005). These findings suggest that abstract concepts are learned by understanding their role in a linguistic context, including semantic relationships with other words (e.g. ‘democracy’ is understood through its relationships to words like ‘people,’ ‘parliament,’ ‘politics,’ etc., e.g. Jones et al., 2012). However, neurobiological data also support the view that subcategories of abstract concepts retain sensorimotor information (Harpaintner et al., 2022; Harpaintner et al., 2020; Harpaintner et al., 2018; Fernandino et al., 2022) as well as social information (Villani et al., 2019; Conca et al., 2021a) and internal/interoceptive/affective experiences (Oosterwijk et al., 2015; Vigliocco et al., 2014), which are also important for learning abstract concepts (Ponari et al., 2018). Thus, abstract concepts constitute a more heterogeneous category (Roversi et al., 2013; Zdrazilova et al., 2018; Villani et al., 2019; Muraki et al., 2020; Muraki et al., 2020; Kiefer et al., 2022).

A limitation of these studies is that they have only investigated the processing of decontextualized concepts (see e.g. Table 1 in a recent review by Del Maschio et al., 2022). That is, they (often implicitly) assume that conceptual representations in the brain are the product of a stable set of regions processing different types of information depending on whether a concept is concrete or abstract. However, this dichotomy may not account for the way in which we typically process concepts (Lebois et al., 2015), given that the information encoded during conceptual processing depends on the contextual information available. For example, the situated (i.e. contextualized in the discourse but also in the physical setting in which processing occurs) processing of concrete concepts like ‘chair’ could be linked to many abstract internal elements like goals (‘I want to rest’), motivations (‘I have been standing for 2 hr’), emotions (‘I would like to feel comfortable’), and theory of mind (‘is that older person more in the need of this chair than me?’). Conversely, an abstract concept like ‘truth’ is no longer particularly abstract when used in reference to a perceived physical situation (such as ‘snowing’) that matches the utterance’s meaning (‘it is true that it is snowing’). Here, ‘truth’ refers to a concrete state of the world (Barsalou et al., 2018).

Indeed, previous work has postulated flexible conceptual processing in experiential brain circuits (Binder and Desai, 2011; Pulvermüller, 2018a). Behavioral data support the view that contextual information can affect conceptual processing (e.g., Chambers et al., 2004; Cooper, 1974; Tanenhaus et al., 1995). For example, when an object is depicted in a visual context consistent with its use, the action associated with using the object is more readily available than when the context is more consistent with picking the object up (Kalénine et al., 2014). There is also neurobiological evidence that objects visually present in a situation can influence conceptual processing (Hoffman et al., 2013; Yee and Thompson-Schill, 2016). For example, task-related color-congruency of objects correlates with less activation of brain regions involved in color perception during processing – likely because less retrieval of detailed color knowledge was necessary (Hsu et al., 2011). Dynamic, context-dependent recruitment of visual and motor-related areas during semantic processing has also been established (Hoenig et al., 2008; van Dam et al., 2012; Popp et al., 2019). An understanding of conceptual knowledge as static and context-independent is insufficient to account for these dynamics (Pulvermüller, 2018b).

However, no previous study has addressed whether the brain areas associated with concrete and abstract concepts are fixed or recruited in a more dynamic way during semantic processing. The present study aims to fill this gap and test the following two predictions. First, we submit that results from previous investigations of conceptual processing, which generally depict a stable dichotomy between concrete and abstract words, reflect the average experiential information of the type of situational context in which concepts are habitually experienced. Therefore, we predict that the neurobiological representation of concrete concepts, will be related to associated brain regions, because they retain experiences related to their physical referents that are predominantly characterized by sensory and motor information, (Pulvermüller, 2018b; Willems et al., 2010). In contrast, because their representations mostly reflect information related to internal/interoceptive/affective experience as well as linguistic information, we expect abstract concepts to activate brain regions associated with emotional, interoceptive, and general linguistic processing (Reinboth and Farkaš, 2022).

Second, the reviewed work also suggests that these habitual representations are not necessarily stable and might change during naturalistic processing depending on the specific contextual information available. We specify two context conditions: a concept is displaced if its context offers little or no visual information related to the concept’s external sensory-motor features. In contrast, a concept is situated, if its visual context contains objects related to its meaning. We predict that when a concrete concept is processed in displaced situations (e.g. ‘cat’ processed when discussing the general character traits of cats vs dogs), response profiles will shift towards more internalized processing shared with abstract concepts. In contrast, when an abstract concept is processed in a situated context (for example the word ‘love’ processed in a scene with people kissing), its representation will more heavily draw on regions involved in processing external, visual information that otherwise characterize more concrete concepts.

We propose that this erosion of the concrete/abstract dichotomy for contextualized processing shows that both concrete and abstract concepts draw on information related to experience (external and internal) as well as linguistic association. Which associated neurobiological structures are engaged during processing depends dynamically on the contextual information available. This way of thinking about the representational nature of conceptual knowledge may help reconcile contradictory evidence concerning the involvement of different brain areas during conceptual processing (for example as discussed in Patterson et al., 2007; Hoffman et al., 2018).

Conceptual processing is typically investigated in experiments where words are stripped away from their naturally occurring context: most studies use isolated words, and sometimes sentences (see Table 1 in Del Maschio et al., 2022). However, conceptual processing in its ecology occurs in rich multimodal contexts. Our study investigated naturalistic conceptual processing during movie-watching to begin to understand the effect of multimodal context on the neurobiological organization of real-world conceptual representation.

The present study analyzed the ‘Naturalistic Neuroimaging Database (NNDb)’ (Aliko et al., 2020). All code is made available on a designated repository under (https://github.com/ViktorKewenig/Naturalistic_Encoding_Concepts, copy archived at ViktorKewenig, 2024).

Conceptual processing across contexts

In this analysis, we tested the prediction that when contextual information is averaged away, the neurobiological organization of conceptual processing will reflect brain systems involved in experiential and linguistic information processing, broadly in line with previous studies. Specifically, sensory and motor system engagement for concrete concepts and internal/interoceptive/affective and more general linguistic processing system engagement for abstract concepts. All statistical analyses on the preprocessed NiFTI files were carried out in AFNI (Cox, 1996; Cox and Hyde, 1997). Individual AFNI programs used are indicated parenthetically or in italics in subsequent descriptions.

Deconvolution analysis

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We used an amplitude (also known as parametric) modulated deconvolution regression to estimate activity associated with concrete and abstract words from the preprocessed fMRI data. Specifically, we estimated four sets of amplitude-modulated impulse response functions (IRF) for (1) abstract words; (2) concrete words; (3) remaining words; and (4) other time points. Both concrete and abstract words included word onset and five modulators, two of interest, and five nuisance modulators. These were the independent ratings of concreteness and abstractness and luminance, loudness, duration, word frequency, and speaking rate (calculated as the number of phonemes divided by duration) for each word. We also estimated the IRFs in the same manner and with the same amplitude modulators for all the remaining words in the movie that were not of interest to our hypothesis. Finally, we generated IRFs (without amplitude modulators) for all time points which did not include any speech. The deconvolution model also included general linear tests for (1) abstract words under the curve; (2) concrete words under the curve; (3) contrasts between concrete and abstract words at each timepoint (for a comparison of significant timing differences see Figure 1—figure supplement 2).

In contrast to a standard convolution-based regression analysis, deconvolution does not assume a canonical hemodynamic response function. Instead, an IRF is estimated over a 20 s time-window from stimulus onset at 1 s steps using multiple basis functions. This produces a better understanding of shape differences between individual hemodynamic response functions and achieves higher statistical power at both individual and group-levels (Chen et al., 2015). Furthermore, there might be differences in timing for the processing of concrete and abstract words (Kroll and Merves, 1986). In particular, the ‘concreteness effect’ indicates that concrete words are processed faster and more accurately than abstract words (Paivio et al., 1994; Jessen et al., 2000; Fliessbach et al., 2006). These timing differences can be captured by our approach. We chose a 20 s time window because this should be sufficient to capture the hemodynamic response function for each word. We selected ‘Csplin’ over the Tent function to deconvolve the BOLD signal because this function offers more interpolation between time points, which might result in a more precise estimate of the individual response function (but is computationally more costly).

Furthermore, traditional ‘main effect’ analysis confounds various non-specific processes, such as acoustic processing, which co-vary with each presented word (especially during dynamic, naturalistic stimuli). In contrast, amplitude modulation allows us to isolate regions that exhibit activational fluctuations specifically in relation to the concreteness modulator beyond the ‘main effect’ and fluctuations in the amplitude response caused by other modulators included in our model. Including nuisance modulators can help serve as controls, mitigating potentially confounding effects - in our case the significant differences between luminance and loudness. By adjusting for these sensory attributes, we ensure that the final betas from this analysis represent the estimated BOLD response specifically associated with concreteness.

Group-level analysis

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We then used the 20 amplitude-modulated beta-coefficients from the concrete-abstract contrasts in a linear mixed effects model for group-level analysis using ‘3dLME’ (Chen et al., 2013). The model included the factors ‘contrast’ with levels ‘abstract’ and ‘concrete’ and ‘time’ with 20 levels. The model also included a centered covariate for age of participant, and covariates for gender (two levels) and movie ID (10 levels). Besides a random intercept for participant we included a control implemented in '3dLME' for the potentially auto-correlative structure of residuals to make sure that we model the true effect estimates of the multiple basis function (Hefley et al., 2017). The final model formula was: contrast + age + gender + movie. We included 20 general linear tests, one for each contrast between concrete and abstract activation at each of the 20 timepoints, because we wanted to see how the amplitude of the activation associated with concreteness and abstractness changes over time. We thought that the timing and/or amplitude of the response for concrete and abstract words might vary and that this might be particularly true of the subsequent context analysis. We provide information on timing differences in the supplementary material (Figure 3—figure supplement 7).

Correction for multiple comparisons

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To correct for multiple comparisons in the LME, we used a multi-threshold approach rather than choosing an arbitrary p value at the individual voxel level threshold. In particular, we used a cluster simulation method to estimate the probability of noise-only clusters using the spatial autocorrelation function from the LME residuals (‘3dFWHMx’ and ‘3dClustSim’). This resulted in the cluster sizes to achieve a corrected alpha value of 0.01 at 9 different p values (i.e. 0.05, 0.02, 0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002, and 0.0001). We thresholded each map at the corresponding z-value for each of these nine p-values and associated cluster sizes. We then combined the resulting maps, leaving each voxel with its original z-value. For additional protection and presentation purposes, we use a minimum cluster size 20 voxels for all results using ‘3dMerge’. For tables, we determined the center of mass for each of these clusters using ‘3dCM.’ See Cox et al., 2017 for a validation of a related method and Skipper et al., 2022 for an earlier application.

Analyses of experiential features

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In order to more closely characterize the functional specificity of the spatial activation maps from the preceding LME analysis, we carried out the following two additional analyses. In both, the goal is to determine whether brain activity associated with concrete and abstract word modulation relates to separable experiential domains that roughly map onto the aforementioned sensory-motor vs internal/interoceptive/affective/linguistic distinction, respectively.

Meta-analytic descriptions

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The resulting coordinates of the center of mass of each cluster associated with modulation of concreteness and abstractness were inputed into Neurosynth (https://neurosynth.org/, Yarkoni et al., 2011), an online tool that includes activation maps of 14,371 neuroscientific studies (accessed April, 2023). Neurosynth automatically mines all words in titles and abstracts of these articles and performs a two-way ANOVA, testing for the presence of a non-zero association between terms reporting activation that overlaps with the input location. We scraped all terms with z scores above 3.12 (p<0.001) (excluding those related to specific brain regions and nondescript terms related to methods, tasks, or results) and repeated this procedure for each concrete and abstract cluster to determine functionally associated terms. We then tested whether any of these terms were more important for concrete or abstract words across clusters using a Kruskal-Wallis test. We did not correct for multiple comparisons, as this analysis was exploratory in nature and we did not have a prediction about how many terms we would end up with.

Peak and valley analysis

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The meta-analytic approach can only provide relatively general functional descriptions of concrete and abstract words as it is based only on high frequency terms in published titles and abstracts. To provide more precise functional specificity, we used a variant of the ‘reverse correlation’ method (Hasson et al., 2004), called the ‘Peaks and Valleys Analysis’ (Hasson et al., 2008; Skipper et al., 2009). For each participant, this analysis averaged the time series of voxels within clusters of modulated activity associated with concreteness and abstractness and relates this directly to features of the perceived stimulus. The approach assumes that, if a brain region encodes certain features, e.g. sensorimotor features, valence, or arousal, then activity will rise (creating peaks) in that region when the feature is present in the stimulus and fall (resulting in valleys) when it is absent.

We first extracted the averaged time series for each activation cluster for the concrete and abstract modulations across voxels using 3dMerge. Next, we determined peaks and valleys by calculating the discrete difference ‘Δ’ along the time series ‘x’ for each value ‘i’ using the ‘Numpy’ Python package (Harris et al., 2020; Figure 7, (1)), where Δx[i]=x[i+1] - x[i]. Given that the canonical model of the hemodynamic response function is said to peak at around 6 s after stimulus onset for stimuli of our length, we extracted the words that were mentioned at each peak and valley in a given cluster’s time series with a 5- and 6 s lag (Figure 7, (2)). We then used the Lancaster sensorimotor norms (Lynott et al., 2020) and norms for valence and arousal (Warriner et al., 2013) to determine a 13-dimensional experience-based representation for each word (Figure 7, (3)), which included the dimensions: ‘Auditory,’ ‘Gustatory,’ ‘Haptic,’ ‘Interoception,’ ‘Visual,’ ‘Hand_Arm,’ ‘Foot_Leg,’ ‘Torso,’ ‘Mouth,’ ‘Head,’ ‘Olfactory,’ ‘Valence,’ and ‘Arousal.’

Figure 7

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Specifically, for concrete clusters, we expected significantly more sensory-motor features (i.e. ‘Foot_Leg,’ ‘Hand_Arm,’ ‘Haptic,’ ‘Visual,’ and ‘Torso’ in the corpora) to be associated with peaks rather than valleys in the time series compared to abstract clusters. Conversely, we expected significantly more experiential features related to internal/interoceptive/affective processing (i.e. ‘Interoception,’ ‘Valence,’ and ‘Arousal’ in the corpora) to be associated with peaks compared to valleys for abstract relative to concrete clusters. It was not clear to us whether the dimensions (‘Auditory,’ ‘Head,’ and ‘Gustatory’) were more related to internal/interoceptive/affective or sensory-motor processing. Therefore, we made no predictions for those.

For each of these dimensions, we created two categorical arrays, one for peaks and one for valleys, noting down 0 if the word mentioned at a peak or valley was not highly rated on the dimension and 1 if it was rated highly. This was defined as a deviation of at least one standard deviation from the mean. Given the distributional nature of this data, we then conducted a Kruskal-Wallis test between these arrays to determine whether a given experiential dimension occurred significantly more with peaks than valleys in the averaged time series of a cluster (Figure 7, (4)). We repeated this procedure for a 4 s, 5 s, and a 6 s time series lag and conducted a cosine-similarity test between each result using the ‘Sklearn’ package in Python Pedregosa et al., 2011 in order to determine if they were significantly different. The results for the 5 s and 6 s lag converge but not for 4 s. This was expected, because the delay of the HRF is somewhere between 5 and 6 s. In the main results, we randomly decided between presenting the 5 s and 6 s lag. The 5 s lag is now displayed in Figure 3—figure supplement 1, the 4 s lag in Figure 3—figure supplement 2.

Conceptual processing in context

The previous analyses tested whether, when activation is considered across contexts, concrete and abstract words are processed in separable brain regions encoding different types of experiential information. Here, we test if these response profiles dynamically change depending on the objects present in the viewing context. We predict that when abstract concepts are situated in highly related contexts (e.g. the word ‘love’ is processed while watching two people kissing), they engage neurobiological regions that are usually involved in processing concrete concepts and are related to processing of external visual information. Conversely, when concrete concepts are displaced from the visual context (e.g. processing the word ‘apple’ while watching the interior of a house), we predict them to engage more abstract-like regions in the brain that are related to processing of internal/interoceptive/affective information. Note that we chose to do the analysis in two stages (first across context, then within context) because only a subset of the 440 words used in our analysis across context for concrete and abstract words are related to the objects present in the scene in a way that situates them in visual context (see below). We wanted to have as much power as possible for the first deconvolution/LME to look beyond the effects of context.

Estimating contextual situatedness

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To test our predictions, we estimated a measure of contextual situatedness for each concrete and abstract word included in the first analysis. To that end, we utilized two pre-trained visual recognition models, Faster R-CNN (He et al., 2015) and OmniSource (Duan et al., 2020), to extract object features using computer vision toolboxes (Mkrtychian et al., 2019), respectively. For each prediction frame (about every four frames, i.e. 4*0.04=0.16 s), the object recognition model generated a list of detected object labels and kept those that had a prediction confidence of 90% or greater (Figure 8, 1). Then, we excluded all objects that were recognized at least three standard deviations more often by the model compared to the mean recognition rate of objects (which was 682 appearances), because they would bias our measure of situatedness. These labels were ‘person’ (17,856 appearances per movie on average), ‘chair’ (9718 appearances per movie on average), and ‘tie’ (8123 appearances per movie on average). After exclusion of these labels, the final object features were represented as the average of the vectorized object labels using GloVe (Pennington et al., 2014; Figure 8, 2), which represents the meaning of each label via global co-occurrence statistics.

Figure 8

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We then estimated a representation of a 2 s (or 60 frames) context window, which should capture the immediate visual context leading up to each concrete and abstract word. We extracted all the labels of objects visually present in each frame within that window. Finally, we calculated the cosine similarity between the vector representation of each word and its context average, using the ‘Sklearn’ package in Python (Pedregosa et al., 2011), to estimate contextual situatedness for each concrete and abstract word (a value c between 0 and 1), Figure 8, (3). After separating into situated (c>0.6) and displaced words (c<0.4), we were left with (on average for each movie) 164 abstract situated words, 201 abstract displaced words, 215 concrete situated words, and 172 concrete displaced words. Given that, as concerns observations, the abstract situated condition was the limiting condition, we randomly selected words from the abstract displaced, concrete situated, and concrete displaced conditions to have an equal number of 164 words in each condition (on average per movie).

Though we use visual nuisance regressors, we note that there may be additional confounding visual information when estimating contextual situatedness: high situatedness may correlate positively with the number of objects present and, therefore, ‘naturally’ engage visual processing more for abstract situated concepts. To alleviate this concern, we counted the number of objects in the abstract situated (8315 objects across movies) and abstract displaced (7443 across movies) conditions. The difference between the two (872) is not statistically significant (H(2)=4.1, p<0.09).

All code and data are publicly available.

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Author details

1. Viktor Nikolaus Kewenig

   Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Software, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   ucjuvnk@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0009-5912-0676

2. Gabriella Vigliocco

   Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

3. Jeremy I Skipper

   Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5503-764X

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