Semantic processing refers to the processing of meanings. It is central to many cognitive functions, such as language, reasoning, and problem-solving. Many neuroimaging studies have investigated brain activation during semantic processing. An important finding is that the brain areas activated during semantic processing largely overlap with the default mode network (DMN), a brain network that is characterized by high activity when individuals are left to think to themselves undisturbedly (Binder et al. 2009; Raichle et al. 2001; Smallwood et al. 2021; Wang et al. 2021). To explain the overlap between the DMN and the semantic network, Binder et al. (1999) proposed that semantic processing constitutes a large component of the cognitive activity occurring during the resting state. They examined this hypothesis by comparing brain activity during a resting state, a perceptual task, and a semantic retrieval task in an fMRI experiment. The DMN showed higher activation during the resting state than during the perceptual task but equal activation during the resting and semantic conditions, which is consistent with their hypothesis. Binder and Desai (2011) further proposed a neurobiological model of semantic processing, which assumes that the DMN contains several hub regions that combine semantic knowledge distributed in the sensory, motor, and emotional systems and supports general semantic processes. We refer to this view as the general semantic view of the DMN.
However, some later studies have indicated that the observed semantic effect in the DMN could be confounded by task difficulty. In an fMRI study, Humphreys et al. (2015) investigated brain activation in several semantic and non-semantic tasks. They found that the core regions of the DMN, including the ventral angular gyrus (AG), posterior cingulate (PC), and medial frontal cortex, showed a task-induced deactivation effect (rest > task) in all tasks. Special attention was paid to the ventral AG, which has been proposed as a hub region of the semantic network. Humphreys et al. (2015) found a negative correlation between ventral AG activation and reaction time (RT), indicating that activation of the ventral AG is sensitive to task difficulty. In a later study, Humphreys and Lambon Ralph (2017) further examined the difficulty effect in the DMN using a semantic task and a visuospatial task. They found a difficulty-induced deactivation effect (difficult < easy) in the ventral AG, PC, and medial frontal cortex in both tasks but found no semantic effect in these regions. Therefore, they proposed that the ventral AG is an automatic bottom-up domain-general buffer of active information, whose function is suppressed when tasks demand executive inputs from the frontoparietal network (Humphreys et al. 2021). More striking evidence has been reported by Graves et al. (2017). They found that the typical greater brain activation pattern to words than non-words during lexical decision, mainly overlapped with the DMN and often explained as the lexical semantic effect, can be flipped by using low-frequency words that are more difficult to process than those typically used. Therefore, the activation of the DMN during lexical decision mainly reflects the difficulty effect rather than the semantic effect. Taken together, these observations lead to the non-semantic view of the DMN (Humphreys et al. 2015; 2021; Jackson et al. 2019), which holds that the semantic effects observed in most DMN regions are confounded by task difficulty and do not reflect semantic processing per se.
Mattheiss et al. (2018) investigated the semantic and non-semantic effects in the DMN using an fMRI experiment and proposed a third view for the DMN, which we refer to as the multifunction view of the DMN. In their experiment, Mattheiss et al. (2018) asked participants to perform a lexical decision task, in which the imageability of the word stimuli was manipulated. They replicated the finding of Graves et al. (2017) that the DMN showed a stronger activation to non-words than words. Using a multivariate analysis, they further found that most of the areas that showed a stronger activation to non-words than words in the study by Graves et al. (2017) contained sufficient information to distinguish high- from low-imageability words. Because the RT and accuracy were matched between the high- and low-imageability conditions, Mattheiss et al. (2018) proposed that the classification of high- and low-imageability words relied on semantic representation. Combining the difficulty effect reflected by the univariate results and the semantic effect reflected by the multivariate results, they concluded that the same areas in the DMN can support both semantic and non-semantic functions.
The last view regarding the function of the DMN in semantic processes holds that the DMN consists of multiple subnetworks that support different aspects of semantic processes separately (Huth et al. 2016; Lin et al. 2020). We refer to this view as the multisystem view of the DMN. Using a data-driven approach, Huth et al. (2016) modeled the impact of multiple semantic features on participants’ brain activation while listening to narrative stories. They identified a semantic network whose activation could be reliably predicted by semantic features while listening to new stories. This semantic network consists mostly of areas of the DMN. Further analysis showed that most areas within the semantic network represent information about specific semantic domains or knowledge types, forming an intricate semantic atlas. Several distinct semantic areas were found in and around the AG, with some areas being selective for social concepts, while others were selective for numeric, visual, or tactile concepts. Similarly, Tamir et al. (2016) found functional dissociation within the DMN when reading fiction. Using fMRI, they found that the dorsal medial prefrontal cortex (DMPFC) subnetwork of the DMN responded preferentially to passages with social content, while the medial temporal lobe (MTL) subnetwork of the DMN responded preferentially to vivid passages. Two latter fMRI studies have demonstrated functional dissociation within the DMN using simpler and more controlled experimental tasks. Lin et al. (2018a) investigated the brain activations evoked by social and sensory-motor semantic information by manipulating the sociality and imageability of words in a word comprehension task. They found functional dissociation between DMN regions, with some regions being sensitive to the social meaning of words and some other regions being sensitive to the sensory-motor meaning of words. Later, using a semantic plausibility judgment task, Lin et al. (2020) further found that another set of regions, which also overlaps with the DMN, is sensitive to the semantic plausibility of phrases. Again, the AG and its surrounding areas showed complex functional dissociation, with the anterior dorsal part being sensitive to semantic plausibility, the anterior ventral part being sensitive to social semantic processing, the posterior part being sensitive to sensory-motor semantic processing, and the middle ventral part being sensitive to both social and sensory-motor semantic processing (Lin et al. 2020). In summary, these studies have collectively indicated that different parts of the DMN are sensitive to different aspects of semantic processing. Therefore, even if task difficulty can explain some of the previously observed semantic effects, it alone cannot explain all semantic effects that are associated with different parts of the DMN.
The relationship between semantic and non-semantic effects in the DMN remains unclear in several aspects. There is compelling evidence that the DMN contains multiple subnetworks that are sensitive to different aspects of semantic processing; however, it remains unclear whether and how each of the semantic subnetworks in the DMN is sensitive to non-semantic factors, such as task difficulty. It is also unclear whether specific types of semantic processing can selectively determine the polarity of the task effect (i.e., task-induced activation/deactivation) in each of these semantic subnetworks, which has been viewed as an important indicator of functional selectivity for semantic processing (Humphreys et al. 2015; 2021; Jackson et al. 2019).
The relationship between two frequently considered non-semantic effects in the DMN, that is, difficulty-induced deactivation (difficult < easy) and task-induced deactivation (task < rest), is also unclear. These two effects together formed the main evidence for the non-semantic view of the DMN; especially, in the ventral AG, they have both been interpreted according to automatic bottom-up buffering processes (Humphreys et al. 2015; 2021). However, although task-induced deactivation in the DMN has been robustly found across many tasks (Humphreys et al. 2015), it remains unclear whether difficulty-induced deactivation in the DMN is stable across multiple tasks. Using five fMRI experiments with 252 participants in total, Yarkoni et al. (2009) investigated the relationship between trial-by-trial differences in the RT and brain activation. A positive correlation between the RT and activation being consistent across experiments was observed in extensive brain regions in the task-positive network (TPN; Fox et al., 2005); however, no gray matter region showed a consistent negative correlation between the RT and activation across experiments. In addition, a recent study indicated that the two types of deactivation may have different distributions in the DMN. Using an fMRI experiment, Meyer and Collier (2020) investigated the effect of difficulty in social and non-social working memory tasks. The difficulty-induced deactivation effect was found only in the non-social working memory task and only in the DMPFC subnetwork of the DMN. Regarding the task effect, they found that the DMPFC subnetwork showed task-induced activation rather than deactivation, while the MTL and core subnetworks showed task-induced deactivation.
One way to clarify the relationships between the multiple semantic effects and non-semantic effects in the DMN is to investigate the semantic processes with distinctive neural correlates separately. Therefore, our study focused on social semantic processing, that is, the processing of meanings about the interaction or interrelationships between individuals. Brain activation associated with social semantic processing has been consistently found in a subset of DMN areas, using univariate analyses (Zhang et al. 2021; Arioli et al. 2021; Binney et al. 2016; Lin et al. 2015; 2018a; 2018b; 2019; 2020; Zahn et al. 2007) and multivariate analyses (Thornton and Mitchell 2018). These areas include the bilateral anterior temporal lobe (ATL), temporoparietal junction (TPJ; overlapping with the ventral AG), DMPFC, and PC/precuneus, which are collectively referred to as the social semantic network (Lin et al. 2020; Zhang et al. 2021). We aimed to investigate the following three questions: First, can the social semantic effect and the difficulty effect collocate in the same areas of the DMN? Second, is the polarity of the task effect in the social semantic network selectively determined by social semantic processing? Third, do difficulty-induced deactivation and task-induced deactivation share the same neural correlates during social semantic processing?
To answer these questions, we conducted an fMRI experiment in which social semantic processing and task difficulty were manipulated. The neural correlates of the social semantic effect, difficulty effect (difficulty-induced activation and deactivation), and task effect (task-induced activation and deactivation) were examined together in this experiment. In addition, to examine the relationship between social semantic processing and the polarity of the task effect across multiple experimental tasks and stimuli, we also examined the results of five previous studies of social semantic processing (Lin et al. 2018a; 2018b; 2019; 2020; Zhang et al. 2021).