Mutual understanding and problems in it are a very complex phenomenon which can and should be tackled by methods coming from different research traditions. In fact, in order to have a full picture of it, we need a phenomenon-driven approach which means open-minded use of various methods. We invite you to collaborate with us and make your own suggestion to this section.
Experimental research on understanding

Conditions of understanding using experimental methods are being examined.  Neuroscientific and psychophysiological research techniques, combined with measurements of task performance speed and accuracy or qualitative interaction analysis, enable exploration of the intertwinement of understanding as a social process and underlying bio-psychological processes. 

In these studies, it was found that task-irrelevant speech during reading sentences and task-irrelevant text during listening to sentences decrease accuracy in classifying attended sentences as logical or illogical (Moisala et al., 2015). Functional magnetic resonance imaging (fMRI) showed that this deterioration in task performance is associated with enhanced activity in the same prefrontal areas that show enhanced activity in a dual-task condition where participants were instructed to attend simultaneously to concurrent written and spoken sentences (see also Moisala et al., 2016). This suggests that also distracting information during semantic processing forces us to dual task, that is, to actively suppress processing of irrelevant sentences while attempting to comprehend the attended sentences. In a new project, fMRI and electro- and magnetoencephalography (EEG and MEG) will be used to study, how increasing acoustic quality (speech comprehensibility), increasing amount of visual-speech (facial-movement) information and semantic predictability in speech affect brain activity and facilitate selective listening in noisy situations, that is, at the presence of irrelevant speech.

Experimental methods can also elucidate understanding of emotions in social interaction. A study combining conversation analysis with psychophysiological measurements has shown that a co-interactant’s ambivalence—i.e. emotional expression where positive and negative stances are mixed—invokes physiological stress in an interaction participant (Voutilainen et al 2014). The physiological stress suggests that ambivalence is hard to understand. On the other hand, the experience of being understood reduces the physiological stress in interaction: when stories are told in social interaction, and the story recipient displays affiliation verbally and non-verbally, the storyteller’s physiological arousal diminishes, while the story recipient’s level of arousal gets higher (Peräkylä et al. 2015). At the moment, the scope of studies of this kind is expanding to include ‘a-typical’ populations, such as persons with autism spectrum disorders. Furthermore, the paradigm is being carried over to naturally occurring psychotherapy sessions. The aim is examine the physiological parallels of understanding in this setting where understanding and being understood are at the core of the professional action.


Moisala, M., Salmela, V., Salo, E., Carlson, S., Vuontela, V., Salonen, O. & Alho, K. Brain activity during divided and selective attention to auditory and visual sentence comprehension tasks. Frontiers in Human Neuroscience, 2015, 9, 86.

Moisala, M., Salmela, V., Hietajärvi, L., Salo, E., Carlson, S., Salonen, O., Lonka, K., Hakkarainen, K., Salmela-Aro, K. & Alho, K. Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage, 2016, 134, 113-121.

Peräkylä, A., Henttonen, P., Voutilainen, L., Kahri, M., Stevanovic, M., Sams, M. & Ravaja, N. (2015) Sharing the Emotional Load: Recipient Affiliation Calms Down the Storyteller. Social Psychology Quarterly, 2015, 78(4):301-323. 

Voutilainen, L., Henttonen, P., Kahri, M., Kivioja, M., Ravaja, N:, Sams, M., & Peräkylä, A. Affective stance, ambivalence and psychophysiology in conversational storytelling. Journal of Pragmatics, 2014, 68, July 2014:1-24. 

Digital tools for mutual understanding

Computational methods and tools can be used to study complex phenomena related to communication. We explore two main tracks. First, we analyze large linguistic corpora in order to formulate models of the contents using statistical machine learning methods. Second, we build computational models of dynamic adaptive phenomena related to communication. This can address aspects of cognitive and social phenomena at different levels of detail.

Large text collections can be used to construct models of lexical semantics in an automatic fashion. This makes it possible to build models of large number of lexemes. Moreover, we pay attention to the contextual interpretation, the pragmatic variation around the prototypical semantics. This enables, at least in principle, development of digital tools that can help in communication. One important application area of this work in statistical language technology is digital humanities. Digital humanities includes many areas in which questions related to understanding between people is of crucial important.

Our central research interest is to consider challenges related to crossing language borders. Machine translation is a widely established research area. Our interest is to evaluate the success of translation, and how translation support crossing cultural borders in addition to linguistic borders.  

Our specific research interest is to model and analyze meaning variation and meaning negotiations. We have developed a method called Grounded Intersubjective Concept Analysis (GICA) that can be used to analyze quantitatively the differences in how people express themselves in different contexts.

Multi-criteria decision analysis as a tool to reach mutual understanding

Public decision making problems are typically complex, large, multidisciplinary, and ill-structured, which makes it difficult for experts and decision makers to make informed decisions. The decisions affect typically various stakeholders, who can have different interests, and prefer different tradeoffs among environmental, social, cultural, and economic impacts. This knowledge has to be systematically incorporated into decision making, as decisions cannot be legitimized without considering public values and preferences.

Multi-criteria decision analysis (MCDA) is a general term for systematic approaches supporting the analysis of multiple alternatives in complex problems involving multiple criteria. The basic idea is that first the different elements of the problem (objectives, criteria, alternatives) are identified and analyzed one by one with an aim to get a view of the various characteristics of the problem (the divergent phase). Next, these are structured into a model which combines objective measurement data about the criteria-wise performances of the alternatives with subjective value judgments about the trade-offs between the criteria. As a result, the model produces commensurate performance scores of the alternatives, which reflect the various views of the participants (the convergent phase). The aim to get a comprehensive overall view of the problem that takes all the different aspects and interactions between these into account.

For supporting group collaboration, there are two main approaches for applying MCDA to deal with preferences of multiple stakeholders, i.e. the aggregative one and the consensus seeking or comparative one. The aggregative group decision support approach uses some techniques to aggregate individual group preferences into the common preferences of the whole group. In the comparative approach, the preferences of each stakeholder are elicited in a separate model. By analyzing these models, we can enhance understanding the most relevant issues of the problem, as well as the pros and cons of various alternatives from the viewpoint of different stakeholders. Especially the comparative approach can be applied to support mutual understanding, as the approach also supports process of learning and discovery, and help finding alternatives that best meet the needs of all the stakeholder groups.

The application areas of MCDA cover all types of decisions, but in recent years, it has increasingly been applied in especially environmental planning. The main reasons for this are that the impacts of environmental problems are typically very complex, and the problems concern multiple stakeholders having different objectives. In this respect, MCDA’s way to support learning and deliberation through a systematic process of analyzing various stakeholders’ views can increase stakeholders’ awareness and participation possibilities, consequently their commitment to the process.


Belton, V., Stewart, T.J. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, Boston.

Gregory, R., Failing, L., Harstone, M., Long, G., McDaniels, T., Ohlson, D. (2012). Structured Decision Making: A Practical Guide to Environmental Management Choices. Wiley-Blackwell, Chichester, UK.

Keisler, J., Linkov, I. (2014). Environment models and decisions. Environment Systems and Decisions, 34, 369–372.

Marttunen M., Mustajoki J., Dufva M., Karjalainen T.P. (2015). How to design and realize participation of stakeholders in MCDA processes? A framework for selecting an appropriate approach. EURO Journal on Decision Processes, 3(1), 187–214.