Objective: Current approaches of routine outcome monitoring (session‐by‐session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions.Method: We tested if high‐frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high‐ versus low‐frequency time series, if ‘not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis (N = 88) used the Therapy Process Questionnaire (TPQ) for daily self‐assessments and the ICD‐10‐based Symptom Rating (ISR) for outcome evaluation.Results: High‐frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome.Conclusion: It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria.

Neurocognitive science represents the modern approach to integrating the subdisciplines aimed at a scientific study of the brain-mind system. This relatively new discipline recognizes, implicitly or explicitly, that this is a complex system whose states and processes are determined by multiple bio-psycho-social variables and order parameters. In a generic perspective, all neurocognitive science is complex, as it is multidisciplinary, but in some studies, complexity has become a more defined scientific paradigm using its own specific empirical and theoretical tools. Some neuroscientists consider complexity science as a specific and formalized paradigm. Between their contributions, the author will try to highlight some current promising paths and new frontiers for neuroscience. In this perspective, he will mostly focus on those contributions directly related to clinical perspectives. This is the reason why some seminal contributions more focused on physiological functioning might not be mentioned.

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. “Static analyses” (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the “dynamic analyses”, based on five coarse-grained descriptors related to variability, the degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature enough to shift from a metaphorical to a fully quantitative status.

We introduce our methodological study with a short review of the main literature on embodied language, including some recent studies in neuroscience. We investigated this component of natural language using Recurrence Quantification Analysis (RQA). RQA is a relatively new statistical methodology, particularly effective in complex systems. RQA provided a reliable quantitative description of recurrences in text sequences at the orthographic level. In order to provide examples of the potential impact of this methodology, we used RQA to measure structural coupling and synchronization in natural and clinical verbal interactions. Results show the efficacy of this methodology and possible implications.

We examine the theoretical implications of empirical studies developed over recent years. These experiments have explored the biosemiotic nature of communication streams from emotional neuroscience and embodied mind perspectives. Information combinatorics analysis enabled a deeper understanding of the coupling and decoupling dynamics of biosemiotics streams. We investigated intraindividual and interpersonal relations as coevolution dynamics of hybrid couplings, synchronizations, and desynchronizations. Cluster analysis and Markov chains produced evidence of chimaera states and phase transitions. A probabilistic and nondeterministic approach clarified the properties of these hybrid dynamics. Thus, multidimensional theoretical models can represent the hybrid nature of human interactions.