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Edited by: Nuno Barbosa Rocha, Politécnico do Porto, Portugal

Reviewed by: Stephan Hau, Stockholm University, Sweden; Miriam Henkel, University of Kassel, Germany; Stefano Carta, University of Cagliari, Italy; Tamara Fischmann, International Psychoanalytic University Berlin, Germany; Christian Sell, University of Kassel, Germany

This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Psychotherapy could be interpreted as a self-organizing process which reveals discontinuous pattern transitions (so-called phase transitions). Whereas this was shown in the conscious process of awake patients by different measures and at different time scales, dreams came very seldom into the focus of investigation. The present work tests the hypothesis that, by dreaming, the patient gets progressively more access to affective-laden (i.e., emotionally charged) unconscious dimensions. Furthermore, the study investigates if, over the course of psychotherapy, a discontinuous phase transition occurs in the patient’s capacity to get in contact with those unconscious dimensions.

A series of 95 dream narratives reported during a psychoanalytic psychotherapy of a female patient (published as the “dreams of Amalie X”) was used for analysis. An automated text analysis procedure based on multiple correspondence analysis was applied to the textual corpus of the dreams, highlighting a 10-factor structure. The factors, interpreted as affective-laden unconscious meaning dimensions, were adopted to define a 10-dimensional phase space, in which the ability of a dream to be associated with one or more local factors representing complex affective-laden meanings is measured by the Euclidean distance (ED) from the origin of this hyperspace. The obtained ED time series has been fitted by an autoregressive integrated moving average (ARIMA) model and by non linear methods like dynamic complexity, recurrence plot, and time frequency distribution. Change point analysis was applied to these non linear methods.

The results show an increased frequency and intensity of dreams to get access to affective-laden meanings. Non linear methods identified a phase transition-like jump of the ED dynamics onto a higher complexity level of the dreaming process, suggesting a non linear process in the patient’s capacity to get in contact with unconscious dimensions.

The study corroborates the hypothesis that, by dreaming, the patient gets progressively more access to affective-laden meaning intended as unconscious dimensions. The trajectory of this process has been reproduced by an ARIMA model, and beyond this, non linear methods of time series analysis allowed the identification of a phase transition in the unconscious process of the psychoanalytic therapy under investigation.

Process research is an essential source of knowledge on how psychotherapy works. Especially in psychoanalysis and in psychodynamic psychotherapies, single-case reports and case studies on change dynamics have a long tradition of more than one century, founded by Sigmund Freud. There is a great variety of case studies, from qualitative reports to sophisticated time series analysis studies. Reviews on the methodology of single-case research in psychodynamic psychotherapy were published by

Usually, process research using high-frequency measures is based on transcripts, video tapes, socio-physiological measures, continuous self-reports, or electronic diaries. In most of the studies, the focus is on intra-session dynamics, with advanced developments in inter-session dynamics by using ecological momentary assessment or internet-based real-time monitoring (

One of the pioneering steps to relate changes of dream patterns to therapeutic change and personality development was done by

In summary, literature suggests that dreams are able to mirror clinical progress and personality development. Dreams prepare and pre-develop new ways of organizing life experiences and new ways of the patient’s mental functioning. In accordance with

Dreams create or construct meaning in a way which is not restricted to the rules of conscious everyday thinking (secondary processes). The mental processes of dreaming are usually more affectively charged, and their functioning lies beyond the restrictions of time, space, causality, physical laws, and logic (primary processes). The unconscious is the mind’s basic way of functioning, but it is also related to rational thinking. This relationship is framed by the concept of

Psychotherapy is an intersubjective process (

The emergence of new meanings is a dialogical and contextual process which creates new narratives of dreams in the clinical setting. Specifically, as highlighted by the two-stage semiotic model (TSSM) (

In line with synergetics (

According to this framework, we hypothesize that through the course of a successful psychotherapy, the dream narratives increase the specificity of their affective charge as a marker of the patient’s capacity to get access to increasingly deep affective-laden dimensions of sensemaking (HP1). Moreover, due to the field dynamics of sensemaking underpinning the clinical exchange, the increased affective charge of the dream narratives should follow a non linear trend (HP2).

Finally, we expect that the self-organizing process of the psychotherapy will show an increase in the complexity of dream dynamics within the phase space of the affective-laden meaning dimension (HP3). The dream dynamics should realize a discontinuous (non-stationary) evolution (phase transition), and a sudden change in complexity should be visible in different complexity measures.

Ninety-five dream narratives reported by a female patient during 517 psychoanalytic sessions (

The female patient known under the pseudonym “Amalie X” was diagnosed with dysthymia (F34.1 in the ICD-10, which corresponds to chronic depression) and with disorder of sexual identity (F64). Since her adolescence, she has suffered from chronic hirsutism—an excessive hair growth on parts of the body where hair is normally absent or minimal. The hirsutism caused psychological distress and social difficulties, avoidance of social situations, and symptoms of anxiety and depression. On the other hand, distress intensified the abnormal hair growth, and this was the reason that she self-referred to psychotherapy in addition to endocrine therapy. The patient suffered from a reduced self-esteem and a distorted body image and challenged her sexual identity as a woman. In consequence, she avoided any closer, especially sexual, contacts with men. The details of the psychoanalytic therapy which was realized by a session frequency of about three times per week are reported in

We performed, by means of T-Lab software (

The text analysis procedure was performed according to the following steps. Firstly, the textual corpus of dream narratives was split into units of analysis, called elementary context units (ECUs). Each ECU corresponds to a dream narrative. Secondly, the lexical forms present in the ECUs have been identified and categorized according to the “lemma” they belong to. A lemma is the citation form (namely, the headword) used in a language dictionary: for example, word forms such as “go,” “goes,” “going,” and “went” have “go” as their lemma; “child” and “children” have “child” as their lemma. Thirdly, lemmas were ranked according to their frequency. The 5% highest-frequency lemmas were omitted by the fact that the higher the frequency of a lemma, the less it contributes to the discrimination among the ECUs. High-frequency lemmas (e.g., words like “and,” “to,” and “of”) are not specific to any ECU. Then the lemmas were ranked according to their frequency, and the 10% most frequent lemmas were selected in order to obtain a digital matrix of the corpus, having as rows the ECU (i.e., the dream narratives), as columns the lemmas, and in the cell x_{ij}_{ij}

Based on the factors, a multidimensional phase space was created in which each dream is represented in terms of a vector whose components are described in terms of the squared cosine, representing the correlation between a dream and a factor. The higher the squared cosine of a dream for a specific factor, the higher the fitting of a dream with the respective affective meaning (factor).

For each dream vector, the Euclidean distance (ED) from the origin of the factor space has been computed. The higher the ED value, the greater the

In order to test the hypothesis of an increased frequency of getting access to affective-laden meanings during psychotherapy (HP1), a logistic binary regression model was calculated with “time” (the sequence of dreams from 1 to 95) as the independent variable and the categorization of dreams as lying inside or outside of the centroid as the dependent variable.

In order to test the extent by which affective-laden meanings are reached (HP2), a non linear model of the process has been tested. Specifically, we adopted an autoregressive integrated moving average [ARIMA_{(}_{p}_{d}_{q}_{)}] model^{1}. ARIMA_{(}_{p}_{d}_{q}_{)} is a data-based modeling procedure which derives stepwise predictions and formalizes the variation of a time series as a function of one or more predictors (e.g., the time series itself) and stochastic noise (_{(}_{p}_{d}_{q}_{)} models, _{(1},_{1},_{1)} model (see section “Results”) with “time” (the sequence of dreams from 1 to 95) as an independent variable and the EDs as a dependent variable.

According to the third hypothesis, the EDs of each dream were subjected to three procedures of complexity analysis: DC, recurrence plot (RP), and time frequency distribution (TFD).

DC (_{min}, _{max}]. The fluctuation is sensitive to the amplitudes and frequencies of a time signal, and the distribution scans the scattering of values over the range of possible values. In order to identify non-stationarity, DC is calculated within a moving window running over the time series (window width: 7, overlap: step 1). The window width was chosen as seven because this corresponds to other applications of the DC method on psychotherapy processes and because with this small width, we do not lose too many time points. On the other hand, seven measurement points is sufficient to calculate the DC.

RPs identify recurrent patterns of time series in a time × time diagram (

TFD is a method to calculate and visualize the frequency of a signal (time series) as it changes with time (

In a last step of testing the non linear phase transition hypothesis, the ED time series, the DC time series, the RP, and the TFD of the ED time series were subjected to a change point analysis (CPA, _{1} and _{2} such that _{1}) + _{2}) < _{1} and _{2} if the sum of the variance of the statistical property of interest, e.g., the mean of the segments, is smaller than the variance of this property of the whole time series; otherwise, no change point is detected. In our application, we used CPA for the detection of changing variance in the ED time series. The analysis was done with the function

The procedure of multiple correspondence analysis (MCA) applied to the dreams × lemmas matrix identified 10 factors which explained 27.84% of the variance of the matrix. In light of the high dispersion of the data in the matrix under analysis, this represents a high percentage of explained variance (see

As highlighted in the section “Materials and Methods,” the affective charge of dream narratives was calculated in terms of ED. The ED from the origin of the phase space and each dream vector was computed, and according to the overall mean of the EDs (arithmetic average = 0.536), the centroid was defined. Sixty-six dreams (70.2%) were proven to lie within the centroid and 28 dreams (29.8%) outside of it.

In order to estimate the probability that the frequency of affective-charged dream narratives (high vs low affective charge according to the calculated centroid) increases a function of time, a binary logistic regression has been performed. The logistic binary regression model identified the significant role of “time” (the sequence of dreams from 1 to 95) as a predictor for the probability of dreams to exceed the centroid threshold, that is, to be more associated to affective-laden meanings (^{2} = 5.07,

Logistic regression model with “time” as a predictor of the probability of dreams to overcome the centroid threshold.

Time | 0.190 | 0.009 | 4.758 | 0.029 | 1.019 |

Constant | −1.829 | 0.524 | 12.195 | 0.000 | 0.161 |

HP2 was tested by means of an ARIMA model having the extent of the EDs as the dependent variable and time as the independent variable. On the basis of a preliminary estimation of the autocorrelation function (ACF) and the partial ACF (PACF) applied to the ED time series (_{(}_{p}_{d}_{q}_{)} model:

The ARIMA_{(1},_{1},_{1)} model fitted to the data (^{2}) of.594, the Ljung–Box test (

Time series of the Euclidean distances (EDs) of each dream from the origin in the factor phase space (black line) and the curve of the fitted ARIMA_{(1,1,1)} model (black dotted line) within a confidence band (dotted gray lines: upper and lower limits of the 99% confidence interval).

The parameters of the ARIMA_{(1},_{1},_{1)} model AR1 and MA1 were statistically significant (_{(1},_{1},_{1)} model confirmed that the data were fully modeled and that “time” predicts the extent of ED.

The parameters of the ARIMA_{(1,1,1)} model.

Constant | 0.011 | 0.000 | 2.578 | 0.012 |

AR lag 1 | –0.240 | 0.106 | –2.272 | 0.025 |

Difference | 1 | – | – | – |

MA lag 1 | 0.995 | 0.176 | 5.654 | 0.000 |

_{(1,1,1)} model. The straight lines above and below the 0.0 line indicate the 95% confidence interval (CI). The correlation values (gray bars) lie within the 95% CI limits, which indicates that the errors of the residuals are white noise. This proves that the model is appropriate for prediction.

With reference to the ED time series of all dreams (

The sequence of 95 dream narratives which were reported during a psychoanalytic therapy of more than 500 sessions realized over a period of more than 4 years—the famous case of “Amalie X”—revealed interesting results. The present work assumes dreams as a context which sustains the re-elaboration of relational meanings regulating the patient’s internal and external worlds. According to this framework, the ACASM was applied to the dreams × lemmas matrix obtaining a 10-dimensional factor space. Each factor represents an unconscious dimension which is based on strongly associated (co-occurring) words in the dream narratives.

According to our first hypothesis, the results highlighted an increased access to affective-laden meanings during the course of the psychotherapy. Concerning the second hypothesis, the results showed that the affective polarization of the dream narratives has been proven to increase following a non linear trend.

Taken together, the results shed light on the nature of the psychoanalysis as an intersubjective process aimed to sustain the patient toward a higher ability to get in touch with affective-laden meanings (i.e., unconscious dimensions) regulating and organizing life experience.

The dream narratives increase their frequency (HP1) in reaching highly affective-laden meanings, and the affective charges of the dream narratives increase in intensity among time (HP2). Taken together, HP1 and HP2 support the view of the clinical process as a recursive dynamics enriching the patient’s ability to explore—increasingly deep—unconscious dimensions (i.e., affective-laden meanings) in order to promote their cognitive–affective elaboration.

The third hypothesis is corroborated by showing a sudden jump of complexity offering a deeper understanding of the clinical process in terms of a non linear phase transition. Taken together, the converging evidence of DC, RP, and TFD of the ED dynamics, around dream number 58, a sudden change in the variability and complexity of the clinical process has been highlighted. The method of CPA following the criterion of changing variance was used to identify the transition in the measures and in the ED time series itself.

Hypothesis 3 enriches the view of hypotheses 1 and 2 on the clinical process: synergetics states that during psychotherapeutic processes, order parameters emerge which enslave the mental states of the patient and therapist. In consequence, the narratives of the dreams, the experienced scenes of the dreams, and the self-related and interactive process of psychotherapy get synchronized.

The phase transition highlighted in the 58th dream narrative—corresponding to the 328th psychoanalytic session—has been highlighted as a clinical turning point by several authors: the chronically depressed female patient who always avoided any close relationship, especially sexual contacts with men, and suffered from a reduced self-esteem and a distorted body image actively started to get in contact with men and had her first erotic and sexual experiences. Additionally, the patient emancipated from her mother with whom she experienced a very close, dependent, and symbiotic relationship. At the time of the phase transition,

The open question is if the phase transition after dream number 58 is relevant for the personality development of the patient. How is the relationship between the content(s) of the dreams, the ongoing psychotherapy, the development of the personality, the symbols and the themes, and the inner process (i.e., the core conflictual themes) related to the phase transition? Is it possible to find this phase transition also in the transcripts of the sessions? These questions will be investigated in the next step of the analysis of Amelie’s dream reports according to the transcripts of the therapeutic sessions.

Taken together, the present results sustain the empirical effort of investigating the process of psychoanalysis by looking at the evolution of dreams. Nevertheless, some limitations that could direct future research efforts should be mentioned.

The study focuses exclusively on dreams without taking other reports or measures of the psychoanalytic process into account. In a next step, the transcripts of the therapeutic conversation should be analyzed in parallel to the dreams. For this case of “Amelie X,” the transcripts of 517 sessions are available. Secondly, in order to get a deeper understanding of therapeutic change processes, further data sources and parameters (e.g., self-assessments by electronic real-time monitoring devices, coding of session-by-session video tapes, and physiological measures during or in between sessions) should be used for a multilevel analysis of much more than one patient. Thirdly, the synergetic approach which was adopted here has a

Future research should take these limitations into consideration in order to relate qualitative changes of the dream dynamics to the therapeutic conversation and to the patient’s everyday life (see

The datasets generated for this study are available on request to the corresponding author.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In the following, we present an example to highlight the factors retrieved by MCA, their interpretation, their EDs, and their categorization as lying inside or outside the centroid. In this illustrative example, the analysis reveals two factors, and the factor interpretation is based on 20 lemmas characterizing the factors. This result is able to restore the conceptual and clinical value of the adopted measures.

Description of factor 1 and factor 2 concerning the first 20 lemmas retrieved by dream narrative analysis.

to see | 0.742 | root | 0.969 |

uncle | 0.681 | tree | 0.968 |

to find | 0.571 | to fall | 0.594 |

Home | 0.351 | to want | 0.593 |

to think | 0.251 | long | 0.593 |

Father | 0.229 | figure | 0.560 |

Image | 0.135 | relationship | 0.481 |

remember | 0.066 | down | 0.468 |

Dream | 0.063 | garden | 0.420 |

to fall | 0.048 | to define | 0.400 |

to bring out | 0.043 | speak | 0.353 |

atmosphere | 0.038 | things | 0.342 |

shoe | 0.037 | wait | 0.331 |

dining room | 0.033 | ground | 0.312 |

family | 0.031 | to wake up | 0.188 |

In

Squared cosine of each dream for the retrieved factors and calculated ED.

10 | 0.021 | 0.113 | 0.114 | Outside |

21 | 0.074 | 0.034 | 0.082 | Outside |

26 | 0.037 | 0.030 | 0.047 | Inside |

42 | 0.004 | 0.083 | 0.083 | Outside |

53 | 0.009 | 0.064 | 0.065 | Inside |

60 | 0.000 | 0.047 | 0.047 | Inside |

64 | 0.022 | 0.117 | 0.119 | Outside |

82 | 0.039 | 0.001 | 0.039 | Inside |

86 | 0.004 | 0.096 | 0.096 | Outside |

92 | 0.035 | 0.010 | 0.036 | Inside |

In

Representation of a sample of dreams related to the mean of the EDs (centroid). The

ARIMA, which stands for autoregressive integrated moving average, is a model for time series data that incorporates both autoregressive and moving average features, along with detrending of the data. The AR part—