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Analyzing psychotherapy process as intersubjective sensemaking: Anapproach based on discourse analysis and neural networksMariangela Nittia; Enrico Ciavolinob; Sergio Salvatorea; Alessandro Gennaroa

a Department of Pedogogy, Psychology, and Teaching, b Department of Philosophy and Social Sciences,University of Salento, Lecce, Italy

First published on: 27 July 2010

To cite this Article Nitti, Mariangela , Ciavolino, Enrico , Salvatore, Sergio and Gennaro, Alessandro(2010) 'Analyzingpsychotherapy process as intersubjective sensemaking: An approach based on discourse analysis and neural networks',Psychotherapy Research,, First published on: 27 July 2010 (iFirst)To link to this Article: DOI: 10.1080/10503301003641886URL: http://dx.doi.org/10.1080/10503301003641886

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Analyzing psychotherapy process as intersubjective sensemaking: Anapproach based on discourse analysis and neural networks

MARIANGELA NITTI1, ENRICO CIAVOLINO2, SERGIO SALVATORE1, &

ALESSANDRO GENNARO1

1Department of Pedogogy, Psychology, and Teaching & 2Department of Philosophy and Social Sciences, University of Salento,

Lecce, Italy

(Received 30 August 2009; revised 13 January 2010; accepted 19 January 2010)

AbstractThe authors propose a method for analyzing the psychotherapy process: discourse flow analysis (DFA). DFA is a techniquerepresenting the verbal interaction between therapist and patient as a discourse network, aimed at measuring the therapist�patient discourse ability to generate new meanings through time. DFA assumes that the main function of psychotherapy is toproduce semiotic novelty. DFA is applied to the verbatim transcript of the psychotherapy. It defines the main meaningsactive within the therapeutic discourse by means of the combined use of text analysis and statistical techniques.Subsequently, it represents the dynamic interconnections among these meanings in terms of a ‘‘discursive network.’’ Thedynamic and structural indexes of the discursive network have been shown to provide a valid representation of the patient�therapist communicative flow as well as an estimation of its clinical quality. Finally, a neural network is designed specificallyto identify patterns of functioning of the discursive network and to verify the clinical validity of these patterns in terms oftheir association with specific phases of the psychotherapy process. An application of the DFA to a case of psychotherapy isprovided to illustrate the method and the kinds of results it produces.

Keywords: process research; discourse flow analysis; competitive neural networks; health services quality

The present work is an attempt to improve quality

research into the therapeutic process by means of the

combination of statistical methodologies, enabling

clinical interaction to be analyzed.

Among several approaches to psychotherapeutic

research, a common way of considering the clinical

process is to look at it as an intersubjective dynamic

of co-construction of new meanings between thera-

pist and patient. Discourse flow analysis (DFA) was

designed by a group of researchers (Salvatore et al.,

2007, in press) to develop an empirical model of

the psychotherapeutic process as intersubjective

sensemaking. According to this model, meaning is

not statically held in signs (e.g., words, gestures).

Rather, it is the product of the way people use signs

in order to regulate their communication and activity

(Harre & Gillett, 1994). People do not choose a

ready-to-use meaning; rather, they co-construct it in

the sense that the interpretation of the signs is a

matter of situated agreement among the participants

of the conversation, performed through the dialogue

and grounded on socially shared generalized mean-

ings (Valsiner, 2007) working as taken-for-granted

assumptions (Rommetveit, 1992). In sum, sense-

making is intrinsically dialogical (Linell, 2009).1

The vision of the psychotherapy process as

intersubjective sensemaking is a meta-theoretical

framework crossing several clinical approaches: psy-

chodynamic (Storolow, Atwood, & Brandchaft,

1994; Hoffman, 1998) and cognitive (Dimaggio &

Semerari, 2004) as well as humanistic (Hermans &

Hermans-Jansen, 1995) and narrative (Santos,

Goncalves, Matos, & Salvatore, 2009). Although

very different in many respects, these approaches

share the basic idea of the clinical exchange as a co-

construction of meanings (i.e., intersubjective sense-

making) aimed at changing the patients’ symbolic

(affective and/or cognitive) modality of interpreting

their experience (Gennaro et al., 2010). Psychother-

apy can, therefore, be seen as a ‘‘transformative

dialog’’ (Gergen, 1999, p. 250), where new mean-

ings are elaborated, new categories are developed,

Correspondence concerning this article should be addressed to Mariangela Nitti, Universita del Salento, Dipartimento di Scienze

Pedagogiche, Psicologiche e Didattiche, Via Stampacchia, Lecce, 73100 Italy. E-mail: [email protected]

Psychotherapy Research

2010, 1�18, iFirst article

ISSN 1050-3307 print/ISSN 1468-4381 online # 2010 Society for Psychotherapy Research

DOI: 10.1080/10503301003641886

Downloaded By: [Nitti, Mariangela] At: 14:29 27 July 2010

and one’s presuppositions (Chambers & Bickhard,

2007) are transformed within an intersubjective

context.

To achieve this aim, DFA analyzes patient�therapist

dialogue in formal terms*that is, by depicting the

structural global qualities of their communicational

exchange*rather than merely in terms of the seman-

tic contents exchanged within their dialogue. In fact,

even though most analysis methods used in psy-

chotherapy process research focus on the semantic

content of the clinical exchange, semantic content

analysis is important, but not sufficient, for a real

understanding of the clinical exchange.

One of the limitations of the semantic content

analysis is the so-called indexicality of linguistic

signs: The semantic content of a sign needs the

context to be understood (Nightingale & Cromby,

1999), so a high level of inference is required.

Furthermore, the indexicality of a sign concerns

not only the discourse semantic dimension but also

the pragmatic sphere: Because producing a sign is

not a mere linguistic operation but rather a speech

act (Austin, 1962), the psychological meaning of a

sign is related to its communicative function as

performed in discourse activity. This communicative

function is not immanent to the sign, but depends on

the way it is used in relation with the other signs

within the intersubjective circumstances of the

discourse (Harre & Gillett, 1994; Wittgenstein,

1953). The temporal dimension of the discourse is,

therefore, an outstanding factor in the process of

constructing the sense.

Consider the following sequences of thematic

contents as characterizing the dialogue of two

patients:

Patient 1 sequence: experience of frustration0anger0pain0desire to be helped by therapist

Patient 2 sequence: pain0desire to be helped by

therapist0experience of frustration0anger

The content of the talk is the same in the two cases;

yet the sequences are different. In addition, it is

evident that the two sequences differ as concerns

their global meaning. The first sequence seems to

concern the patient’s request to be supported by the

therapist in dealing with the negative emotive reac-

tion to a frustrated desire. The second sequence

seems to concern a patient expressing a negative

feelings aroused by his or her desire to be helped by

the therapist.

Among the contributions present in literature, the

matter of the indexicality and the temporal dynamics

of sensemaking has been ignored (researchers have

focused only on the semantic dimension of sense-

making) or dealt with by adopting a qualitative

discourse analysis in order to properly consider the

context and the communicative function of the signs

and analyze the discourse dynamic over time.

Our contribution seeks to construct a robust,

flexible, and automatic, although not uncritical,

procedure for seriously taking into account the

temporal dynamics of the discourse, which is a basic

(although not exhaustive) aspect of sensemaking.

As the procedure leads to the synthetic description

of the discourse network through some indexes,

such indexes are processed by a competitive neural

network that classifies them based on their common

properties. Each discourse network will be then

interpreted with reference to the average character-

istics of the category to which it belongs.

This work is organized in two parts. The first is

devoted to the description of the DFA method. The

latter concerns the exemplificative application of

DFA to a case: Pietro’s psychotherapy.

Discourse Flow Analysis

DFA was designed to develop an empirical model of

the psychotherapy process as intersubjective sense-

making. In order to go beyond the limits of semantic

content analysis described previously, DFA allows a

dynamic analysis of sensemaking, focusing on the

temporal patterns of meanings rather than on the

survey of discrete contents, by adopting an auto-

mated low-inferential procedure of content analysis,

yet at the same time is able to take the contextuality

of meaning into account. Let us now consider how

DFA operates in order to reach such results.

The main assumption of this model is the idea that,

because the relationships among contents basically

arise from associations for temporal adjacency,

sensemaking strongly depends on the way contents

are combined throughout the discourse flow.

Coherently, sensemaking is a matter not of con-

tents but of sequential combination of contents.

Starting from this assumption, DFA maps the

psychotherapeutic dialogue in terms of associations

for adjacency between semantic contents occurring

within the clinical exchange. In order to operate in

this way, DFA refers to the concept of discourse

network. A discourse network is made of nodes, each

of which represents one of the units of semantic

contents, those that are active in the communi-

cational exchange between patient and therapist.

Reducing the communicational exchange in terms of

network allows us to analyze the systemic and

dynamic structure of sensemaking.

To construct and analyze the clinical process as a

discourse network, the DFA method works in three

steps and is applied to the verbatim transcript of

therapist�patient dialogue. First, a content analysis

2 M. Nitti et al.


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identifies and categorizes the semantic contents

active in the text. Second, a sequence analysis is

applied to the results of the first step in order to

calculate the probability of pairs of contents being

adjacent; this procedure is designed to construct the

discourse network. Third, through quantitative

analysis, it is possible to obtain a synthetic descrip-

tion of the way the discourse network functions by

means of structural-dynamic indexes. Finally, the

quantitative indexes describing the discourse net-

work are processed, in the fourth step, into a

competitive neural network that can classify the

psychotherapy sessions on the basis of the index

patterns characterizing them. The steps of the

analysis are shown in Figure I.

Step 1: Content Analysis

Content analysis is applied to the integral verbatim

transcription (henceforth corpus) of the patient�therapist talk. Using text analysis software (T-Lab

5.3 Beta; Lancia, 2005), the lexical corpus is

transformed into a digital matrix (co-occurrence

matrix) and subjected to a multidimensional proce-

dure of analysis.

Preliminary Operations

First, T-Lab performs the segmentation of the text.

It divides the whole text into elementary context

units (ECUs), each of which represents a meaningful

sentence or group of sentences. To this end, T-Lab

adopts the following criterion: An ECU ends

with the first punctuation mark (‘‘.’’ or ‘‘!’’ or ‘‘?’’)

after the 250th character and it does not exceed

500 characters in length.

At the same time, T-Lab operates the lemmatization

of the lexical corpus. This substep is carried out in

terms of the several subprocedures, discussed next.

Listing. All the textual forms present in the text

are listed just as they appear in the corpus. T-Lab

considers a form to be any string of characters (even

if made up of one character) between two blank

spaces (or a blank space and a punctuation mark).

Filtering. Punctuation is eliminated from the

analysis. At the end of this procedure, the original

list of textual forms is filtered into a list of lexical

forms.

Disambiguation. This procedure is designed to

change the lexical forms that can have more than one

semantic content (e.g., ‘‘subject’’). It is performed by

modifying the lexical form in the various contexts

where it occurs. In this way, the original form is

differentiated into two or more forms with a single

semantic content. For instance, the original form

‘‘subject’’ could be differentiated into two new

forms: ‘‘subject_p’’ when its meaning is ‘‘person’’

and ‘‘subject_k’’ when, according to the context, it

means ‘‘branch of knowledge.’’ Once the disambi-

guation has been performed, listing and filtering

need to be repeated. Finally, it is worth noting that

the disambiguation may be performed only on the

most frequent words and/or on the lexical forms that

are relevant for the aim of the analysis (e.g., in the

case of an analysis of the psychotherapy process, it

could be relevant to differentiate between ‘‘sense’’ as

concerning perception and ‘‘sense’’ as synonymous

of meaning).2

Lemmatization. The lexical forms are grouped

according to their common lexical root. In other

words, the various forms of the same word are

turned into their corresponding lemma. This sub-

procedure entails the application of a dictionary to

the list of lexical forms. A dictionary is a list of the

main lemmas of a given language (e.g., English,

Italian), each of them put in correspondence with

Figure I. Steps of the analysis.

Analysis of psychotherapy through statistics 3


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the set of forms constituting its lexical variant (i.e., in

the dictionary, the lemma ‘‘do’’ is in correspondence

with the lexical forms ‘‘does,’’ ‘‘done,’’ ‘‘did,’’ and so

forth). To empower the coverage of the lemmatiza-

tion (i.e., the percentage of textual forms recognized

by the dictionary and, therefore, lemmatized), we

integrated the T-Lab dictionary with another one

elaborated by us. The DFA dictionary was produced

by listing the textual forms of a large number of texts

retrieved from a variety of discursive fields and

sources (sport, politics, novels, verbatim transcripts

of psychotherapy sessions). Eventually, the auto-

matic lemmatization is checked and, if necessary,

completed by the researcher.

Not all the lemmas are kept in the analysis.

Because of T-Lab’s computational constraints, only

the 500 more frequent lemmas are used for the

analysis.

Identification of the Co-Occurrence of ECUs for the Sake

of the Definition of the Thematic Nuclei

As these preliminary text operations are done, it is

possible to proceed with the content analysis. Con-

tent analysis is realized by using criteria of categor-

ization of the text intrinsic to the text itself rather

than external parameters of classification. The

method operates the text classification by means

of a composite multidimensional procedure of ana-

lysis combining multiple correspondence analysis

(MCA; Benzecri, 1973) and cluster analysis (CA;

Anderberg, 1973).

To perform these analyses, T-Lab produces a co-

occurrence matrix with ECU as rows and lemmas as

columns.3 In the generic cell xij we will find the value

‘‘1’’, if the jth lemma is present in the ith ECU, ‘‘0’’

otherwise, as shown in Table I.

This matrix undergoes MCA. Each factorial

dimension extracted by MCA maps the joint behavior

of groups of lemmas. Generally speaking, MCA

breaks down the lexical variability into patterns of

co-occurrence of lemmas (i.e., joint presence of

lemmas within the same ECU). Each factorial

dimension represents a pattern of this kind: a rather

stable combination of words present through the

text (or parts of the text). Accordingly, a factorial

dimension can be interpreted as a semantic micro-

dimension active in the text.

The factorial dimensions are then used as criteria

of classification in the subsequent CA (in T-Lab

language, the thematic analysis of elementary con-

text). Thus, CA groups the ECUs (and the lemmas)

according to their similarity on the semantic micro-

dimensions. In other words, a cluster represents a

subset of utterances (i.e., ECUs) that share similar

patterns of co-occurrence of lemmas; or, in a

complementary way, each cluster represents a subset

of words tending to occur in the same sentences.

Therefore, each cluster can be understood as a

thematic nucleus (or semantic content or node),

made up of a set of words whose aggregation reflects

the ‘‘isotopy’’ of semantic traits (Lancia, 2005).

To clarify this concept, let us take an example

from a previous application of DFA (Lisa’s case;

Salvatore et al., in press). Table II shows the three

Table I. Co-occurrence Matrix

ECU/Lemma Be Your Name Pen Red

What is your name? 1 1 1 0 0

The pens are red. 1 0 0 1 1

Note. ECU, elementary context unit.

Table II. Selected Part of One Cluster: Thematic Nucleus

Resulting from the Application of the DFA Method to a

Transcript of Psychotherapy (Lisa’s Case)

ECU 1 (in Session 9; score: 185.32)

T: How would you do that?

L: Uh, just I either stay in the house or do housecleaning or

whatever needs to be done um.

T: How would you do that right now if you were to just sort of like

put her aside, put her, what do you do, put her in a box almost?

L: Um.

T: Trap her. Try to do that now, to trap her and put her away.

L: Okay, um, just, just stay home.

ECU 2 (in Session 12; score: 172.70)

T: Tell her she scares you and you feel small, tell her about what

it’s like, speak from, it’s almost like saying ‘‘I’m small and

I feel . . .’’ Finish it off.

L: I’m ah. I’m small and I feel ah, ah, just helpless um.

T: Mm-hm.

L: Just giving out myself.

T: Mm-hm, a little lost?

L: Yeah, I feel um lost.

T: Tell her.

L: InsECUre.

ECU 3 (in Session 9; score 160.59)

T: So you, you sort of keep her, shield her from the people, from

the world in a sense.

L: Yeah.

T: Okay, be the shield, be the shield and speak from that and tell

her what you feel.

L: Um, I don’t want you to desert me, or just, just stay with me

and and we’ll make it through together.

T: Mm-hm. Tell her how you protect her.

Note. The score is a chi-square parameter depicting the associa-

tion between the ECU (token) and the cluster (type). The higher

the index, the higher the representativeness of the ECU with

respect to the cluster. Underlined words are word forms belonging

to the characteristic lemmas of the identified cluster (adjusted

from Salvatore et al., in press). ECU, elementary context unit; T,

therapist; L, Lisa.

4 M. Nitti et al.


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most representative ECUs of one of the clusters

defined by the analysis (the cluster in question

globally encompasses 94 ECUs, corresponding to

5.25% of the classified ECUs in the text; cf.

Salvatore et al., in press). The underlined words

are the ones whose co-occurrence made the ECU

cluster together. The thematic interpretation of the

cluster entails identifying a thematic area overlap-

ping/passing through the clustered ECUs. In the

case of the three ECUs reported in Table II, it seems

that they share a common thematic area concerning

the recognition of needs/desires in relations with

others.4

Text Indexing

The thematic nuclei identified as active in the corpus

by the combination of MCA-CA are then used for

the subsequent text indexing carried out as follows.

Each ECU is assigned to the cluster (therefore to a

thematic nucleus) with which the ECU has the

highest index of association. In this way, each ECU

is marked with the most representative thematic

nuclei identified through content analysis. Thus,

the output of this step is the transformation of

the text into a sequence of thematic nuclei, each of

which represents the semantic content of a nucleus.

Table III shows an example of the output of a

content analysis applied on a hypothetical text

segmented into a sequence of 20 ECUs and assigned

to a set of four clusters.

Evidence Supporting the Procedure of Content Analysis

In the last 20 years, there has been a dramatic

development of automatic procedure of textual

analysis and textual mining. Nowadays, we have

major evidence of the validity of this kind of

approach. Here we briefly recall two kinds of

findings, converging to highlight the power of the

automatic process of content analysis.

One source of evidence is represented by studies

dealing with the modeling of the cognitive processes

entailed in the mapping and understanding of mean-

ing, especially in the case of the reading of written

texts. Some authors within this stream of studies

have developed specific procedural models that

have proved able to simulate the competence of a

human being in mapping meanings (Andersen,

2001; Landauer & Dumais, 1997).

The other source of evidence is given by the

studies more specifically aimed at testing the validity

and reliability of automatic procedures of content

analysis. A strong tradition in this sense is provided

by the many studies concerning the validity of the

procedure of data mining for the classification of

documents. In a study aimed at comparing models

of cluster analysis applied to the classification of

documents, Steinbach, Karypis, and Kumar (2000)

provided estimates of the efficacy of various algo-

rithms of cluster analysis related to a previously

established classification. Based on their analysis,

according to the type of documents, all the proce-

dures range between .58 to .85, with most of the

performance greater than .70 (1�perfect capacity

of classification; that is the clustering capacity of

fully reproducing the normative classification).

Rosenberg, Schnurr, and Oxman (1990) have com-

pared two automatic procedures of content analysis,

based on the calculation of the frequencies of

occurrences of words (one not context sensitive,

the other context sensitive), and a clinically informed

method of content analysis based on human coding.

The three methods were applied to the transcripts

of interviews with 71 patients from four clinical

groups (cancer, depression, somatization, paranoid)

and compared regarding their capacity of rightly

classifying the subjects in accordance to the diag-

nosis. At this end, the authors performed three

separate stepwise discriminant analyses, one for

each method. The classification performances of

the three methods were then compared in a mixed

analysis of variance design, with clinical group as a

between-subjects factor and method as a within-

subject factor. The two computerized methods

showed significantly better performance than the

method based on human coding.

Table III. Cluster and ECU Sequences

ECU sequence Cluster sequence

ECU 1 CL3

ECU 2 CL1

ECU 3 CL2

ECU 4 CL1

ECU 5 CL4

ECU 6 CL1

ECU 7 CL3

ECU 8 CL2

ECU 9 CL3

ECU 10 CL1

ECU 11 CL2

ECU 12 CL4

ECU 13 CL1

ECU 14 CL3

ECU 15 CL2

ECU 16 CL3

ECU 17 CL1

ECU 18 CL2

ECU 19 CL4

ECU 20 CL1




With regard to DFA, the reliability of the proce-

dure of content analysis adopted by the method was

studied within the framework defined by the line of

study we just mentioned (Salvatore et al., 2010).

Two complementary approaches were adopted. On

the one hand, trained judges were asked to evaluate

the output of the content analysis provided by the

DFA procedure. On the other hand, we compared

the content analysis produced by the DFA with the

series of content analysis provided by judges. Both

kinds of tests were performed on a sample of

transcripts randomly and blindly extracted from an

Italian language psychotherapy (Katia; see Nicolo &

Salvatore, 2007). The main findings are as follows:

1. The judges were asked to rate the semantic

similarity of a set of 70 ECUs (i.e., utterances;

see prior discussion), five for each of the

14 clusters produced by the DFA procedure

of content analysis. The intracluster similarity

(i.e., the mean of the rates of similarity of each

couple of the five ECUs grouped in the same

cluster) was significantly higher than the inter-

cluster similarity (i.e., the mean of the rates of

similarity of each couple of ECUs belonging to

different clusters). This means that the DFA

procedure seems to be able to differentiate the

utterances and group them in classes that

are semantically meaningful. In other words,

DFA puts together what a human coder con-

siders more semantically homogeneous and

separates what the human coder considers

more dissimilar.

2. DFA procedures calculate an index of the

similarity between each couple of the clusters

defined: the Euclidian distance between the

centroids of the clusters. The higher this index,

the more dissimilar the cluster. We then com-

pared the judgments of similarity between each

couple of clusters with this DFA index. We

found a significant negative correlation. This

means that the DFA’s way of representing the

relationship of (dis)similarity among the ECUs,

and therefore among the clusters, tends to over-

lap the one produced by the human coders. If

one considers that the human coders in this case

were engaged in a task of semantic coding, one

can conclude that the DFA procedure yields a

classification of ECUs reflecting a network of

relationships of similarity�dissimilarity consis-

tent with the one provided by the human coders.

3. Three judges were asked to group the 70 ECUs

on the basis of their semantic similarity in terms

of the same partition produced by DFA (14

clusters, each of five ECUs). Then we com-

pared the interrater agreement between each

couple of judges and between the judges and

the DFA procedure. The comparison between

the judge�judge agreement and DFA�judge

agreement could not be differentiated; in all

cases, we recorded a fair level of agreement

(Cohen’s k between .30 and .40). Note that in

order to appreciate the level of such agreement

one has to take into account the large amount

of degrees of freedom associated with the

classification at stake. As a matter of fact, the

probability that the classifier orders 70 ECUs

(n) in 14 groups (g) of five items (k) is equal

to 6,19�100, as resulting from the formula:

P(n,g,k)�(k! (n�k)!n!)g�(5! (70�5)!70!)14.

In sum, different sources of results support the

conclusion that automatic procedures of information

retrieval are able to perform content analysis with

at least a sufficient level of reliability. Within this

scenario, the procedure of content analysis adopted

by DFA seems to be able to discriminate the

utterances in accordance with their semantic simi-

larity and to classify the semantic content of the text

in a way that does not differ from a trained human

coder (as measured by the interrater agreement).

Step 2: Creation of the Discourse Network

The clusters of ECU are used for the definition

of the discourse network. In its most elementary

version, a network is a collection of elements (nodes)

linked to each other: In our case, the nodes are

represented by the cluster of ECUs, and the con-

nections between them represent the strength of

their association. The strength of a cluster’s associa-

tion is calculated using a sequence analysis.

To explain simply how the sequence analysis

works, we give an example in Table III, which

considers 20 ECUs grouped into four clusters. The

four clusters represent, as we said, the nodes of the

discourse network. Given the sequence of clusters in

Table III, it is possible to calculate the transition

matrix deriving from the matrix of the successors.

Table IV shows the matrix of the successors. This

matrix has the ECU clusters both in rows (prede-

cessors) and columns (successors) and shows, in its

generic cell, the number of times the jth cluster

follows the ith cluster. For instance, the number of

Table IV. Frequencies of Successors

Cluster CL1 CL2 CL3 CL4

CL1 0 3 2 1

CL2 1 0 2 2

CL3 3 2 0 0

CL4 3 0 0 0

6 M. Nitti et al.


times Cluster 1 (CL1) follows Cluster 3 (CL3) is

equal to 3, as is shown in the tables. The total

transition number is equal to the number of ECUs

minus 1 (score of 19 in Table IV), because the first

item of the sequence has no predecessors.

The resulting transition matrix, similarly to the

matrix of successors, has the clusters both in rows and

columns and shows in its generic cell the strength of

the association between two clusters. The strength

of that association is the probability, calculated by

a Markovian algorithm, that a cluster i follows j

immediately afterward: This probability is nothing

but the relative frequency of the successor, that is, the

ratio between the absolute frequency of a cluster’s

successor (the frequency in the successor matrix cell)

and the total number of the cluster’s successors (the

marginal row of the matrix). In our example, the

resulting transition matrix is shown in Table V.

The values contained in the transition matrix are

the strength of the associations among the discourse

network nodes. The direction of the connections

goes from the predecessor (i) to the successor (j).

The intensity of the association is represented by the

thickness of the arrow connecting two clusters: In

Figure II, CL4 and CL1 have the maximum

association (equal to 1; that is, every time CL4

appears throughout the discourse, it will be certainly

be followed by CL1), represented by the thickest

arrow; CL4 has less probability of following CL1

(0.167) and so the arrow is very thin.

Moreover, Markovian sequence analysis produces

as output, for each node, a set of essential informa-

tion, such as:

. Frequency of occurrence (how many times a

node is present through the discourse). In the

example, CL3 appears five times during the

interaction.

. Incoming and outgoing nodes (including the

node considered, because a node can be asso-

ciated with itself) and the corresponding fre-

quencies. In the example, CL3 has two

predecessors (CL1 and CL2), with a frequency

equal to four, and two successors (CL1 and

CL2), with a frequency of five.

This descriptive information is useful for carrying

out the quantitative analysis of the discourse network.

The aim of this step is to measure the structural and

dynamic properties of the network and to interpret

them as relevant aspects of the sensemaking dynamic.

It is worth emphasizing that sequence analysis is

applied to a single time ‘‘window,’’ consisting of a

specific temporal period of the psychotherapy under

analysis. The sequence analysis can, therefore, be

applied to single sessions of psychotherapy as well

as to part of a session (i.e., the first half hour) or to

a group of sessions. According to the aim and the

conditions of the analysis, the windows can be

defined in order to cover all the sessions or to

sample parts of them. In sum, both discursive

networks and windows of analysis will be defined

by the segmentation and/or the sampling of the

transcript the research produces.

Step 3: Quantitative Analysis of the Discourse

Network

The quantitative analysis of the discourse network is

mainly carried out by means of the following

indexes: connectivity, activity, and super-order

nodes.

Connectivity (C) is the density of the association

among nodes, that is, the relative amount of con-

nections among the nodes. It is calculated as the

percentage of connections that are active in the text

compared with the theoretical number of connec-

tions. It results from:

C�c=K2 (1)

where c is the number of connections active in the

text and K2 is the square of the number of nodes

(every node can be theoretically associated with

every other node, including itself). In our example,

the number of active connections is nine (the

number of cells of Table IV with a non-zero value)

and the theoretical number of connections is 42�16,

so the connectivity is equal to (9/16)�0.5625.

Activity (A) is the global network ability to extend

the spectrum of associations among nodes thro-

ugh time. High levels of activity depict discourse

Table V. Transition Matrix

Cluster CL1 CL2 CL3 CL4

CL1 0 0.5 0.333 0.167

CL2 0.2 0 0.400 0.400

CL3 0.6 0.4 0 0

CL4 1 0 0 0

CL1

CL3 CL4

CL2

Figure II. Example of discourse network.



dynamics capable of expanding the paths of sense-

making by enriching the possible combinations

among meanings. It is calculated as the ratio

between network generative and absorbing power:

A�GEN=ABS (2)

These two parameters express the capability of the

discourse flow to, respectively, increase and decrease

the meaning variability over time. It is based on

the measure of the single node’s contribution to

the discourse meaning variability.

To calculate the contribution of a node, S(CNTj),

the sum and the difference of the frequencies of

outgoing and incoming connections (respectively, ouj

and inj) are multiplied and weighted by the number

of theoretically possible connections (two times the

number of nodes, or 2K) and the frequency of the

node (freqj):

S(CNTj)�(ou j � in j)

2K�

(ou j � in j)

freq j

(3)

According to the measure of the contribution,

nodes can be classified as ‘‘generative,’’ ‘‘relay,’’ or

‘‘absorbing.’’ The distinction is based on the differ-

ence between outgoing and incoming connections.

In a generative node, the difference is positive; in an

absorbing node, negative; and in a relay node, null.

The network’s generative power (GEN) and absorb-

ing power (ABS) are calculated as the average

contribution of the generative nodes and of the

absorbing nodes, respectively.

Super-order nodes (SN) are particular nodes

carrying out the function of super-ordered meaning

working as taken-for-granted assumptions (e.g.,

concepts of self and others, affective schemata,

metacognitive modalities, relational and attachment

strategies, unconscious plans) regulating patients’

interpretation of the experience (see Teasdale

& Barnard, 1993) and, more in general, sense-

making*in psychotherapy dialogue too.

The super-order meanings’ regulative function

results from the high frequency of occurrence and

the strong connectivity they have with other nodes.

The index is calculated as the percentage of highly

frequent nodes with high (generative or absorb-

ing) contribution as well. DFA defines the high-

frequency node as a node with a frequency higher

than 1.5 times the average ratio between token and

type. The contribution is considered high if a node

has outgoing or incoming connections with more

than 33% of the nodes in the network.

All these indexes, taken singly, give no information

on the quality of the psychotherapy process. They,

therefore, have to be taken into account together, in

their interactions, with the function of describing the

internal dynamic of the discourse network, without

any specific reference to the clinical significance of

this dynamic.

Step 4: Neural Network for the Sessions’

Classification

Neural networks can be a useful instrument for the

analysis of the psychotherapy process. As discussed

in the previous steps, the implementation of DFA

leads to the representation of the whole commu-

nicative exchange between therapist and patient in

terms of discourse networks and provides a descrip-

tion of such networks by means of structural and

dynamic indexes. The aim of this fourth step is to

provide an interpretation of these indexes based on

their classification into homogeneous classes.

Therefore, the neural network we are about to

construct has to be able to:

1. Discriminate patterns of indexes according to

regularities and differences between them;

2. Classify the sessions in classes in accordance to

these patterns of indexes; and

3. Compare the classification provided by the

network with a theoretical based classification

in order to test the convergence between the

two classifications.

The building block of an artificial neural network

is a processing unit called neuron (or neurode, node,

or unit), which captures many essential features of

biological neurons. It receives input from other

neurons or from an external source. Each input has

an associated weight (w), which can be modified so

as to model synaptic learning. The outcome of each

unit (a) is merely the result of a function like this:

ai �f (XR

j�1

wij pj )

where p is the incoming input signal and the function

argument is called net input (n) to unit i. A neural

network is composed of such units and the weighted

unidirectional connections between them; in some

neural nets, the number of units may be in the

thousands. For a correct functioning of the network,

a further element is introduced in the network, the

so-called bias. The bias is much like a weight, except

that it has a constant input of 1; like weights, biases

are scalar parameters of the neuron, adjustable by

the learning rule used. This element is nothing but a

threshold term, regulating the ‘‘activation’’ or the

‘‘inhibition’’ of the neuron (for more details, see

Floreano, 1996); for a linear output unit, the bias

term is equivalent to an intercept in a regression

model.

8 M. Nitti et al.


The output of one unit typically becomes an input

for another; they may also be units with external

inputs/outputs.

To simplify the notation, we can consider all the

items of the network in terms of vectors and

matrixes.

a�f (wp)�f (n)

Function f is called activation function or transfer

function; in the simplest case, f is the identity

function, and the unit output is just its net input

(this is called ‘‘linear unit’’). According to the goals

of the network, it is possible to choose among many

activation functions; one of the most used is the

threshold function: If the net input is greater than a

given constant, the activation value is 1 and is 0

otherwise.

Commonly, one neuron, even with many inputs,

may not be sufficient; we might need five or 10,

operating in parallel, in what we will call a layer (see

Figure III).

A single-layer network of S neurons includes the

weight matrix (W), the sum operators, the transfer

function boxes, and the output vector a. It is possible

to design a single layer of neurons having different

transfer functions.

A multilayer network is simply the combination in

sequence of layers: In this architecture, the input

vector p is connected to each neuron through the

weight matrix W, and the resulting output vector a

represents, in turn, the input to the following layer.

The second layer may be, respectively, a hidden layer,

if at least one other layer comes directly after it, or an

output layer, if its output corresponds to the network

final outcome.

The most interesting aspect of the way neural

networks function is their autonomy in learning

information and correct behaviors. In a neural

network, knowledge is represented by the weights of

the connections among units, so learning consists

of modifying such weights. There are many types of

neural network learning rules, or training algorithms.

They fall into two broad categories: supervised

learning and unsupervised learning.

In supervised learning, the learning rule is pro-

vided with a set of examples, called training set, of

proper network behavior. The training set is made up

of pairs of input�target (desired output). The output

of the function can be a continuous value or can

predict a class label of the input object. The task of a

network based on supervised learning is to predict

the value of the function for any valid input (or, most

probably, input vector) after having seen a consider-

able number of training examples. The network then

has to generalize from the presented data to unseen

situations in a ‘‘reasonable’’ way. The learning

process operates as follows: As the inputs are applied

to the network, the network outputs are compared

with the targets. The learning rule is then used to

adjust the weights of the network in order to move the

network outputs closer to the targets. The mechan-

ism of adjusting is defined by the algorithm adopted

(e.g., perceptron learning rule, back-propagation).

In contrast, in unsupervised learning, the weights

are modified in response to network inputs only.

There are no target outputs available. Most of these

algorithms perform some kind of clustering opera-

tion. They learn to categorize the input patterns

into a finite number of classes (this is the case, for

instance, of competitive networks, which is the

model we adopt in this work).

The abilities of different networks are then related

to their structure and learning methods5; each type

of network has a different strength particular to its

applications. In the case study, we present a network

that can classify patterns by discovering regularities

or differences among them. Such a network has a

single-layer structure, and its learning is based on a

competitive mechanism.

We now have to detect the proper architecture of

the network and the corresponding learning rule.

First, the network should perform a pattern recogni-

tion: The network responds when an input vector

close to a learned vector is presented.

Because we want to verify the hypothesis of

different phases in psychotherapy, we cannot provide

the network with correct behavior examples, so data-

driven recognition is required (Floreano, 1996).

We believe that these tasks could be effectively

performed by a competitive network for these main

reasons:

1. It is a self-organizing network: The structure

and the classification parameters are defined

merely by the input vector presented to the

network.

2. After training, each neuron is in competition

with the others in order to represent a subset ofFigure III. A layer of neurons.



input patterns; this competition is won by the

most representative neuron.

The mechanism of competition (Hagan, Demuth,

& Beale, 1996) between neurons is very simple

and effective. The network is supplied with a great

number of example patterns of inputs; such exam-

ples are called characteristics and occur in the space of

characteristics. A suitable distance function, applied on

each pair of items, calculates the degree of likeness

between them.

At the end of the training, the network will

respond to the input by activating only one neuron,

the one closest to the input presented. This neuron is

called the winning neuron (i*), and its output, ai is the

average of the characteristics of the cluster (subset of

inputs) represented by the neuron.

The network, therefore, groups the examples

together into subsets with the highest inner homo-

geneity and the highest outer unlikeness. To do so,

the following transfer function is defined:

a�compet (n) (4)

where the network outcome (a) results from the

application of the transfer function compet to the

net input (n), that is, the inner product between

the input vector and the weights matrix.

The compet function operates by assigning ‘‘1’’ to

the neuron with the highest net input and ‘‘0’’ to the

others. The reason for this choice is that the higher

the inner product between input vector and weights,

the lower the distance between them and the

characteristics space. The winner is, therefore, the

neuron whose weights are most similar to the inputs

presented to the network.

In a competitive network, each neuron corresponds

to a subset of inputs: For example, if the network

consists of three neurons, it will classify inputs into

three classes; if there are seven neurons, the network

will fit a seven-class classification. The researcher,

therefore, has to set the structure of the network on

the basis of the number of classes expected. Gen-

erally, this is a limitation for those who want to

discover relations between data in an explorative way.

However, in our case, we need to adopt a confirmative

approach: We want to verify the existence of regular

trends within different phases of the therapy.

Theoretical Based Classification

We adopt as the criterion of comparison the two-

stage semiotic model (TSSM; Gennaro et al., 2010;

Salvatore et al., in press). This model considers the

psychotherapy process to be composed of two phases,

with different patterns of functioning. The TSSM

sees psychotherapy as a dialogical process involving

the deconstruction of old meanings and the creation

of new ones. The patient arrives at psychotherapy

with a predefined, more or less rigid system of*explicit, implicit, and latent*assumptions (e.g.,

concepts of self and others, affective schemata,

metacognitive modalities, relational and attachment

strategies, unconscious plans), which are taken for

granted and work as super-ordered meanings regulat-

ing the interpretation of experience (see Teasdale &

Barnard, 1993). This system of assumptions can be

conceived of as the source of the patient’s psycholo-

gical problems: symptoms, at the intrapsychic as well

as at the relational level. One of the main therapeutic

activities consists, therefore, of triggering the reorga-

nization of the patient’s super-ordered meanings.

TSSM claims that the change process in the

communicational system made up of patient�therapist interaction follows two stages. In the first

stage, the patient�therapist exchange works funda-

mentally as an external source of limitation on the

patient’s system of assumptions. The first stage is,

therefore, fundamentally a deconstructive process,

with therapeutic dialogue aimed at placing con-

straints on the regulative activity of the patient’s

problematic assumptions. The weakening of the

patient’s critical super-ordered meaning opens the

way for the emergence of new super-ordered ones.

This is what happens in the second, constructive,

stage, when the patient�therapist dialogue imple-

ments new super-ordered meanings, replacing the

previous ones in regulating the meaning-making

experience.6

Following the TSSM, we expect that the incidence

of the regulative assumption (i.e., of the super-

ordered meaning depicted by the DFA’s index:

super-ordered nodes) will decrease in the first stage

(deconstructive stage) and then increase in the

second (constructive stage).

Following TSSM, we differentiate the psychother-

apy process into two macrophases, a deconstructive

and a constructive phase, adopting as a cutoff point

the session with the first negative peak of the SN

index.7

Finally, we compare the TSSM-based classifica-

tion and the one provided by the operation of pattern

recognition performed by the neural network (we use

a nonparametric test* Fisher’s exact test*for this

purpose). Insofar as these two classifications prove to

be consistent (i.e., their association is statistically

significant), then it could be concluded that the

sessions of each of the two phases defined on the

basis of the TSSM criterion are characterized by a

specific pattern of functioning of the psychotherapy

process. On the other hand, such a result would

mean that the neural network is able to discriminate

the sessions in a clinically meaningful way.

10 M. Nitti et al.


A Case Study: Pietro’s Psychotherapy

To explain better the way the DFA method works,

we proceed with the examination of an empirical

case. The analysis is based on the verbatim tran-

scriptions of 43 sessions of a single case of supportive

psychodynamic psychotherapy.

At the time of the therapy, Pietro was a 22-year-

old undergraduate student at the University of

Padua who sought psychological counseling at the

university’s psychological support service because of

his difficulties with human relationships, resulting,

in his opinion, from insecurity and self-confidence

problems. Pietro received therapy approximately

once a week for 2 years, for a total of 79 sessions.

Results of several outcome measures indicate that

Pietro’s therapy was successful.

We now show how the DFA method operates in

practice by transforming therapy�patient conversa-

tions in discourse networks and evaluating them

in terms of dynamic systems able to generate new

intersubjective meanings. To achieve this aim, the

steps described in the previous section are followed

and some considerations about the outcomes pro-

vided.

Step 1: Content Analysis

The corpus consists of the verbatim transcriptions of

43 psychotherapy audiotaped sessions (randomly

sampled from the total of 79 sessions). Table VI

shows the output of the preliminary treatment of the

corpus (see section above).

A distinction must be made between word token

and word type: The former is the total amount of

words present in the text; the latter is the number of

words that are different from each other, subjected

to lemmatization.

The co-occurrence matrix derived by this pre-

liminary treatment of the corpus is made up of 5,367

rows (ECU) and 4,337 columns (lemmas). The

subsequent application of the MCA and CA to this

matrix led to the identification of 20 clusters/

thematic nuclei (inertia between clusters, in our

case equal to 49.1% on the total inertia). A sample

of the most representative ECUs of one of the

20 clusters is given in the Appendix. The following

text indexing was performed in accordance with

them, each ECU being attributed to the cluster with

which it was mainly associated.

Step 2: Creation of the Discourse Networks

The indexing of the ECU represents a transforma-

tion of the text into a sequence of thematic nuclei.

This sequence is the input of sequence analysis. In

this study, we have adopted the single session as a

window. That is, we performed a sequence analysis

separately for each sequence of clusters correspond-

ing to a session. This means that we created 43

discourse networks.

Step 3: Quantitative Analysis of the Discourse

Network

Indexes of the discourse network for each of the

43 sessions were calculated. Here we focus on the

main ones: connectivity, activity, and frequencies of

super-order nodes (SN) (it is worth remembering

that a proper interpretation of the results needs a

joint evaluation of all index trends).

Activity: As shown in Figure IV, the activity can be

depicted as having two phases. While in the first

part of the therapy (Sessions 1�23) we find a

continuous basic line of low activity, from the

middle of the therapy there are precise, discrete

bursts of variability (see Sessions 24, 34, and 38).

SN: The SN globally follows a U-shape trend

(Figure V). In particular, one can distinguish a

first period (roughly from Session 1 to 18, with

Session 15 being the first negative peak) with a

downward trend, followed by a middle period

(roughly Sessions 19�30) characterized by a flat

trend (with the exception of some deviant ses-

sions, in particular Session 19). In the last third of

the sessions, the trend shows a slight inversion: a

weak rise associated with a considerable increment

of the variability between the sessions.

Connectivity: The shape of the connectivity index

curve (with exception of the peaks in Sessions

19 and 32) is slowly decreasing (Figure VI).

Taken together, these trends depict a double-

phase course. Whereas connectivity is constantly

Table VI. Output of Preliminary Treatment of the Corpus of

Pietro’s Therapy

Descriptive parameter Amount

Number of sessions 43

Number of ECUs 5,367

Mean number of ECUs per session 124.81

Number of occurrences in the text

(token)

140,869

Number of lemmas the text (type) 8,053

Token-type ratio 17.49

Number of lemmas in analysis 4,616

Frequency threshold for selecting the

lemma for analysis

5




decreasing throughout the therapy, activity and SN

vary during that time. The first phase is character-

ized by low and constant activity and a slow but

constant reduction of SN; the second phase presents

positive peaks of activity and a stabilization or slight

increase of the SN level.

Based on the meaning of the indexes, the two

phases can be interpreted as follows: (a) In the first

part of the therapy, a reduction of the connections

among meanings occurs (decreasing connectivity)

and, at the same time, the patient’s assumptions lose

ground (reduced SN); (b) in the second part, the

discourse dynamic develops the capability of recom-

bining meanings (increasing activity variability),

without, however, this leading to the development

of new assumptions (SN level becomes stable).

This interpretation leads to the following scenario

as a plausible description of the therapy process.

Presumably, at the beginning of the therapy, Pietro

explicitly and implicitly expressed and acted out his

super-order meanings, in particular the ones con-

cerning his system of beliefs associated with his

problems. As the clinical dialogue proceeded, these

assumptions were subjected to constraints.8 Once

this process of constraining ended, a condition of

stability was achieved (cf. the stabilization of SN in

the second phase). This new state allowed space for

new ways of thinking and speaking to emerge,

enabling the patient�therapist dyad to access a

higher level of production of meaning variability

(see the increasing variability of activity in the second

phase).

0,000

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Z S

core

Activity

Figure IV. Activity trend.

y = 0,0002x2 - 0,0115x + 0,3193R2 = 0,1776

0,000

0,050

0,100

0,150

0,200

0,250

0,300

0,350

0,400

0,450

0,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 323 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Z S

core

SN Poli. (SN)

Figure V. SN trend.

12 M. Nitti et al.


It is worth emphasizing that the picture just

provided is consistent with the supportive, rather

than elaborative, aim of the therapy (Gabbard,

2000). In a supportive therapy, the clinic dialogue,

coherently with its goals, works as a limitation on the

patient’s beliefs instead of creating new assumptions.

In fact, supportive therapy is aimed at increasing the

strength of the patient’s ego rather than changing its

structural way of thinking and interpreting the

experience. The limited incidence of the construc-

tive stage can, therefore, be interpreted in the light of

this characteristic of the clinical setting.

Step 4: Construction and Training of the

Network

We proceed now with the construction of the

competitive network for the classification of the

input vector into two classes. In other words,

the first output of this step is the classification by

the neural network of the sessions in two classes,

each of them characterized by a specific pattern of

values of the three DFA indexes.

After this, we compare the classification per-

formed by the neural network with the one based

on the TSSM. Following the TSSM, we adopt

Session 15 (i.e., the first negative peak of the SN

trend; cf. Figure IV) as a cutoff point. Therefore, we

take Sessions 1 to 15 as the first stage characterized

by a deconstructive dynamics and Sessions 30 to 43

as the second, constructive, stage.9

The neural network proposed consists of one layer

of two neurons; the input vector is a group of three

indexes combined resulting from the quantitative

analysis of the discourse network obtained using the

DFA method. The output number corresponds to

the number of index patterns; each output is

represented by ‘‘1’’ if the pattern belongs to the first

class of indexes or ‘‘2’’ if it belongs to the second

class. The architecture of the network is shown in

Figure VII.

As we said, the transfer function is:

a�compet (n)

which finds the neuron with the highest net input

and assigns it a ‘‘1’’ and the others ‘‘0’’ (winner-take-

all competition).

The network has been created with the statistical

software MATLAB version 7.1 using the parameters

given in Table VII. The table presents two elements

to improve the learning process: the Kohonen learning

rate and conscience. The former is a training para-

meter that controls the size of weights and bias

changes during learning. The latter allows the so-

called dead neurons to be avoided: Some neuron

weight vectors may start out far from any input

vectors and never win the competition, so that their

weight is never learned. To stop this from happening,

biases are used to give neurons that rarely win the

competition an advantage over neurons that often

win. Once the neuron’s weights have moved into a

group of input vectors and the neuron is winning

consistently, its bias will decrease to 0.

Figure VII. Competitive network for the classification into two

classes.

10,000

15,000

20,000

25,000

30,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Z Sc

ore

Connectivity

Figure VI. Connectivity trend.



As regards the input ranges, those reported in

the table are [0, 1] for both the neurons, while the

indexes resulting from the quantitative analysis have

different values, as shown in Table VIII.

The real index values have been converted into a

[0, 1] range through the expression:

Inew�Iold � Imin

Imax � Imin

(5)

This solution, adopted each time new inputs are

introduced, allows different vectors to give compar-

able results obtained from different sets of data.

The network weights are initialized, by means of

the midpoint function, as the center of the input

variation range (0, 5). The biases are assigned in a

random way, through the initcon function.

Once the weights and the biases are set, there is

nothing left to do but train the network for the

classification task. The transfer function detects

the winning neuron, but this is not sufficient for

effective learning: The weights of the neuron that

win the competition need to be modified in order to

make it even closer to the input vector. To this end,

the following learning rule is applied on the winning

neuron’s weights:

iwnew�i w

old �a (pnew�iwold )

�(1�a) iwold �apnew (6)

Using this rule, the weight vector of the winner is

moved toward the input vector along a line between

the old weight vector and the input vector. The

distance the weight vector moves depends on the

value of a (the learning rate). The winning weight

vector then ‘‘learns’’ the input vector, so that it will

most probably win the competition when a similar

vector is presented to the network.

The training function trainr operates as follows:

At each training stage, all training vectors are

presented singly in random order; weights and biases

are updated at each repetition. Once the training is

ended, the inputs are presented to the network and

the classification starts.

For each session of the therapy, we presented the

corresponding index pattern as input of the network;

this input, passing through the network, was as-

signed to a subset by the corresponding winning

neuron. Table IX shows the correspondence be-

tween each session and index pattern class. The

result of the classification is discussed next.

Comparison between Network and TSSM Classification

Is it possible to state that two distinct stages of the

therapy really exist? If they do exist, what are their

characteristics?

The answer to the first question is provided by

Fisher’s exact test, which will demonstrate whether

or not there is statistical independence between the

two classifications, that is between each stage of the

therapy and one of the index classes. As mentioned

previously, if there is no independence, we can state

that the therapy can be divided into two phases, each

characterized by the prevalence of a specific class of

indexes and, therefore, by a specific trend of the

communicative exchange.

In our case, the one-sided Fisher’s test has a value

equal to 0.023 (i.e., the probability of the contin-

gency table analyzed is equal to 2.3%). This prob-

ability is small enough to reject the null hypothesis of

independence between the two variables. In other

words, a strong connection between one period of

the therapy and a specific class of indexes exists, and

it is, therefore, possible to clearly discriminate two

different stages of the therapy.

Table VIII. Index Variation Ranges

Variable Minimum Maximum

Connectivity 11.000 31.953

Activity 0.159722 14.000

SN 0.000 0.320833

Table IX. Results of the Classification

Stage I No. Stage II No.

Session 2 1 Session 45 2















Session 79 2

Table VII. Parameters of the Network

Network type Input range No. neurons Kohonen learningrate Conscience learning rate

Competitive [0, 1; 0, 1; 0, 1] 2 0.01 0.001

14 M. Nitti et al.


We now have to answer the second question: How

is each stage of the psychotherapeutic process

characterized? As described previously, at the end

of the training, each neuron represents the average

characteristics of the input patterns it classifies. In a

neural network, memory and knowledge are stored

in weights and biases of the neurons. Thus, in a

competitive network, the weights and the biases are

simply the prototypical index configurations found

in the patterns presented.

The final network parameters leading to our index

classification are presented in Table X. As regards

weights, each row corresponds to a neuron (a class)

and each column to a quantitative index: respec-

tively, connectivity, activity, and frequencies of SN.

The first neuron classifies the patterns of indexes

that are prevalent in the first stage of the therapy,

whereas the second neuron classifies those dominant

in the second stage.

We can summarize the main characteristics of

each stage as follows: The first stage sessions present

a high level of connectivity (0.555 on a 0�1 scale)

and SN (0.6) and a very low activity level (0.061); in

a mirror image, connectivity and SN drastically

decrease while the activity increases, even if it does

not reach a very high level. Therefore, in the second

stage of the therapy, a constraint on the discourse

network structure intervenes: The connections

among nodes become loose, with an important effect

on the regulative function of meanings (SN). The

reduction of super-order nodes does not involve a

reduction of the discursive dynamic, which, in fact,

is strengthened (as shown by the increasing activity).

It is finally possible to interpret the outputs of the

network in terms of clinical effects that the therapy is

able to produce on the patient. In the first phase of

the therapy, the interaction between therapist and

patient operates toward a limitation of the patient’s

beliefs and ways of perceiving experiences. The

therapy starts with a consistent wealth of meanings

regulating the patient’s interpretation of experience;

this is confirmed by the high level of super-order

nodes at the beginning of the therapy. During the

first stage, this level slowly decreases: The initial

assumptions are renegotiated through the discourse.

The reduction of super-order meanings is an effect

of the high connectivity among meanings and, only

marginally, of the activity.

The second one is the stage of re-elaboration of

sense. In this phase, the discourse dynamic enriches

the variability of the connections among meanings.

As a matter of fact, the second neuron, correspond-

ing to the second stage of the therapy, classifies

pattern combination with higher levels of activity.

Then, in the second stage, the communicative

exchange between patient and therapist becomes

capable of enlarging the paths of sensemaking by

enriching the combination among meanings that

these paths are made of. The activity then leads to

the recombination of the super-order meanings.

In summary, the competitive neural network

constructed here detects, among the index patterns

presented, such regularities that allow two classes to

be distinguished, corresponding with the two stages

(limitation and recombination) of the psychotherapy

process.

Conclusions and Potential Developments

The work presented here is an attempt to contribute

to the development of a technique, conceived and

tested in recent years, for analyzing the psychother-

apeutic process: discourse flow analysis.

The main strength and originality of such an

approach is the attempt to depict a model representing

the psychotherapeutic process with its temporal-

dynamic dimensions, by means of integrated statis-

tical methods allowing the creation of an automatic

procedure of analysis. Such methods are here

used in an innovative way: The cluster analysis,

usually adopted for the aims of classification, be-

comes a precious instrument for the content analysis;

the sequence analysis is implemented to discover

the sensemaking dynamics and represents the dis-

course network; the neural networks allow us to

identify the patterns characterizing the psychotherapy

process.

It is worth highlighting that because of its auto-

matic procedures of analysis the method consumes

relatively little time. Depending on the specific

operation involved, the function of researchers is to

define the parameters of the analysis and control or

complete the intermediate output (e.g., the case of

the lemmatization).

The introduction of these devices, and in parti-

cular of the neural network, represents an innovative

contribution to psychotherapy research. Researchers

are aware of the necessity of developing dynamic

approaches to the study of the psychotherapy

process so that it can be analyzed in terms of pattern

modification through time. Nevertheless, this theo-

retical awareness has not yet been translated into

consistent strategies and procedures of empirical

Table X. Parameters of the Network

Weights

Variable Connectivity Activity SN Biases

1st neuron 0.555 0.061 0.60 5.442

2nd neuron 0.214 0.173 0.18 5.430



analysis. The DFA has been elaborated in order to

favor the overcoming of this methodological gap.

Needless to say, the encouraging results reported

in this study must not let its limits be under-

estimated. Our study is based on just one case. We

need more analyses to validate the DFA in terms of

both construct and convergent validity. We have

already obtained some positive results in this direc-

tion (Gennaro et al., 2010; Salvatore et al., 2007, in

press). Other case analyses have been planned to test

with a larger variety of types of psychotherapies,

clinical problems, and outcome.

Moreover, we are aware of several criticisms that

can be prompted by DFA. First, the method is quite

abstract and highly demanding of computational

competence. One could be led to ask: Where is the

clinical side in all this? How could it be useful for

clinicians?

We recognize the relevance of this issue. However,

the fact that a given technical competence is thought

of as sector-based or part of the common ground is a

matter of historical evolution of the field; for

instance, the statistical competence shared among

clinical researchers nowadays are very different in

quantity and quality compared with 20 years ago.

Above all, we believe that the development of a

clinical understanding of the psychotherapy process

can be enhanced by contributions coming from more

abstract and formal models. A method has to be

evaluated not on its complexity but on the mean-

ingfulness of its results in terms of implications for

clinical thought. So the right question is: Could DFA

be useful for the clinical side?

This study allows us to think that the answer to this

question could be positive. The DFA can contribute

to elaborate and validate a general model of the

psychotherapy process (TSSM), a goal that has

theoretical as well as methodological implications.

From a theoretical point of view, DFA proposes an

intersubjective and semiotic view of the psychother-

apy process. According to this view, the clinical

quality of psychotherapy as well as the mechanisms

of change need to be sought at the level of the

functional, structural, and dynamic characteristics

of the communication between therapist and patient

rather than in isolated aspects concerning the

patient’s and/or therapist’s state of mind (feeling,

attitude, thoughts, and the like) or behaviors. From a

methodological point of view, the DFA highlights

the need to move psychotherapy process research

toward a more dynamic and pattern-focused

approach (Greenberg, 1991, 1994; Russell, 1994)

to make it closer and more consistent to the actual

clinical experience and psychotherapists’ professional

culture.

A second critical issue concerns the DFA’s way of

interpreting the indexes. Focusing on the most

important example, we have to say that the inter-

pretation of the SN index as super-ordered meaning,

and thus in terms of the patient’s assumptions,

cannot be considered more than a plausible hypoth-

esis that requires further testing. This means devel-

oping the qualitative side of the DFA. In other words,

it means retrieving the content of the super-order

nodes and analyzing their discursive characteristics

and functions. For the present, what we can only say

is that our preliminary results are consistent with the

hypothesis grounding the TSSM model and the DFA

method.

Finally, we have to come back to the claim made at

the beginning of the study concerning the capability

of DFA to take the contextuality of meanings into

account. Needless to say, DFA has a limited rather

than absolute sensibility to the context. Many con-

textual dimensions and sources (e.g., paralinguistic

cues, nonverbal behavior, and syntactic, lexical, and

pragmatic markers) are not considered, because the

method focuses on just one aspect of contextuality:

the temporal dynamics of the discursive flow. Ob-

viously, no method can fully grasp the contextuality

of communication; suffice to say that this complete-

ness is already lost in the translation from the clinical

exchange to the transcript. On the other hand, the

levels of contextuality are not fully orthogonal among

themselves. Rather, because of the redundant char-

acter of the language, there is systematically a certain

extent of vicariousness through the dimensions of

the context. For instance, a patient striving to keep

himself emotively distant from the content of his

utterances could perform this communicative feat by

using redundantly a variable set of elements (e.g., an

impersonal syntactic expression, a distancing tone,

an abstract lexicon, and a controlled paralinguistic

and nonverbal behavior).

However, it is not possible to say to what extent

the redundancy of the language is a comfortable and

stable ground for the validity of the DFA with the

findings produced as far. The next fundamental step

in the development of the DFA will address this

point, it being the focal issue of any effort toward a

low inferential and time-efficient procedure of tex-

tual analysis aiming to deal with the complexity,

contingency, and dynamism of communication, in

particular the communication in a clinical setting.

Notes1 More precisely, the dialogical nature of sensemaking concerns

three complementary aspects (authors disagree in terms of the

relevance given to each of them). First, any sign (i.e., a word, an

utterance) takes place in the flow of other signs mobilized by the

participants in the dialogue. Every time people use signs, for

16 M. Nitti et al.


speaking as well as for thinking or whatever use (e.g., as an

answer to the earlier signs of the flow, as an anticipation of the

future signs), those utterances are made by a present inter-

locutor, or by an imagined or imaginary other (Linell, 2009). In

that sense, sensemaking*also when it is a product of an

individual thinker*is itself always and already a dialogical

activity, inherently social. Second, signs specify their meanings

according to the position in that flow. The same utterance can

produce different meanings depending on which other utter-

ances come before and after. In that sense, sensemaking is

carried out through dialogue. Third, sensemaking is not merely

a matter of description of the reality. Rather, it has an inherent

social purpose: It is shaped by the intersubjective circumstances

motivating its activation. To use Wittgenstein’s terminology, the

meaning of a sign is its function of regulating the forms of life

carried on by linguistic games. Of course, broader contextual

dimensions (cultural models, ideologies, or structures of power)

are as much constitutive of the dialogical nature of thinking and

communicating as the situated circumstances of these activities

(Ratner, 2008). However, these dimensions have to be activated

within the contingence of the life of people influenced by them,

and it is at this level that we examine them here.2 However, one can observe that the method of content analysis

that DFA adopts makes the disambiguation not strictly

required. Indeed, the method is based on the co-occurrences

of the word within the utterances (see later discussion).

Consequently, the words are interpreted in accordance with

the context of their occurrence (i.e., the other words they tend

to occur with in the same utterance). Therefore, the disambi-

guation can be performed downhill, at the moment of the

interpretation of the cluster of co-occurring words. For instance,

imagine that the word ‘‘subject’’ has not been undertaken to

disambiguation. Moreover, imagine that the output of the

analysis shows that it is associated with words such as ‘‘under-

stand,’’ ‘‘to study,’’ ‘‘book,’’ and ‘‘examination.’’ One can then

conclude that in this context of co-occurrence the semantic

content that is pertinent is probably the one concerning ‘‘branch

of knowledge’’ rather than ‘‘person.’’3 Each lemma is considered a category of the ‘‘discourse’’ variable

and each ECU*that is, the context where a specific lemma is

used*is the statistical unit of analysis.4 Needless to say, this interpretation needs to be deepened and

perhaps articulated through a comparison of the other ECUs of

the cluster as well as of the whole distribution of the ECU of the

other clusters, a task that goes beyond the illustrative aim of our

discussion here (for details, see Salvatore et al., 2010).5 Another important aspect is the dynamic of the network, that is,

the direction of the information flow between and/or within

layers.6 Needless to say, the two stages are not totally distinct and

mutually exclusive. Both of them are present throughout the

whole psychotherapy, within every session, although to different

extents. However, the two-stage assumption asserts that, at the

macroanalytical level, in a clinically efficacious psychotherapy

process, it is possible to discriminate between a first stage

where deconstructive sensemaking is dominant and a second

one where the dynamics of sensemaking take on a constructive

function.7 Obviously, this criterion is valid insofar as the SN trend is

consistent with the U-shape model expected by the TSSM.8 The clinical literature provides various conceptualizations of the

clinical exchange in terms of a dialectical source of constraints

on a patient’s beliefs (inter alia; Salvatore, Tebaldi, & Potı,

2009).9 We have excluded from the analysis the sessions of the middle

period (Sessions 16�30). This is because the variability of

the SN trend in this period leads us to think that, at least in

the case of Pietro, the middle period may have worked as a

phase of transition, with no clear and discriminative pattern

emerging.

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