# Personality Style Recognition via Machine Learning: Identifying Anaclitic
and Introjective Personality Styles from Patients’ Speech

Semere Kiros Bitew∗<EMAIL_ADDRESS>
Vincent Schelstraete∗∗∗<EMAIL_ADDRESS>
Klim Zaporojets∗<EMAIL_ADDRESS>
Kimberly Van Nieuwenhove∗∗<EMAIL_ADDRESS>
Reitske Meganck∗∗<EMAIL_ADDRESS>
Chris Develder∗<EMAIL_ADDRESS>∗IDLab, Department of Information
Technology, Ghent University – IMEC, Ghent, Belgium ∗∗Department of Psycho-
analysis and Clinical Consulting, Ghent University, Ghent, Belgium
∗∗∗TechWolf, Ghent, Belgium

###### Abstract

In disentangling the heterogeneity observed in psychopathology, personality of
the patients is considered crucial. While it has been demonstrated that
personality traits are reflected in the language used by a patient, we
hypothesize that this enables automatic inference of the personality type
directly from speech utterances, potentially more accurately than through a
traditional questionnaire-based approach explicitly designed for personality
classification. To validate this hypothesis, we adopt natural language
processing (NLP) and standard machine learning tools for classification. We
test this on a dataset of recorded clinical diagnostic interviews (CDI) on a
sample of 79 patients diagnosed with major depressive disorder (MDD) – a
condition for which differentiated treatment based on personality styles has
been advocated – and classified into anaclitic and introjective personality
styles. We start by analyzing the interviews to see which linguistic features
are associated with each style, in order to gain a better understanding of the
styles. Then, we develop automatic classifiers based on  (a) standardized
questionnaire responses;  (b) basic text features, i.e., TF-IDF scores of
words and word sequences;  (c) more advanced text features, using LIWC
(linguistic inquiry and word count) and context-aware features using BERT
(bidirectional encoder representations from transformers);  (d) audio
features.  We find that automated classification with language-derived
features (i.e., based on LIWC) significantly outperforms questionnaire-based
classification models. Furthermore, the best performance is achieved by
combining LIWC with the questionnaire features. This suggests that more work
should be put into developing linguistically based automated techniques for
characterizing personality, however questionnaires still to some extent
complement such methods.111Code will be available in
https://github.com/semerekiros/personapredict \- upon acceptance.

##  1 Introduction

Personality is considered a crucial variable in explaining the heterogeneity
observed in psychopathology (?). It has been demonstrated that the large group
of people meeting the commonly used criteria for depression (e.g., DSM or ICD
criteria) – which affects over 280 million people worldwide (according to the
WHO’s 2021 report),222https://www.who.int/news-room/fact-
sheets/detail/depression – is not homogeneous. On the contrary, the
heterogeneity of people covered by the general label of depression is widely
recognized (?).

From multiple perspectives, a two-polarity model has been put forward to
distinguish between depressed patient groups on the basis of two personality
dimensions, namely relatedness and self-definition (?, ?, ?). Drawing on
Freud‘s (?) early distinction between hysteria (libidinal, oral fixation) and
obsessional neurosis (aggressive, anal fixation), Blatt (1974, 2004)
distinguishes between an anaclitic and introjective personality style,
respectively. The _anaclitic_ personality style is focused on interpersonal
relationships and dependency, whereas the _introjective_ personality style is
focused on self-definition, autonomy, and interpersonal distance. Beck (1983)
proposed similar interpersonal characteristics, sociotropy and autonomy, to
discern different personality styles in depression. Sociotropy indicates an
intense need for intimate relationships, whereas autonomy is associated with a
desire for independence and achievement. As such, sociotropy and autonomy
correspond with the anaclitic and introjective personality dimensions,
respectively. Similar to Blatt (1974), Beck (1983) assumes that individuals
are more prone to react to certain stressors, dependent on their personality
style. Anaclitic/sociotropic individuals are vulnerable for interpersonal
stressors (e.g., breakups), whereas introjective/autonomous individuals are
more sensitive to stressors on the level of achievements and self-definition
(e.g., job loss). Consequently, a predominance of either personality dimension
leads to the development of distinct symptoms when confronted with these
specific stressors: different phenotypical manifestations of depression have
been described from this perspective. The symptom-specificity hypothesis (?)
assumes that anaclitic depression, typified by an overdetermination of
dependency and a preoccupation with interpersonal consolidation, is associated
with somatic symptoms and phobias. An introjective depression on the other
hand, is dominantly focused on self-definition and interpersonal distance and
involves symptoms on a more cognitive level, exemplified by self-criticism,
perfectionism and pathological doubt. Noting these different manifestations,
it has been argued that also treatment should be tailored according to
patients’ personality styles, with anaclitic patients benefiting more from
structured, supportive therapy approaches, whilst patients with an
introjective personality style would profit more from insight-focused
treatments (?). Thus, a first fundamental step supporting this would be
effective determination of a patient’s dominant personality style.

Strikingly, notwithstanding their solid theoretical underpinnings, after
almost half a century, personality style assessment remains a challenge, which
is embodied by research failing to yield consistent support for the symptom-
specificity hypothesis (?, ?) and outcome studies, as of yet, not having
convincingly demonstrated the benefits of matching personality styles to
different treatment strategies (?, ?). At the heart of this predicament
probably lie the methodological difficulties associated with the assessment of
personality styles (?). The determination of personality styles typically
includes the administration of self-report questionnaires, such as the
Depressive Experience Questionnaire (DEQ) (?) and the Personal Style Inventory
(PSI) (?). However, research literature is inconsistent as to whether these
questionnaires are apt (valid) to distinguish between the clinical
manifestations (symptoms, interpersonal functioning) associated with an
anaclitic and introjective personality style (?, ?). To make further progress,
there is a need to explore alternative approaches to detect personality styles
with greater precision. More recent approaches such as prototype matching (?)
are promising, since they combine scientific rigor with clinical expertise.
Yet, they are time consuming because they rely on the expertise of several
well-trained human raters. Natural Language Processing (NLP) and Machine
Learning (ML) techniques, originating from the field of computer science,
might alternatively provide ways to assess psychological features and
phenotypes based on complex data without the interference of human raters (?).
NLP focuses on automated processing of human language, learning and applying
the underlying linguistics and semantics in computerized systems (?). Today,
NLP applications heavily rely on machine learning techniques. Machine learning
(ML) is a subset of artificial intelligence that “automatically determines
(i.e., learns) methods and parameters to reach an optimal solution to a
problem rather than being programmed by a human a priori to deliver a fixed
solution” (?). In other words, ML offers techniques to identify patterns,
phenotypes or subgroups in a dataset (e.g., text, audio, self-report
questionnaires, biological data) through learning from the data with minimal
prior assumptions. Although ML techniques are mostly known for their
commercial (e.g., personalized advertisements via internet searches) and
medical applications (e.g., algorithms to detect cancer), there is over two
decades worth of research in the psychological field, concerning the
diagnosis, prognosis and treatment of diverse mental conditions (?), such as
depression (?) and suicide contemplation (?). Unfortunately, the use of mostly
complex biological data (e.g., neuroimaging and MRI data) complicates the
translation of ML solutions to practical and clinical implementations (?). In
the context of psychotherapy research, however, what patients say about
themselves and their symptoms is an essential, if not the most important
source of information, which makes patients’ narratives a particularly
suitable source of data for NLP and ML. For the detection of depression in
general, there are several studies using NLP on textual data (?, ?, ?, ?). As
an example, the Linguistic Inquiry and Word Count (LIWC), a text analysis
program that counts the frequency of words and types of grammar typically used
in certain contexts, Tausczik and Pennebaker (2010), found that depressed
people use more negative words and terms related to death on social media,
while also using the first person more frequently than healthy controls. Next
to the detection of depression using textual information from social media (?,
?), depression has been successfully inferred using transcripts from screening
interviews (?) and therapy sessions (?). However, as Dwyer et al. (2018) note,
it does not suffice to use ML to detect broad categories (e.g., depression
yes/no), as treatment for depression in general shows relatively low efficacy
(?). Especially for the heterogeneous depressed population, it would be
fruitful to use ML techniques to predict more specific features, such as
personality style, which would allow more tailored clinical decision making.

Thus, several works were proposed that employ ML for automatic personality
prediction using different personality frameworks (measures) such as Big Five
(?), MBTI (?), and Catell’s 16PF (?). The majority of research makes use of
the Big Five personality measure for personality trait classification (?, ?,
?, ?, ?) followed by MBTI (?, ?). The Big Five personality measure defines
personality through the following five dimensions: extroversion,
agreeableness, conscientiousness, neuroticism, and openness. Similar to
depression detection systems, automatic personality detection ML models use a
wide range of data sources; these sources include digital footprints in social
media platforms (e.g., Reddit (?), Twitter (?), Facebook (?, ?)), essays
written in a controlled environment by volunteers (?, ?), video and audio
recordings of meetings (?). The input modalities that these ML models use
include text, audio, visual cues, and multimodal inputs. Many personality
detection ML models that use _textual data_ as input extract features using
two approaches: closed-vocabulary and open-vocabulary. Closed-vocabulary
approaches rely on a priori determined word categories, each represented by a
fixed set of words (where some may belong to multiple categories). For
example, LIWC, which is the most common closed-vocabulary method for
personality detection from text, categorizes the words in a text into various
psychologically relevant clusters like ‘affective processes’ (e.g., ‘happy’,
‘cried’, ‘nervous’) and ‘social processes’ (e.g., ‘pal’, ‘buddy’, ‘cousin’,
‘woman’). The frequency counts of words for each cluster are used by ML models
to predict the personality of the text author (?). On the other hand, open-
vocabulary methods rely on extracting a comprehensive collection of language
features from textual input such as n-gram features, part of speech (POS)
tags, usage of emoticons, length of posts of users from social media, and use
of word embeddings (e.g., Word2vec, GloVe) to build ML models for personality
detection (?, ?). Apart from the actual words spoken, additional features can
be extracted from the _audio signal_ , e.g.,  loudness, spectrals, pitch,
interruptions, acoustic features, etc. For instance, as the first stage in
their deception detection pipeline, (?) use prosodic, acoustic, and LIWC
features to predict the personality traits of speakers engaging in a dialogue.
They analyzed how each LIWC element contributed and discovered, among others,
that the dimensions of “focus-future” and “drives” are the most helpful
features for determining “extroversion” personality traits, whereas “time” and
“work” are significant for determining “conscientiousness.” They also show
that incorporating the personality traits as an input improves their deception
detection model.

_Visual-based_ ML models for personality prediction mainly extract features of
the body, especially the face. Facial features such the shape of nose and
eyebrows, eye openness, and mouth have been used to infer personalities of
users (?). For example, Liu et al. (2016) trained an ML model to predict
personality of people by analyzing their Twitter profile pictures and found
users high in ‘openness’ prefer more aesthetic pictures, while users with
strong ‘agreeableness’ and ‘conscientiousness’ display more positive emotions
in their photos. Similarly, Cristani et al. (2013) studied the relationship
between personality of users and the types of pictures they prefer (e.g., by
examining the types of pictures they like). They found that pictures posted as
“favourite” by users in Flickr333https://www.flickr.com/ were correlated with
their personality traits. For example, they found that extrovert individuals
show a preference of pictures portraying people while introvert show the
opposite preference.

Other works combine one or more of the above discussed modalities to predict
personality traits (?). For example, Kindroglu et al. (2017) combine features
from audio and visual modalities to predict extraversion and leadership
traits. As of yet, to the best of our knowledge, ML techniques have not been
used to learn typical linguistic properties that assess personality styles at
a specific level of behaviors (i.e., anaclitic vs. introjective) for clinical
psychology purposes.

Therefore, the aim of our study is twofold, namely to  (a) better understand
anaclitic versus introjective primary personality traits through the analysis
of natural language; and  (b) investigate possible ML solutions for automatic
personality prediction.

First, we explore important linguistic characteristics of anaclitic versus
introjective patients by performing feature analysis on data from intake
interviews (transcripts and audio) of depressed patients. Notwithstanding the
explorative nature of feature analysis, there are certain expectations that
can be drawn from a recent line of preliminary descriptive research into the
different linguistic styles of anaclitic and introjective patients (?, ?).
Since anaclitic patients are highly concerned with the development of
meaningful and satisfying interpersonal relationships (e.g., expressed through
LIWC categories as ‘social’, ‘family’, ‘affiliation’), we expect them to use
more pronouns in the second and third person such as ‘you’, ‘he’, ‘she’, etc.
Conversely, we expect introjective patients to frequently use the word ‘I’,
since they are rather preoccupied with achieving a differentiated and
consolidated identity (e.g., LIWC category ‘achieve’). Furthermore, we expect
anaclitic patients to use more emotional words (e.g., LIWC categories ‘affect’
and ‘feel’) and speak with an affective invested intonation, whereas more
cognitive and causal words (e.g., LIWC categories ‘CogProc’, ‘Insight’,
‘Cause’) are expected in introjective patients’ speech. In a nutshell, the
first objective is to show how analyzing verbal behaviors coalesces into an
interpretable and meaningful distinction in personality.

Second, we investigate whether personality classification into anaclitic or
introjective can be automated via ML techniques, using basic and more advanced
classifiers with diverse data as input (text, audio, questionnaires, and
different combinations of these). The aim hereof is to  (a) assess whether ML
allows automatic personality style prediction as good (or better) than via the
traditionally used self-report questionnaires that are crafted by experts, and
(b) which data and selected ML method attains the better personality
classification performance.

The remainder of the paper is organized as follows: Section 2 describes how
the clinical diagnostic Interview (CDI) was conducted, as well as the data
pre-processing, the feature extraction and the machine learning models we
used. Section 3 presents the analytical findings followed by the predictive
experimental results. Section 4 summarizes our conclusion and discussion.

##  2 Materials and Methods

###  2.1 Participants

The dataset comprises the intake interviews of 79 patients enrolled in the
Ghent Psychotherapy Study (GPS), a randomized controlled trial studying the
differential efficacy of cognitive behavioral therapy and short-term
psychodynamic psychotherapy for depressed patients with a predominant
anaclitic or introjective personality style (?). All patients are diagnosed
with Major Depressive Disorder (MDD), assessed by the Hamilton Rating Scale
for Depression (?) and Structured Clinical Interview for DSM-IV-TR (?).
Exclusion criteria were primary diagnosis of substance abuse/dependence, acute
psychotic symptoms or suicidal ideation. The sample consists of 50 anaclitic
patients (12 males (24%), 38 females (76%); age range 20-60 years (mean =
38.3, standard deviation = 12.3)) and 29 introjective patients (12 males
(41.4%), 17 females (58.6%); age range 21-59 years (mean = 37.6, standard
deviation = 11.0)).

###  2.2 Procedures and Measures

Before enrollment in the study, patients filled in a large test battery at
home consisting of the _Depressive Experience Questionnaire_ (DEQ) (?) and the
_Personal Style Inventory_ (PSI) (?) to assess personality characteristics;
and the _Beck Depression Inventory-II (BDI-II)_ (?), the _Depression Anxiety
Stress Scale_ (DASS) (?), the _Symptom Checklist-90_ (SCL-90) (?), the
_Inventory of Interpersonal Problems-32_ (IIP-32) (?), the _Self-rating
Inventory for Posttraumatic Stress Disorder_ (SIL) (?), the _Outcome
Questionnaire-45_ (OQ-45) (?), _Experiences in Close Relationships_ (ECR) (?)
and ten emotions on a _Visual Analogue Scale_ (VAS) (?) to assess symptoms and
complaints related to depression, anxiety, trauma, interpersonal problems,
overall well-being and emotional experiences.

The Clinical Diagnostic Interview (CDI) (?) was administered and audio-taped
by a member of the GPS research team upon the intake of the patient. The CDI
is a semi-structured interview that questions both current clinical complaints
and meaningful lifetime events. The interview guide was built using open
questions to evoke rich narratives concerning inter- and intra-personal
experiences. The raw audio files of these intake interviews were transcribed
using preset standards, and the resulting textual transcripts saved as Word
documents.

Personality style was assessed through an intensive prototype matching
procedure (?) conducted by three independent and trained GPS researchers
(including the researcher who conducted the interview). The procedure consists
of each researcher rating the CDI individually on a scale of 1 to 5 for both
the anaclitic as well as the introjective personality style using prototype
vignettes (?). In a second step, these independent ratings were discussed to
reach consensus. To assign a personality style to a participant, a score of at
least 3 for one of the personality styles and a minimum of 2 points difference
with the score on the other personality style were required. When no agreement
could be reached, the difference in consensus scores between dependent and
self-critical personality dimensions was less than 2 points, or there was no
score of at least 3 on either dimension, patients were excluded from the
trial.

###  2.3 Data Preprocessing

Our data analysis is based on features obtained from the CDI intake
interviews, by processing both audio files and human produced transcripts
thereof, as sketched in Fig. 1. The transcripts (i.e., Word documents) of the
CDI were segmented into questions, answers, and background noises according to
the text formatting. Anonymization was performed by first extracting
capitalized words, thus obtaining a list that was manually filtered to retain
a list of unique person and location names to anonymize (i.e., replace them
with a special $<$name$>$ or $<$location$>$ tag). Using the _Natural Language
Toolkit_ (NLTK) (?), the text was then further tokenized and standardized
(e.g., lowercasing, mapping dialect words and numbers to standard language,
applying tags for sounds and anonymizations) and eventually appended into a
JSON-file. The audio (i.e., recorded in MP3-format) of the CDI, was converted
to a mono-recording and stored as a WAV-file with a normalized volume, a
sample rate of 16 kHz and a bit-depth of 16 bits per sample. Speaker
segmentation and speaker clustering (?) was used to separate speech into
interviewer, interviewee utterances and non-speech, using the Open Speech and
Music Interpretation by Large Space Extraction
(openSMILE).444https://www.audeering.com/research/opensmile/ openSMILE is a
modular and flexible toolkit used for feature extraction from audio files. The
resulting segments were stored as separate TextGrid files.

###  2.4 Feature extraction and Machine Learning Models

In this section, we discuss how the features extracted from the CDI intake
interviews are further processed before they can be fed to machine learning
models. Additionally, we discuss which machine learning techniques were used
in order to create models that classify the personality styles of patients.
Figure 1 shows the complete feature extraction pipeline. We built our machine
learning models based on the feature categories we will discuss next.

Figure 1: The input data and the feature extraction pipeline. The features
extracted from the CDI intake interviews include: TF-IDF, LIWC, BERT features,
and audio features. The questionnaire features are filled by the patients.

####  2.4.1 Questionnaire features

As briefly introduced in Section 2.2, patients filled a large battery of
questionnaires before they were enrolled in the study. The joint outputs of
all collected questionnaires yielded a feature vector with a total of 358
elements. This feature vector contains both categorical and numerical values.
It is then fed to several classifiers for determining the personality trait of
the the patients. For our questionnaire feature set, we trained a simple
logistic regression (LR), a random forest (RF), and a CatBoost (CB)
classifier. We use the LR as our baseline because it is one of the most basic
and popular algorithms to solve binary classification problems. The second ML
model, random forest (RF) is an ensemble of decision tree classifiers, in
which it is ensured that each tree is an acceptably good classifier on its own
and that the trees are uncorrelated. Finally, we use CatBoost as it is
specifically designed for dealing with categorical variables and has proven to
attain state-of-the-art results for classification tasks based on tabular
data.

####  2.4.2 TF-IDF features

To process the textual transcripts, for each word in a document we calculated
the TF-IDF scores as features (?). An inverse document frequency (IDF) weight
indicates the relevance of a certain word within a certain document, with
lower weights being associated with words that are common in the corpus and
thus have small discriminating power. This word IDF is then multiplied by the
term frequency (TF) in a particular document to obtain the TF-IDF score of
that word for the respective document. Calculating those TF-IDF scores for
each word, we thus summarize a document as a vector of these per-word scores,
indicating which words are important in the document and which are not. This
procedure was applied to the interview transcriptions, converting each
document to a high-dimensional sparse real-valued vector, with a value for
each different word encountered. The same TF-IDF scores were also calculated
for N-grams. An N-gram is a sequence of N consecutive words appearing in a
document, thus allowing longer grammatical constructions to be captured as
well. In sum, the set of features for the TF-IDF based classifiers consists of
a vector for each document containing the TF-IDF score for that document of
each N-gram (N = 1, 2, 3) in the corpus (i.e., 840,834 elements).

We used logistic regression (LR), random forest (RF) and CatBoost (CB) to
classify the patients. Rather than considering all transcribed speech of the
patient in the complete interview as a whole, we also experimented by
considering “chunking” their utterances in the individual answers, treating
each per-answer “chunk” as a separate document sample. We then applied
majority voting on the per-chunk classification to predict the personality of
a given patient.

####  2.4.3 Psychological features

Next, we turned to text processing techniques specifically tailored to an
application with features known to be indicative of psychological
characteristics and/or sentiment. First, we employed the 2015 Dutch version of
the _Linguistic Inquiry Word Count-Lexicon_ (LIWC), a computerized text
analysis tool for counting words in 73 psychology-relevant word categories,
established through psychometric studies on linguistics (?). The output of the
LIWC tool is a vector that contains the absolute number of times a word from
each category was used. We normalized these absolute count values to fractions
of all uttered words in patient’s answers. Mann-Whitney-Wilcoxon tests were
performed to establish which categories are relevant discriminators between
the two personality styles (?) where the significance of each category was
expressed as a $p$-value. The results will be discussed in Section 3.1.

Second, we used the sentiment analysis functionality from the _pattern.nl_
library. The sentiment-function takes a sentence or paragraph as input and
returns a tuple of two values, subjectivity and polarity. The subjectivity has
a value between 0.0 and 1.0, with low values when the text is objective (e.g.,
“The movie was about squirrels.”) and higher values indicating subjective
statements (e.g., “The movie was very good!”). The polarity indicates whether
the sentiment expressed in a sentence (e.g., “I absolutely hated the movie”,
“it was quite good”) is negative or positive, with values ranging between –1.0
and +1.0. The greater the absolute value, the stronger the sentiment. To
extract sentiment features from transcripts, we first compute the polarity and
subjectivity of each sentence in the transcript, then use the mean and
standard deviation for both dimensions as the feature set. Again, Mann-
Whitney-Wilcoxon tests were performed to examine whether the differences in
terms of subjectivity and polarity between the two personality styles were
significant and significance was expressed as a $p$-value. See Section 3.1 for
details.

####  2.4.4 BERT features

Another feature set we extracted makes use of advanced deep neural models
called transformers (?), which represent words depending on their semantic
role in the context of the text. Bidirectional Encoder Representations from
transformers (BERT) (?) is the most popular pre-trained language model that
has been widely used to extract rich representations from textual data and
proved to attain state-of-the-art results in several NLP tasks such as
sentiment analysis, question answering, machine translation, etc. Moreover,
several downstream tasks such as depression detection (?), suicide risk
assessment (?), and personality detection (?) have benefited from such
context-aware representations. Since our data is in Dutch, we use RobBERT (?),
a variant of BERT that is pretrained on Dutch language, which has achieved
state-of-the-art performance for a wide range of tasks in Dutch. Typically, a
BERT based model gets an input text (i.e., the textual transcripts) prepended
by a special token called _CLS_ , and produces output representations (i.e., a
vector of 768 elements) for each of the words in the input and the CLS token.
The CLS token is pretrained to represent the entire input sequence. Thus, the
vector representation of CLS is used as the extracted feature for the input
transcript. Another method is to combine the word representations excluding
the CLS token, for example, taking the average or the maximum of over all
vector representations. In this paper, we experiment with both CLS
representation and max-pooled representation.

Since the input to BERT models is limited to only 512 tokens, we segment our
textual transcriptions into chunks of 512 tokens. For determining the class
label of a transcript, we look at the predicted labels of its chunks and
decide the final class label based on majority voting over the individual of
the chunks labels. For this feature set, we built a simple logistic regression
model.

####  2.4.5 Audio features

Since in depression studies, noticeable differences in speech production have
been suggested as potential biomarkers for depression (?), we also collected
such features to investigate whether they are useful to detect personality
differences. As it has also been established that there are language
differences between males and females (?), we will also consider gender as a
confounding variable. From the audio, features were extracted from the
patients’ speech using openSMILE, a modular and flexible toolkit used for
feature extraction from audio files (?). We chose to use the extended version
of the _Geneva Minimalistic Acoustic Parameter Set_ (GeMAPS) as configuration,
as it obsoletes feature selection (?). This resulted in a feature vector with
88 low-level audio descriptors for each segment in which the patient was
speaking. These descriptors include frequency-, energy- and amplitude-related
parameters, as well as spectral and temporal parameters. These parameters were
normalized to have zero mean and unit variance across all segments belonging
to one patient. We also calculated the speech rate for the patient, using two
different metrics. The first is the so-called _speaking rate_ , calculated as
the number of words spoken per second, where the time counted includes the
hesitations between the words where no words are pronounced. The second is the
_articulation rate_ , expressed in characters per second, where silences or
hesitations where no speech is produced are excluded from the timekeeping.
Finally, we also calculated the fraction of time a patient hesitates while
speaking and recorded it as the _hesitation fraction_. The audio feature set
thus contains 91 features in total (i.e., 88 low-level audio features and 3
speech rate-related features). Again for this feature set, we experimented
with logistic regression, random forest and CatBoost machine learning models.

##  3 Results

In this section, we present and discuss the analytical and experimental
findings. Section 3.1 provides a thorough analysis of both textual and audio
modalities to distinguish anaclitic from introjective personality traits.
Section 3.2 reports the results of the predictive ML models that were built
using the features introduced in Section 2.4.

###  3.1 Analysis

####  3.1.1 Text Analysis

Figure 2: Distribution of the answer length (in number of tokens without
punctuation) of the patients’ aggregated answers in the CDI transcripts: (a)
empirical cumulative distribution function (ECDF), (b) histograms

  * •

Answer Length The average transcript length of the CDIs is 15,226 words
(disregarding punctuation), with a standard deviation of 5,208 words. The
patient’s answers constitute the bulk thereof, i.e., 13,556 words (standard
deviation of 5,131 words). This standard deviation is fairly large, which can
be explained by the rather limited size of the dataset. The distribution of
the document length is shown in Fig. 2 for the complete transcription set and
for the different personality styles separately. This figure shows that the
length of answers does not seem to significantly differ between personality
styles or sexes.

(a) TF-IDF score distributions

(b) Fraction of tokens in given LIWC category

Figure 3: Lexical features that are potentially useful for distinguishing
anaclitic (A) versus inrojective (I) personality styles: (a) TF-IDF score
distribution for the most discriminating words, (b) LIWC category frequency
(measured as fraction of text tokens). The stated $p$-values result from a
Mann-Whitney-Wilcoxon with null hypothesis that the feature distributions are
the same for anaclitics and introjectives. Note: × represents the mean value.

  * •

TF-IDF Analysis: Before considering specific word categories in LIWC, we
analyzed the pure vocabulary usage by means of individual words’ TF-IDF
scores. In Fig. 3(a), we plot the distributions of TF-IDF scores in the
anaclitic and introjective patient populations for words that (i) occur in all
transcripts; and (ii) occur significantly in one of the personality
categories, according to a Mann-Whitney-Wilcoxon test at $p<0.05$. Even though
these features consider documents as bags of words, and thus ignore word order
or the context in which they occur, we still observe some meaningful insights.
We note the higher frequency of personal pronouns — which are notable features
in distinguishing personality styles (?) — in anaclitics (see “ik (I)”, “mij
(me)”, “mijn (my)”, “ze (she/they)”). For introjectives, we further note more
prevalent “denk (think)”, “wel (well)”, “ook (also)” which one would expect to
occur more in statements reflecting cognitive processes, nuanced reasonings,
etc. and thus is in accordance with our a priori expectations. We also remark
that introjectives utter more non-word sounds (the “<sound>” marker),
vocalized as “uh”, “pff” and the like, which could be seen as hesitations that
reflect an ongoing cognitive process while talking.

Table 1: LIWC categories whose relative frequency among the words used by introjective versus anaclitic patients is significantly different, at least at $p<0.10$ .  Rank | Category |  $p$-value | Dominant personality style
---|---|---|---
1 | ppron | 4.43E–05 | anaclitic
2 | percept | 2.74E–03 | anaclitic
3 | focuspast | 3.69E–03 | anaclitic
4 | pronoun | 4.06E–03 | anaclitic
5 | hear | 4.19E–03 | anaclitic
6 | shehe | 4.47E–03 | anaclitic
7 | tentat | 2.43E–02 | introjective
8 | i | 2.56E–02 | anaclitic
9 | cause | 2.99E–02 | introjective
10 | focuspresent | 3.31E–02 | anaclitic
11 | home | 3.31E–02 | anaclitic
12 | body | 4.24E–02 | anaclitic
13 | verb | 4.35E–02 | anaclitic
14 | social | 4.91E–02 | anaclitic
15 | sad | 5.27E–02 | anaclitic
16 | motion | 6.34E–02 | anaclitic
17 | male | 6.94E–02 | anaclitic
18 | adverb | 7.59E–02 | introjective
19 | they | 8.47E–02 | anaclitic
20 | filler | 9.04E–02 | introjective
21 | focusfuture | 9.23E–02 | introjective
22 | death | 9.63E–02 | introjective
  * •

LIWC Analysis: Because of the variation in interview length (see earlier),
instead of absolute word counts, we opted for relative usage of LIWC word
categories (i.e., the fraction of words used belonging to a given LIWC
category). Analysis of these statistics is summarized in Table 1, which
reports the $p$-values of a Mann-Whitney-Wilcoxon test with null hypothesis
that the feature distribution in anaclitic and introjective patient
populations is the same. Note that we only consider word categories that are
used at least once by all patients, and at least 10 times per interview on
average over all patients. We find that personal pronouns ( ‘ppron’, ‘shehe’,
‘I’, ‘they’) as well as pronouns ( ‘pronoun’) in general, comprise a larger
fraction of uttered words for anaclitics than for introjectives. Further, also
the categories ‘home’ and ‘sad’ are significantly more prominent in anaclitic
than introjective patients (at $p<0.05$ ). In line with our expectations,
words belonging to the cognitive process sub-categories ‘cause’ (causation,
e.g., ‘because’, ‘effect’) and ‘tentat’ (tentative, e.g., ‘maybe’, ‘perhaps’)
are significantly (at $p<0.05$) more common in introjective patients’ speech
compared to anaclitic patients and, vice versa, words belonging to the
category ‘social’ are more frequently used by anaclitic patients. Another
interesting finding is that anaclitic patients seem to be preoccupied with the
present and past (categories ‘focuspresent’ and ‘focuspast’), while
introjective patients seem more invested in the future (category
‘focusfuture’, only marginally significant at $p<0.10$). Figure 3(b) shows the
distributions of these LIWC category usage frequencies in the form of box
plots. This confirms that mainly (personal) pronouns, as well as ‘sad’ and
‘focuspast’ are discriminative word categories to distinguish anaclitics from
introjectives.

Figure 4: Answer and question polarity distributions. Polarity is determined
positive for values $>0.3$, neutral in the range [-0.3, +0.3], and negative if
$<–0.3$

  * •

Sentiment Analysis: we analyzed the sentiment of the answers given by the
patients during their interview with therapists. First, we discarded short
answers (i.e., containing <10 sentences because the sentiment of a patient is
more confidently inferred from longer replies). Then, as introduced in Section
2.4.3, we calculated the polarity and subjectivity of patients’ answers by
taking the average of the polarity and subjectivity of all individual answers.
Studying the distributions of the patients’ answer polarity and subjectivity
did not reveal any statistically significant conclusion.

Because the answer sentiment analysis did not yield conclusive findings, we
opted to use an alternative approach by comparing question-answers pairs (?).
From these pairs, we wanted to investigate two dimensions:  (1) whether
therapists are more likely to ask positive or negative questions to patients
with anaclitic or introjective personality styles, and  (2) whether anaclitic
and introjective patients respond more positively or negatively to these
questions.

To examine the first dimension, we look at the polarity scores of the
questions and answers. Since polarity scores range between $-1$ and $1$, we
divided them into three equal parts to reflect positive, neutral and negative
polarity. A question/answer was regarded positive if the average polarity over
its sentences was larger than $0.3$, negative if it was less than $-0.3$, and
neutral otherwise. Figure 4 suggests that interviewers/therapists are not
biased towards a certain personality style in terms of differentiating the
proportions of positive and negative questions.

Figure 5: Question-answer pair sentiment polarity combinations: per question
sentiment, we calculate the fraction of corresponding answers for each of the
positive/neutral/negative sentiment categories. The sentiment category of a
question/answer is assigned according to the most polarized sentence therein

Next, to understand the second dimension, we studied how patients’ responses
were affected by the polarity of the questions posed to them by the
interviewers. To achieve this, we first used the most polarizing sentence in
the question/answer as our approximation of its sentiment (negative for an
extreme polarity $<-0.3$ , positive for $>$ 0.3, else neutral). Then, for a
given question, we calculated the the fraction of answers that fall in each
polarity category for both personality styles as depicted in the heat map in
Fig. 5. Anaclitic patients seem to stick more to giving neutral answers, while
introjectives seem more inclined to follow the question’s polarity.

####  3.1.2 Audio Analysis

We first analyzed the potential difference in audio feature measurements
between anaclitic and introjective patients. For the 88 raw audio features, we
calculated ANOVA F-scores to determine their respective importance. A high
F-score indicates that a feature explains a higher fraction of the total
variance and thus is more informative. Using this approach, the 10 most
relevant features are mean F3-frequency, mean F2-frequency, mean alpha ratio,
20th percentile loudness, mean harmonic difference H1-A3, standard deviation
local shimmer (dB), mean local shimmer (dB), mean pitch, mean Hammarberg index
(voiced) and mean loudness, respectively. (?) describes the acoustic meanings
of these features. For example, the mean F2 and F3 frequencies indicate a
resonant voice while the mean harmonic difference H1-A4 correlates to a creaky
voice. However, we did not find indicative features that are meaningfully
associated with one of the personality styles, i.e., whether any of the
features has different group means between introjective versus anaclitic
patients.

Figure 6: High-level audio feature measure distribution in anaclitic and
introjective patient populations. Note. × represents the mean value

Additionally, for the more high-level features related to speech rate and
hesitations, (a) speaking rate, (b) articulation rate, and (c) hesitation
fraction, we performed Mann-Whitney-Wilcoxon tests. Yet, again we found no
statistically significant differences in those measures between anaclitic and
introjective patient populations, as can also be observed from the largely
overlapping box-plots in Fig. 6.

###  3.2 Machine Learning for Automatic Personality Classification

In this section, we provide the results of the several classification ML
models we built to predict personality traits of patients. We use F1-score and
Cohen’s kappa as evaluation metrics to measure the performance of our models.
F1-score can be defined in terms of true/false positives, and true/false
negatives. For a given class (i.e., personality style), a true positive (TP)
is a positively classified sample (i.e., having the given personality style)
that was actually positive and a true negative example (TN) can be defined
similarly. A false positive (FP) is a positively classified patient that in
reality is negative for the given class, while a false negative is the
counterpart of this. As such, the precision (P) of a classifier is the
fraction of positive classifications that was actually positive, as shown in
equation Eq. (1). The recall (R) of a classifier is the fraction of positive
training examples that is actually identified as being such (see equation Eq.
(2)). Finally, the F1-score is calculated as the geometric mean of precision P
and recall R as shown in equation Eq. (3).

${P}=\frac{TP}{TP+FP}$ (1) ${R}=\frac{TP}{TP+FN}$ (2) ${F1}=\frac{2*P*R}{P+R}$
(3)

On the other hand, _Kappa_ is a measure originally designed as a measure of
agreement between two judges, based on the accuracy but corrected for chance
agreement. In a classification setting, it is used to evaluate the agreement
between the actual and the predicted classes by the model. We use it because
it implicitly accounts for class imbalance. Cohen’s Kappa is calculated with
the following formula:

$\kappa=\frac{P_{o}-P_{e}}{1-P_{e}}$ (4)

where $P_{o}$ is the overall accuracy of the model (i.e., calculated as
(($TP+TN)/TP+TN+FN+FP$) and $P_{e}$ is the expected agreement between the
actual classes & predicted classes. For a binary task like ours, $P_{e}$ is
the sum of $P_{e1}$, the probability of the predictions agreeing with actual
values of class 1 (i.e., anaclitic) by chance, and $P_{e2}$, the probability
of the predictions agreeing with the actual values of class 2 (i.e.,
introjective) by chance. Assuming that the actual class values and model
predictions are independent, these probabilities (i.e., $P_{e1}$ and $P_{e2}$)
are calculated by multiplying the proportion of the actual class and the
proportion of the predicted class. $P_{e}$ is calculated as follows:

${P_{e}}={P_{e1}+P_{e2}}={P_{e1,actual}\cdot
P_{e1,predicted}+P_{e2,actual}\cdot P_{e2,predicted}}$ (5)

To evaluate our proposed classifiers’ performance, we used stratified 5-fold
cross-validation, where the dataset of 79 patients was split into 5 data
segments that (approximately) have the same ratio of anaclitic and
introjective patients (63% resp. 37%). We used the same splits for each
individual classifier, training a classifier model using 4 data segments as
training data and evaluating it on the remaining unseen data segment. To
report our results, we calculate each metric as an average over the 5-fold
cross-validation runs. We repeat this split into 5 folds randomly for 100
times, which results in 100 scores for each metric. Our final results are
averages over these iterations and the corresponding standard deviation, as
shown in Table 2. It is important to note that the comparison between
different models was done using a parallel analysis over the 100 runs. We do a
simple t-test between two sets of 100 scores (i.e., coming from two models) to
check if one model reliably outperforms the other.

Additionally, since there is a class imbalance in our dataset (i.e., 50
anaclitic and 29 introjective patients), we experiment with oversampling
techniques to balance the dataset and examine the effect on the results.
Particularly, we use synthetic minority oversampling technique (SMOTE) (?) to
oversample from the introjective minority class so that the class distribution
becomes 1:1. The approach generates new instances by interpolating positive
instances (i.e., represented by vectors in the feature space) in the minority
class that are close to each other.

The model evaluation for each feature category is shown in Table 2. For the
_questionnaire features_ , the Logistic regression model achieves the best
performance with a macro F1 score of 0.588 and Kappa 0.194. Similarly, for the
basic _TF-IDF features,_ the LR model outperforms the other models with macro
F1 score of 0.578 and kappa score of 0.194. The difference in performance
between the best questionnaire model and best TF-IDF model is found to be not
statistically significant. Moreover, the LR model based on advanced _BERT
features_ , which take into account the contextual information, outperforms
the best model based on the TF-IDF features. We found that this improvement is
statistically significant. The t-test with the null hypothesis that the means
of the two models are equal at 0.05 level of significance is strongly rejected
($p<0.001$). Another interesting finding is the max-pooled BERT feature
representation yields better classification performance when compared with the
the CLS representation confirming conclusions of previous works such as (?,
?).

The best classifier performance using only _one feature category_ is achieved
using the more advanced features, designed for psychological analysis, i.e.,
LIWC features — the Random Forest classifier employing these features
significantly outperforms all the other classifiers with F1 score of 0.896 and
kappa score of 0.801. We observe that adding sentiment features and/or patient
gender (i.e., the psychological features) does not benefit classification
performance (i.e., F1 score drops to 0.884 and kappa drops to 0.778). However,
combining the LIWC features with the questionnaire features produces the best
classification performance. The Catboost model that takes the concatenation of
the LIWC and questionnaire features as an input achieves 0.93 of F1 score and
0.867 of kappa $\kappa$ score. The improvement is statistically significant
with $p=0.001$. Table 3 in Appendix A lists the significance analyses that
compare the various models.

Looking at the _audio-based_ classification models, we find that the random
forest model yields the best results with 0.621 F1 score and 0.268 kappa
score. Moreover, this model reliably outperforms the best TF-IDF model.
Analyzing our models’ means using the t-test results in rejecting the null
hypothesis that they are equal with $p<0.001$.

Finally, the oversampling technique (SMOTE) we used helps achieve higher
classification performance as can be seen in Table 2. All the best-performing
models in each feature category are built using the balanced data. We run a
t-test on the best models in each feature category to determine the
statistical significance of the stated improvements following dataset
balancing. The SMOTE models’ performance improvement is statistically
significant as reported in Table 4 in Appendix A.

Table 2: Classification performance of 5-fold cross-validation results over 100 runs reported as mean $\pm$ standard deviation. The F1 scores are macro averages of the two classes. The best scores for each feature category are underlined, and the best scores over all categories are indicated in boldface.  Features | Models | imbalanced data | balanced data with SMOTE
---|---|---|---
|  | F1 | Kappa | F1 | Kappa
Questionnaire | LR |  0.586 ± 0.101 |  0.188 ± 0.194 | 0.588 ± 0.097 | 0.194 ± 0.187
RF |  0.498 ± 0.113 |  0.115 ± 0.160 |  0.554 ± 0.118 |  0.182 ± 0.188
CB |  0.453 ± 0.128 |  0.086 ± 0.171 |  0.573 ± 0.116 |  0.194 ± 0.204
TF-IDF | LR |  0.570 ± 0.138 |  0.185 ± 0.228 | 0.578 ± 0.130 | 0.195 ± 0.210
RF |  0.503 ± 0.095 |  0.114 ± 0.158 |  0.524 ± 0.113 |  0.128 ± 0.192
CB |  0.526 ± 0.112 |  0.137 ± 0.188 |  0.543 ± 0.121 |  0.144 ± 0.209
LIWC | LR |  0.822 ± 0.129 |  0.662 ± 0.229 |  0.802 ± 0.129 |  0.625 ± 0.230
RF |  0.882 ± 0.148 |  0.779 ± 0.268 | 0.896 ± 0.127 | 0.801 ± 0.238
CB |  0.838 ± 0.221 |  0.717 ± 0.363 |  0.873 ± 0.153 |  0.763 ± 0.273
Psychological | LR |  0.851 ± 0.091 |  0.709 ± 0.175 |  0.845 ± 0.112 |  0.701 ± 0.20
RF |  0.874 ± 0.161 |  0.766 ± 0.295 | 0.884 ± 0.142 | 0.778 ± 0.271
CB |  0.824 ± 0.221 |  0.689 ± 0.366 |  0.868 ± 0.172 |  0.758 ± 0.306
RobBERT-CLS | LR |  0.575 ± 0.080 |  0.182 ± 0.149 |  0.619 ± 0.090 |  0.248 ± 0.178
RobBERT-max | LR |  0.606 ± 0.081 |  0.257 ± 0.127 | 0.627 ± 0.110 | 0.282 ± 0.202
Audio | LR |  0.388 ± 0.006 |  0.076 ± 0.253 |  0.270 ± 0.014 |  0.082 ± 0.196
RF |  0.529 ± 0.143 |  0.169 ± 0.217 | 0.621 ± 0.129 | 0.268 ± 0.248
CB |  0.556 ± 0.149 |  0.156 ± 0.288 |  0.577 ± 0.099 |  0.173 ± 0.190
LIWC + Questionnaire | LR |  0.572 ± 0.081 |  0.159 ± 0.151 |  0.591 ± 0.094 |  0.199 ± 0.180
RF |  0.822 ± 0.175 |  0.674 ± 0.300 |  0.865 ± 0.138 |  0.749 ± 0.247
CB |  0.812 ± 0.163 |  0.653 ± 0.286 | 0.930 ± 0.108 | 0.867 ± 0.206

##  4 Discussion and Conclusion

The _first aim_ of this study was to gain a better understanding of anaclitic
versus introjective personality traits through the analysis of natural
language. We investigated whether using NLP tools to process natural
utterances from patients (as recorded in interviews with a therapist) could
uncover typical language features for anaclitic and introjective personality
styles. We discovered several language characteristics common to one or the
other personality style, such as word use, grammar structures, and language
polarity. In line with our expectations, we found that anaclitic patients
typically use words related to the interpersonal sphere (personal pronouns,
‘social’ category words). In addition, we found that the category ‘home’ was
more common for anaclitic patients. This might be linked to the dominance of
interpersonal themes, as the boundary between an anaclitic person and the
outer world is weaker than for an introjective person. As a result, they tend
to talk more about their home situation. For example: _“I moved to the family
home and my parents moved to the house next door.”_ This example also helps
explain why anaclitic patients — contrary to our a priori expectation — use
the first person more frequently than introjective patients. In focusing on
their relationships with others, they will use more personal pronouns than
introjective patients, for talking about others (_‘shehe’_) in relation to
themselves (_‘I’_). Interestingly, we also found that anaclitic patients tend
to be more preoccupied with the present and the past (see LIWC categories
_‘focuspresent’_ , _‘focuspast’_), while introjective patients seem to care
more about the future (LIWC category _‘focusfuture’_). With a linguistic pun,
this corresponds with hysterics’ emphasis on history (hysteria) (?), framing
their complaints and symptoms in the context of their life story, much more
than an obsessional or introjective patient would, the latter being more
likely to isolate these different domains. Minimally, this finding suggest
that a descriptive approach to personality styles must be supplemented with
theory to understand the phenomenology of the different personality styles and
comprehend the clinical usefulness of discriminating between them.

From the sentiment analysis, we note that the polarity of the questions asked
by the interviewer is not differently distributed between the two personality
styles. This suggests that polarized sentiment is not evoked by a different
attitude of the therapist based on the patient’s personality. We also note
that introjective patients seem to have answers that are more aligned in
sentiment with the interviewer, compared to anaclitic subjects who stick to
giving more neutral answers (i.e., the fraction of answers corresponding in
sentiment to that of the question asked is slightly higher for introjectives).
Our findings within this regard are preliminary at best, but nonetheless
suggest potential avenues for further research into turn taking in therapy.

The _second goal_ of this research was to see whethear personality
classification into anaclitic or introjective personality styles can be
automated well using machine learning techniques. We conclude from our
findings that the advanced psychological features based on LIWC provide the
most discriminating features. The best model, which achieves the maximum
classification performance, is a random forest utilizing these features. When
comparing these results to the ones obtained using only questionnaire
features, the improvement is significant (increasing from 0.588 to 0.896 and
0.194 to 0.801 for F1 and kappa scores, respectively). More importantly, we
find that combining these two feature categories further improves the
classification performance. A CatBoost model trained on this combined feature
set achieves 0.93 F1 score and 0.867 kappa score. See Table 3 in Appendix A
for the t-test significance tests.

Classification of personality styles based on of self-report surveys have been
criticized in the past (?, ?) due to concerns about the validity of the
questionnaires introduced in Section 2.2. The findings of our study confirm
that the questionnaires’ discriminatory power is questionable, as demonstrated
by the poor performance of machine learning models built with these as the
only features when compared to (textual) data extracted directly from patient
interviews. However, it is worth noting that questionnaires complement
advanced textual characteristics, as seen by the rise in F1 score and Kappa
score (i.e., LIWC-based model vs. LIWC+Questionnaire model) of an additional 3
and 6 percentage points, respectively. Our results suggest that we should go
back to the basics when addressing the assessment issue, meaning that
patients’ narratives, what people say when they first come to consult a health
care professional, is most indicative for the personality style of the
patient. Our results can aid researchers and clinicians in their attempt to
determine the personality style of their patients. Despite the good
performance of our proposed automatic personality classification, we believe
the classifying features that stem from our study should only be used to
assist a human psychologist to confirm or challenge his/her thoughts. They
should not replace the psychologist as a decision taker.

A limitation of this study is the relative small sample size (N = 79
patients). Even though that the main goals of this study were achieved, a
larger sample size might have led to more robust and significant findings.
Further improvements can thus possibly be established using larger samples.
Further, the classifiers in this study were based on only a single intake
interview per patient. Thus, we cannot confidently claim that we can
generalize to intake conversations with structures different from the adopted
CDI. Further research should aim to replicate our findings using alternative
interviews or natural therapy intake sessions. As we focused specifically on a
depressed population, further research could also aim to investigate a more
heterogeneous (clinical) sample. Moreover, it would be interesting to
investigate associations between personality styles and the process of therapy
from diverse therapy orientations in an attempt to discover patterns that have
remained obscure in traditional outcome-process research.

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##  5 Appendices

##  A Statistical Significance test details

This section provides the statistical significance tests we run to check
whether one model reliably outperforms another. Table 3 shows the $p$-value
after running a t-test to compare the best models in each feature category.
First, we run each model 100 times, which thus yields 100 kappa scores. We
then use a t-test to test the null hypothesis that the means of the two models
are equal at a 0.05 level of significance. The only comparison where we found
no significant difference is between the questionnaire and TF-IDF-based models
with $p$=0.847. We found that the difference was significant for each of the
other comparisons.

Table 3: The comparison of best-performing models (in terms of kappa score k) from each category using the t-test. Each model is run 100 times yielding 100 kappa scores. We then use a t-test to compare the different models. Null hypothesis: the means of the two models are equal. |  Question-naire |  TF-IDF |  LIWC |  Psycho-logical |  BERT-CLS |  BERT-max |  Audio |  LIWC+ Questionnaire
---|---|---|---|---|---|---|---|---
|  $\kappa$=0.194 |  $\kappa$=0.194 |  $\kappa$=0.801 |  $\kappa$=0.778 |  $\kappa$=0.248 |  $\kappa$=0.282 |  $\kappa$=0.268 |  $\kappa$=0.867
Questionnaire ($\kappa$=0.194) |  - |  $p$=0.847 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001
TF-IDF ($\kappa$=0.194) |  - |  - |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001
LIWC ($\kappa$=0.801) |  - |  - |  - |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001
Psychological ($\kappa$=0.778) |  - |  - |  - |  - |  $p<$0.001 |  $p<$0.001 |  $p<$0.001 |  $p<$0.001
BERT-CLS ($\kappa$=0.248) |  - |  - |  - |  - |  - |  $p<$0.001 |  $p<$0.001 |  $p<$0.001
BERT-max ($\kappa$=0.282) |  - |  - |  - |  - |  - |  - |  $p<$0.001 |  $p<$0.001
Audio ($\kappa$=0.268) |  - |  - |  - |  - |  - |  - |  - |  $p<$0.001

Similarly, Table 4 shows the $p$-values for statistical significance tests
using the t-test. The aim is to examine whether the performance increase
gained by balancing the dataset is significant. Again, we test the null
hypothesis that the means (in terms of kappa scores) of balanced- vs.
unbalanced-based models are equal at a 0.05 level of significance. As shown in
the table, the null hypothesis was rejected for all the comparisons,
indicating that improvements of using SMOTE are significant.

Table 4: The statistical significance test results for comparing models based on a balanced vs. unbalanced dataset. For each feature category, we suse the best performing model type (i.e., RF, LR, CB) and compare a model that uses SMOTE (in terms of kappa score) with the counterparts that uses the unbalanced dataset. Each model is run 100 times yielding 100 kappa scores. We then use a t-test to compare the different models. Null hypothesis: the means of the SMOTE vs. unbalanced dataset-based models are equal. Features | Dataset |
---|---|---
| Unbalanced | Balanced (SMOTE) | t-test
| _Kappa $\kappa$_ | _Kappa $\kappa$_ |
Questionnaire | 0.188 | 0.194 |  $p<$0.001
TF-IDF | 0.185 | 0.194 |  $p<$0.001
LIWC | 0.779 | 0.801 |  $p<$0.001
Psychological | 0.766 | 0.778 |  $p<$0.001
BERT-CLS | 0.182 | 0.248 |  $p<$0.001
BERT-max | 0.257 | 0.282 |  $p<$0.001
Audio | 0.169 | 0.268 |  $p<$0.001
LIWC+Ques’ire | 0.653 | 0.867 |  $p<$0.001


# Optical Mode Control, Switching and Shaping In Few Mode Fiber Using a Fiber
Piano

Shuin Jian Wu  Centre for Quantum Technologies, National University of
Singapore, 3 Science Drive 2, Singapore 117543<EMAIL_ADDRESS><EMAIL_ADDRESS>these authors contributed equally to this work  Anindya
Banerji  Centre for Quantum Technologies, National University of Singapore, 3
Science Drive 2, Singapore 117543<EMAIL_ADDRESS><EMAIL_ADDRESS>these
authors contributed equally to this work  Ankush Sharma  Centre for Quantum
Technologies, National University of Singapore, 3 Science Drive 2, Singapore
117543  Zohar Finkelstein  Racah Institute of Physics, The Hebrew University
of Jerusalem, Jerusalem 91904, Israel  Ronen Shekel  Racah Institute of
Physics, The Hebrew University of Jerusalem, Jerusalem 91904, Israel  Yaron
Bromberg  Racah Institute of Physics, The Hebrew University of Jerusalem,
Jerusalem 91904, Israel  Alexander Ling  Centre for Quantum Technologies,
National University of Singapore, 3 Science Drive 2, Singapore 117543
Department of Physics, Faculty of Science, National University of Singapore, 2
Science Drive 3, Singapore 117551

###### Abstract

This work investigates the use of a fiber piano in controlling spatial modes
in few mode fibers. It has been found that together with sub-optimal coupling
into SMF-28 fibre and half and quarter waveplates, the fiber piano is capable
of producing and reproducing desired spatial modes up to $LP_{11}$ when using
808 nm light and up to $LP_{21}$ when using 632.8 nm light. The control of
spatial mode profile extends down to the single photon level. This is
demonstrated with the help of correlated photon pairs generated via
spontaneous parametric down conversion.

## Introduction

Although light is a common means of transmitting information in modern
communications, not all of its available degrees of freedom (DoF) can be
freely controlled [1]. For instance, methods to control polarization and
frequency are easily found, but light coupled into a multimode fiber leads to
uncontrolled interference between various spatial modes that creates a speckle
pattern at the output [2]. Being able to control and sort those spatial modes
would allow for higher data transmission rates using multimode fibers as
compared to standard single mode fibers [3], along with other applications.

In current free space optical communications, including quantum communications
[4], the signal is often collected into a single mode fiber. This could be
quite challenging, especially when the signal passes through turbulent
conditions that cause wavefront distortions. In such cases, adaptive optics
[5] together with active feedback control is necessary to ensure sufficient
coupling of the signal into the fiber. Using a multimode or even a few mode
fiber [6] can relax these requirements a bit due to the larger numerical
aperture (NA) of these fibers. This leads to higher coupling efficiencies [7]
which result in better signal to noise ratios. But, this comes at the cost of
the signal getting scrambled due to uncontrolled interference of the spatial
modes. Hence, some means of spatial mode control is needed in order to benefit
from the higher coupling efficiencies of few or multimode fibers.

One method of control involves using spatial light modulators to shape the
wavefront before it enters the fiber, the goal being to selectively excite
only the desired modes to be propagated [8]. Unfortunately, for a fiber
supporting N modes, shaping the input wavefront only affords up to 2N degrees
of control, whereas the fiber’s transmission matrix contains $N^{2}$ elements
[9]. Furthermore, mode groups of order higher than $LP_{01}$ have degenerate
modes that easily couple amongst themselves, altering the output spatial
profile [8]. Hence, a more productive approach would be to use the
transmission matrix.

With deep learning methods the transmission matrix of a fiber could be solved
for in order to compensate for it [10]. However, matrix inversion problems are
complex and computationally costly, and ultimately a spatial light modulator
will be needed to modify the input wavefront to obtain the desired output.
Such spatial light modulators tend to be lossy, and could offset the gains
from the increased coupling efficiency. In addition, the main means of spatial
mode control would still be the spatial light modulator, which as stated
before lacks the degree of control available to the transmission matrix [9].

Rather than simply learn the transmission matrix, it would be better to gain
control of it. A fiber piano has been proposed previously as a means to do so.
The fiber piano consists of ’keys’ made of piezoelectric elements that allow
for controlled mechanical perturbations to be performed on a fiber to induce
local bends [9, 11, 12]. These bends allow for coupling across spatial mode
groups, allowing for a Particle Swarm Optimization (PSO) algorithm [13, 14] to
find key configurations that produce the desired spatial modes at the output
given a certain input. The fiber piano has been able to perform as a tunable
bandpass filter [15] as well as increase the coupling from a multimode fiber
to a single mode fiber and shape single photons [16].

In this work, we explore the use of a 15 key fiber piano in guiding single
spatial modes through a few mode fiber given an arbitrary superposition of
spatial modes excited at the input. We find that spatial mode control can be
enhanced with the use of a half waveplate (HWP) and quarter waveplate (QWP)
just before light is coupled into the few mode fiber, along with tuning the
misalignment of light into said fiber. Once the fiber piano has solved for the
desired spatial modes, it can swap back and forth between them easily (see
supplementary video). We have managed to obtain $LP_{01}$, $LP_{11}$ and
$LP_{21}$ modes using this method, and shown that it works on single photons
as well.

## Methods

The experimental setup used is shown in Figure 1. Light sources were first
coupled into 780HP single mode fiber before entering the setup. The 15 ’key’
(piezo actuator, ThorLabs PB4NB2W) fiber piano used is of the same
construction as Bromberg et al’s design [9, 11, 15, 16]. Each actuator creates
a three-point contact on the optical fiber running under it. This creates
local bends on the fiber of varying radius of curvature when different
voltages are applied to the actuators. Testing was done with He-Ne (632.8 nm)
and 808 nm lasers, and the few mode fiber used was SMF-28 (NA: 0.14, diameter
= 8.2 $\mu m$), about 2 m in length. PSO was used to search a 15 dimensional
space, each dimension corresponding to the voltage setting of each piezo
actuator. PSO settings used were 30 particles and 80 iterations, with inertia
damping ratio $w_{damp}=0.99$, personal learning coefficient $c_{1}=1.5$ and
global learning coefficient $c_{2}=2$. The cost function used was 2D
correlation with respect to reference images generated from python module
ofiber as seen in Figure 2. When fitting for $LP_{11}$ and $LP_{21}$, the
reference images are rotated at various angles to find the best 2D correlation
value (corr2).

Figure 1: Experimental setup for guiding laser light through the few mode
fiber with the fiber piano. For testing with single photons the lasers were
later swapped with a photon pair source and the camera at the reflected arm of
the PBS with an EMCCD. Figure 2: Reference images generated from python module
ofiber used to calculate 2D correlation (corr2) values with images obtained
from the cameras. The $LP_{11}$ and $LP_{21}$ references are rotated at
various angles to find the best corr2 values.

## Results

Figure 3: Results obtained with the 808 nm laser when the PSO algorithm was
set to solve for $LP_{11}$ in the reflected arm (top row) and transmitted arm
of the PBS (bottom row). Corr2 values given are the 2D correlations of the
images obtained with respect to $LP_{01}$ and $LP_{11}$ reference images.
Figure 4: Replication of fiber piano settings for $LP_{01}$ and $LP_{11}$ in
SMF-28. The camera was running at about 27 frames per second, which makes each
video frame displayed here about 1 second apart (See supplementary video).
Swap rates can potentially enter the kHz range as the piezoelectric elements
are rated for submillisecond response.

The setup used is in Figure 1 with SMF-28. With the 808 nm laser, The PSO
algorithm was set to maximize the corr2 value for $LP_{11}$ in the reflected
arm of the PBS, and then later in the transmitted arm. The results are shown
in Figure 3. Interestingly, decent $LP_{01}$ images were obtained in the
complementary arm even though the cost function did not take that into
account. However, it should be noted that sub-optimal coupling efficiency was
required for this result. When coupling efficiency was maximized, only
$LP_{01}$ was observed, and the fibre piano was unable to produce $LP_{11}$ in
any of the arms.

Overall losses in generating the spatial modes were not severe. Input power
entering the fibre piano was about 978 $\mu W$. When solving for $LP_{11}$ in
the reflected arm, about 645 $\mu W$ was measured after the output reflective
collimator, yielding a coupling efficiency of about 66$\%$. Of that 645 $\mu
W$, about 333 $\mu W$ ($\approx 34\%$ of input power) was transmitted through
the PBS and 251 $\mu W$ ($\approx 26\%$ of input power) was reflected. When
solving for $LP_{11}$ in the transmitted arm, about 609 $\mu W$ was measured
after the output reflective collimator, yielding a coupling efficiency of
about 62$\%$. Of that 609 $\mu W$, about 153 $\mu W$ ($\approx 16\%$ of input
power) was transmitted through the PBS and 390 $\mu W$ ($\approx 40\%$ of
input power) was reflected. Thus the piano was able to perform mode separation
with well tolerable losses.

Once the fibre piano has solved for a desired setting, the result could be
consistently replicated at a later time so long as the setup had not been
disturbed. This allows for the fibre piano to be used as a fast spatial mode
switch, as seen in Figure 4 and the supplementary video. The swapping speed is
limited by the piezoelectric elements. ThorLabs states that the PB4NB2W models
have sub-millisecond response time, so in principle a kHz swap rate is
possible.

It was also observed that once the fibre piano had found a setting that
produced $LP_{01}$ in the transmitted arm and $LP_{11}$ in the reflected arm,
the modes in each arm could also be swapped by rotating the HWP and QWP in
between the fixed Glan-Taylor prism and the coupling lens, as shown in Figure
5. The HWP produced more drastic changes while the QWP allowed for refinements
in the mode shape. The input power into the fibre piano was 215 $\mu W$ and
the output power right after the reflective collimator was 35 $\mu W$, a
coupling efficiency of $16\%$. With the HWP at 312∘ and the QWP at 258∘, the
transmitted $LP_{01}$ had 19.2 $\mu W$ ($\approx 9\%$ of input power) and the
reflected $LP_{11}$ had 14.9 $\mu W$ ($\approx 7\%$ of input power). After
rotating the HWP to 287∘ and QWP to 270∘, there was no significant change to
coupling efficiency, but then the transmitted $LP_{11}$ had 6.1 $\mu W$
($\approx 3\%$ of input power) and the reflected $LP_{01}$ had 28.8 $\mu W$
($\approx 13\%$ of input power). It should be noted that without the use of
the fiber piano, the waveplates themselves were insufficient to isolate
desired modes given arbitrary initial conditions (ie coupling efficiency and
fiber curvature).

Figure 5: The fiber piano was first used to solve for a configuration where
$LP_{01}$ was in the transmitted arm and $LP_{11}$ in the reflected arm. The
use of a HWP and QWP allowed for the modes to be swapped in both arms, albeit
with $LP_{11}$ undergoing some rotation.

Attempts were made to obtain higher order modes like $LP_{02}$ and $LP_{21}$
with the 808 nm laser to no avail, so a He-Ne laser (632.8 nm) was used
instead in the hopes that the shorter wavelength allowed for those modes to be
easily supported in the SMF-28. At the transmitted arm of the PBS, the fibre
piano was able to produce $LP_{01}$, $LP_{11}$ and $LP_{21}$, but was unable
to get $LP_{02}$ as shown in Figure 6. However, solving for $LP_{21}$ was only
possible after reducing the power exiting the reflective collimator by about
$60\%$ relative to that used to solve for $LP_{01}$ and $LP_{11}$. Even with
that reduction in coupling efficiency, the fibre piano was still unable to get
the 4 lobes with even shape and brightness. It appears that modes of higher
order than $LP_{11}$ pose greater difficulty, but the potential to isolate
them is definitely present.

Figure 6: Results obtained with the He-Ne (632.8 nm) laser when the PSO
algorithm was set to solve for various spatial modes in the transmitted arm of
the PBS. Obtaining $LP_{21}$ required a reduction in coupling efficiency of
about $60\%$ compared to that for $LP_{01}$ and $LP_{11}$. Corr2 values given
are the 2D correlations of the images obtained with respect to $LP_{01}$,
$LP_{11}$ and $LP_{21}$ reference images.

Since the fibre piano could shape the spatial modes of laser light, it was
reasonable to believe that it could do the same for single photons. To check
this, the lasers were replaced with a degenerate photon pair source centred at
810 nm generated from Spontaneous Parametric Downconversion (SPDC) and an
Electron-multiplying Charged Coupled Device (EMCCD) took the place of the
camera in the reflected arm of the PBS. As the difference between the central
wavelength of the photon pair source and that of the 808 nm laser was only a
few nm, prior settings solved for by the piano using the 808 nm laser should
be adequate for decent mode images. The results are shown in Figure 7. The
distinct mode shapes are evident to the eye, confirming that the fibre piano
is also able to shape the spatial modes of single photons.

Figure 7: $LP_{01}$ and $LP_{11}$ modes imaged by an EMCCD. This verifies that
the fibre piano can also shape the spatial mode profiles of single photons.

## Discussion

The fiber piano displays strong potential as a spatial mode control tool,
albeit with limitations that can be improved on. While unable to produce any
desired spatial mode given arbitrary coupling conditions into the few mode
fiber, this could be due to the ThorLabs PB4NB2W piezo actuators being only
able to produce 450 $\mu$m of travel. It was found that when coupling
efficiency was maximised in SMF-28 with either 808 nm or 632.8 nm lasers, only
$LP_{01}$ was clearly excited and the fiber piano was unable to produce
$LP_{11}$. Some degree of off-angle coupling was required for the fiber piano
to access both modes. If stronger bends could be produced, stronger
interactions between different spatial mode groups could be effected,
affording the fiber piano better mode control and range of exploration.

Further improvement in mode control could be achieved by incorporating the
coupling angle as part of the PSO search space. Sharma and Chaudhuri have
demonstrated mode control with this principle by using an electric field to
deflect a fiber tip coated in an electrostrictive material [17], thereby
tuning the coupling angle. This would also allow for optimization of power
transmission through the system while being able to access all desired modes
at the output. In addition, the use of a HWP and QWP can provide additional
tuning and even mode swapping at the outputs without requiring the fiber piano
to search for a new configuration. Integrating them as part of the
optimization process would also be simple with standard rotation mounts.

However, it seems that the polarization and spatial mode degrees of freedom
cannot be completely decoupled from each other. The PBS is essential for mode
separation, and the fibre piano cannot work without it. In addition, input
polarization greatly affects how modes are separated at the end. Observe that
the $LP_{11}$ modes obtained at the transmitted and reflected arms are rotated
relative to each other, and the piano was unable to generate $LP_{11}$ modes
with a specific orientation. Also, the distribution of powers at both arms of
the PBS are significantly influenced by input polarization. This could be due
to the limitations in the piano’s ability to transfer power between spatial
modes of orthogonal polarizations, but more work will be needed to understand
this. In any case, the transmission matrix of the optical fibre likely has
fewer than $N^{2}$ controllable elements.

Excitation and control of higher order modes beyond $LP_{11}$ is challenging
but potentially possible. The main ways to access them more easily are
increasing the core diameter of the fiber or using lasers of shorter
wavelength. This work has demonstrated that a He-Ne laser is sufficient to
obtain $LP_{21}$ in SMF-28, but with significantly higher coupling losses and
lower corr2 values. Standard multimode fibres were tested as well but the
fibre piano was unable to tame the resultant speckle patterns. That is an
expected outcome, as the more spatial modes that can be excited in the fiber
the more keys the fiber piano will require to isolate the desired modes [9].
Custom made fibres with core sizes smaller than standard multimode fibres
could be of use in going beyond $LP{21}$ while avoiding the speckled mess.
However, coupling losses required to excite those modes are likely to be
higher judging from the trend observed. An alternative could be modifying the
spatial mode of the light entering the fibre piano. This work has used a
Gaussian beam for the input, so the low overlap with higher order modes could
be responsible for additional coupling losses being necessary. Launching light
from a few or multimode fibre into the fibre piano should be sufficient to
test this idea.

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## Acknowledgements

We thank M. Mukherjee (Centre for Quantum Technologies) for loaning his EMCCD.

## Author contributions statement

S.J.W and A.B. conducted the experiments and analysed the results. S.J.W.
wrote the manuscript. A.S. designed the electronics used to control the
piezoelectric elements. Z.F and R.S helped set up the fiber piano. A.L.
supervised the study. Y.B. conceptualized and developed the fiber piano. All
authors reviewed the manuscript.

## Availability of data and materials

The data used in this study is available from the corresponding author on
reasonable request.

## Additional information

### Competing interests

The authors declare no competing interests.

### Funding

This research is supported by the National Research Foundation, Singapore
(Grant NRF2020-NRF-ISF004-3538) and A*STAR under its CQT Bridging Grant.