Automated coherence measures fail to index thought disorder in individuals at risk for psychosis (2021)

Abstract: Thought disorder – linguistic disturbances including incoherence and derailment of topic – is seen in individuals both with and at risk for psychosis. Methods from computational linguistics have increasingly sought to quantify thought disorder to detect group differences between clinical populations and healthy controls. While previous work has been quite successful at these classification tasks, the lack of interpretability of the computational metrics has made it unclear whether they are in fact measuring thought disorder. In this paper, we dive into these measures to try to better understand what they reflect. While we find group differences between at-risk and healthy control populations, we also find that the measures mostly do not correlate with existing measures of thought disorder symptoms (what they are intended to measure), but rather correlate with surface properties of the speech (e.g., sentence length) and sociodemographic properties of the speaker (e.g., race). These results highlight the importance of considering interpretability and front and center as the field continues to grow. Ethical use of computational measures like those studied here – especially in the high-stakes context of clinical care – requires us to devote substantial attention to potential biases in our measures.

## introduction individuals with psychosis exhibit language disturbances, often referred to as thought disorder. at the discourse level, this includes poverty of speech (low quantities of speech), poverty of speech content (vague, repetitive speech), as well as the focus of this work: incoherence and derailment (slow but steady loss of topic; e.g., 'i always liked geography. my last teacher in that subject was professor august a. he was a man with black eyes. i also like black eyes. there are also blue and grey eyes and other sorts.') @xcite @xcite . these symptoms are used to diagnose psychotic disorders and are thought to have predictive clinical value @xcite @xcite @xcite . similar, but attenuated, symptoms are observed in individuals who do not have psychosis, but who meet criteria for being at clinical high-risk for psychosis (chr). in this population, the presence of these linguistic symptoms predicts later transition to psychosis @xcite @xcite . however, despite the clinical value of these measures, these symptoms have generally been evaluated via self-report and/or overall clinician impressions, which may capture only the most extreme disturbances. manual annotations of specific linguistic features may allow for more nuanced measures; however, they are time-intensive and infeasible to apply on a wide scale. as a result, these linguistic measures, despite their clinical value, have been underused in the field. there is a growing body of literature trying to automatically quantify these linguistic differences using methods from computational linguistics, both in psychosis @xcite @xcite and chr populations @xcite @xcite . this work has been quite successful, replicating group differences between patient and healthy populations and accurately categorizing individuals into appropriate groups. however, much of the focus of this work has been on separating groups, and there has been less of a focus on relating these metrics to symptoms. work examining this relationship has sometimes found correlations between these computational metrics and relevant symptoms, but has often failed to find such relationships. in order for these measures to be useful clinically, it is important to establish their construct validity: do they relate to relevant symptoms? or, do their instead reflect other linguistic/demographic factors? establishing trust for a system's predictions is particularly important in the clinical/medical setting where these systems could have substantial consequences @xcite . this is especially true as the machine learning systems that these metrics rely on are known to exhibit potentially harmful biases in other domains @xcite @xcite . in this paper, we dive into measures utilized in previous work to try to understand what they reflect. following this work, we use a suite of models to quantify incoherence and derailment on speech produced by the chr vs. hc groups (individuals who meet criteria for being at high-risk for psychosis vs. healthy controls). we examine group differences, finding significant differences using a subset of measures (at uncorrected α = .05). we then critique these measures to determine if they reflect the target thought disorder symptoms -and fail to find specific correlations. finally, we consider what these measures do reflect, finding that they partially reflect surface properties of the speech (sentence length) and sociodemographic properties of the speaker. these results highlight the need to consider the interpretability of these measures as the field continues to grow. ## a note on terminology past work applying computational methods to study thought disorder in psychosis has used the words 'incoherence' or 'tangentiality' to describe their object of study, which has focused on the cohesion between sentences. however, this terminology is somewhat misaligned with the terminology discussed in the original thought disorder literature, which uses 'incoherence' to describe a lack of cohesion within sentences and 'tangentiality' for cases where participants give an off-topic response to a question @xcite . in this paper, we follow the naming conventions of past computational work in this area. we will refer to methods measuring the cohesion between neighboring sentences as 'coherence measures' and methods measuring how much a text drifts off topic as 'tangentiality measures'. however, it is very important to note that these methods better relate to derailment as defined in andreasen (1986), as they measure how much a participant shifts topics between sentences. ## results as shown in for tangentiality, we do not find any significant differences in tangentiality between the chr and hc groups ( these results suggest that these automated measures of thought disorder are very sensitive to the particular method used to derive it. notably, previous work has not found any particular method to be consistently successful in separating groups. one of the methods where we find a significant difference is also successful in @xcite , who find significant coherence differences using tf-idf glove and no significant differences in tangentiality. however, @xcite only found differences in coherence using sif word2vec, while other papers @xcite @xcite have found significant differences using lsa mean(all). overall, while we do not find group differences in tangentiality, we do find the predicted group differences in coherence between chr and hc in a subset of cases. however, more work needs to be done to understand whether these are meaningful effects and what they reflect. to this end, for the remainder of the paper, we ask whether these automated linguistic methods of coherence relate to symptoms or other linguistic/sociodemographic factors. for these analyses, we zoom in on the sentence/word embedding models that separate chr from hc groups. we present glove mean(content) analyses in the main text; all other analyses are presented in appendix c. do lower coherence scores (within the chr group) relate to worse thought disorder? we examine this using symptoms in the sips that are related to thought disorder. as shown in figure 1 , we find generally poor correlations. the computational measures intended to measure thought disorder do not show any correlation with currently used clinical interviews measuring thought disorder in the chr group. this result adds to a growing but mixed literature on the relationship between automated linguistic measures and the symptoms they are intended to measure. of past work that has reported correlations, corcoran et al. (2018) and @xcite found no correlation between coherence scores and clinical interview symptoms, while @xcite found their coherence measures did correlate negatively with symptom severity as measured by the scale for the assessment of negative symptoms @xcite . @xcite included coherence in a canonical correlation identifying the maximal correlation between a linear combination of 3 linguistic features -coherence, maximal word phrase length, and number of determiners -and a linear combination of the positive and negative sips subscales. they found an overall positive correlation, but it's unclear what role coherence played in driving this correlation. taken together, our results and previous results suggests that coherence scores are not reliably related to clinical measures of thought disorders; however, a high-powered investigation is warranted. if these measures are not capturing thought disorder symptoms, what are they measuring? to examine this issue, we examine the relationship of these computational measures to surface linguistic features of the speech samples and sociodemographic factors of the speakers. we focus on three features that show a significant relationship to this 'coherence' measure -sentence length, a measure of general intelligence, and racial identity of the speaker -and report non-significant correlations in appendix c. ## discussion we tested methods of quantifying coherence and tangentiality, applying them to speech samples produced by individuals at clinical high-risk for psychosis. we found group differences between the chr and hc groups for a subset of the tested methods (3 out of 13, significant only at uncorrected α = .05). surprisingly, we did not find significant correlations with items from clinical interviews that measure thought disorder (i.e. what these measures are meant to capture). in order for these measures to be useful clinically, it is important to show construct validity -that the measures actually index what they are meant to, rather than other features of the speech/speaker. this is especially true as the methods we use here have been shown to exhibit potentially harmful biases in other work. to this end, our final exploratory analyses were designed to better understand what these measures are capturing. we found correlations with sentence length, wrat scores, and race, which suggests that these methods partially reflect properties that these measures are not intended to measure. these results suggest that there is substantial and careful work that needs to be done for these methods to be useful clinically.