The Impact of Aphantasia on Mental Healthcare Experiences

All materials from Stage 1 and 2, including anonymized quantitative data and anonymized interview transcripts, are publicly available on the Open Science Framework (OSF; https://osf.io/v7krh/). Stage 1 materials were made available on OSF on 29/07/2023 (https://osf.io/uamcp/), along with the Stage 1 preprint on PsyArXiv (https://osf.io/dsjah). Stage 2 data and materials were made available on OSF on 16/07/2024, along with the Stage 2 preprint on PsyArXiv (https://osf.io/preprints/psyarxiv/f6h5q). All appendices (Appendix A: Recruitment questionnaire; Appendix B: Interview questions; Appendix C: CBT schematic; and Appendix D: Qualitative quote table) are available in the Supplementary Materials file.

In collaboration with the Aphantasia Network, we disseminated the details of this study to over 16,000 aphants in their newsletter. We also disseminated the study to over 92,000 subscribers on r/aphantasia on the forum website, Reddit. We had planned to collect responses from readers of The Conversation (where the senior author previously collected responses from approximately 2,500 aphants and 5,000 imagers), but this was ultimately unnecessary, as we reached our goal number of participants with the other two methods of recruitment. According to a sample size calculation (see Sample size justification), we aimed to recruit 4,248 aphants to fill out the questionnaire, to ensure at least 7-8 interviews per target group. To obtain the largest sample and because one of our hypotheses pertains to potential misdiagnosis, we recruited both individuals who suspect they may have a disorder (but are currently undiagnosed) and those who are clinically diagnosed with a mental health condition.

We distributed the Plymouth Sensory Imagery Questionnaire (PSI-Q; Andrade et al., 2014) to measure self-assessed imagery vividness in seven modalities: visual, auditory, tactile, olfactory, gustatory, bodily sensations, and emotional imagery. This was delivered along with a brief mental healthcare questionnaire (see Supplementary Materials: Appendix A) on the Aphantasia Network and r/aphantasia to achieve our target sample size. For individuals who were hesitant to document their mental healthcare experiences in a questionnaire, they had the option to e-mail the researchers directly if they believed they met inclusion criteria and were open to an interview. If we had still been unable to recruit 8 aphants who could comment on the efficacy of CBT using these methods (see Stopping rule), we had planned to further recruit participants through providers of CBT-based mental health support. This was ultimately unnecessary, as we achieved our interview numbers using the two methods described above.

The PSI-Q was used to identify different mental imagery abilities, particularly aphantasia. The mental healthcare questionnaire was analyzed quantitatively, in addition to being used as a tool to recruit participants into three interview groups:

1. aphants experiencing mental health symptoms that impact their quality of life who have either not been diagnosed or feel they have been misdiagnosed, due to a perceived lack of understanding of aphantasia by mental health professionals (target N=7, see Sample size justification below)

2. aphants who have been clinically evaluated and diagnosed with a mental health condition, but believe they have received ineffective treatment, potentially due to having aphantasia (target N=7)

3. aphants who have been clinically evaluated and diagnosed with a mental health condition, and believe they have received effective treatment (target N=7)

Of the final two groups (N=14), we required 8 to have tried CBT, regardless of the distribution (i.e., whether they believe CBT worked for them or not). Ideally, we would have liked 4 from each group, but did not want to exclude the possibility that CBT may work better than expected (and therefore receive more CBT participants from group 3), or not at all (and therefore receive all CBT participants from group 2). For this, an information power approach (Malterud et al., 2016, also see Sample size justification) was appropriate in determining the number of participants needed. See Supplementary Materials: Appendix B for details of the interview questions tailored for each group.

Individuals across the mental imagery spectrum were included in the questionnaire, so that we could compare aphants to a typical imagery control group during quantitative analysis. The questionnaire was also used as a screening tool for inclusion in the interview, since we only interviewed aphants. Q1 requires participants to indicate whether they have aphantasia or not. If participants responded positively to Q1, we checked this against their responses on the visual section of the PSI-Q (a 0-10 rating of mental imagery vividness for five visualized items). The average rating across the five items must be <4 (consistent with previous research: Königsmark et al., 2021; Reeder, 2022) for inclusion in the aphantasia group. Any individual who reported having aphantasia with a score >3, or who reported not having aphantasia with a score <4, had their data excluded from analysis, due to inconsistency in responses.

To avoid potentially activating memories of trauma, we did not ask participants whether their aphantasia is acquired or congenital. We acknowledge that there is a distinction between the two: congenital aphantasia is the lifelong absence of mental imagery (Zeman et al., 2015), and acquired aphantasia is the loss of mental imagery due to neurological or psychological trauma (Zeman et al., 2010). We do not deem this distinction relevant to the current study, and included participants as long as they believed they were experiencing aphantasia at the time they sought mental healthcare.

As an additional measure, we analyzed the data separately for individuals who did and did not provide their email address to be contacted for interview (based on the assumption that participants interested in an interview would be less likely to fabricate having aphantasia). If there was strong evidence for a difference in questionnaire responses based on this data-splitting procedure, we would present the results of these data separately, but not exclude data based on this difference.

Imagers who responded on the questionnaire (responded negatively to Q1 and rated their average visual imagery vividness as >3 on the PSI-Q) were excluded from taking part in the interview, but we used their quantitative data from the questionnaire, as they can serve as a control group for aphants. Individuals who responded negatively on Q1 but indicated that their visual imagery vividness is <4 on the PSI-Q were excluded from analysis due to inconsistency in responses.

We excluded participants from analysis based on further inconsistent questionnaire responses: for example, if they answered “Yes” to both a. (I have never been diagnosed with a mental health condition) and c. (I now have a diagnosis, and did not have trouble receiving a diagnosis) of Q5. This excluded participants who did not read the questions carefully or did not comprehend the questions.

We had exceptionally narrow inclusion criteria for our three interview groups, to ensure we could collect the relevant data. At the beginning of the interview, we ensured that all participants had aphantasia by asking them a few questions about their mental imagery experience compared to the wider imagery spectrum (~5-minutes; see Supplementary Materials: Appendix B). This assessment was also used to further categorize individuals into “complete aphantasia” (no mind’s eye) or “hypophantasia” (dim or vague mind’s eye). Should it have been revealed that the participant did not have aphantasia or hypophantasia, they would have been thanked for their time and the interview would have been terminated. We did not ultimately exclude any participants by these criteria.

If participants passed the aphantasia check, their responses on the questionnaire that led to interview group inclusion were then verbally confirmed prior to interview. That is, to be considered for interview group 1, participants must respond that they have had trouble receiving a diagnosis (Q5) and believe it is related to their aphantasia (a or b on Q6), or respond that they believe they have been misevaluated or misdiagnosed (Q11). To be considered for group 2, participants must respond that they have been evaluated for a mental health condition (Q4) but believe they have received ineffective treatment for it (Q14). To be considered for group 3, participants must respond that they have been evaluated for a mental health condition (Q4) and have received adequate treatment for it (Q13), which included some form of therapy (Q17, Q18, Q21). Those who answered positively on Q21 (related to CBT) received priority for interview. If this check had revealed inconsistencies in questionnaire responses, we were to clarify these with participants prior to interview. If a participant no longer met inclusion criteria following clarification of responses, the participant was to be thanked for their time and the interview would have been terminated. We did not ultimately exclude any participants by these criteria.

In a previous study, 24% of aphants (64/267) reported a history of mental illness (Dawes et al., 2020). According to the UK Adult Psychiatric Morbidity Survey 2014 (McManus et al., 2016, p. 86), 39% of adults with a mental health condition ultimately seek treatment³; 6% of those use CBT. Using these estimates, we calculated our sample size as follows:

The required sample size to find the prevalence of X in a population (Duncan & Humphry, n.d.) can be calculated using the following formula:

where P is the expected prevalence in the population, N is the population size (estimated as number of aphants on Reddit, the Aphantasia Network, and The Conversation readership = approximately 100,000⁴), and p is the confidence level (95%). To obtain 14 responses from individuals who have been evaluated for, or diagnosed with, a mental health condition (24% x 39% = 9.36%), this would require a minimum sample size of 31 (chance of detecting 1 or more) x 14 = 434. To ensure 8 participants have tried CBT (9.36% x 6% = .5616%), this would require a minimum sample size of 531 (chance of detecting 1 or more) x 8 = 4,248. Therefore, we estimate that we will require 4,248 aphant respondents on our survey to achieve our target sample size for the interviews.

For interviewing, we determined target sample sizes based on a quantitative power analysis, an information power model specific to qualitative interview studies, and “rules of thumb” from previous literature. First, as an initial quantitative measure, we calculated the sample size required to find a difference between general population efficacy of CBT (approximately 50% across a variety of mental health disorders; Hofmann et al., 2012) and the hypothesized efficacy of CBT among aphants (0-10%). For this, we conducted a power analysis using the online calculator ClinCalc (https://clincalc.com/stats/samplesize.aspx), with a dichotomous primary endpoint (CBT was effective: Yes or No), power set at 90%, beta at 0.1, and alpha at 0.05. The result of this test suggests a sample size of 4-12 is required. It should be noted that quantitative approaches are not ideal for qualitative sample size justification, but are worth considering in combination with the other two approaches described below.

Next, we calculated the required sample size based on an information power model (Malterud et al., 2016). Information power models take into account 5 dimensions that may contribute to a decrease or increase in estimated required sample size for qualitative interview studies: aim (narrow, broad), specificity (dense, sparse), theory (applied, none), dialogue (strong, weak), and analysis (case, cross-case). A narrow, dense, applied, strong case study will require the fewest participants (N=1 in the optimal scenario).

The current study has a narrow aim (the phenomenon being measured is therapy efficacy in aphants with mental health conditions); dense specificity (the participants in our study are targeted for their specific experiences and characteristics); applied theory (we use an evidence-based theory from aphantasia research and general population studies of CBT to motivate our predictions); moderate-strength dialogue (one author has a background in interviewing people with aphantasia, and two have a background in mental health); and a cross-case analysis (we wish to include individuals with different mental healthcare experiences for a variety of disorders). Based on these factors, we estimated that a sample size of 6-10 should provide sufficient information power for descriptions of different mental healthcare experiences, although the precise number was updated continuously throughout the process of data collection.

Finally, according to rules of thumb, we can estimate an appropriate sample size based on various factors. First, we took individual participant interviews to understand individuals’ lived experiences using an interpretative approach: thematic analysis (TA). According to a recent systematic review of 200 qualitative studies (Bartholomew et al., 2021), sample sizes can be extremely diverse across studies (N=1-308), but “higher quality” studies require lower sample sizes around or below the average sample size for the particular type of study. For interpretative studies (like ours), the average sample size was 11.61, with a minimum sample size of 5. Pre-identifying the data collection and analysis method significantly predicted higher quality. Furthermore, individual participant interviews were found to contribute the highest quality data compared to other data collection methods, such as focus groups. Finally, our chosen research methodology is congruent with our research questions, our philosophical perspective, our proposed representation and analysis of data, and the proposed methods used to collect the data – all factors that contribute to high quality data. These factors indicated that we should glean sufficient power for our study with 5-12 interviews.

The triangulation of these three methods suggests a minimum sample of 4-6, although information power suggests updating these numbers throughout the data collection process. We therefore estimated that we would need to recruit at least 7 participants for each interview group (with 8 in the CBT sub-group) to ensure sufficient power in our analyses, although these numbers could have changed as we gathered data. The estimated maximum sample is between 10-12. To allow for flexibility, we therefore left open the possibility of doubling our estimated number of participants (to 42) if necessary.

We stopped recruitment for the questionnaire once we had reached our target sample size for each interview group. During TA, we anticipated a potential need to recruit additional participants, and broadly estimated a maximum sample size of 42 total participants (double the estimated required sample size for each group, to allow for flexibility in the TA).

Only participants that passed both the aphantasia check and the group inclusion check (see Exclusion and inclusion criteria) continued with the interview. We continued to recruit participants for the interview until the target sample per group had been reached. We sent out interview requests to the first 7 who met each group’s inclusion criteria. If participants did not schedule their interview within two weeks, or if participants were excluded during interview for the above-stated reasons, we worked our way down the list of participants who met the inclusion criteria until our target sample was reached.

Prior to the survey, participants read a Participant Information Sheet, providing details of the research and open data policy, and contact information of the researchers, ethics board, and external help lines. They then provided written, informed consent to participate in the survey. If participants opted to e-mail the researchers directly for interview, the researchers would have sent these forms via e-mail before the scheduled meeting date. This situation, however, did not occur. Participants then optionally entered demographic data (age, gender, country of residence, country where mental health services were sought, English language level, level of education, socioeconomic status, ethnic background). The PSI-Q preceded the mental healthcare survey, and took approximately 5 minutes to complete. Participants provided a 0-10 rating of the vividness of seven modalities of imagery (visual, auditory, gustatory, olfactory, tactile, bodily sensations, emotional), with five items per modality, for a total of 35 ratings. The mental healthcare survey took approximately 10 minutes to fill out and consists of 23 questions regarding mental healthcare experiences (evaluation, diagnosis, and treatment; see Supplementary Materials: Appendix A). The survey ended with a debrief form and contact information to request withdrawal of personal information (email) within two weeks of completing the survey.

Prior to the interview, participants received an interview-specific Participant Information Sheet and consent form. Additionally, if they were being interviewed about their CBT experience, they received a short schematic of the most commonly used CBT tools (see Supplementary Materials: Appendix C) and to indicate those they have experience with, which helped steer the interview questions concerning CBT. Here, they were also asked whether they would like to opt-in to have their anonymized interview transcript made available in a public repository. Understanding of the study, ethical information, and consent was reconfirmed during the interview.

Interviews took place online via Microsoft Teams meeting, and participants were allowed to turn off their video prior to audio recording. Audio of interviews was originally planned to be auto-transcribed via Otter (Otter.ai) speech-to-text, to assist with initial transcription; however, Microsoft Teams has its own in-built auto-transcription, so we ultimately used this, instead. Interviews lasted approximately one hour but were sometimes longer or shorter depending on the amount of information the participant was willing to share. Interviewers first reconfirmed consent verbally, and then proceeded to verbal validation of the participant’s imagery classification and interview group membership lasting approximately 10 minutes. This was followed by the core interview questions (approximately 50 minutes; see Supplementary Materials: Appendix B). All participants who passed the initial checks received £12 (1.5 hours of participation) via PayPal in the currency of their country. Participants were informed that the interview could take around 1 hour, but they could take as much or as little time as they were comfortable with. Following the interview, participants were sent copies of the debrief form and contact information to request withdrawal of their data within two weeks.

For the quantitative survey, we first pre-processed the data for exclusions (see Exclusion and inclusion criteria), then calculated descriptive statistics on demographic data and questionnaire responses. Data analysis was planned to be performed in JASP (JASP Team & others, 2019) and Python; however, we have made one deviation from the registered analysis plan in that several analyses were conducted in jamovi (The Jamovi Project, 2021) rather than JASP or Python. Specifically, we experienced technical issues conducting contingency tables tests in JASP during analysis, and therefore switched to jamovi for all contingency tables tests. We additionally used jamovi’s survey plot feature to generate summary result plots rather than writing Python code.

Contingency tables tests and Multinomial tests were performed on count data. We calculated Bayes Factors (BF) where applicable, to evaluate the strength of evidence for the different hypotheses. To increase power, we included data both from individuals who have been diagnosed with a disorder, and those who have sought mental health evaluation for a disorder but have not been diagnosed. We also included data from individuals with complete aphantasia and hypophantasia (dim or vague imagery). We additionally conducted analyses on these groups separately (complete aphantasia = 0/10 and hypophantasia = an average of 1-3/10 on the PSI-Q visual scale) if there were at least 5 individuals in each cell for contingency tables tests. For analyses that included a typical imagery control group, this group was defined as individuals who responded negatively to Q1 on the mental healthcare questionnaire, and scored an average >3 on the PSI-Q visual scale.

To address Hypothesis 1 (Psychiatric disorders will manifest with a lack of imagery-related symptomology in aphantasia), we first present descriptive statistics for answers to Q7 (I have had intrusive sensory experiences (for example: flashbacks, unpleasant imagery, hallucinations) because of my mental health condition Yes/No) and Q8 (If you answered yes to the last question, what kind of experience affected you? Visual, Auditory, Tactile, Body, Emotional, Other: Significantly—Somewhat—A little bit—Not at all). These are split by answers to Q20 (The mental health condition(s) I have/may have) where possible. If there were at least 5 individuals who answered “Yes” to Q7, we performed the analyses detailed below. If we had not found at least 5 individuals out of our sample who reported intrusive sensory experiences, further analyses would be unnecessary.

To determine whether there are differences in the lifetime prevalence of intrusive images in aphantasia versus a typical imagery control group, we conducted 2 (aphantasia, imagery) x 2 (yes, no) Bayesian contingency tables tests. In a meta-analysis by Brewin et al. (2010), the authors reported lifetime prevalence of intrusive images across various anxiety disorders, depression, PTSD, eating and body perception disorders, and psychotic disorders. In anxiety neurosis and panic disorders, there are much higher reports of intrusive imagery when anxious or during panic (90-100%) compared to “any time in the last three weeks” (32-37%; Brewin et al., 2010), suggesting that intrusive images are tied to illness-related episodes. We therefore predicted the most extreme differences by comparing aphantasia and control responses concerning lifetime prevalence. We conducted analyses within specific disorders (e.g., PTSD) and classes of disorders (e.g., anxiety and panic disorders) where possible.

To determine if there are differences in the prevalence of visual versus other imagery experiences within our aphantasia versus control sample, we split answers to Q8 into visual, auditory, tactile, body, emotional, and other groups; and split responses into “Yes” (Significantly, Somewhat, A little bit) and “No” (Not at all) for 2 (aphantasia, imagery) x 2 (yes, no) Bayesian contingency tables tests. If there were at least 5 responses per cell, we also performed a series of 2 x 4 (“Significantly”, “Somewhat”, “A little bit”, “Not at all”) Bayesian contingency tables tests. Within the aphantasia group, we performed tests with responses split into visual versus “other sensory” (auditory, tactile, body, other), and sensory (visual, auditory, tactile, body, other) versus non-sensory (emotional) imagery. These tests address how aphants’ experience of their mental health condition is affected by visual, other sensory, and non-sensory imagery, and which form of intrusive imagery is most prevalent. Finally, we performed the same tests in the control group, then entered these results as our expected counts in Bayesian Multinomial tests comparing the aphantasia group to the control group. This tells us whether aphants’ experience of their mental health condition is affected differently by imagery intrusions compared to imagers.

To determine if there are differences in the prevalence of any imagery-related symptomology in different disorders between the aphantasia and control sample, we conducted Bayesian contingency tables tests on answers to Q7 (I have had intrusive experiences because of my mental health condition), in a 2 (aphantasia, imagery) x 2 (yes, no) test for each disorder, separately. Disorders must have at least 5 responses in each cell (yes, no) to be tested. We then performed a 2 (answer to Q7: yes, no) x N (Disorder: yes, no) Bayesian contingency tables test within the aphantasia group, to determine whether imagery symptoms are more prevalent in certain disorders. We performed a final Bayesian Multinomial test with responses from the control group input as expected values, to determine whether imagery-related symptomology occurs differently across disorders in aphants compared to typical imagery controls.

To address Hypothesis 2 (Aphants will report a perceived “lack of awareness or understanding of aphantasia” as a common factor in misevaluation, missed diagnosis, or misdiagnosis by mental health professionals), we first present descriptive statistics from the aphantasia sample for answers to Q6 (Do you think that aphantasia prevented you from being diagnosed in any way?: a. I believe my aphantasia impacted my diagnosis; b. I did not know I had aphantasia at the time, but looking back I believe it may have impacted the diagnosis; c. my issues were unrelated to aphantasia, or I do not have aphantasia), Q9. (Do you believe you were properly diagnosed?), Q10. (If you answered “Yes” to the last question, do you believe you received a diagnosis you agree with because: a) my mental healthcare professional was aware of, and understood, aphantasia; b) my mental health care professional did not understand aphantasia but was able to diagnose me because of other symptoms; c) my condition is unrelated to aphantasia, or I do not have aphantasia), Q11. (Do you believe you have been misevaluated and/or misdiagnosed?), and Q12. (If you answered “Yes” to the last question, do you believed your misevaluation/misdiagnosis was: a) because the person assessing me did not understand my aphantasia; b) unrelated to aphantasia, or I do not have aphantasia).

We then performed a series of Bayesian contingency tables tests within our aphantasia sample, collapsed across disorders, provided there were at least 5 data points per cell. As a reference, misdiagnosis is, generally, extremely common among anxiety and mood disorders: from 65.9 – 97.8%; and missed diagnoses range from approximately 50 – 99%, depending on the condition (Vermani et al., 2011). We expect roughly these ratios among our aphantasia sample, as well, but we performed an additional Bayesian Multinomial test using results from our typical imagery control sample as expected values to check whether the rate of missed and misdiagnoses are similar. The analyses detailed below target proportions of accurate diagnoses, missed diagnoses, and misdiagnoses thought to be related or unrelated to aphantasia.

To determine whether there is a different proportion of missed or misdiagnoses related to aphantasia compared to accurate diagnoses, we first performed a 2 (accurately diagnosed, missed/misdiagnosed) x 2 (related to aphantasia, unrelated to aphantasia) test within our aphantasia sample. To determine whether missed or misdiagnoses are differently thought to be due to misunderstandings about aphantasia, we conducted a 2 (missed, misdiagnosed) x 2 (due to aphantasia, not due to aphantasia) test. To find out whether accurate, missed or misdiagnoses related to aphantasia are more prevalent in certain disorders, we then performed three Bayesian contingency tables tests (one each for accurate, missed, and misdiagnoses) with each disorder (with at least 5 values per cell) as a separate group, split by whether individuals believed their diagnosis was related to aphantasia or not (i.e., three N x 2 tests). If there were differences between disorders, we conducted post-hoc comparisons between pairs of disorders. Because these tests require individuals to have aphantasia, we cannot compare the results of these tests to a control sample.

To address Hypothesis 3 (Aphants will report that imagery-related therapies (specifically CBT) are ineffective in their mental health treatment), we first present descriptive statistics for answers to Q17 (I only tried therapy and it a. helped, b. didn’t help), Q18 (I tried a combination of prescribed medication and therapy, and they a. helped in combination, b. only medication helped, c. only therapy helped, d. nothing helped), Q19 (The type of therapy I tried included visual imagery exercises), Q20 (The type of therapy I tried included other types of mental imagery), and Q21 (The type of therapy I tried was cognitive behavioral therapy – CBT). The responses on Q19-21 were split by whether participants reported therapy was effective or not, either in combination with pharmaceutical intervention or on its own, where applicable. These descriptive statistics were reported for the aphantasia and typical imagery control group, separately.

To determine whether there is a difference in effectiveness between therapy that included imagery techniques versus non-imagery techniques among our aphantasia group, we conducted four Bayesian contingency tables tests, collapsed across all mental health conditions, provided there were at least 5 data points per cell: a 2 (imagery-based, not imagery-based) x 2 (effective, ineffective) test for a general effect of imagery-based therapies; a 2 (CBT, other psychotherapy) x 2 (effective, ineffective) test for CBT effectiveness versus other forms of therapy; a 2 (visual imagery-based, other imagery-based) x 2 (effective, ineffective) test to find out whether therapies that use other modalities of imagery (e.g., auditory, tactile, body, emotional) are more effective than visual imagery therapies; and a 2 (CBT with imagery, CBT without imagery) x 2 (effective, ineffective) test to determine whether CBT is judged more effective if imagery techniques were not used. Finally, we performed the same tests in the control group, then entered these results as our expected counts in Bayesian Multinomial tests comparing the aphantasia group to the control group. This tells us whether there is a difference in imagery versus non-imagery-based therapy effectiveness between aphants and imagers.

Based on our prevalence calculations, it was deemed unlikely that we would be able to carry out contingency tables tests for conditions that look at the effectiveness of CBT (e.g., CBT with imagery versus CBT without imagery). For these cases, we address Hypothesis 3 with our thematic analysis, described below. For all analyses that could be performed with at least 5 data points per cell, we additionally performed all above stated analyses within each mental health condition separately, where feasible.

For the interviews, we conducted a thematic analysis (TA) to extract themes from the different interview groups. TA is a flexible method for detecting meaningful patterns, or themes, in acquired data. According to Braun & Clarke (2012), we should pre-determine whether the data would be collected using an inductive or theory-driven approach, an experiential or critical orientation, and an essentialist or constructionist theoretical framework. It is important to note that we stayed open to the possibility of adapting our pre-determined approach to best fit the data we got, and there is always an element of both deductive and inductive methodology in TA. Therefore, the following is a guide we started with, but is by no means a plan to which we strictly adhered, should the data have led us in another direction. For this reason, our TA remained exploratory (Braun et al., 2022).

For the current study, it was most appropriate to use a deductive, theory-driven approach, as our hypotheses are motivated and steered by previous research findings. We took an experiential orientation to the data, as it is important to understand the needs of individuals with the lived experience of mental illness, and we do not presume to know what those needs are. For example, we hypothesized that clinical ignorance about aphantasia negatively impacts patient satisfaction with mental healthcare, but how this impact manifests can only be gleaned from learning about people’s lived experiences. Finally, we took a constructionist theoretical perspective. This means we did not only focus on recurring patterns in interviews (which could include those that were unanticipated and not directly related to our hypotheses), but also concentrated on patterns we deemed meaningful for our study, which are those that address the three hypotheses (Byrne, 2022). During the TA (particularly at the end of Phase 4), we used member checking (Birt et al., 2016), which entails verifying with interviewees whether they agree with our interpretations of their words, allowing them to make amendments as necessary.

TA followed 6 phases, as described in Braun & Clarke (2006). In Phase 1, interviews were audio-recorded, then transcribed verbatim. The researchers listened to every interview: one to perform the initial transcription, and the other to validate the accuracy of the transcription. The researchers then independently immersed themselves in the data and made notes on potential patterns of interest prior to a deeper analysis. We used peer debriefing and investigator triangulation here to ensure a high amount of information sharing throughout the process and improve credibility (Nowell et al., 2017).

In Phase 2, data were coded for both semantic and latent content, meaning that we focused on both specific language used in the interviews and potential implied meanings, as they may both come through in the data. Our coding technique was exploratory, and our aim was to identify particular features in the data. Our three hypotheses were used as inclusion criteria for interview: that is, we recruited individuals who indicated that they believe their aphantasia affected their ability to receive adequate assessment, diagnosis, and/or treatment; that clinician ignorance of aphantasia interfered with their mental health assessment, diagnosis, and/or treatment; that CBT was not an effective psychotherapeutic treatment for them; or that CBT was an effective treatment for them. Once we had obtained our groups, we explored more deeply how individuals had come to these feelings. For example, to summarize some of the discussion points for interview (the full list can be found in the Supplementary Materials: Appendix B): 1) Given that the individual believes their aphantasia affected their ability to seek mental healthcare, in what ways do they believe the process was affected? Which symptoms do/did they experience that caused them to seek mental healthcare? 2) Given that the individual expresses clinician ignorance of aphantasia as a factor in their misevaluation/misdiagnosis, in what ways do they feel this occurred? What made them feel this way?; 3) Given that the individual feels that CBT was ineffective for them, what aspects of CBT do they feel were ineffective? 4) Given that the individual feels that CBT was effective for them, what aspects of CBT do they feel were effective? We therefore aimed to focus coding on these points; however, if unexpected, but meaningful patterns occurred at this stage, we followed them up with exploratory analyses and coding. Therefore, no potential themes were ignored.

In Phase 3, we sorted codes into broader themes and sub-themes. Phase 4 required reviewing and refining themes. Together, the researchers worked reflexively and collaboratively (Byrne, 2022), using peer debriefing to discuss identified themes; review the codes associated with each theme; and discard, add, and change themes as necessary. We kept reflexive diaries to account for some of the researcher bias inherent in the analysis process. We all agreed on the final thematic map and its representation of the data, and checked that each aim had been addressed. Phase 5 required defining the scope of each theme, determining whether themes were sufficiently distinct from one another, and giving them each a clear, appropriate name. In Phase 6, we reported the final analysis, which addressed each aim. A thematic map was produced as part of the coding process.

We conducted Bayesian analyses in JASP (JASP Team & others, 2019) and jamovi (The Jamovi Project, 2021), with the prior concentration set to the default of 1, and all tests two-sided. We used the default prior concentration of 1 because we did not want to make any assumptions about how our distributions deviate from a null distribution; nevertheless, we repeated all tests with prior concentrations set between 1-10, which allowed us to test a range of weakly informative priors, and report whether the direction of the results changed across these values. For all analyses that include Bayes Factors (BF), the strength of evidence for the different hypotheses is defined in accordance with the standards as stated in Quintana and Williams (2018):

BF₁₀>10 = strong evidence for a difference between groups

3<BF₁₀<10 = moderate evidence for a difference between groups

1<BF₁₀<3 = weak evidence for a difference between groups

BF₀₁>10 = strong evidence for no difference between groups

3<BF₀₁<10 = moderate evidence for no difference between groups

1<BF₀₁<3 = weak evidence for no difference between groups

We clearly state below which groups we expected to show differences, which we did not, and in which direction this difference was expected to take. BFs provide relative evidence for two competing hypotheses; strong evidence (BF₁₀>10 for a difference between groups; BF₀₁>10 for no difference between groups) was taken as confirming or disconfirming evidence depending on the hypothesis. Moderate or weak evidence in either direction was reported as inconclusive. If we did not have hypotheses about the direction or difference between groups, these were listed as exploratory.

For Hypothesis 1 (Psychiatric disorders will manifest with a lack of imagery-related symptomology in aphantasia), confirming evidence would include: fewer than five total “Yes” responses on Q7 of the questionnaire (I have had intrusive sensory experiences (for example: flashbacks, unpleasant imagery, hallucinations) because of my mental health condition Yes/No) within the aphantasia sample, combined with more than 5 total “Yes” responses on the same question within the typical imagery control group; a lower lifetime prevalence of imagery-related symptomology in aphantasia versus a typical imagery control group; and/or a lower prevalence of visual intrusive imagery compared to other sensory and non-sensory imagery. Disconfirming evidence would include a higher lifetime prevalence of imagery symptomology in aphantasia compared to control, or a higher prevalence of visual intrusive imagery compared to other forms of imagery. Strong evidence for no difference in any of these tests is also taken as disconfirming. We did not have a hypothesis as to whether imagery-related symptomology is stronger in some disorders compared to others, so these planned analyses and post-hoc contrasts are exploratory. We did not have a hypothesis concerning potential differences due to group splits (diagnosed versus undiagnosed, aphantasia versus hypophantasia), so these analyses are also exploratory.

For Hypothesis 2 (Aphants will report “lack of awareness or understanding of aphantasia” as a common factor in misevaluation, missed diagnosis, or misdiagnosis by mental health professionals), confirming evidence would be: more than five total “Yes” responses to item a. (because the person evaluating me did not understand my aphantasia) on Q12 of the questionnaire combined with fewer than five total “Yes” responses to item b. (unrelated to aphantasia); or a higher likelihood that aphants will attribute missed/misdiagnosis to aphantasia-related factors compared to those that received an accurate diagnosis. On the same analysis, if we found that accurate diagnoses are more likely to be related to aphantasia than missed/misdiagnoses, or that missed/misdiagnoses are more likely to be unrelated to aphantasia compared to accurate diagnoses, this would be taken as disconfirming evidence. Strong evidence for no difference in any of these tests would also be taken as disconfirming evidence. For the analysis comparing missed versus misdiagnoses, we predicted that these are similarly affected by misunderstandings of aphantasia; we therefore predicted to find strong evidence for no difference on this test. Finally, we did not have specific hypotheses concerning whether a certain disorder is more prone to misdiagnosis due to a misunderstanding of aphantasia compared to others; these analyses are therefore exploratory. We did not have a hypothesis concerning potential differences due to an aphantasia versus hypophantasia group split, so this analysis is also exploratory.

For Hypothesis 3 (Aphants will report that imagery-related therapies (specifically CBT) are ineffective in their mental health treatment), confirming evidence includes: fewer than five total responses of “No” on Q14 of the questionnaire (If you answered no to the last question, do you believe treatment was ineffective because you have aphantasia?), combined with more than five total “Yes” responses; a greater ineffectiveness of imagery-based therapies compared to non-imagery therapies; a greater ineffectiveness of CBT compared to other forms of psychotherapy; strong evidence that non-visual imagery therapies are more effective than visual imagery therapies; and/or strong evidence that CBT without imagery is more effective than CBT with imagery. Disconfirming evidence would include: fewer than five total “Yes” responses on Q14 of the questionnaire, combined with more than 5 total “No” responses; strong evidence for the opposite effects (e.g., effectiveness where ineffectiveness is expected); or strong evidence for null effects (i.e., no difference in effectiveness between types of therapy). In our tests comparing aphants to typical imagery controls, we also expected that aphants are more likely to report ineffectiveness of imagery-based therapies compared to control, whereas strong evidence in the opposite direction, or no difference between groups, would be taken as disconfirming evidence. All analyses that could be performed within each mental health condition separately were considered exploratory. We did not have a hypothesis concerning potential differences due to an aphantasia versus hypophantasia group split, so this analysis was also exploratory.

We additionally addressed all aims with a theory-driven thematic analysis (TA) of the interviews to obtain a richer, multidimensional dataset. Some questions can only be answered with a qualitative analysis; as previously stated, how participants have come to feel satisfied or dissatisfied with mental health services is best captured in a discussion of lived experiences. TA is inherently exploratory in its interpretation. Our interpretative plan is described in Phases 5 and 6 of the planned TA.

Reality checks were performed both for questionnaire and interview data. These checks are described in the Exclusion and inclusion criteria. To summarize, we first checked that participants responded consistently on different questions in the questionnaire (e.g., they indicated that they have aphantasia and score themselves <4 on the PSI-Q visual scale). We also checked responses from individuals who provided an email address compared to those who did not, to find out whether participants interested in an interview would be less likely to fabricate responses. At the beginning of the interview, we performed a short aphantasia assessment. We then double checked questionnaire responses verbally. If any responses were inconsistent between questionnaire and interview, we either clarified with the participant (if they still met inclusion criteria) or terminated the interview. We did not have to terminate any interviews based on these criteria.

The authors confirm that all analyses we performed adhered to the registered analysis plan at Stage 1, and any deviations from this plan, including additional exploratory analyses, are explicitly stated as such in the Quantitative Results section.

At the time of completion of the interviews, we had received 4,005 responses on the online survey (demographics, PSI-Q, and mental-healthcare questionnaire). 934 did not respond to any mental healthcare questions, and were excluded. A further 6 were excluded who reported that they did not have aphantasia, but had average PSI-Q visual scores <=3. A further 251 were excluded for reporting that they had aphantasia, but had average PSI-Q visual scores >3. 2,815 participants contributed to the final sample (total aphantasia N = 2,405; total control N = 410). Demographic data can be found in Figure 1. Although we registered an expected sample size of 4,248, this was estimated based on the sample size required to find at least 7-8 participants per interview group for the qualitative analysis, rather than the sample size required to perform quantitative analyses. Since we were able to meet our sample size goals for interview, we terminated data collection prior to achieving our target 4,248 participants. Furthermore, as the quantitative analyses required a much lower sample size (at least 5 samples per cell in contingency tables tests), we deemed 2,815 to be sufficient.

To summarize, we did not specifically seek to match aphantasia and typical imagery control samples as we recruited indiscriminately from our online resources, but our demographic distributions are quite similar between the two. Bayesian contingency tables tests revealed strong evidence for no difference in the distributions of reported gender (BF_01Poisson = 649.351, N = 2,807), English proficiency (BF_01Poisson = 1,118.568, N = 2,801), or socioeconomic status (BF_01Poisson = 252.525, N = 2,569). A Bayesian Mann-Whitney U test conducted on the age distributions of the two groups revealed moderate evidence for a difference (BF₁₀ = 9.01, W = 535,580), with the aphantasia sample being slightly older (Median age = 42 years, standard deviation (SD) = 15.2, N = 2,321) than the typical imagery controls (Median age = 37 years, SD = 14.4, N = 392). We then coded highest level of education attained on a 1-10 scale, from “primary school” to “doctorate”, and conducted a Bayesian Mann-Whitney U test on the difference in median level of education between the two groups. This revealed moderate evidence for no difference (BF₀₁ = 4.785, W = 474,530), with median education for both groups being at the level of Bachelor’s degree.

We have included the distribution of PSI-Q visual scores reported for each group (note that some participants were excluded from each group so there could be no overlap in scores). This shows that most aphants (whose PSI-Q visual scores could range from 0-3) had a median visual imagery vividness rating of 0, with 75% of scores including ratings up to 2.5. Imagers (whose PSI-Q visual scores could range from 3-10), had a median visual imagery vividness rating of 9.10, with a high number of individuals also concentrating around scores of 10, and 75% of scores including ratings down to 5.

Prior to our main analyses of interest, we conducted analyses on the reported prevalence of different mental health conditions in aphants compared to imagers. These were exploratory analyses, as they were not specifically addressing one of the registered hypotheses, but we determined during analysis that it would be important to elucidate the prevalence of different mental health conditions prior to an analysis of the prevalence of intrusions. In terms of the general lifetime prevalence of different mental health conditions reported, we conducted 2 (aphant, control) x 2 (specific disorder: yes, no) Bayesian contingency tables tests. The data are graphically summarized in Figure 2, and statistics are shown in Table 1. In summary, we found evidence for no difference in prevalence between the two groups for the majority of named disorders tested, although we found strong evidence for a difference in the prevalence of OCD, and moderate evidence for a difference for bipolar disorder.

Supported: Yes

To address Hypothesis 1, we first present descriptive statistics, graphically summarized in Figure 3.

First, individuals were excluded who reported that they did not believe they had a mental health condition, since we were only interested in mental health-related intrusions. This left us with 1,363 aphants and 236 imagers. To determine whether there was a difference in the lifetime prevalence of intrusive sensory experiences in aphantasia versus a typical imagery control group, we conducted a 2 (aphantasia, imagery) x 2 (any intrusions yes, no) Bayesian contingency tables test. This revealed strong evidence for a difference (BF_10Poisson = 1.28e+32, N = 1,599), in that typical imagery controls reported a higher prevalence of intrusions as part of their mental health condition (74%) compared to individuals with aphantasia (31%). BF₁₀ remained high across varying prior concentrations up to 10.

We then performed the same analysis within different classes of disorders, starting with anxiety and panic disorders. Individuals were included who reported GAD, social anxiety, and panic disorder (included from “other” write-in responses). This created a sub-sample of 921 aphants and 165 controls. We found strong evidence that controls are more likely to experience intrusions compared to aphants (BF_10Poisson = 3.12e+26, N = 1,086) with BF prior concentrations up to 10. The general prevalence of intrusions was not significantly different between our total sample and the sub-sample of those with anxiety and panic disorders (χ² goodness of fit = 1.16, p = 0.282). We then analyzed the sub-sample who reported depression, which included 1006 aphants and 164 controls. This again revealed strong evidence for a difference in the proportion of intrusions experienced (BF_10Poisson = 1.11e+22, N = 1,170) up to BF prior concentrations of 10, and this was also no different from expected proportions obtained from the total sample (χ² goodness of fit = 0.353, p = 0.552).

We then performed the same tests for a sub-sample who reported either PTSD or other symptoms of trauma for which they sought mental healthcare; this led to a sub-sample of 408 aphants and 86 controls. This test revealed strong evidence for a difference in intrusion prevalence between the two groups (BF_10Poisson = 4.60e+9, N = 494) with BF prior concentrations up to 10, with aphants being much less likely to report intrusions of any kind. A higher proportion of individuals reported intrusions as part of their PTSD compared to the total sample (χ² goodness of fit = 130, p < 0.001): specifically, there was nearly a perfect flip in the proportion of yes-to-no intrusion responses, with 63% of the PTSD sample reporting intrusions, compared to 37.7% across all conditions.

Next, we analyzed the sub-sample with complex mental health conditions and personality disorders, including OCD, DID, and BPD, along with those who responded with a personality disorder under “other”: these included avoidant personality disorder/sociopathy, depersonalization disorder, schizoid personality disorder, schizoaffective personality disorder, and schizotypal personality disorder. This resulted in a sub-sample of 239 aphants and 57 controls. There was again strong evidence for a difference in the proportion of intrusions between the two groups (BF_10Poisson = 106,070, N = 296) with BF prior concentrations up to 10. Individuals with complex or personality disorders generally showed a higher proportion of intrusions than observed in the total sample (χ² goodness of fit = 45.7, p < 0.001).

We wished to analyze intrusions in schizophrenia spectrum disorders and psychosis (written under “other” disorders), but there were too few individuals to submit to contingency tables tests (aphant N = 16, control N = 3). We instead report numerically that all three controls and 13/16 aphants experienced intrusions as part of their disorder.

For ED and body dysmorphic disorders, there were also not enough responses per cell to conduct contingency tables tests; however 83/178 (46.63%) aphants and 27/31 (87.10%) controls experienced intrusions as part of ED, and 52/100 (52%) of aphants and 22/23 (95.65%) controls experienced intrusions as part of a body dysmorphic disorder.

We then performed 2 (intrusions: yes, no) x 2 (specific disorder: yes, no) Bayesian contingency tables tests within the aphantasia group only, to determine whether intrusive symptoms are prevalent in certain disorders and not others. Intrusions were more prevalent if individuals had PTSD (BF_10Poisson = 6.39e+35, N = 1,363), BPD (BF_10Poisson = 10,439, N = 1,363), ED (BF_10Poisson = 3,018, N = 1,363), OCD (BF_10Poisson = 49.5, N = 1,363), DID (BF_10Poisson = 301, N = 1,363), or bipolar disorder (BF_10Poisson = 152, N = 1,363), compared to individuals who did not have each of those disorders. Intrusions were not more prevalent in GAD, social anxiety, or depression (all BF₁₀s < 1).

Finally, we performed the same series of 2 x 2 tests within the control group and input their observed intrusions as expected values in a χ² goodness-of-fit test (this was mistakenly referred to as a Multinomial Test in the Stage 1 registered methods), to determine whether intrusions occur with different proportions across different disorders in aphants compared to typical imagery controls. The control group, compared to aphants, experienced significantly more intrusions in all analyzable disorders (see Table 2). There were not enough observations for an analysis of DID or BPD, though 5/5 (100%) controls with DID and 9/10 (90%) controls with BPD experienced intrusions, compared to 60.9% and 56.6% of aphants with DID and BPD, respectively.

To determine if there are differences in the prevalence of different types of intrusions within our aphantasia versus control sample, we then split the data by modality of intrusions: visual, auditory, tactile, body, and emotional intrusions; and merged Likert-scale responses into binary “yes” (significantly, somewhat, a little bit) and “no” (not at all) for 2 (aphantasia, imagery) x 2 (yes, no) Bayesian contingency tables tests. Note that these analyses only include individuals who reported having experienced intrusions of some kind. This analysis revealed strong evidence for a difference in visual intrusion prevalence between aphants and controls (BF_10Poisson = 1.08e+19, N = 594), an effect that remained stable across BF prior concentrations up to 10. We found strong evidence for no difference in the prevalence of emotional intrusions (BF_01Poisson = 10.917, N = 598), though this fell to moderate evidence when BF prior concentration was set to 10 (BF_01Poisson = 6.431). We found moderate evidence for no difference in tactile (BF_01Poisson = 3.367, N = 592) and bodily sensations intrusions (BF_01Poisson = 5.348, N = 591), and anecdotal evidence for no difference in auditory intrusions (BF_01Poisson = 1.862, N = 589).

Within the aphantasia group only, we then performed 2 (visual versus “other modality”) x 4 (significantly, somewhat, a little bit, not at all) Bayesian contingency tables tests to investigate the difference in visual versus other sensory intrusion reports. We found strong evidence for a difference between visual and auditory intrusions (BF_10Poisson = 459, N = 418), and strong evidence for no difference in reports of visual versus body (BF_01Poisson = 52.632, N = 418) and visual versus touch intrusions (BF_01Poisson = 25.126, N = 419). Next, still within the aphantasia group only, we performed 2 (sensory versus non-sensory) x 4 (significantly, somewhat, a little bit, not at all) Bayesian contingency tables tests, to investigate the difference between emotional (non-sensory) versus sensory intrusion reports. These tests revealed several cells with fewer than 5 responses: specifically, there were not enough responses for “no, not at all” or “a little bit” for emotional intrusions (i.e., nearly all participants experienced emotional intrusions at least somewhat or significantly), so we could not analyze these contingency tables.

Finally, we performed the same tests (visual versus “other modality” intrusions) in the control group, to then enter these results as our expected counts comparing the aphantasia group to the control group. However, we were unable to perform these tests because there were not at least 5 cells in which imagers responded “no, not at all” or “a little bit” for visual intrusions (i.e., nearly all imagers experienced visual intrusions at least somewhat or significantly).

We conducted a Bayesian contingency tables test within the aphant group who believed they had a mental health condition of some kind, on the proportion of individuals who experienced intrusions as part of their disorder, split by diagnosis status: diagnosed, and did not have trouble receiving a diagnosis; diagnosed, but had trouble receiving a diagnosis in the past; or never diagnosed. This revealed strong evidence for a difference in the prevalence of reported intrusions (BF_10Poisson = 80.3, N = 1,363), with the lowest prevalence of intrusions (22.6%) among undiagnosed individuals, compared to those who did not have trouble receiving a diagnosis (28.6%) and those who were currently diagnosed but had trouble receiving a diagnosis in the past (39.4%). To investigate whether removing individuals who had never received a diagnosis changed the difference in the prevalence of intrusions between aphants and imagers, we then removed the group that had never been diagnosed and re-ran the Bayesian contingency tables test on the prevalence of intrusions between aphants and imagers. This test still revealed strong evidence for a difference between aphants and imagers (BF_10Poisson = 1.27e+29, N = 1,498), with more imagers still experiencing intrusions than aphants (70.8% compared to 29.2%, respectively). We therefore deemed that although individuals with different diagnosis statuses experienced different prevalences of intrusions, this difference did not affect the main finding that aphants are much less likely to experience intrusions compared to imagers.

We conducted a Bayesian contingency tables test within the aphant group who believed they had a mental health condition of some kind, on the proportion of individuals who experienced intrusions as part of their disorder, split by whether the individual reported complete aphantasia (PSI-Q visual rating = 0) or hypophantasia (PSI-Q visual rating between 1-3). This revealed strong evidence for a difference in intrusion prevalence (BF_10Poisson = 49,992, N = 1,363), with hypophants being more likely to experience intrusions compared to aphants (41.9% compared to 27.4%, respectively). We therefore re-ran the Bayesian contingency tables test on the difference in intrusion prevalence between aphants and imagers with complete aphants removed, to determine whether the main finding was still intact. This revealed strong evidence for a difference in intrusion prevalence between imagers and hypophants (BF_10Poisson = 7.23e+12, N = 613), with imagers being still more likely to experience intrusions compared to hypophants (74.2% versus 41.9%, respectively). This suggests that even though there is a difference in intrusions experienced by aphants and hypophants, both are much less likely than imagers to experience intrusions as part of their disorder. The imagery group difference in intrusion prevalence was still intact after further removing the data of undiagnosed individuals (BF_10Poisson = 9.76e+11, N = 574), thus suggesting a robust effect of mental imagery ability on the prevalence of intrusions.

We conducted a Bayesian contingency tables test to determine whether individuals who provided contact information for interview would report a different pattern of responses compared to those who did not. We filtered out individuals who did not provide contact information and re-ran the test for a difference in the prevalence of intrusions between aphants and imagers. This test revealed strong evidence for a difference (BF_10Poisson = 2.65e+24, N = 1,275), with imagers consistently showing a higher prevalence of intrusions compared to aphants (75.8% compared to 31.1%, respectively).

Supported: Partially – aphants reported this as a potential factor, but they were not more likely to believe they had been misevaluated or misdiagnosed compared to imagers.

To address Hypothesis 2, we first present descriptive statistics, graphically summarized in Figure 4.

To summarize, 57% of aphants who believed they had a mental health condition of some kind believed they had been properly diagnosed compared to 17% who believed they had not been properly diagnosed, and 26% who had never received a diagnosis. Among the 57% who felt they were currently accurately diagnosed, 57% believed their aphantasia was unrelated to their mental health condition, 39% believed their mental health professional was able to diagnose them based on symptoms unrelated to aphantasia, and 3% believed they received an accurate diagnosis because their mental health professional knew about aphantasia and understood how to treat individuals with aphantasia.

Among the 43% of aphants who reported a mental health condition of some kind but believed they had not been properly diagnosed or had never been diagnosed, 38% believed aphantasia was unrelated to their inability to receive a correct diagnosis. 54% had not known they had aphantasia at the time of diagnosis, but in retrospect, believed that aphantasia may have been related to their inability to receive an accurate diagnosis. 8% believed their aphantasia had contributed to their inability to receive an accurate diagnosis.

25% of aphants evaluated for a mental health condition believed they had been misevaluated or misdiagnosed at some point in their mental healthcare journey. This group included individuals with and without a diagnosis; among those with a diagnosis, this included not only individuals who agreed with their current diagnosis, but also individuals who had had trouble receiving a diagnosis in the past, and also those who did not agree with their current diagnosis. 47% of these individuals believed their misevaluation or misdiagnosis was unrelated to having aphantasia. 29.3% of individuals believed their misevaluation or misdiagnosis was due to a lack of understanding of aphantasia, whereas 23.6% believed their misevaluation or misdiagnosis was related to aphantasia in some other way.

We first performed a series of Bayesian contingency tables tests comparing our typical imagery control sample to our aphantasia sample to check whether the rate of missed and misdiagnoses are similar. First, we found strong evidence for no difference in the proportion of aphants and imagers who felt they were properly diagnosed, not properly diagnosed, or were undiagnosed at the time of testing (BF_01Poisson = 89.9), and this remained intact across BF prior concentrations up to 10. We found strong evidence that aphants and imagers also reported a similar level of misevaluations and misdiagnoses (BF_01Poisson = 10.8), which was also found with BF prior concentrations up to 10. Finally, we found no evidence for a difference in diagnosis status between the two groups (i.e., diagnosed, and had no trouble receiving a diagnosis; diagnosed, but had trouble receiving a diagnosis in the past; never diagnosed with a mental health condition; BF_10Poisson = 0.902, N = 1,666). However, the null result was only supported by anecdotal evidence (BF_01Poisson = 1.11); this went up to moderate evidence with a BF prior concentration of 10 (BF_01Poisson = 8.47).

We then performed a series of Bayesian contingency tables tests within our aphantasia sample, collapsed across disorders. The analyses detailed below target proportions of accurate diagnoses, missed diagnoses, misdiagnoses, and misevaluations thought to be related or unrelated to aphantasia.

To determine whether there was a different proportion of missed- or misdiagnoses related to aphantasia compared to accurate diagnoses, we first performed a 2 (accurately diagnosed, missed/misdiagnosed) x 2 (related to aphantasia, unrelated to aphantasia) test within our aphantasia sample. This revealed strong evidence that individuals who believed they were accurately diagnosed felt they were able to receive a diagnosis based on factors unrelated to aphantasia, whereas people who believed they were misdiagnosed or had not been diagnosed indicated aphantasia-related factors as a barrier (BF_10Poisson = 8.43e+68, N = 1,390); this effect remained stable with BF prior concentrations up to 10.

We then repeated the analysis but removed individuals who reported that their missed/misdiagnosis was related to aphantasia in some other way, rather than due to a specific lack of understanding of aphantasia by their mental healthcare professional. This again revealed strong evidence for a difference (BF_10Poisson = 1.69e+43, N = 1,294), in that individuals were likely to attribute an accurate diagnosis to factors unrelated to aphantasia, whereas they were likely to attribute an inaccurate or missed diagnosis to factors related to aphantasia, specifically a lack of understanding of aphantasia. This effect remained with BF prior concentrations up to 10.

To determine whether missed or misdiagnoses are differently thought to be due to misunderstandings about aphantasia, we conducted a 2 (missed diagnosis, misdiagnosed) x 2 (due to aphantasia, not due to aphantasia) test. This revealed strong evidence for a difference (BF_10Poisson = 3.43e+30, N = 565), in that 57.7% of individuals who felt they were misdiagnosed attributed their misdiagnosis to aphantasia-related factors, whereas only 7.4% of undiagnosed individuals who felt they had a mental health condition attributed their lack of a diagnosis to aphantasia-related factors. This difference remained when a lack of understanding of aphantasia was analyzed as the only aphantasia-related factor (BF_10Poisson = 2.62e+22, N = 474), and also with BF prior concentrations up to 10.

To find out whether an understanding of aphantasia benefited the accurate diagnosis of specific disorders, we performed Bayesian contingency tables tests on whether aphants were diagnosed with a certain disorder or not, with responses split by whether individuals believed their diagnosis benefited from an understanding of aphantasia; whether they could be diagnosed based on other symptoms; or whether their condition was unrelated to aphantasia. This revealed that the accurate diagnosis of PTSD benefited from an understanding of aphantasia, and individuals diagnosed with PTSD were also less likely to say that their condition was unrelated to aphantasia, compared to other disorders (BF_10Poisson = 180, N = 1,031). This effect remained with BF prior concentrations up to 10. The diagnosis of no other specific disorders showed a benefit over others from an understanding of aphantasia.

We then performed a series of Bayesian contingency tables tests for different mental health conditions to determine whether a lack of understanding of aphantasia seemed to contribute more to misdiagnoses in specific conditions compared to others. These tests revealed no evidence that any mental health condition over another was more affected by this factor (all BF_10Poissions < 1). The same tests performed for missed diagnoses (i.e., in individuals who believed they had a mental health condition and had sought mental healthcare, but currently remained undiagnosed), revealed strong evidence that individuals believed their PTSD remained undiagnosed due to aphantasia-related factors (BF_10Poisson = 433, N = 509). Specifically, 73.8% (135/183) of individuals who believed they had PTSD but had never been diagnosed with PTSD believed this may have been due to aphantasia-related factors, compared to 56.4% (184/326) of individuals who believed they had another disorder.

We conducted Bayesian contingency tables tests to assess whether there were any differences in diagnostic experiences of complete aphants versus hypophants (Table 3). In summary, the results suggest that although a similar proportion of hypophants and complete aphants felt properly diagnosed, misevaluated, or remained undiagnosed, we found strong evidence that complete aphants were more likely to report that aphantasia-related factors may have contributed to misevaluation and missed diagnosis.

We re-ran Bayesian contingency tables tests on whether aphants and imagers had different experiences of being misevaluated (BF_01Poisson = 13.4, N = 1,583), misdiagnosed (BF_01Poisson = 99.3, N = 1,653), or had trouble receiving a diagnosis (BF_01Poisson = 41, N = 1,309), in individuals who provided contact information only. We found strong evidence for no group differences for all tests.

Supported: Partially – specifically visual imagery-related techniques, including visual imagery techniques used in CBT, were considered less effective than non-visual imagery-related techniques in therapy; imagery-related therapies could still be considered effective, especially if visual imagery techniques were not involved

To address Hypothesis 3, we first present descriptive statistics, graphically summarized in Figure 5.

In summary, both aphants and imagers were most likely to have tried a combination of medication and therapy in their mental health treatment, followed by only therapy, and then only medication. Among individuals who had tried a combination of treatments, both aphants and imagers reported that the combination of treatments helped the most, followed by medication only, then therapy only, in that order. 13% of both aphants and imagers who tried a combination of treatments indicated that nothing helped. Among the individuals who had tried therapy either in combination with medication or on its own, both aphants and imagers reported that emotional imagery was the most common modality of imagery used in therapeutic exercises, followed by visual, body, auditory, and tactile imagery, in that order. Finally, more than two thirds of both aphants and imagers whose treatment included a form of therapy, had tried CBT.

For aphants and imagers who had tried any form of treatment, there were more individuals from both groups who indicated that their treatment had been effective rather than ineffective; among those who had tried CBT, more individuals from both groups reported that CBT was effective compared to ineffective. If CBT exercises included a visual imagery component, an equal number of aphants considered it effective versus ineffective, whereas more imagers considered CBT with visual imagery effective. If individuals took part in CBT without a visual imagery component, more than two thirds of each group felt that it was effective.

To determine whether there is a difference in perceived effectiveness between therapy that included imagery techniques versus non-imagery techniques among our aphantasia group, we first conducted a 2 (imagery, no-imagery) x 2 (effective, ineffective) Bayesian contingency tables test among individuals with aphantasia, collapsed across disorders. This revealed strong evidence for a difference in the perceived effectiveness of therapy (BF_10Poisson = 1.06e+6; N = 1,355) across BF prior concentrations up to 10. Specifically, more aphants felt their treatment was effective if therapy did not include an imagery component, compared to those whose therapy did include imagery techniques in one or more modalities.

We then performed a 2 (visual imagery-based, other imagery-based) x 2 (effective, ineffective) Bayesian contingency tables test to find out whether therapies that use other modalities of imagery (i.e., auditory, tactile, body, emotional) are considered more effective than visual imagery therapies. Because several individuals reported multiple imagery modalities used in therapy, we first split groups into those who reported techniques that used any visual imagery (including visual-only and visual-combined) versus any non-visual imagery modality. This test revealed strong evidence for a difference in perceived therapy effectiveness (BF_10Poisson = 7.76e+10, N = 1,019), with aphants finding therapy more effective if imagery techniques were non-visual (e.g., bodily sensation imagery). We then repeated the test with groups split by those who used only visual imagery techniques in therapy, versus those that used any non-visual imagery techniques. This test also revealed strong evidence for a difference in the same direction (BF_10Poisson = 225, N = 412). These effects remained stable with BF prior concentrations up to 10.

Next, we conducted a 2 (CBT, other therapy) x 2 (effective, ineffective) test for perceived CBT effectiveness versus other forms of therapy. This revealed strong evidence for a difference in perceived effectiveness (BF_10Poisson = 1,963, N = 1,092) with BF prior concentrations up to 10, in that aphants felt their therapy was more effective if it did not include CBT.

We then split the group who reported having tried CBT into those that also tried imagery techniques in any modality versus those that did not use imagery techniques as part of their therapy. The 2 (CBT with imagery, CBT without imagery) x 2 (effective, ineffective) revealed strong evidence for a difference in perceived CBT effectiveness (BF_10Poisson = 1,471, N = 757), in that CBT was considered effective more often if it did not include imagery techniques. Among those that reported using imagery techniques of some kind in CBT, non-visual imagery techniques were considered more effective than visual imagery techniques (BF_10Poisson = 1.27e+7, N = 599). These effects remained intact with BF prior concentrations up to 10.

Finally, we performed the same series of 2 x 2 tests within the control group and input their observed proportions of therapy effectiveness as expected values in a χ² goodness-of-fit test (this was mistakenly referred to as a Multinomial Test in the methods). The results are summarized in Table 4.

These tests revealed significant differences between aphants and imagers in the perceived effectiveness of different techniques used in therapy. Specifically, therapies that included mental imagery, visual imagery, CBT, CBT and imagery, and CBT and visual imagery techniques were all considered less effective for aphants compared to imagers. On the other hand, therapies that used no imagery, nonvisual imagery, non-CBT, CBT without imagery, and CBT with non-visual imagery were all considered similarly effective for aphants and imagers.

Because we obtained a high number of respondents who had tried CBT, we were able to look at the perceived effectiveness of CBT across different mental health conditions. The results are collated in Table 5. In summary, imagers found CBT generally more effective than aphants, particularly in treating depression and PTSD.

To determine whether hypophants and complete aphants felt the different therapeutic techniques were differently effective, we re-ran the χ² goodness of fit tests between these two groups. This revealed a single significant difference, in that more hypophants (63.9%) felt imagery techniques were effective compared to complete aphants (59.4%; see Table 6). Otherwise, there were no significant differences between these groups.

To determine whether the consistency of our results depended on whether participants provided contact information, we re-ran the χ² goodness of fit tests between aphants and imagers only in individuals who provided this information (see Table 7). This analysis revealed that more imagers found therapy effective compared to aphants if it included imagery, visual imagery, CBT, CBT with imagery, and CBT with visual imagery; whereas more aphants found therapy effective compared to imagers if it relied on non-imagery, non-visual imagery, non-CBT techniques, and non-visual imagery techniques with CBT. The only technique that did not show a difference in perceived effectiveness between groups was non-imagery CBT. We deemed these results consistent with the findings from the larger samples.

Participants in the qualitative analysis were selected based on diverse demographic data from the quantitative survey, to represent as wide a demographic sample as possible. We selected participants across age bands (young adults (18-39 years), middle-aged adults (40-54 years), and older adults (55+ years)), socioeconomic status (SES; low, middle, and high), geographic region of nationality, and gender expression. Since most participants reported a generally high level of education, we were unable to diversify our sample based on this metric. Participants were initially invited via e-mail to represent one of the target groups detailed in the methods; however, during the interview process, these groups proved to be unnecessary, as most individuals had experiences pertaining to all three target groups at some point in their mental health journeys. 18 individuals were invited from target group 1, of which six were interviewed; 19 individuals were invited from target group 2, of which eight were interviewed; and 18 individuals were invited from target group 3, of which nine were interviewed. 23 interviews were conducted by two researchers. One interview from group 3 had to be discarded due to incomprehensible responses. Demographic data are summarized in Table 8.

Transcripts were checked, coded and analyzed by three researchers working independently. Initial codes were discussed, and candidate themes identified. This was then checked with a fourth researcher to improve the robustness of the analysis. The initial coding primarily followed the deductive approach outlined in the methodology, from which we generated the theme of Quest for Identity, with sub-themes Being Different, Memory, and Help Seeking. Following a constructivist and inductive approach (Byrne, 2022), a second main theme – Mental Health Journey – was identified, based on differences in mental health/care experiences. Sub-themes here corresponded to mental health classifications that were either self-identified or formally diagnosed by mental health professionals. These were Anxiety & Depression, Neurodiversity, and Trauma & Complexity. The thematic map (Figure 6) graphically summarizes the main themes and sub-themes and their relationship to each other.

In the following narrative of the qualitative analysis, exemplar quotes that represent each theme and sub-theme are presented within the text. A selection of additional and expanded quotes related to the exemplars are provided in a supplementary table (see Supplementary Materials: Appendix D).

Core experiences of living with aphantasia were similar across participants and were classified into three sub-themes: Being Different, Memory, and Help Seeking. Participants identified aphantasia as a key part of their Quest for Identity and described a range of different ways of processing their world in the absence of visual imagery or visual memory.

Participants identified their experience as different from others, particularly from imagers. For all participants, this included pragmatic statements about how their minds functioned:

There were many instances of individuals comparing themselves with others.

Many reported an early awareness of being different. Individual reflections included negativity about aphantasia, describing themselves as seeming to lack common sense, and not understanding the world very well.

Some expressed a pragmatic understanding and acceptance of their individual differences.

Others expressed enthusiasm and delight at being different, describing themselves as being “different, but in a good way” and finding benefits from aphantasia.

The lack of visual imagery was often accompanied by memory challenges resulting specifically from an impairment of memory for visual information, including visual information about oneself.

There were also more global memory impairments and severely deficient autobiographical memory (SDAM), characterized by profoundly impaired autobiographical re-experiencing (Palombo et al., 2015). Many participants included strategies that they had devised to manage this in their daily life.

This was further exemplified by the understanding that their experiences with memory challenges meant that their life is lived “in the moment”.

Participants reported a wide range of experiences of help seeking, including visits to general practitioners, hospital assessments, therapies of different types, prescribed medication, and self-assessments (usually online). Most expressed experiences of being misunderstood – either that they themselves were “wrong” or “broken” or were doing something “wrong”. Several expressed having been misunderstood or let down by professionals.

At its core, identity is defined by the differences and similarities that enable the recognition of individual uniqueness, while simultaneously defining the extent of belonging to, and separation from, familial, social, professional, and more global human groupings (Swann & Bosson, 2010). As such, identity formation is a key developmental task and fundamental to the ability to navigate human worlds. The relatively recent appellation of aphantasia currently defies categorization, being neither a form of neurodiversity nor a type of disability. While its precise position within the diversity of human experience is yet to be fully determined, our research demonstrates that those who identify as aphants share characteristics and experiences that they believe differentiate them from imagers. This similarity of experience further supports the growing understanding of aphantasia as part of the spectrum of neurodiversity that characterizes the human condition (Monzel et al., 2023). Participants in this study described a range of ways in which awareness of these differences became apparent to them. For many, early experiences of being different included the experience of personal deficit or failure. Prior to the entry of aphantasia into popular awareness (Zeman et al., 2015), aphantasic experiences often resulted in a negative or deficient self-identity. In the absence of a framework for understanding their fundamental nature and functioning, there may be a degree of distress arising from aphants’ early efforts to fit into an imager’s world.

Some part of this is encapsulated in the sub-theme of Being Different, with most participants’ language loading in the direction of deficit, based on unfavorable comparisons with others. This often fueled the Quest for Identity. The relationship between self-identity and the sub-theme of Memory hinges on the process of reflexivity as a way of deepening self-understanding and identity formation. For those with limited access to memories, this process becomes challenging and explains why Memory enjoys a relatively central position in the narrative. Almost all participants felt that a key consequence of being aphantasic was memory impairment. This often had an impact on social relationships through the inability to recall faces or to recognize familiar people who had made relatively minor changes in their appearance. For some, it manifested as an almost global level of memory impairment, impacting all areas of life. Individuals with SDAM felt it had a profound impact on their core identity. Finally, from a subjective perspective, while Help Seeking focuses primarily on distress reduction, it also forms part of the Quest for Identity. For some, differences in processing attributed to aphantasia are described and sometimes celebrated as manifestations of human diversity – potentially indicating that aphantasia may emerge as an identity in its own right. For the most part, however, help seeking involved multiple re-referrals and conversations that left many participants feeling severely misunderstood.

Using a more inductive and ultimately constructionist approach, we identified three different types of experiences that were linked to different groups of symptoms or diagnoses for which the participants sought support as part of the mental health journey: Anxiety & Depression, Neurodiversity, and Trauma & Complexity.

Participants who self-identified their primary area of difficulty as either anxiety or depression tended to express a degree of satisfaction and a capacity to manage themselves most of the time.

Participants reporting ADHD and/or autism spectrum conditions (ASC) seemed to spend more time and energy trying to understand themselves, and frequently experienced significant degrees of distress as a result of their experiences.

Participants who reported diagnoses or experiences of complex mental health difficulties and/or trauma described significant and enduring difficulties accessing treatment. Their journeys to better mental health tended to take longer, and in many, was still ongoing at the time of interview. This group was also more likely to have confusing symptomology.

One participant shared that they believed that the experience of having aphantasia was protective against some of the visual aspects of PTSD.

Deeply important to this group was the interaction with healthcare professionals, as they felt they were often misunderstood, not only by the systems of support, but by the people aiming to support them.

A positive therapeutic relationship, however, was a significant support.

The first group, who reported experiences of depression and/or anxiety, had relatively fewer interactions with mental health services, seemed to agree more readily with their diagnosis (which was also more easily given), and tended to believe that the treatment worked for them. Some had taken medication as part of their treatment, but all found that, to some extent, the therapeutic treatment on offer was enough to support their journey to wellness. Once they received the help they sought, and daily experience more closely mirrored their desires or expectations, they “stopped looking” for further answers. It is as if their need to understand themselves was satisfied at that point and no further support was sought.

The second group, characterized by their neurodiversity, including ASC and ADHD, demonstrated a reasonably positive interaction with professional support systems. While they had few issues with the diagnostic process itself, there was a sense that their journey of self-discovery was not yet complete. Neither the diagnosis of neurodiversity, nor the recognition of themselves as aphants, seemed to offer enough for self-understanding. For some participants, their journey continued as a self-discovery process, enabling them to recognize more about themselves, to either self-diagnose or to seek a later diagnosis of neurodiverse conditions. If they felt that their labels – whether self-described, professionally recognized, or diagnosed – explained the differences between their expected experience of the world and their lived reality, they too seemed to “stop looking” for professional support. For many, this journey of self-discovery was longer than for those with more easily diagnosed mental health challenges, but this could be due to the nature of the diagnostic process and the more complex criteria that they needed to pass through on their journey.

The last group, with more fundamental mental health needs – including PTSD, personality disorders, and complex trauma – had the most tempestuous interaction with mental health services. They were most likely to struggle to receive a diagnosis that they felt authentically captured their experience. It was often expressed that this was down to the nature of the diagnostic criteria for some of these conditions, especially visual flashbacks, which are rarely experienced by aphants. Instead, participants identified emotional or physiological flashbacks that were disconnected from the current physical reality. This poses a challenge for diagnosticians due to the crossover between flashbacks, disconnected emotions, and dissociative states associated with a range of complex mental health conditions. Emotional dissociation is one of the diagnostic symptoms of BPD, for example, but may in fact be present in the expression of PTSD flashbacks in those with aphantasia. Such is the diversity and complexity of individual experience, and the nature of these conditions, that reaching a “correct” diagnosis – or one which feels authentic – is a long and complex process. It is also heavily dependent on a relationship of mutual trust between a supportive and adaptable therapist and a willing and introspectively reflective patient. Of all the groups, this participant group valued the therapeutic relationship the most and reported more positive outcomes when this relationship was based on respect and professional curiosity.

Two participants speculated that aphantasia may act as a protective factor against PTSD flashbacks. One, diagnosed with anxiety and depression, had engaged in the literature and expressed this belief from a theoretical perspective, rather than from personal experience. The other had a diagnosis of PTSD, but despite awareness that they had experienced past trauma, did not relive it over and over, which they felt would have been the case if they could visualize. The remaining participants within the trauma and complexity group did not seem to share this belief, and had PTSD experiences despite their aphantasia, albeit not visually oriented. The interpretation of aphantasia-related research literature by one participant highlights potential challenges for this group. In their quest to understand themselves and their mental health challenges, some may assimilate theory-driven notions into their self-concept, believing that they are somehow immune to conditions such as PTSD despite the more experiential or authentic beliefs of other aphants with these diagnoses.

It is worth identifying here that all the interview participants in this study were resilient enough to identify that they had a mental health challenge, that they had experiences of aphantasia, and were willing to talk about their deeply personal experiences with independent researchers. This means that these individuals were self-aware enough to identify a challenge in their daily life and to seek support. This is not the case for all of those with mental health problems. For those who are able to seek help, particularly those who fall into the category of trauma and complexity, sympathetic and knowledgeable services are a critical source of support. For those unable or unwilling to seek help, the services and their flexibility remain irrelevant. However, there are many individuals at the edges of enquiry who have initiated support-seeking, or who have identified a difficulty with their interaction with the world. For these individuals, seeking support and finding professional curiosity, understanding, and acceptance, versus a “one size fits all” approach, may be the difference between continuing to pursue self-discovery, and identifying oneself as beyond the reach of help. It is for this group that the continued investigation and understanding of therapeutic interventions must be prioritized.

This mixed-methods study was conducted to investigate the impact of aphantasia on mental healthcare experiences, with three hypotheses: that aphants will experience a lack of imagery-related mental health symptoms; that aphants will report a perceived “lack of awareness or understanding of aphantasia” as a common factor in misevaluation, missed diagnosis, or misdiagnosis by mental health professionals; and that aphants will be more likely to report that imagery-related psychotherapies (specifically CBT) are ineffective in their mental health treatment compared to a typical imagery control group.

It is important to note that the prevalence of most mental health conditions – including PTSD, other anxiety and panic disorders, and depression – was no different between aphants and imagers. This suggests that mental imagery ability does not generally contribute to a higher prevalence of most mental health disorders, contrary to dominant theories (Di Simplicio et al., 2016; Hirsch & Holmes, 2007; Moritz, Hörmann, et al., 2014; Weßlau et al., 2015). We propose that, instead of a difference in condition prevalence, aphants express different symptom profiles compared to imagers, especially in conditions for which intrusive imagery is a diagnostic indicator. In support of this hypothesis, we found that aphants in our study had a much lower lifetime prevalence of sensory intrusions as a symptom of any mental health condition compared to imagers. Emotional intrusions were the most prevalent for both groups; however, aphants were much less likely to experience visual intrusions compared to imagers, and virtually all imagers who reported intrusions of some kind had visual intrusions. Aphants who reported intrusions were more likely to experience them as a symptom of trauma-related, personality, and eating disorders compared to other disorders. This ties in with research showing that intrusions are a dominant symptom of all of these disorders (Hirsch & Holmes, 2007; Kadriu et al., 2019; Moritz, Claussen, et al., 2014). Although, as identified by the qualitative interviews, mapping intrusions to specific diagnoses may not be appropriate for aphants and may further contribute to “missed” diagnoses reported by those who experience intrusions in an unconventional way.

In line with this, our qualitative interviews conducted with aphants about their mental health/care revealed a marked lack of imagery-related symptoms in trauma disorders. For those participants who had a diagnosis of PTSD, there was not one report of visual flashbacks, which are a key diagnostic feature of the condition (American Psychiatric Association, 2013). Interviewees consistently reported emotional flashbacks that many described as a sudden experience of an emotion unrelated to the reality of their “in-the-moment” experience. In particular, they reported that the experience of these “out of context” emotions was disturbing and confusing, possibly exacerbating issues of identity and reinforcing prior interactions with therapeutic professionals that may have led to the belief that they were “broken” or that the interventions could not work because of some supposed fault of the participant. While in most cases these emotional intrusions eventually led to a tentative diagnosis and treatment of PTSD, it may not always be wise to include this as part of an expected diagnostic protocol for an aphant presenting with suspected PTSD, as emotional intrusions are a feature of other mental health pathologies such as DID and schizophrenia.

For those with PTSD, it is also possible that some elements of reduced or absent visual imagery could be a result of the trauma itself (Knowles et al., 2021) or be a natural part of the mind protecting itself from the traumatic event (Mary et al., 2020), as some memories are repressed as part of the resilience of the human mind. Keogh et al. (2023) further suggest that aphantasia may act as a buffer against developing trauma-related mental health conditions. Our qualitative results point to another, more nuanced position, in that a lack of visual recall may support the recovery from some effects of trauma, rather than conferring immunity to trauma in the first place. This could be due to differences in the way flashbacks occur, in that the absence of graphic images of trauma may reduce some of the impact of the flashback process. Further research is needed to investigate this more fully.

Sensory intrusions were not more commonly experienced in anxiety disorders and depression compared to other disorders in our sample. Although intrusions often occur during panic states in anxiety disorders (Brewin et al., 2010), previous research indicates that anxious, depressive, and intrusive thoughts form distinct categories of negative thought in these disorders (Clark, 1992), and intrusive thoughts can be further split into thoughts, images, and impulses (Arnáez et al., 2021). Worrying that a family member has a serious illness after hearing them cough would be an example of a non-image-based intrusive thought. Therefore, although intrusive sensory experiences are not common in individuals with a combination of anxiety and aphantasia, these individuals are likely still susceptible to anxious, depressive, and non-image-based intrusive negative thoughts. While there is more space for investigation around the specific expression of these symptoms in aphantasia, our qualitative analysis suggests that the key diagnostic symptoms of depression and anxiety are unaffected by differences in visual imagery.

Quantitative group differences remained intact following additional data quality checks (analyses conducted only in diagnosed individuals and those who provided contact information), and also when aphants and hypophants were compared to imagers separately. We conducted these analyses based on the possibility that individuals with a diagnosis may experience more extreme symptoms of their condition; that individuals who provide contact information would respond more truthfully on the questionnaire due to the likelihood of being interviewed; and that “extreme” aphants (i.e., those who scored an average of 0/10 on the PSI-Q visual scale) may have more severe imagery-related impairments compared to hypophants (i.e., those who scored an average of 1-3/10 on the PSI-Q visual scale). Our results suggest that group differences do not fundamentally change with any of these variations, although effects seem to be strongest in diagnosed individuals with extreme aphantasia.

Hypothesis 2 was partially supported, in that many aphants believed a lack of awareness or understanding of aphantasia contributed to misevaluation, missed diagnosis, or misdiagnosis by mental health professionals. Specifically, aphants who believed they were accurately diagnosed felt they were able to receive a diagnosis based on factors unrelated to aphantasia, whereas aphants who believed they were misdiagnosed or had not been diagnosed indicated that a lack of understanding of aphantasia may have been a barrier to accurate diagnosis. One reason for this could be that different mental health conditions rely to different extents on imagery-based diagnostic criteria. For example, the diagnostic tools used to identify anxiety and depression conditions (primarily those of self-report) tend to focus on internal feelings and are not concerned with visual experiences (American Psychiatric Association, 2013). In support of this, individuals we interviewed in this category reported no issues in getting mental health support from the various systems with which they engaged.

In terms of neurodivergent conditions, the diagnostic criteria for ASC and ADHD as outlined by the DSM-V (American Psychiatric Association, 2013) or the International Classification of Diseases (ICD-11; World Health Organization, 2022), have no reference to visual phenomena, so diagnosis of these conditions is also not dependent on visual imagery-based experiences. However, there is some trait overlap between aphantasia and ASC on other measures, such as the Autism-Spectrum Quotient (AQ; Baron-Cohen et al., 2001), which contains a dimension related to impoverished imagination. This could potentially contribute to misdiagnosis, as one interview participant in our study disagreed with her autism diagnosis, believing it to be a result of trait overlap with aphantasia. However, most interview participants with neurodiverse conditions agreed with, and accepted, their diagnosis, or were still seeking a diagnosis and believed that trait overlap with aphantasia did not sufficiently explain their experience.

There is evidence that missed diagnosis and misdiagnosis rates are generally high in many mental health conditions (Vermani et al., 2011), and there are myriad reasons why individuals may feel they have been misdiagnosed or that their diagnosis is inauthentic to their experience. Generally, aphants in our quantitative study were no more likely to feel they were misevaluated or misdiagnosed compared to imagers. This could be due to the overwhelming majority of our participants experiencing conditions that do not have imagery-related diagnostic criteria (anxiety, depression, ASC). Another possible reason for a missed diagnosis may be due to differences in intrusive experiences, with more lifetime intrusions reported by aphants with a diagnosis (whether it was easily obtained or more of a challenge) compared to those without a diagnosis. This supports the idea that diagnosing professionals are looking for these key identifying features to support diagnoses.

Misdiagnoses were more likely attributed to aphantasia-related factors compared to missed diagnoses. This is possibly because symptoms may present in unconventional ways, specifically leading to misdiagnoses. For example, aphants overwhelmingly report negative mood and cognition in response to trauma rather than sensory intrusions (Dawes et al., 2020). This may lead to a diagnosis of depression, rather than PTSD. This is in line with two other quantitative results from the current study: aphants reported that an accurate diagnosis of PTSD, specifically, benefited from an understanding of aphantasia by professionals; and aphants who believed they had a mental health condition but remained undiagnosed were more likely to believe the lack of a diagnosis was due to aphantasia-related factors if they believed they had PTSD compared to other conditions. Results from our qualitative analysis suggest that for those with PTSD, schizophrenia, BPD, or DID – all conditions for which intrusive sensations and images are diagnostic criteria – a lack of imagery-related symptoms did significantly impact the ability to receive an accurate diagnosis. Specifically, interviewees with these conditions reported delays in receiving a diagnosis and starting treatment, and some felt that their diagnosis was not reflective of their experiences. Together, these findings point to a specific risk of missed and misdiagnosis in individuals with aphantasia and complex mental health conditions, including trauma-related conditions, which merits further investigation.

Hypothesis 3 was partially supported, in that aphants felt that visual imagery-related therapeutic techniques (including those used in CBT) were ineffective in their mental healthcare. Compared to imagers, aphants were more likely to feel that any therapy involving imagery techniques in any modality were ineffective – especially for depression and PTSD. This could be due to a particular reliance on imagery-related techniques in treating these conditions. For example, restructuring one’s self-image, imagining hypothetical scenarios (e.g., “what is the worst that could happen”), and humorously exaggerating problems with imagery (e.g., imagining smoke coming out of one’s ears when angry) are all common imagery-based techniques used to treat depression (Munoz & Miranda, 2000); and various psychotherapies for PTSD dominantly make use of imagery techniques such as imaginal exposure, and reliving and updating traumatic memories (Schnyder et al., 2015). However, techniques that did not rely on visual imagery, including CBT, could be considered effective for aphants in our study. Extrapolating from this, aphants may have success in therapy if practitioners are able to promote more personalization in their chosen techniques. CBT could be a particularly good candidate therapy for personalization, as it has the potential to make use of diverse techniques, including many non-imagery techniques. Therefore, we highlight the overarching importance of adapting therapeutic techniques to individual preference and ability.

This brings us to the centrality of the therapeutic relationship for positive therapeutic outcomes (Dobson, 2022; Okamoto et al., 2019). Our qualitative analysis suggests that when practitioners emphasized technique over relationship, aphantasic individuals interpreted the inability to benefit from therapy as a personal failure, which exacerbated their difficulties. For those who were fortunate enough to seek help from a professional who was able to meet them on their terms, techniques could be mutually adapted (as exemplified in the discussion related to the sub-themes of Help Seeking and Neurodiversity). Otherwise, adaptation of the technique had to be self-managed, or the technique discarded completely. This frequently led to mutual distrust, as the client did not believe the professional was able to help them, with the therapist stating that the lack of progress was due to poor adherence to technique on the client’s side. This culminated in a breakdown in the client/therapist relationship. The relationship must involve trust and understanding, and if the practitioner does not accept the experiences of the individual, they are unable to provide the “safe space” that is needed for good client progress; this is particularly exemplified in the discussion on the theme Mental Health Journey, and specifically the sub-theme Trauma & Complexity. The quality of the relationship and professional understanding – whether of the individual as part of a person-centered approach, or as a specific understanding of aphantasia – are therefore key in effective treatment.

Irrespective of aphantasia status, mental health help-seeking is driven by the need of the individual to reduce stress and alleviate distress in everyday life. Our qualitative findings emphasized the importance of the quest for identity amongst a population that has only recently been identified in the public eye. They experience a pervasive feeling of being different from imagers and have specific memory challenges that impacted their help-seeking success. Importantly, our analysis highlighted multidimensionality in mental health journeys, which were different for individuals with anxiety/depression, neurodiversity, or trauma/complex mental health conditions in combination with aphantasia. Specifically, individuals with these different conditions had different experiences of their mental health journeys, their quests to understand themselves, and their interactions with the world.

Altogether, this research reveals that aphantasics experience similar levels of mental health distress to imagers, but symptomatic experiences and response to treatment techniques are fundamentally different. The application of this knowledge into practice may support more personalized pathways into mental healthcare. Key to this is the therapeutic relationship; a relationship built on mutual trust, respect, and professional curiosity, can transform the sometimes painful quest for identity into a positive mental health journey toward self-acceptance and compassion, for aphants and imagers alike. By including an understanding of aphantasia in these discussions, it is our hope that these insights will support positive mental healthcare outcomes for everyone.

Reshanne R Reeder: Conceptualization (Equal), Data curation (Lead), Formal Analysis (Equal), Funding acquisition (Lead), Investigation (Equal), Methodology (Equal), Project administration (Lead), Visualization (Lead), Writing – original draft (Equal)

Bridget Mawtus: Conceptualization (Equal), Formal Analysis (Equal), Investigation (Equal), Methodology (Equal), Writing – original draft (Equal)

Fran Renwick: Formal Analysis (Supporting), Methodology (Supporting), Writing – review & editing (Supporting)

Bethany R Thomas: Methodology (Supporting), Validation (Supporting), Writing – review & editing (Supporting)

The authors declare no conflicts of interest, and no author will receive a financial advantage from the direction of any result in this work.

This research was funded by the SIPS-Collabra Registered Report Funding Partnership. The authors would like to thank the Aphantasia Network for their significant contribution to participant recruitment and study advertising, which were both imperative to the success of this study. We also thank Dr Gray Atherton and Dr Liam Cross for their valuable insights on the qualitative analysis, as well as Emily Cooper and Anna Baker for their help with interview transcription.

All materials from Stage 1 and 2, including anonymized quantitative data and anonymized interview transcripts, are publicly available on the Open Science Framework (OSF; https://osf.io/v7krh/). Stage 1 materials were made available on OSF on 29/07/2023 (https://osf.io/uamcp/), along with the Stage 1 preprint on PsyArXiv (https://osf.io/dsjah). Stage 2 data and materials were made available on OSF on 16/07/2024, along with the Stage 2 preprint on PsyArXiv (https://osf.io/preprints/psyarxiv/f6h5q). All appendices (Appendix A: Recruitment questionnaire; Appendix B: Interview questions; Appendix C: CBT schematic; and Appendix D: Qualitative quote table) are available in the Supplementary Materials file.