Thursday, September 30, 2021

A Curious Case of Auditory-Gustatory Synesthesia... in someone who can't smell


A fascinating case study from 1907 describes the self-reported sensory “taste” experiences evoked by hearing specific words, names, or sounds (Pierce, 1907). The subject was a young woman about to graduate from college. As far as she could tell, she's always had these experiences, and for most of her life she didn't know they were unusual. This surprise upon discovering the uniqueness of one's one internal experience is similar to what is reported by many contemporary individuals with less typical phenomenology, such as aphantasia (the inability to generate visual images).

Pierce noted that the subject was anosmic (had a loss of smell), although this was not formally tested. Nonetheless, he observed:

Coffee burning upon a stove is not noticed, though she may be close by. Camphor placed in the nostrils gives only a stinging sensation. Ammonia can be sniffed without discomfort. And, as with all anosmics, foods are discriminated on the basis of the pure taste qualities, or by the characters of texture, astringency, and so on, which any food-complex may possess. These sensory defects are worth noting for their possible significance in connection with the theory of this special case of synæsthesia.


Some aspects of the report were rather quaint by modern standards (e.g., the first person narration), but others were quite prescient.

But what evidence have we that an actual case of synæsthesia is here being reported, and not a case of artificial association due to a lively dramatic fancy? This is a point that must be raised, for it is very easy to entertain a suspicion that these phenomena are essentially ungenuine. Now, of course, in matters of this kind general impressions and personal knowledge of the subject count heavily. And on both these grounds I have no hesitation in asserting my conviction that the above-cited equivalents are the expression of a genuine synæsthesia.

One key piece of evidence was the consistency of gustatory experiences associated with the same words at a later date:

...the equivalents possess a constancy which would hardly be possible apart from a true synæsthesia. After an interval of six months a number of words were given at random from the original list, with the result that the identical equivalents were described in almost precisely the same language.


Also, there was a somatotopic-like arrangement for some of the gustatory experiences:

Many of the experiences are given quite definite locations in the mouth. Thus the equivalents of Ethel and Hall (tactual) are felt at the tip of the tongue; of lox (irritation) at the back of the throat; of Judith (salt) at the sides of the tongue; of Sarah (cold) on the lips; of amethyst (bitter) "at the back of the mouth, on the roof, where the root of the tongue seems to hit it" ...


Pierce tried to discern whether similar sounds could be related to similar gustatory experiences, but that wasn't the case. Nor were they related to similarities in articulation. He still preferred a physiological explanation, yet...
In attempting to decide whether the above-cited experiences are to be explained by the physiological or by the psychological theory, we are, apparently, in no better and in no worse case than in respect to all varieties of synæsthesia. No decisive facts are at hand.


An interesting commentary in JAMA predated MRI connectivity studies of synesthesia by nearly a century in its speculation about alternative brain wiring:

There is little evidence one way or the other as to whether or not this phenomenon is to be explained on purely physiologic grounds, depending on a cross circuiting of the association fibers between different brain centers.

Pierce concludes his paper with a pithy aphorism that acknowledges the inconclusiveness of the study:
Still, here no less than in all known cases of synaesthesis, we can only regret that our theory is so lame while our facts are so secure.


Pierce AH. (1907). Gustatory audition; a hitherto undescribed variety of synæsthesia. The American Journal of Psychology. Jul 1:341-52.

Commentary (1907). GUSTATORY AUDITION. JAMA. XLIX(10):857-858.

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Tuesday, August 31, 2021

Reading Aloud without a Mask, Olfactory Bulbs, Omega Variant

Here's the latest alarming COVID news to distract you from fires and hurricanes.
{I'm very sorry if you are experiencing either of these disasters personally. Donations ideas: El Dorado Community Foundation and The Mutual Aid Response Network.}

Caldor Fire: Karl Mondon/Bay Area News Group. Hurricane Ida: Edmund D. Fountain/New York Times.



Outbreak Associated with SARS-CoV-2 B.1.617.2 (Delta) Variant in an Elementary School — Marin County, California, May–June 2021

An unvaccinated elementary school teacher was experiencing nasal congestion and fatigue but continued working. The teacher read aloud to the students while maskless and two days later received a positive test for the Delta variant. Half of the class was infected, with the greatest risk among those sitting in front. Transmission to siblings and parents ensued, for a total of 27 cases.

"Among the five infected adults, one parent and the teacher were unvaccinated; the others were fully vaccinated. The vaccinated adults and one unvaccinated adult were symptomatic with fever, chills, cough, headache, and loss of smell."


The CDC created a figure showing the seating chart, transmission pattern, and whether the individual was symptomatic. The desks were six feet apart, an air filter in front, and the door and windows were open. All five children in the front row were infected.


from the CDC: Classroom layout and seating chart for 24 students in index patient’s class, by SARS-CoV-2 testing date, result or status, and symptoms—Marin County, California, May–June 2021

Conclusion and recommendation:

"Ineligibility because of age and lack of vaccination contribute to persistent elevated risk for outbreaks in schools, especially as new SARS-CoV-2 variants emerge. However, implementation of multiple prevention strategies within schools can mitigate this risk."

Olfactory Bulbs 

Speaking of smell, over 20 papers show MRI signal abnormalities in the olfactory bulbs of COVID-19 patients with anosmia (loss of smell). This isn't new, but anosmia has been reported in breakthrough cases as well. The images below show some resolution in a patient from time 1 to time 2.

Magnetic Resonance Imaging Alteration of the Brain in a Patient With Coronavirus Disease 2019 (COVID-19) and Anosmia

"...we can speculate that SARS-CoV-2 might invade the brain through the olfactory pathway and cause an olfactory dysfunction of sensorineural origin."


modified from Fig 2 (Politi et al., 2020). A coronal 2-dimensional FLAIR image shows a slight reduction of the hyperintensity and the thickness of the olfactory bulbs [right side], suggesting a postinfection olfactory loss.

modified from Fig 2 (Politi et al., 2020). FLAIR image showing complete resolution of the previously seen signal alteration within the cortex of the right gyrus rectus [bottom image].

Omega Variant 

OK, I made that up. However (alpha, beta, delta, epsilon, iota, gamma, OTHER):

Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California (preprint):

"These findings suggest that vaccine breakthrough cases are preferentially caused by circulating antibody-resistant SARS-CoV-2 variants, and that symptomatic breakthrough infections may potentially transmit COVID-19 as efficiently as unvaccinated infections, regardless of the infecting lineage."


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Saturday, July 31, 2021

Why would nasally-transferred coronavirus only affect the left side of the brain?


Q – “I survived a mild case of COVID. Should I be worried about the volume of gray matter in olfactory-related structures in the left hemisphere of my brain?”

A – Most of what you've read on social media may be overstated.

One of the scariest things about SARS-CoV-2 (other than possible death) is that it affects multiple organs, including the brain. The vast majority of studies have compared measures in COVID survivors to those obtained from participants without COVID. These cross-sectional studies cannot determine whether pre-existing differences can account for disease-related 'changes'.

An important new preprint by Douaud and colleagues reported results from a longitudinal study that obtained MRIs from participants before and during the pandemic. Carefully matched cohorts of COVID+ (n=394) and COVID- (n=388) people were given a second scan 3 years after their initial entry into the study. The majority of patients were not hospitalized. The authors hypothesized that brain areas related to smell and taste, senses which show notable decrements in infected patients, would be altered in the COVID-19 population.


from Vaughan & Jackson (2014). The “piriform axis” is an unusual oblique angle that shows primary olfactory cortex (Pir, piriform cortex) in the orbitofrontal region of the frontal lobe and nearby medial temporal lobe structures related to emotion and memory (Am, amygdala and Hip, hippocampus).

The frightening entrée of the virus into the brain may be through the nose. In mice, the S1 subunit of the coronavirus spike protein crosses the protective blood brain barrier and ends up in the olfactory bulb within 10-30 min (Rhea et al., 2021). After that, we don't really know what happens. So it's a stretch to suggest that neurotropism (viral infection of brain cells) causes alterations at a macro level in humans that can be detected by structural MRI. Anyway, that's the hypothesis.

What was unclear to me, however, was the number of other analyses conducted as part of the study. Other than the smell/taste regions of interest (ROIs), there were 2,360 distinct measures of brain structure or function (including resting-state fMRI and task-related fMRI, which we assume were not significantly different in the patients).

Sticking to the ROIs, the authors ran permutation tests that corrected for multiple comparisons and found that only the left hemisphere was affected. WHY?? Despite the Proustian speculation...

“...where the left hemisphere seem [sic] to be more involved in the emotional aspect of olfactory memory (the famous madeleine de Proust association that seemed particularly targeted in COVID-19).”'s mysterious why the virus would have deleterious effects on the brain by only invading the left nostril.


Fig. 1. (Douaud et al., 2021). The three main regions showing significant loss of grey matter (thickness, volume) between the two time-points specifically for the COVID patients are the parahippocampal gyrus, the lateral orbitofrontal cortex, and the superior insula. All results were localised to the left hemisphere.

The other notable aspect of the results was the massive overlap in distribution between the COVID+ and COVID- participants (see snarky commentary in the first figure). The authors helpfully showed the full spread of values for the time 1 vs. time 2 difference (clustered around zero). But did gray matter volume and thickness actually increase in some people? Although the group differences were statistically significant, were there any functional consequences? Severity of smell/taste loss? Effects on memory or emotion? We don't know.

COVID-19 can have persistent, disabling effects in some people, including young and previously healthy individuals (the “long haulers”, see Davis et al., 2021). There is no doubt about the reality of Long Covid.

On the other hand, alarmist coverage of preliminary neuroimaging findings is not helpful. The differences in Fig. 1 of Douaud et al. do not depict a shrinkage of 4 standard deviations, despite what some widely circulated tweets may claim. As one of the authors explains:


In fact, the exploratory analysis showed the largest loss of brain volume was non-specific (and not discussed). And perhaps not due to neurotropic invasion?


Longitudinal studies are extremely valuable, and the authors are to be commended for this. We'll wait for future papers to verify these findings, but for now I'm not totally convinced.


Davis HE, Assaf GS, McCorkell L, Wei H, Low RJ, Re'em Y, Redfield S, Austin JP, Akrami A. (2021). Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021 Jul 15. Epub ahead of print. PMID: 34308300.
Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, Lange F, Andersson JL, Griffanti L, Duff E, Jbabdi S, Taschler B. (preprint). Brain imaging before and after COVID-19 in UK Biobank. medRxiv. June 20, 2021.

Rhea EM, Logsdon AF, Hansen KM, Williams LM, Reed MJ, Baumann KK, Holden SJ, Raber J, Banks WA, Erickson MA. (2021). The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice. Nature Neuroscience 24(3):368-78.

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Tuesday, June 29, 2021

The rs-FC fMRI Law of Attraction (i.e., Resting-State Functional Connectivity of Speed Dating Choice)

Feeling starved for affection after 15 months of pandemic-mandated social distancing? Ready to look for a suitable romantic partner by attending an in-person speed dating event? Just recline inside this noisy tube for 10 minutes, think about anything you like, and our algorithm will Predict [the] Compatibility of a Female-Male Relationship!

This new study by Kajimura and colleagues garnered a lot of attention on Twitter, where it was publicized by @INM7_ISN (Simon Eickhoff) and @Neuro_Skeptic. The prevailing sentiment was not favorable (check the replies)...



Full disclosure: I was immediately biased against the claims made in this study...

This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

...and have covered earlier attempts at linking speed dating choice to a proxy of neural activity. But I wanted to be fair and see what the authors did, since their results reflect an enormous amount of work.

Here I will argue that a 10 minute brain scan cannot predict who you will choose at a speed dating event. The resultant measures are even further away from identifying a compatible mate for you, since only 5% of speed dating interactions result in a relationship of any sort (6% for sexual relationships and 4% for romantic relationships, according to one study).

I was flabbergasted that anyone would think a “resting” state MRI scan (looking at “+” for 10 min) and its resulting pattern of correlated BOLD signal fluctuations would reflect a level of superficial desirability that can be detected by a potential mate at greater than chance level. Another disclosure: this is far from my field of expertise. So I searched the literature. Apparently, “patterns of functional brain activity during rest encode latent similarities (e.g., in terms of how people think and behave) that are associated with friendship” (Hyon et al., 2020). However, that study was conducted in a small town in South Korea (total population 860), allowing a detailed social network analysis. Plus, people knew each other well and experienced many of the same day to day events, which could shape their functional connectomes. Not exactly relevant for predicting strangers' speed dating choices, eh?

Another paper identified a “global personality network” based on data from 984 participants in the Human Connectome Project (Liu et al., 2019). The sample was large enough to support a training set of n=801 and a “hold-out” dataset (n=183) for validation purposes. The results supported the authors' “similar brain, similar personality” hypothesis. But in the dating world, how much do “similars” attract (compared to the popular saying, “opposites attract”)? Well why not construct (dis)similarity profiles between potential pairs by taking the absolute value of differences in functional connectivity (FC), and combine those with values of similarities in FC? Does that make sense?? And in order to arrive at this metric, there's a whole lot of machine learning (but with much smaller training sets)...

Identity Classification 

A separate sample of 44 individuals from the Human Connectome Project was used to construct the Similarity of Connectivity Pattern between pairs (Kajimura et al., 2021). These 44 participants had each been scanned twice, allowing 44 self-self pairs (Jessica at time 1 vs. Jessica at time 2), which were compared to 44 self-other pairs (Jessica at time 1 vs. Jennifer at time 2). Self-self “feature values” always show a positive correlation, and these were used to define “individual-specific information.”

26,680 feature values?

To start, 116 regions of interest (ROIs) were defined by Automated Anatomical Labeling (AAL). Pairwise comparisons of these for Self scan #1 vs. Self scan #2 (or vs. Other scan #2) resulted in a vector of 6,670 functional connectivities for each data point [(116 × 115)/2]. Then multiply this by four (!!) and you get 26,680 values fed into a machine learning classifier. Why four? Because the slow fluctuating BOLD signals were decomposed into four frequency bands for the classification procedure. Was this necessary? Does it add robustness, or merely more opportunities for false positive results?

Fig. 3 (Kajimura et al., 2021). Top 100 feature values, i.e. absolute values of differences between functional connectivity that contributed to identity classification for three frequency bands [the fourth was eliminated because the classifier could not distinguish between self-self and self-other pairs].

The machine learning algorithm was sparse logistic regression with elastic-net regularization (SLR-EN), which usually prevents overfitting, but I don't know if the algorithm can overcome 26,680 feature values with only 44 subjects. Maybe I'm misunderstanding (and others can correct me if I'm wrong), but the number of participants is rather low for SLR-EN given the number of input parameters? Then...
The classification accuracy was evaluated using a stratified k-fold cross-validation procedure. ... The ratio of the number of correctly classified labels was then obtained as the classification accuracy.

The regional results are below, showing a 7 x 7 brain network matrix with similarity in red (positive coefficients) and dissimilarity in blue (negative coefficients). We're still in the realm of correctly classifying self-self, so dissimilarites were considered artifacts of overfitting [but similarities were not?]. If the contribution from similar > dissimilar with binomial tests, this was considered an indicator of self. This was true of F1 (53 out of 67, p<.001) and F2 (52 out of 67, p<.001), but not F3, which was at chance (33 out of 67).

Fig. 4 (modified from Kajimura et al., 2021). Ratio of self-self classification connectivity in terms of brain networks. Red and blue matrices display the results of similarity- and dissimilarity-based contributions [at three frequency bands]. ... Vis, visual network; Som, somatosensory-motor network; Sal, salience network; Lim, limbic system; Con, executive control network; Def, default mode network; Cer, cerebellum.

Separate Statistical Analysis — a bevy of Pearsons 

Before we turn to speed dating, two more analyses are shown below for the identity classification study. The first involved a boatload of FDR-corrected Pearson’s correlations of the functional connectivity vectors for self–self pairs vs. self–other pairs (Fig 2A). The next shows the effectiveness of the machine learning (ML) algorithm in classifying these pairs (Fig 2B).


Fig. 2 (modified from Kajimura et al., 2021). Identity classification. (A) Similarities in overall functional connectivity profile was significantly higher for the self–self pair (dark-colored distribution) than the self–other pair (light-colored distribution) for all frequency bands. [I've included arrows to point out where they start to diverge] (B) Distribution of differences between ML classification accuracy.

As the authors predicted, self-self comparisons yielded more similar connectivites than self-other pairs. The ML algorithm identified self for three of the frequency bands (F1-F3) at greater than chance levels (12.4%. 14.8%, and 16.3% better than chance, respectively). However, the algorithm is still wrong a lot of the time. This is especially important for the matchmaking study...

Speed Dating

The authors provided a nice self-explanatory graphic presenting an overview of the Speed Dating study (click on image for a larger view). Data collection and analysis followed the flow of the Identity experiment.

Participants and Social Event

The participants were 42 heterosexual young adults (20-23 yrs), with 20 females and 22 males. Why these numbers were not perfectly matched, I do not know. The resting state fMRI scan was several days before the first speed dating session. [I'm assuming it was the first, because the Methods say there were three speed dating events. There was also a post-dating scan, which was described in another paper]. The three hour event was held in a large room where pairs of participants had 3 min long conversations with every member of the opposite sex. After each conversation, all the men moved to the next table. When all the speed dates were over, each person was asked to identify at least half of the opposite sex individuals they'd like to chat with again.

Well, there's a problem here — a requirement to select half the dates could result in less-than-optimal choices in some individuals. This requirement was necessary for sampling purposes, but it makes you wonder about the quality of the matches. Also, there was a strong possibility of unilateral matches — one individual thinks they've found their dream partner but that feeling was not reciprocated. When both members of a pair said "yes" they were considered compatible. Out of a total of 440 possible pairs, 158 were compatible and 282 incompatible.

The Compatible vs. Incompatible comparisons are the key findings of the study (Fig. 5, with A and B panels as above). Unlike the Identity comparison, compatible male-female pairs did not show more similar functional connnectivity patterns than incompatible pairs (Fig. 5A).

Well then...

“This indicates that the compatibility of female–male relationships is not necessarily represented by the similarity of functional connectivity patterns.”


“Unlike identity classification, compatibility classification was supported by the considerable negative coefficients of the features” (shown in Fig. 6 of the paper). We shall not interpret this as opposites attract


Fig. 5 (Kajimura et al., 2021). Compatibility classification. (A) Similarity of overall functional connectivity profile. There was no significant difference between compatible (dark-colored distribution) and incompatible (light-colored distribution) pairs. (B) Distribution of differences between the classification accuracy with true labels of pairs and that with a randomized label for each frequency band. Vertical lines indicate chance levels.

Fig. 5B shows classification accuracy for compatible pairs, which was above chance for F1 and F2. Before investing in a commercial venture,  however, you should know that the benefit beyond guessing is only 5.47% and 4.95%, respectively. Thus, I disagree with the claim that...
...the current results indicate that resting-state functional connectivity has information about behavioral tendencies that two individuals actually exhibit during a dyadic interaction, which cannot be measured by self-report methods and thus may remain hidden unless we use neuroimaging methods concurrently.

To review the potential limitations of the study, we can't assess the quality of matches (meh vs. enthusiastic), we don't know what the participants were thinking about during their rsfMRI scan (see Gonzalez-Castillo et al., 2021), and we don't know their mental state during the scan. Although rs-FC fMRI is often considered a stable trait”, state factors and motion artifacts can affect the results on a given day (Geerligs et al., 2015). Indeed, ~35% of the time, the present paper was unsuccessful in classifying the same person run on two different days (and that's excluding one of four frequencies that was not above chance).

Is there something intrinsic encoded in BOLD signal fluctuations that can predict who we will find appealing (and a potential “match) after a three minute interaction?  Decisions at speed dating events are mostly based on physical attractiveness, so it seems very implausible to me.

Further Reading (the Speed Dating Collection)

The Neuroscience of Speed Dating Choice

The Electroencephalogram Cocktail Party

EEG Speed Dating

The Journal of Speed Dating Studies

Winner of Best Title

How I Meditated with Your Mother: Speed Dating at Temples and Shrines in Contemporary Japan



Geerligs L, Rubinov M, Henson RN. (2015). State and trait components of functional connectivity: individual differences vary with mental state. Journal of Neuroscience 35(41):13949-61.

Gonzalez-Castillo J, Kam JW, Hoy CW, Bandettini PA. (2021). How to Interpret Resting-State fMRI: Ask Your Participants. Journal of Neuroscience 41(6):1130-41.
Hyon R, Youm Y, Kim J, Chey J, Kwak S, Parkinson C. (2020). Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village. Proceedings of the National Academy of Sciences 117(52):33149-60.


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Monday, May 31, 2021

Did dreams evolve to transcend overfitting?

A fascinating new paper proposes that dreams evolved to help the brain generalize, which improves its performance on day to day tasks. Incorporating a concept from deep learning, Erik Hoel (2021):

“...outlines the idea that the brains of animals are constantly in danger of overfitting, which is the lack of generalizability that occurs in a deep neural network when its learning is based too much on one particular dataset, and that dreams help mitigate this ubiquitous issue. This is the overfitted brian [sic] hypothesis.”


The Overfitted Brain Hypothesis (OHB) proposes that the bizarre phenomenology of dreams is critical to their functional role. This view differs from most other neuroscientific theories, which treat dream content as epiphenomenal — a byproduct of brain activity involved in memory consolidation, replay, forgetting, synaptic pruning, etc.  

In contrast, Hoel suggests that “it is the very strangeness of dreams in their divergence from waking experience that gives them their biological function.”

The hallucinogenic, category-breaking, and fabulist quality of dreams means they are extremely different from the “training set” of the animal (i.e., their daily experiences).
. . .

To sum up: the OBH conceptualizes dreams as a form of purposefully corrupted input, likely derived from noise injected into the hierarchical structure of the brain, causing feedback to generate warped or “corrupted” sensory input. The overall evolved purpose of this stochastic activity is to prevent overfitting. This overfitting may be within a particular module or task such a specific brain region or network, and may also involve generalization to out-of-distribution (unseen) novel stimuli.

Speaking of overfitting, I was reminded of Google's foray into artificial neural networks for image classification, which was all the rage in July 2015. The DeepDream program is a visualization tool that shows what the layers of the neural network have learned:

One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation.

The image above is characteristic of the hallucinogenic output from the DeepDream web interface, and it illustrates that the original training set was filled with dogs, birds, and pagodas.  DeepDream images inspired blog posts with titles like, Do neural nets dream of electric sheep? and Do Androids Dream of Electric Bananas? and my favorite, Scary Brains and the Garden of Earthly Deep Dreams.


Hoel E. (2021). The overfitted brain: Dreams evolved to assist generalization. Patterns 2(5):100244.


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Sunday, April 25, 2021

Hoarders and Collectors

Andy Warhol's collection of dental models

Pop artist Andy Warhol excelled in turning the everyday and the mundane into art. During the last 13 years of his life, Warhol put thousands of collected objects into 610 cardboard boxes. These Time Capsules were never sold as art, but they were meticulously cataloged by museum archivists and displayed in a major exhibition at the Andy Warhol Museum. “Warhol was a packrat. But that desire to collect helped inform his artistic point of view.” Yet Warhol was aware of his compulsion, and it disturbed him: “I'm so sick of the way I live, of all this junk, and always dragging home more.”

Where does the hobby of collection cross over into hoarding, and who makes this determination? 

Artists get an automatic pass into the realm of collectionism, no matter their level of compulsion. The Vancouver Art Gallery held a major exhibition of the works of Canadian writer and artist Douglas Coupland in 2014. One of the sections consisted of a room filled with 5,000 objects collected over 20 years and carefully arranged in a masterwork called The Brain. Here's what the collection looked like prior to assembly.

Materials used in the The Brain, 2000–2014, mixed-media installation with readymade objects. Courtesy of the Artist and Daniel Faria Gallery. Photo: Trevor Mills, Vancouver Art Gallery.

Hoarding, on the other had, lacks the artistic intent or deliberate organization of collection. Collectors may be passionate, but their obsessions/compulsions do not hinder their everyday function (or personal safety). According to Halperin and Glick (2003):
“Characteristically, collectors organize their collections, which while extensive, do not make their homes dysfunctional or otherwise unlivable. They see their collections as adding a new dimension to their lives in terms of providing an area of beauty or historical continuity that might otherwise be lacking.”
The differential diagnosis for the DSM-5 classification of Hoarding Disorder vs. non-pathological Collecting considers order and value of primary importance.

Fig. 2 (Nakao & Kanba, 2019).
If possessions are well organized and have a specific value, the owner is defined as a ‘collector.’ Medical conditions that cause secondary hoarding are excluded from Hoarding Disorder. The existence of comorbidities such as obsessive-compulsive disorder (OCD), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) must be excluded as well.

I've held onto the wish of writing about this topic for the last eight months...

...because of the time I spent sorting through my mother's possessions between July 2020 and November 2020 after she died on July 4th. This process entailed flying across the country five times in a total of 20 different planes in the midst of a pandemic.
Although my mother showed some elements of  hoarding, she didn't meet clinical criteria. She had various collections of objects (e.g., glass shoes, decorator plates, snuff bottles, and ceremonial masks), but what really stood out were her accumulations — organized but excessive stockpiles of useful items such as flashlights, slippers, sweatshirts, kitchen towels, and watches (although most of the latter were no longer useful).

Ten pairs of unworn gardening gloves

During the year+ of COVID sheltering-in-place, some people wrote books, published papers, started nonprofits, engaged in fundraising, held Zoom benefit events, demonstrated for BLM, home-schooled their kids, taught classes, cared for sick household members, mourned the loss of their elder relatives, or endured COVID-19 themselves.
I dealt with the loss of a parent, along with the solo task of emptying 51 years of accumulated belongings from her home. To cope with this sad and lonely and emotionally grueling task, I took photos of my mother's accumulations and collections. It became a mini-obsession unto itself. I tried to make sense of my mother's motivations, but the trauma of her suffering and the specter of an unresolved childhood were too overwhelming. Besides, there's no computational model to explain the differences between Collectors, Accumulators and Hoarders.

Additional Reading

Compulsive Collecting of Toy Bullets

Compulsive Collecting of Televisions

The Neural Correlates of Compulsive Hoarding

Welcome to Douglas Coupland's Brain


Halperin DA, Glick J. (2003). Collectors, accumulators, hoarders, and hoarding perspectives. Addictive Disorders & Their Treatment 2(2):47-51.

Nakao T, Kanba S. (2019). Pathophysiology and treatment of hoarding disorder. Psychiatry Clin Neurosci. 73(7):370-375. doi:10.1111/pcn.12853

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Wednesday, March 31, 2021

Overinterpreting Computational Models of Decision-Making

Bell (1985)

Can a set of equations predict and quantify complex emotions resulting from financial decisions made in an uncertain environment? An influential paper by David E. Bell considered the implications of disappointment, a psychological reaction caused by comparing an actual outcome to a more optimistic expected outcome, as in playing the lottery. Equations for regret, disappointment, elation, and satisfaction have been incorporated into economic models of financial decision-making (e.g., variants of prospect theory).

Financial choices comprise one critical aspect of decision-making in our daily lives. There are so many choices we make every day, from the proverbial option paralysis in the cereal aisle... decisions about who to date, where to go on vacation, whether one should take a new job, change fields, start a business, move to a new city, get married, get divorced, have children (or not).

And who to trust. Futuristic scenario below...

Decision to Trust

I just met someone at a pivotal meeting of the Dryden Commission. We chatted beforehand and discovered we had some common ground. Plus he's brilliant, charming and witty.

“Are you looking for an ally?” he asked. 

Neil, Laura and Stanley in Season 3 of Humans


Should I trust this person and go out to dinner with him? Time to ask my assistant Stanley, the orange-eyed (servile) Synthetic, an anthropomorphic robot with superior strength and computational abilities.

Laura: “Stanley, was Dr. Sommer lying to me just then, about Basswood?”

Stanley, the orange-eyed Synth: “Based on initial analysis of 16 distinct physiological factors, I would rate the likelihood of deceit or misrepresentation in Dr. Sommer's response to your inquiry at... EIGHTY-FIVE PERCENT.”

The world would be easier to navigate if we could base our decisions on an abundance of data and well-tuned weighting functions accessible to the human brain. Right? Like a computational model of trust and reputation or a model of how people choose to allocate effort in social interactions. Right?

I'm out of my element here, so this will limit my understanding of these models. Which brings me to a more familiar topic: meta-commentary on interpretation (and extrapolation).

Computational Decision-Making

My motivation for writing this post was annoyance. And despair. A study on probabilistic decision-making under uncertain and volatile conditions came to the conclusion that people with anxiety and depression will benefit from focusing on past successes, instead of failures. Which kinda goes without saying. The paper in eLife was far more measured and sophisticated, but the press release said:

The more chaotic things get, the harder it is for people with clinical anxiety and/or depression to make sound decisions and to learn from their mistakes. On a positive note, overly anxious and depressed people’s judgment can improve if they focus on what they get right, instead of what they get wrong...

...researchers tested the probabilistic decision-making skills of more than 300 adults, including people with major depressive disorder and generalized anxiety disorder. In probabilistic decision making, people, often without being aware of it, use the positive or negative results of their previous actions to inform their current decisions.

The unaware shall become aware. Further advice:

“When everything keeps changing rapidly, and you get a bad outcome from a decision you make, you might fixate on what you did wrong, which is often the case with clinically anxious or depressed people...”

...individualized treatments, such as cognitive behavior therapy, could improve both decision-making skills and confidence by focusing on past successes, instead of failures...


The final statement on individualized CBT could very well be true, but it has nothing to do with the outcome of the study (Gagne et al., 2021), wherein participants chose between two shapes associated with differential probabilities of receiving electric shock (Exp. 1), or financial gain or loss (Exp. 2).

With that out of the way, I will say the experiments and the computational modeling approach are impressive. The theme is probabilistic decision-making under uncertainty, with the added bonus of volatility in the underlying causal structure (e.g., the square is suddenly associated a higher probability of shocks). People with anxiety disorders and depression are generally intolerant of uncertainty. Learning the stimulus-outcome contingencies and then rapidly adapting to change was predictably impaired.

Does this general finding differ for learning under reward vs. punishment? For anxiety vs. depression? In the past, depression was associated with altered learning under reward, while anxiety was associated with altered learning under punishment (including in the authors' own work). For reasons that were not entirely clear to me, the authors chose to classify symptoms using a bifactor model designed to capture “internalizing psychopathology” common to both anxiety and depression vs. symptoms that are unique to each disorder [ but see Fried (2021) ]1

Overall, high scores on the common internalizing factor were associated with impaired adjustments to learning rate during the volatile condition, and this held whether the outcomes were shocks, financial gains, or financial losses. Meanwhile, high scores on anxiety-unique or depression-unique symptoms did not show this relationship. This was determined by computational modeling of task performance, using a hierarchical Bayesian framework to identify the model that best described the participants' behavior:

We fitted participants’ choice behavior using alternate versions of simple reinforcement learning models. We focused on models that were parameterized in a sufficiently flexible manner to capture differences in behavior between experimental conditions (block type: volatile versus stable; task version: reward gain versus aversive) and differences in learning from better or worse than expected outcomes. We used a hierarchical Bayesian approach to estimate distributions over model parameters at an individual- and population-level with the latter capturing variation as a function of general, anxiety-specific, and depression-specific internalizing symptoms. 

We've been living in a very uncertain world for more than a year now, often in a state of loneliness and isolation. Some of us have experienced loss after loss, deteriorating mental health, lack of motivation, lack of purpose, and difficulty making decisions. My snappish response to the press release concerns whether we can prescribe individualized therapies based on the differences between the yellow arrows on the left (“resilient people”) compared to the right (“internalizing people” — i.e., the anxious and depressed), given that the participants may not even realize they're learning anything.


1 I will leave it to Dr. Eiko Fried (2021) to explain whether we should accept (or reject) this bifactor model of “shared symptoms” vs. “unshared symptoms”.


Bell DE. (1985) Disappointment in decision making under uncertainty. Operations research 33(1):1-27.

Gagne C, Zika O, Dayan P, Bishop SJ. (2020). Impaired adaptation of learning to contingency volatility in internalizing psychopathology. Elife 9:e61387.

Further Reading

Fried EI. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry 31(4):271-88.

Guest O, Martin AE. (2021) How computational modeling can force theory building in psychological science. Perspect Psychol Sci. Jan 22:1745691620970585.

van Rooij I, Baggio G. (2021). Theory before the test: How to build high-verisimilitude explanatory theories in psychological science. Perspect Psychol Sci. Jan 6:1745691620970604.

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