Friday, June 30, 2017

What Is Thought?

Is that some sort of trick question? Everyone knows what thought is. Or do they...  My questions for you today are:

  • How do you define “a thought” (yes, a single thought)? Where is the boundary from one thought to the next?
  • What is “thought” more generally? Does this cognitive activity require conscious awareness? Or language? We don't want to be linguistic chauvinists, now do we, so let's assume mice have them. But how about shrimp? Or worms?

What is “a thought”?

Can you define what a discrete “thought” is?  This question was motivated by a persistent brain myth:
You have an estimated 70,000 thoughts per day.
Where did this number come from? How do you tally up 70,000 thoughts? Do some thoughts last 10 seconds, while others are finished in one tenth of a second?

Over 24 hours, one thought per second would yield 86,400 thoughts. If “thoughts” are restricted to 16 waking hours, the number would be 57,600. But we're almost certainly thinking while we're dreaming (for about two hours every night), so that would be 64,800 seconds, with an ultimate result of one thought every 0.9257 seconds, on average.

LONI®, the Laboratory of Neuroimaging at USC, included this claim on their Brain Trivia page, so perhaps it's all their fault.1

How many thoughts does the average person have per day?

*This is still an open question (how many thoughts does the average human brain processes in 1 day). LONI faculty have done some very preliminary studies using undergraduate student volunteers and have estimated that one may expect around 60-70K thoughts per day. These results are not peer-reviewed/published. There is no generally accepted definition of what "thought" is or how it is created. In our study, we had assumed that a "thought" is a sporadic single-idea cognitive concept resulting from the act of thinking, or produced by spontaneous systems-level cognitive brain activations.

Neuroskeptic tried to find the origin of The 70,000 Thoughts Per Day Myth five years ago. He found a very bizarre post by Charlie Greer (“Helping Plumbing, HVAC, and Electrical service contractors Sell More at Higher Profits”):
Several years ago, the National Science Foundation put out some very interesting statistics. We think a thousand thoughts per hour. When we write, we think twenty-five hundred thoughts in an hour and a half. The average person thinks about twelve thousand thoughts per day. A deeper thinker, according to this report, puts forth fifty thousand thoughts daily.

If this “NSF report” exists, no one can find it (NSF is a funding agency, not a research lab). Were the LONI® researchers funded by NSF?  No one knows...

Maybe we're approaching this in the wrong way. We shouldn't be relying on descriptions of mental events to define a thought, but rather discrete brain states.

Using this definition, “a thought” is what you can capture with your fancy new imaging technique. Therefore, a thought conveniently occupies the available temporal resolution of your method:
“A thought or a cognitive function usually lasts 30 seconds or a minute. That’s the range of what we’re hoping to be able to capture,” says Kay Tye, an assistant professor in the Department of Brain and Cognitive Sciences at MIT.
In this case, the method is FLARE, “an engineered transcription factor that drives expression of fluorescent proteins, opsins, and other genetically encoded tools only in the subset of neurons that experienced activity during a user-defined time window” (Wang et al., 2017).

But what if your method records EEG microstates, “short periods (100 ms) during which the EEG scalp topography remains quasi-stable” (Van De Ville et al., 2010). In this case, thoughts are assembled from EEG microstates:
One characteristic feature of EEG microstates is the rapid transition from one scalp field topography into another, leading to the hypothesis that they constitute the “basic building blocks of cognition” or “atoms of thought” that underlie spontaneous conscious cognitive activity.

And for good measure, studies of mind wandering, spontaneous thought, and the default mode network are flourishing. To learn more, a good place to start is Brain signatures of spontaneous thoughts, a blog post by Emilie Reas.

What is “thought”?

What is called thinking? The question sounds definite. It seems unequivocal. But even a slight reflection shows it to have more than one meaning. No sooner do we ask the question than we begin to vacillate. Indeed, the ambiguity of the question foils every attempt to push toward the answer without some further preparation.

- Martin Heidegger, What Is Called Thinking?

Philosophers have filled thousands of pages addressing this question, so clearly we're way beyond the depth and scope of this post. My focus here is more narrow, “thought” in the sense used by cognitive psychologists. Is thought different from attention

Once we look at the etymology and usage of the word, no wonder we're so confused...

Does Beauty Require Thought?

Speaking of philosophy, a recent study tested Kant's views on aesthetics, specifically the claim that experiencing beauty requires thought (Brielmann & Pelli, 2017).

Participants in the study rated the pleasure they felt from seeing pictures (IKEA furniture vs. beautiful images), tasting Jolly Rancher candy, and touching a soft alpaca teddy bear. In one condition, they had to perform a working memory task (an auditory 2-back task) at the same time. They listened to strings of letters and identified when the present stimulus matched the letter presented two trials ago. This is distracting, obviously, and the participants' ratings of pleasure and beauty declined. So in this context, the authors effectively defined thought as attention or working memory (Brielmann & Pelli, 2017).2 

Alternate Titles for the paper (none of which sound as exciting as the original Beauty Requires Thought)

Aesthetic Judgments and Pleasure Ratings Require Attention

Judgments of Beauty Require Working Memory and Cognitive Control

...or the especially clunky Ratings of “felt beauty” Require Attention — but only for beautiful items.

Dual task experiments are pretty popular. Concurrent performance of the n-back working memory task also disrupts the execution of decidedly non-beautiful activities, such as walking and timed ankle movements. So I guess walking and ankle movements require thought...


1 This claim was still on their site as recently as March 2017, but it's no longer there.

2 They did, however, show that working memory load on its own (a digit span task) didn't produce the same alterations in beauty/pleasure ratings.


Brielmann, A., & Pelli, D. (2017). Beauty Requires Thought. Current Biology, 27 (10), 1506-1513.

Van de Ville D, Britz J, Michel CM. (2010). EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci 107(42):18179-84.

Wang W, Wildes CP, Pattarabanjird T, Sanchez MI, Glober GF, Matthews GA, Tye KM, Ting AY. (2017). A light- and calcium-gated transcription factor for imaging andmanipulating activated neurons. Nat Biotechnol. Jun 26.

gif from palerlotus

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Monday, June 19, 2017

The Big Bad Brain

I’m high, staring at the ceiling
Sending my love, what a wonderful feeling
What comes next, I see a light
I’m along for the ride as I’m taking flight

Plus a cool brain tattoo to boot. AND the song is an earworm (at least it is for me).

It feels good to be running from the devil
Another breath and I'm up another level
It feels good to be up above the clouds
It feels good for the first time in a long time now

A monument to love unspoken
Carved into stone “Unwilling to come undone”

Here's what singer Landon Jacobs had to say about those specific lyrics:
“in the face of what I incorrectly assumed was an impending brain aneurysm, I decided that the best way to spend my final moments was to push my love through the universe to the people I cared about. I was terrified of dying, but that’s not reason to squander a potential death bed situation.”

(he had gotten way too high on one occasion and had a panic attack... he thought he was dying)

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Thursday, June 08, 2017

Terrorism and the Implicit Association Test

Induced Stereotyping?

Imagine that you're riding on a very crowded bus in a busy urban area in the US. You get on during a shift change, when a new driver takes over for the old one. The new driver appears to be Middle Eastern, and for a second you have a fleeting reaction that the situation might become dangerous. This is embarrassing and ridiculous, you think. You hate that the thought even crossed your mind. There are 1.8 billion Muslims in the world. How many are radical Islamist extremists? For example, in the UK at present, the number comprises maybe 0.00000167% of all Muslims? 1

Language matters.

Theresa May:
“First, while the recent attacks are not connected by common networks, they are connected in one important sense. They are bound together by the single, evil ideology of Islamist extremism that preaches hatred, sows division, and promotes sectarianism. 

It is an ideology that claims our Western values of freedom, democracy and human rights are incompatible with the religion of Islam. It is an ideology that is a perversion of Islam and a perversion of the truth.”

Donald Trump:
“That means honestly confronting the crisis of Islamist extremism and the Islamist terror groups it inspires. And it means standing together against the murder of innocent Muslims, the oppression of women, the persecution of Jews, and the slaughter of Christians.
. . .


In both of these cases, the world leaders did acknowledge that Islamist extremism is not the same as the religion of Islam. Nonetheless, in terms of statistical co-occurrence in the English language, the root word Islam- is now associated with all that is bad and evil in the world. Could the constant exposure to news about radical Islamist terrorism and Trump's proposed Muslim Ban result in an involuntary or “forced” stereotyping in the bus scenario above?

A recent study found that semantics derived automatically from language corpora contain human-like biases, which means that machines (which do not have cultural stereotypes) become “biased” when they learn word association patterns from large bodies of text, such as Google News. The authors used a word embedding algorithm called Global Vectors for Word Representation (GloVe) to improve the performance of the machine learning model. As a measure of human bias, they used the popular implicit association test (IAT), from which they developed the Word-Embedding Association Test (WEAT). Instead of response times (RTs) to a specific set of words, WEAT used the distance between a set of vectors in semantic space. The authors were able to replicate the associations seen in every IAT they tested (Caliskan et al., 2017), suggesting:
The number, variety, and substantive importance of our results raise the possibility that all implicit human biases are reflected in the statistical properties of language.

Arab-Muslim Implicit Association Test

Because of the relationship between word associations and implicit bias, I decided to take the Arab-Muslim IAT at Project Implicit, an organization interested in “implicit social cognition — thoughts and feelings outside of conscious awareness and control.” This definition seemed to fit with the bus scenario, which involved an impulse to profile the driver based on a rapid evaluation of perceived ethnicity.

In the Arab-Muslim IAT, the participant classifies words as good (e.g, Fantastic, Fabulous) or bad (e.g, Horrible, Hurtful), and proper names as Arab Muslim (e.g., Akbar, Hakim) or “Other People” (e.g, Ernesto, Philippe, Kazuki).2 The bias is revealed when you have to sort both of these categories at the same time. Are you slower when Good/Arab Muslim are mapped to the same key, compared to when Bad/Arab Muslim are mapped to the same key? (and vice versa).

My results are below.

- click on image for a larger view -

I showed a moderate automatic preference for Arab Muslims over Other People. But this wasn't completely unique compared to the population of 327,000 other participants who have taken this test:

The summary of other people's results shows that most people have little to no implicit preference for Arab Muslims compared to Other People - i.e., they are just as fast when sorting good words and Arab Muslims than sorting good words and Other People.”

The aggregate results above covered a period of 11.5 years ending in December 2015. The strength of semantic associations between words can vary over time and contexts, so we can wonder if this has shifted any in the last year. In addition, different results have been observed when faces were used instead of names, and when a better list of “Other People” names was used to specify ingroup vs. outgroup (see explanation in footnote #2).

A Muslim-Terrorism test has in fact been developed by Webb et al. (2011). They used a variant of the IAT (the GNAT) with Muslim names (e.g., Abdul, Ali, Farid, Khalid, Tariq), Scottish names (e.g., Alistair, Angus, Douglas, Gordon, Hamish), terrorism-related words (e.g., attack, bomb, blast, explosives, threat) and peace-related words (e.g., friendship, harmony, love, serenity, unity). In an interesting twist, the authors varied “implementation intentions” to flip the Muslim-Terrorism test to the Muslim-Peace test in half of the subjects:
Following the practice trials, one-half of the participants (implementation intention condition) were asked to form an implementation intention to help them to respond especially quickly to Muslim names and peace-related words. Participants were asked to tell themselves ‘If Muslim names and peace are at the top of the screen, then I respond especially fast to Muslim words and peace words!’. Participants were asked to repeat this statement several times before continuing with the experiment. The other half of the participants (standard instruction condition) were given no further instructions.

I actually discovered this strategy on my own in 2008, when my IAT results revealed I was Human AND Alien and NEITHER Dead NOR Alive.

And indeed, the Muslim-Peace instructions neutralized the strong Muslim-Terrorism association seen in the control participants Webb et al. (2011).

Calvin Lai and colleagues conducted a high-powered series of experiments showing that instructions such as implementation intentions and faking the IAT can shift implicit racial biases (Lai et al., 2014), but these interventions are short-lived (Lai et al., 2016).

I wrote about the former study in 2014: Contest to Reduce Implicit Racial Bias Shows Empathy and Perspective-Taking Don't Work. Failed interventions all tried to challenge the racially biased attitudes and prejudice presumably measured by the IAT. These interventions are below the red line in the figure below.

- click on image for a larger view -

Figure 1 (modified from Lai et al, 2014). Effectiveness of interventions on implicit racial preferences, organized from most effective to least effective. Cohen’s d = reduction in implicit preferences relative to control; White circles = the meta-analytic mean effect size; Black circles = individual study effect sizes; Lines = 95% confidence intervals around meta-analytic mean effect sizes. IAT = Implict Association Test; GNAT = go/no-go association task.

The major message here is that top-down cognitive control processes can affect thoughts and feelings that are purportedly outside of conscious awareness — and can apparently override semantic associations that are statistical properties of language obtained from a large-scale crawl of the Internet (containing 840 billion words)!

Now whether the IAT actually measures implicit bias is another story...

ADDENDUM (June 11 2017): Prof. Joanna J. Bryson, a co-author on the machine learning/semantic bias paper, wrote a very informative blog post about this work: We Didn't Prove Prejudice Is True (A Role for Consciousness).


1 I cannot imagine what it's like to be a survivor of the recent Manchester and London attacks, and my deepest condolences go out to the families who have lost loved ones

2 Notice I put “Other People” in quotes. That's because the names are not all from the same category (country/ethnicity)  Latino, French, and Japanese in the examples above. This lack of uniformity could slow down RTs for the “Other People” category. A better alternate category would have been all French names, for instance. Or use common European-American names to differentiate ingroup (Michael, Christopher, Tyler) vs. outgroup (Sharif, Yousef, Wahib)


Caliskan A, Bryson JJ, Narayanan A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183-186.

Lai CK, Marini M, Lehr SA, Cerruti C, Shin JE, Joy-Gaba JA, Ho AK, Teachman BA, Wojcik SP, Koleva SP, Frazier RS, Heiphetz L, Chen EE, Turner RN, Haidt J, Kesebir S, Hawkins CB, Schaefer HS, Rubichi S, Sartori G, Dial CM, Sriram N, Banaji MR, Nosek BA. (2014). Reducing implicit racial preferences: I. A comparative investigation of 17 interventions. J Exp Psychol Gen. 143(4):1765-85.

Lai CK, Skinner AL, Cooley E, Murrar S, Brauer M, Devos T, Calanchini J, Xiao YJ, Pedram C, Marshburn CK, Simon S, Blanchar JC, Joy-Gaba JA, Conway J, Redford L, Klein RA, Roussos G, Schellhaas FM, Burns M, Hu X, McLean MC, Axt JR, Asgari S, Schmidt K, Rubinstein R, Marini M, Rubichi S, Shin JE, Nosek BA. (2016). Reducing implicit racial preferences: II. Intervention effectiveness across time. J Exp Psychol Gen. 145(8):1001-16.

Webb TL, Sheeran P, Pepper J. (2012). Gaining control over responses to implicit attitude tests: Implementation intentions engender fast responses on attitude-incongruent trials. Br J Soc Psychol. 51(1):13-32.

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