Category Archives: cognition

Computer metaphor not accurate for brain’s embodied cognition

It’s common for brain functions to be described in terms of digital computing, but this metaphor does not hold up in brain research. Unlike computers, in which hardware and software are separate, organic brains’ structures embody memories and brain functions. Form and function are entangled.

Rather than finding brains to work like computers, we are beginning to design computers–artificial intelligence systems–to work more like brains. 

https://www.wired.com/story/tech-metaphors-are-holding-back-brain-research/ 

Brain’s facial-recognition mechanism revealed

Caltech researchers have identified the brain mechanisms that enable primates to quickly identify specific faces. In a feat of efficiency, surprisingly few feature-recognition neurons are involved in a process that may be able to distinguish among billions of faces. Each neuron in the facial-recognition system specializes in noticing one feature, such as the width of the part in the observed person’s hair. If the person is bald or has no part, the part-width-recognizing neuron remains silent. A small number of such specialized-recognizer neurons feed their inputs to other layers (patches) that integrate a higher-level pattern (e.g., hair pattern), and these integrate at yet higher levels until there is a total face pattern. This process occurs nearly instantaneously and works regardless of the view angle (as long as some facial features are visible). Also, by cataloging which neurons perform which functions and then mapping these to a relatively small set of composite faces, researchers were able to tell which face a macaque (monkey) was looking at.

These findings seem to correlate closely with Ray Kurzweil’s (Google’s Chief Technology Officer) pattern-recognition theory of mind.

Scientific American article

BMCAI library file (site members only)

Should AI agents’ voice interactions be more like our own? What effects should we anticipate?

An article at Wired.com considers the pros and cons of making the voice interactions of AI assistants more humanlike.

The assumption that more human-like speech from AIs is naturally better may prove as incorrect as the belief that the desktop metaphor was the best way to make humans more proficient in using computers. When designing the interfaces between humans and machines, should we minimize the demands placed on users to learn more about the system they’re interacting with? That seems to have been Alan Kay’s assumption when he designed the first desktop interface back in 1970.

Problems arise when the interaction metaphor diverges too far from the reality of how the underlying system is organized and works. In a personal example, someone dear to me grew up helping her mother–an office manager for several businesses. Dear one was thoroughly familiar with physical desktops, paper documents and forms, file folders, and filing cabinets. As I explained how to create, save, and retrieve information on a 1990 Mac, she quickly overcame her initial fear. “Oh, it’s just like in the real world!” (Chalk one for Alan Kay? Not so fast.) I knew better than to tell her the truth at that point. Dear one’s Mac honeymoon crashed a few days later when, to her horror and confusion, she discovered a file cabinet inside a folder. A few years later, there was another metaphor collapse when she clicked on a string of underlined text in a document and was forcibly and instantly transported to a strange destination.

Having come to terms with computers through the command-line interface, I found the desktop metaphor annoying and unnecessary. Hyperlinking, however–that’s another matter altogether–an innovation that multiplied the value I found in computing.

On the other end of the complexity spectrum would be machine-level code. There would be no general computing today if we all had to speak to computers in their own fundamental language of ones and zeros. That hasn’t stopped some hard-core computer geeks from advocating extreme positions on appropriate interaction modes, as reflected in this quote from a 1984 edition of InfoWorld:

“There isn’t any software! Only different internal states of hardware. It’s all hardware! It’s a shame programmers don’t grok that better.”

Interaction designers operate on the metaphor end of the spectrum by necessity. The human brain organizes concepts by semantic association. But sometimes a different metaphor makes all the difference. And sometimes, to be truly proficient when interacting with automation systems, we have to invest the effort to understand less simplistic metaphors.

The article referenced in the beginning of this post mentions that humans are manually coding “speech synthesis markup tags” to cause synthesized voices of AI systems to sound more natural. (Note that this creates an appearance that the AI understands the user’s intent and emotional state, though this more natural intelligence is illusory.) Intuitively, this sounds appropriate. The down side, as the article points out, is that colloquial AI speech limits human-machine interactions to the sort of vagueness inherent in informal speech. It also trains humans to be less articulate. The result may be interactions that fail to clearly communicate what either party actually means.

I suspect a colloquial mode could be more effective in certain kinds of interactions: when attempting to deceive a human into thinking she’s speaking with another human; virtual talk therapy; when translating from one language to another in situations where idioms, inflections, pauses, tonality, and other linguistic nuances affect meaning and emotion; etc.

In conclusion, operating systems, applications, and AIs are not humans. To improve our effectiveness in using more complex automation systems, we will have to meet them farther along the complexity continuum–still far from machine code, but at points of complexity that require much more of us as users.

Mathematical field of topology reveals importance of ‘holes in brain’

New Scientist article: Applying the mathematical field of topology to brain science suggests gaps in densely connected brain regions serve essential cognitive functions. Newly discovered densely connected neural groups are characterized by a gap in the center, with one edge of the ring (cycle) being very thin. It’s speculated that this architecture evolved to enable the brain to better time and sequence the integration of information from different functional areas into a coherent pattern.

Aspects of the findings appear to support Edelman’s and Tononi’s (2000, p. 83) theory of neuronal group selection (TNGS, aka neural Darwinism).


Edelman, G.M. and Tononi, G. (2000). A Universe of Consciousness: How Matter Becomes Imagination. Basic Books.

How to Convince Someone When Facts Fail

Cognitive bias article of the day: How to Convince Someone When Facts Fail

A concise, timely look at how worldview-driven cognitive dissonance leads people to double down on their misbeliefs in the face of challenging evidence. It also recommends steps for having more meaningful conversations with others whose irrational positions differ from your own. 😉

Excellent article on the history and recent advances in AI

This NY Times article is worth your time, if you are interested in AI–especially if you are still under the impression AI has ossified or lost its way.

AI Creativity

Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required.

Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human):

Click Play button to listen->

Is it conceivable that AI may one day be able to synthesize new made-to-order creations by blending features from a catalog of existing works and styles? Imagine being able to specify, “Write me a new musical composition reminiscent of Rhapsody in Blue, but in the style of Lynyrd Skynyrd.

There is already at least one human who could instantly play Rhapsody in Blue in Skynyrd style, but even he does not (to my knowledge) create entirely original pieces.

Original article: https://www.technologyreview.com/s/601642/ok-computer-write-me-a-song/

See also: https://www.technologyreview.com/s/600762/robot-art-raises-questions-about-human-creativity/

Living in a ‘post-fact’ world

Studies find that people with higher numeracy and understanding of the scientific method and its tools are more likely to challenge or twist the results of scientific studies that challenge their ideologies. For example, it’s the more scientifically competent persons on the political right (those who are most identified with a free-market ideology) who mount the most vehement assaults against claims of human contributions to global warming.

This article delves into the extent of cognitive biases against facts (rigorously validated knowledge claims) and the apparent variables affecting when those biases are triggered. It also raises possible ways to mitigate biases.