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With all of the health problems I’ve had recently, I was surprised to find this in Mitcham’s Thinking through Technology: The Path between Engineering and Philosophy, in which he is discussing Heidegger’s conceptualization of technology:

When we suffer or are in pain, we are simply too close to what we are experiencing; we need distance, some self-knowledge, appreciation of who we really are and of our limitations. But this is acquired not through rejection or repression of the pain; it comes only with time and through naming the source of our pain by asking questions and talking about it, rendering our suffering or recalling its background of happiness in poetry and art, sitting quietly and experiencing its presence–or rather what is immediately and unobtrusively there, just on the other side of the curtain of our disturbed feelings–gradually standing back and becoming detached from the tossed surface of our conscious calculations.

Tags:cognition, experience, Heidegger, perception technology
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The cultural schemas we share include the meaning we make of spatial relationships.  Scale is an important factor in the way we cognize space. Scale becomes an element of our spatial schemas, which are closely linked to other cognitive and cultural schemas within our neural networks. Put another way, resolution and experience are important factors in perception.

Interpreting hybrid images « Neurophilosophy
This experiment showed the importance of scale information in perception. The researchers specifically manipulated the spatial resolution of the painting that is, the periodicity with which image intensity changes. Large scale features change little over a given distance, and therefore have a low spatial resolution, while fine-grained features change much more over the same distance, and so have a high spatial resolution. In a second experiment, the participants were shown random noise patterns before the cropped greyscale painting. One group was shown a pattern with a high spatial resolution, the other a pattern with a low spatial resolution. Afterwards, the former reported seeing the bust of Voltaire, while the latter reported seeing the nuns. This showed that previous experience is an important factor in perception. The participants had selectively perceived the frequency channels presented to them before they viewed the image.

Tags:cognition, cultural schemas, geospatial, neural networks, neurophilosophy, perception spatial
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[This is not meant to be a well-written, coherent essay. Rather, it is simply my reflections of some of the concepts discussed in: Uschold, M. (2003) Where Are the Semantics in the Semantic Web? AI Magazine 24(3): 25-36.]

Talking about an agent’s ontology, Uschold says:

If it were written only for people to understand, this specification could be a glossary.

It is a simple statement that belies the complexity of human cognition and its ability to function as a connectionist network. Humans are able to read a word and its definition and recall a complex of associations and experiences to create meaning, to understand its semantic content and the ontological commitments that are shared within a community. An agent doesn’t have this ability and must rely on explicit specifications (ala Gruber) and formal languages to communicate its meaning to other agents. Successful communication is the goal, but machines are confronted with heterogeneous sources, “different ontology representation languages, different modeling styles, and an inconsistent use of terminology.”

Uschold describes the semantic continuum:

Semantics can be implicit, existing only in the minds of the humans who communicate and build web applications. They can also be explicit and informal, or they can be formal. The further I move along the continuum, the less ambiguity there is, and the more likely it is to have interoperable, robust, and correctly functioning web applications.

The difficulty with implicit semantics is their inherent ambiguity. People don’t always agree about the meaning of a term. Yet this is exactly where people live, so to speak. Humans engage in dialogue and discourse in order to clarify meaning. Through this interaction, we make the implicit semantics explicit. Engaging with machines isn’t as interactive, typically. We don’t expect our machines to be intelligent in terms of understanding semantic content and to be able to express that understanding as humans do. We also don’t expect that machines will be able to assess contextual cues that we use as humans to make meaning of our interactions with others, nor do we expect machines to remember our previous interactions with them (unless we explicitly encode that into the preferences of each particular application or service).

As Uschold continues to describe the continuum, moving towards the more formally expressed semantics for machine processing, it occurs to me that what we are doing is conforming our natural ways of interacting to the requirements of a machine. Why are we not trying to make the machine conform to our natural way of doing things? Shouldn’t the goal be to enable the machine to dynamically discover the meaning of content and how to use it? This, as it turns out, is what Uschold tackles next, machine-proccessible semantics:

For a computer to automatically determine the intended meaning of a given term in an ontology is an impossible task, in principle; it would require seeing into the mind of the author. Therefore, a computer cannot determine whether the intended meaning of two terms is the same.

Getting an agent to understand semantic content can be accomplished by hardwiring the meaning of terms and procedures into them, or through accessing external, publicly agreed to declarations such as ontologies. The heterogeneity of information and meaning makes both of these strategies limited. Information from different web sites may need to be integrated, so we require some way to map the different meanings to each other. Ontologies in conjunction with semantic mapping and translation techniques play a key role in semantic integration (Uschold citing Bradshaw et al. 2003).

Uschold speaks of human consensus regarding the use of terms. He refers to this consensus as an implicit shared semantic repository. I use the term cultural schemas to refer to this shared cognitive “repository.”

So, where are the semantics in the semantic web? Uschold offers these six explanations:

1. They are often just in the human-as-unstated assumptions derived from implicit consensus (e.g., price on a travle or bookseller web site).
2. They are informal specification documents, e.g., the semantics of UML or RDF SCHEMA.
3. They are hardwired in implemented code (e.g., in UML and RDF tools and in web shopping agents).
4. They are in formal specifications to help humans understand or write code (e.g., a modal logic specification of meaning of inform in an agent communication language).
5. They are formally encoded for machine processing, (e.g., fuel-pump has (superclasses SHO: pump)).
6. They are in the axiomatic and model-theoretic semantics of representation languages (e.g., the formal semantics of RDF).

Tags:cognition, concepts, cultural schemas, formal ontology, information, language, ontologies, semantics semantic web
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Icon Analysis

One of the research areas in which I’m interested is that of spatial cognition. I find it fascinating how we cognize our bodily experiences and make meaning from them. Instantaneously, we create meaningful interpretations of our physical experiences through our situated and embedded cognition.

The article above examines human computer interaction (HCI) in relation to the icons we encounter on a daily basis. It approaches it from a neurological point of view and examines how we discriminate in terms of spatial distance, color, shape, and so on. Cool stuff.

Tags:cognition, hci, icons, perception spatial
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