Using RAAM to model human sequence representation and processing

• The human mind has been studied from various perspectives. Some adopt an analytical approach by analyzing humans, yet others attempt to construct automated systems exhibiting certain aspects of mind. Here we argue that connectionist architectures generally fail to exhibit important aspects of mind. We present a number of aspects, relating to human short-term and long-term memory during sequence representation and processing. These aspects are then used as a means to measure the explanatory power of connectionist architectures. We find that connectionist architectures (specifically Recursive Auto-Associative Memories, RAAM) generally fail to model important aspects of the short-term and long-term memory, when representing and processing sequences. Some aspects are correctly modeled, whereas others are modeled incorrectly or it is an open question whether or not they can be modeled at all. From this we go on to present the areas in which more research is needed, before connectionist RAAM-like architectures can be finally claimed to model important aspects of short-term and long-term memory.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Keyword

Recursive Auto-Associative Memory

sequence representation

sequence processing

long-term memory

short-term memory

models of memory

Computer and systems science

Data- och systemvetenskap

Publication and Content Type

vet (subject category)

rap (subject category)