The central role of the parietal lobe in the neural cognitive architecture - Randall C. O'Reilly UC Davis eCortex, Inc - VISCA-2021
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The central role of the parietal
lobe in the neural cognitive
architecture
Randall C. O’Reilly
UC Davis
eCortex, Inc.
Tripartite Neural Cognitive
Architecture
Broadest “cut” of the
neural cognitive
wn b iasing architecture
top-do
gating Explains many aspects
Basal Ganglia of cognitive function
(action selection)
Consistent with many
Frontal Cortex well-established
(active maintenance) theories and data
across many labs
Posterior Cortex
(sensory & semantics)
Hippocampus (e.g., O’Reilly et al, 2016;
(episodic memory) O’Reilly et al, 2014)
Neural, Cognitive Isomorphs: ACT-R
Frontal Cortex
(active maintenance)
g
sin
gating
bia
wn
-do
top
Basal Ganglia
(action selection)
Posterior Cortex Hippocampus
(sensory & semantics) (episodic memory)
Same functional architecture discovered in two different architectures: neural specializations
vs. functional specializations in ACT-R. Mapping between multiple levels of models very
informative, and more abstract ACT-R model can simulate more complex cognition
Common / Modal Model of Cognition
Laird et al, 2017 LTM remains homogenous
Temporal
Stocco et al, 2021 (ish), dissociated from
sensory / motor areas
BG PFC
WM / STM is central
bottleneck / coordinator
Atkinson & Shiffrin, 1968
CMC Assumptions
l Declarative and procedural long-term memories contain
symbol structures and associated quantitative metadata
- ACT-R: Chunks = slot, filler bindings
l Perception yields symbol structures with associated metadata
in specific working memory buffers
l Motor control converts symbolic relational structures in its
buffers into external actions
-> highly modular, information passing architecture
A Different, Neural Perspective
l Functional roles, relationships (i.e., structure) learned through
motor action and perception, in parietal lobe
- parietal lobe provides the slots
l Abstract, invariant, semantic representations of content
encoded in temporal lobe (e.g., visual & auditory learning)
- temporal lobe provides the fillers
l LTM involves both separate learning in each pathway (new
structure, new content), and combinations thereof (e.g.,
episodic memory = specific structure + content bindings)
- sensory & motor & memory all integrated, hippo special for episodic
PM / AT Networks (Ranganath)
Fodor & Pylyshyn (1988): Structure
sensitive processing is key for
systematicity
O’Reilly, Russin, & Ranganath (submitted)
Ranganath & Ritchey, NRN (2012)
Structure / Content Cognitive Arch
Parietal = links of relational /
structural graph
Temporal = nodes w/ content
Highly dynamic, interactive architecture where structure / content paths coordinate to
encode overall situation model. But separation = systematicity and generativity.
Three Parietal Pathways
(Kravitz et al, 2011)
1. Looking (parieto-prefrontal)
2. Reaching (parieto-premotor)
3. Navigating (parieto-medial)
Navigation is Primordial Structure
l We inherited our cognitive architecture from
rodent-like beasts whose survival depended
upon superior navigational abilities.
l Tight interactions between goals (frontal cortex)
spatial structure (parietal lobe) and episodic
memory of specific places, events
(hippocampus).Deep predictive learning
(O’Reilly et al, 2021)
Pulvinar receives from all
over visual cortex
and projects back out to
Pulvinar! these same areas
Two inputs:
1. Few strong feedforward:
“what happens”
2. Many weaker feedback:
prediction
(Sherman & Guillery, 2006)Spiking Predictive Learning Model
• Multimodal sensory prediction: full field depth, foveal vision, somatosensory
(whiskers, vestibular), body state (thirst, hunger)
• Error-driven predictive learning based on action: predict next state
• New fully spiking error-driven learning (bio backprop) model!Spiking Predictive Learning Model Navigation through prediction: no explicit allocentric map, just enough context to disambiguate predictions at different locations! Parietal learns structural reps of state-transitions caused by actions.
Computational Model Results MST PCC SMA Clear progression from MST to PCC to SMA: visual flow, borders to more action modulated (consuming spots). Provides rich tapestry of representational bases for behavior.
Maps are nice to look at, but… Maps are complex, high-dimensional, static (hard to rescale, recenter), allocentric, expensive as info grows State-to-state predictions are dynamic, compact, generalizable (easy to rescale, recenter), egocentric (always recenter!)
Structure (syntax) vs. Content
(Russin et al, 2020)
Jake Russin
DNN exhibits systematic o.o.d. generalization on SCAN task (Lake & Baroni, 2017)
via architectural distinction between syntax pathway (can see all words across time)
and semantics pathway (can only see current word). Syntax only influences
response via attention. Fully trained end-to-end via backprop.Thanks To
CCN Lab Funding
l Andrew Carlson l ONR – Hawkins &
l Riley DeHaan McKenna
l Tom Hazy
l Seth Herd Collaborators
l Kai Krueger l Charan Ranganath (Davis)
l April Luo
l Erie Boorman (Davis)
l Jessica Mollick
l Ananta Nair l Ignacio Saez (Davis)
l John Rohrlich l Jonathan Cohen
l Jake Russin (Princeton)
l Maryam Zolfaghar
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