Harrison Ritz

@hritz.bsky.social

cybernetic cognitive control computational cognitive neuroscience Connected Minds Fellow @ Queens + RRI he/him 🇨🇦 harrisonritz.github.io

Xiaoyi Liu giving a presentation at CCN2026

Nothing in neural activity makes sense except in the light of dynamics and cybernetics
• temporal structure in addition to the spatial structure
• abstract mechanisms and organizing principles
• real-time coupling with the environment (embodied)
• agency and control objectives (cybernetic)
sensory
environment
Park, I. M., Vermani, A., de Polavieja, G. G., Gallego, J. Á., Esfahany, K., Saxena, S., Orger, M., Ijspeert, A., Dowling, M., McNamee, D., Turaga, S. C., Mainen, Z., Paton, J.J. and Renart, A. (2026). Integrative Neurocybernetic Modeling in the Era of Large-Scale
Neuroscience.. http://arxiv.org/abs/2604.23903

Slide from my poster describing the item response theory model: review ~ N(discrim*quality + bias, discrim^2controversy^2 + noise^2)

Talk title: Orthogonal Task Representations Enable Flexible Task Switching in Human OPM-MEG and Neural Networks

Huge raccoon climbing down from a tree and on to a roof.

265 homemade pierogis on a kitchen counter

major pitfalls include participant strategies introducing task confounds, neglecting variants within decision architecture, or failures to consider alterantive decision architectures.

LCA models do a good job capturing choices, and reproduce the spurious threshold-control effects

If anything, difficult effects on threshold reversed.
But now there were larger effects of overall value on threshold.

collapsing bound DDMs fit the behavior much better

The original model has lower threshold on harder trials, but this model doesn't capture the data well.

task: participation chose between bundles of outcomes within a deadline

DDM: their model proposed that vigor reduces the threshold on hard trials

the log odds of a response depend on value difference plus effort.

effort maximizes the value of the response, minus an effort cost

Scene from Jackie chan’s police story. He is on a hill looking over Hong Kong. Since it is 1985, Hong Kong is much smaller than it is now

But the hardware of computers is very far from being the only matter relevant to their
functioning. In order to understand how a computing system works one must enquire into the
logic of the system software and the semantics of the programming languages in which the
system can be addressed. The corresponding questions about human beings are those
asked by the science of psychology - though admittedly, psychological theories seldom
attain a degree of sophistication worthy of their subject matter. An outstanding exception to
this stricture is the science of linguistics, and perhaps it is no coincidence that the most
impressive achievement of artificial intelligence to date is a working model of the
comprehension of natural language.
I would go further and hazard the prediction that for some time to come the most valuable
work in artificial intelligence will be that which attempts to express, in the form of computer
programs, abstract theories of our various cognitive faculties, rather than mathematical
models of the brain itself - this in spite of some excellent recent work on the possible role of
the neocortex as a classifying device. This view is based not only on the obvious vitality of
current artificial intelligence work on language and vision, but also on an evident
dissatisfaction among psychologists with the naive stimulus-response theory of behaviour as
it has been applied to human beings.
It is now plain that a central problem in cognitive psychology is to understand how our
knowledge is represented and deployed, and the computer program is the only medium
which at present offers us the possibility of formulating adequately sophisticated theories of
cognition. The elimination of inadequate theories is no longer the main problem; the defects
of a programmed theory become immediately apparent as soon as it is run on a computer.
In short whatever the technological prospects of artificial intelligence, its principal scientific
value, in my view, is that it sets …

Graph showing that monkeys are more likely to generate a response when side-specific neurons are stimulated

$Our analyses show that during a decision-making task, coding subspaces do not autonomously generate the observed dynamics. Although these dimensions account for a sizable fraction of the population variance, they depend on interactions with other, often weakly selective and low-variance residual dimensions. These residual dimensions lie outside task-coding subspaces and are typically dismissed as noise or irrelevant variability in dimensionality-reduction analyses. However, our results show that they actively drive the dynamics, and perturbing them can flip the choice outcome. The mechanism for this phenomenon is non-normal amplification of neural trajectories that occupy a relatively high number of latent dimensions (∼10). Weak selectivity along the residual dimensions is dynamically amplified through non-normal interactions in the underlying network. As a result, even small perturbations along residual dimensions, which are dominated by neurons with low firing rates and weak task selectivity, can transiently grow and redirect trajectories within the coding subspace. This mechanism explains how seemingly untuned or weakly selective populations can exert strong$