SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Discriminative Cognitive Steering (Target 1 - Target 2)
Figure 8: Results of the LRM3 architecture are shown, for both side-by-side and overlay composites tests. Subplots show different discriminative cognitive steering signal variations. Target-present conditions indicate percent of times the target category was predicted, when steering towards that target. Target-absent condition indicate the percent of times that the target-absent category was predicted, when steering towards the target absent category (false alarm). Error bars indicate the  $95\%$  bootstrapped confidence interval across composite images. Horizontal, dashed gray lines indicate the accuracy of classifying the individual target images alone, computed for the LRM3 model for its default modulated pass.
A.6 Steering modulates face-tuned and scene-tuned units
Classic studies in cognitive neuroscience have shown that category- and object-based attention dramatically alter neural responses in the ventral stream, even within category-selective brain regions. For instance, in the human visual cortex, there are face-selective and scene-selective brain regions: e.g., the fusiform-face area (FFA) responds more to faces than scenes or any other category of object [71], whereas the parahippocampal place area (PPA) responds more to scenes than to faces or other categories of object [72]. When presented with overlapping faces and scenes, the activity of these
regions varies dramatically based on the attended category [37, 73], demonstrating that top-down guidance signals have a strong influence on processing within the ventral stream (Fig. 6A).
We tested whether the LRM3 model would show similar effects of category-based attention within analogues of the human-FFA and human-PPA. To do so, we employed a method we introduced in prior work [74] to identify both face-selective and scene-selective units within the model using the same stimuli and statistical corrections used in the fMRI literature. Specifically, we used a "category localizer" image dataset composed of 400 images, 80 per category for 5 categories (Faces, Bodies, Objects, Scenes, Words) known to selectively activate different regions of the human visual cortex. We passed each image through the network and stored the activations separately for each layer. To determine whether a unit (channel, x, y) responds selectively to faces, we performed a t-test on activation scores for 80 face images vs. activations to 80 images from another category (separately for each other category one-by-one). The p-values were corrected using a false-discovery-rate (FDR) of .05 to adjust for multiple comparisons. A unit was considered face-selective if its activation was significantly greater for faces than each of the other categories in their head-to-head t-tests. The same procedure was used to identify scene-selective units, identifying units that showed higher activation for scenes than all other categories.
For steering signals, we computed the average model output for all face images (face-prototype) and all scene images (scene-prototype). To determine the effect of steering on face-units and scene-units, we presented overlapping face and scene pairs (each face image was paired with a scene image, and they were blended 50/50: composite  $1 = .5$  * face  $1 + .5$  * scene  $1$ ). We measured the activation of face-units and scene-units when the network was steered towards the face-prototype, or the scene-prototype, with an alpha of 3.0. As can be seen in Fig. 6B, the activation of model face-units and scene-units is strongly modulated by the steering target, with face-units showing stronger activation when steering towards faces, and scene-units showing stronger activation when steering towards scenes. Thus, LRM models with cognitive steering are able to qualitatively capture this well-known signature of category-based attention in the human brain.
A.7 Steering in LRM-additive models
Initial analyses were conducted to explore the impact of LRM pathways with additive modulatory influence on destination layers. We hypothesized that during cognitive steering, models with additive feedback would have more 'hallucinations' (or false alarms) to the steered category, even if it was not present in the image.
Table 4 focuses on a subset of the results, considering prototype steering, with overlaid images. The LRM3-multiplicative model showed peak performance at  $\alpha = 3$ ; the LRM3-additive model showed higher hit rates, but also higher false alarm rates. Further, when steering the LRM-additive models, the false alarm rate climbs with increasing attentional gain ( $\alpha = 5$ ), but that does not happen with the LRM-multiplicative models.
Considering the signal detection measure A' (sensitivity of hits relative to false alarms [75]), the LRM3-multiplicative model has an A' that is high and fairly stable across the alpha-gain range. Meanwhile, the LRM3-additive model has an A with a steeper decline of sensitivity with increasing attention modulation. Qualitatively similar results were obtained with the side-by-side composites. Thus, these LRM-additive models help clarify the role of the multiplicative feedback mechanisms in reducing hallucinations during high attentional gain states.
LRM-multiplicative	LRM-additive
hit rate	% false alarm	hit	% false alarm
LRM3, α=3	53.3%	5.8%	62.3%	21.2%
LRM3, α=5	35.0%	3.2%	67.0%	38.9%
Table 4: Comparison between LRM3 models with either multiplicative or additive feedback modulation. These results show the impact of cognitive steering, on the overlaid image composite test.
A.8 Steering with CORnet
CORnet [40] is a recurrent neural network, with similar consecutive-forward-pass dynamics as LRM models. That is, the image is processed serially through the hierarchical layers in the first time step. In CORnet, the activations from each layer are carried forward in time, and then add to the same layer's activation on the next time step (as opposed to earlier layers, as in our LRM models). Pre-trained CORnet models perform 5 forward passes by default.
To outfit this model with steering capacity, we replaced the previous time point's activation with a steering signal. We used a steering signal at every layer at every time point. For instance-level steering, the steering inputs consisted of the activations of the exact image, at every layer and every time point-i.e. perfect oracle instance-level steering. For category-prototype steering, the steering inputs consisted the average activation map over all exemplar images of that category (in the ImageNet Training Dataset), computed at each time step and layer. We additionally included a steering modulation strength factor, multiplying the steering signals by a constant  $(s = 1,2,3,4)$ .
Note that our implementation of CORnet steering uses a stored representation at every layer of the visual encoding backbone at every time point, per category. This steering interface differs dramatically from the LRM networks, which leverages a single 1000-dimensional vector per category. We note that there are likely many ways to outfit recurrent neural networks to inject steering—we opted for these choices because they seemed the most likely to lead to the largest consequences of cognitive steering.
The results are shown in Figure 9, with varying strengths of both instance steering and prototype steering, assessed on composite overlaid images. It is clear that increasing the strength of steering increases correct target recognition but also introduces false alarms when steering to a category that is not present in the image (target-absent conditions).
We used the signal detection measure A' (A-prime) [75] to quantify this effect. A' is a measure of sensitivity for distinguishing between signal (target present) and noise (target absent), and is a non-parametric version of d' (d-prime). A' values range from 0 to 1, with .5 indicating chance, 1 indicating perfect target detection with no false alarms (values less than .5 are uncommon, e.g., 0 indicates  $100\%$  false alarms and  $0\%$  hits). The A' effect is plotted for LRM3 and CORnet models on the same plot (though note the way the steering strength is implemented differs between these models). With increasingly strong steering, the LRM models maintain their ability to correctly detect target present categories without false alarms, while the same is not true for the CORnet model (as we have implemented steering here).
A Implementing Steering in CORnet
B CORnet: Instance-Steering
D CORnet: Category-Steering
C Sensitivity Comparison: Instance-Steering
Figure 9: Steering in CORnet. (A) Diagram of the flow of information in CORnet (adapted from Kubilius et al., 2018). Steering signals are calculated for each image (or the average of all images in a category), at each layer and each time point (yellow arrows). During steering, the state is overwritten with these arrows. (B) Instance-steering in CORnet models, on composite overlay images. Probability that a target is the max logit is plotted for steering when the target category is present or absent, for different levels of steering strength. (C) Sensitivity measure, A' (reflecting corrected Hit rate taking into account False Alarms) is plotted for both LRM3 and CORnet_RT models with increasing steering strength. (D,E) Same plots but for category-level steering signals.
E Sensitivity Comparison: Category-Steering