Modelling perception as a hierarchical competition 3 Figure 1. A hierarchical network modelling interference between externally and internally generated stimuli Note. (a) A hierarchical network model of visual perception. Layer size indicates the relative dimensionality of the encoded representation. Information is pooled via feedforward processes (red, upward arrows) and extrapolated via feedback processes (blue, downward arrows). Lock icons indicate input layers with clamped representations during simulations where mental imagery is attempted during veridical perception. Clamped representations do not evolve with time. Note that a 'thought' here could be any internally generated representation. (b) Evolution rule for unclamped layers. Each value r in the representational matrix of each layer l evolves at each time point t according to a weighted average of the adjacent layers and the current layer at the previous time step with weights w. The ascending value array was convolved with an averaging kernel before being weighted to facilitate feedforward dimensionality reduction. Under these assumptions, we constructed a simple compu-tational model of a serially connected network with bidirec-tional information flow as a first approximation to simulating the hierarchical interaction of internally and externally gener-ated stimulus representations (Fig. 1). We used this model to evaluate the degree to which internally generated (i. e. imagined) stimulus representations are able to spread throughout a hier-archical system, with and without competition from externally generated stimuli. We demonstrate that competition from exter-nal sensory input could prevent internally generated content from ever recruiting the low-level neural infrastructure best suited to representing highly sensory, highly modal information. In doing so, we show that accounting for perceptual interference, even with preliminary models, can provide an arguably more coher-ent explanation for the unique phenomenology of mental imagery than approaches that consider mental imagery in isolation. We explore the perceptual implications of such predictions on the subjective experience of mental imagery, provide a neurochemi-cally grounded hypothesis for variance in imagery across individ-uals and states, and delineate how perceptual interference may relate to hallucinations and mental imagery in non-visual modal-ities. Ultimately, we demonstrate that reconceptualizing mental imagery as an intrinsically competitive process can resolve major lingering questions in imagery research. Materials and Methods Computational model Our goal was to assess the degree and extent to which internally generated stimuli are able to spread throughout a hierarchical system, with and without competition from externally generated stimuli. To do this, a dynamic, serially connected, five-layer com-putational network was constructed as a simple model of how stimulus information may flow bidirectionally in the hierarchical human visual system (Fig. 1). Each layer l encoded a represen-tation r consisting of a square matrix of values. The number of units available to represent information (i. e. dimensionality) in each layer shrank in size from 256 × 256 matrix elements at the lowest layer ( l=0) to 208 × 208 elements at the higher layer (l =4) by linearly decreasing the square root of the number of elements in each layer. This mimics feedforward dimensionality reduction in the visual system such that upper layers encode information that is more abstract than lower layers, analogous to neural struc-tures towards the parietal and temporal lobes ( Binder 2016 ). Each element in the matrix could vary in value along a single dimen-sion, from 0 to 1, encoding the variation of an arbitrary stimulus feature. The lowest layer acted as an input layer for externally generated stimuli and never received feedback, analogous to the retina in visual perception. The network allowed representations to propagate between layers and for bottom-up processes to interact with top-down pro-cesses. Hence, the representation r of each layer l was updated at each time step t according to a weighted average of the ascending representation rl-1 from the layer below, descending representa-tion rl+1 from the layer above, and representation rl of the current layer, each at the previous time step t-1 (Fig. 1b). We note that despite the simplicity of this approach, weighted averaging is approximately equivalent to the biologically plausible additive combination of neural inputs, followed by a decay of activity pro-portional to the strength of activation. However, unlike in real neurons, decay here occurs instantaneously. We allow for this sim-plification given that we simulate the perception of static images only and are therefore most concerned with the steady-state, rather than moment-to-moment, profile of the system. Each layer was updated sequentially, first as part of a feedfor-ward sweep, then as part of a feedback sweep, and then alternating with every subsequent iteration. Weightings wl-1, wl+1, and wl for the ascending, descending, and current representations, respec-tively, were each equal to one except where the effect of weighting ratios was explicitly investigated. However, if a layer acted as a constant source of information in the network (representing either an internally or externally generated stimulus), the values in its representational matrix were locked in place (clamped) by set-ting the weightings of ascending and/or descending inputs to that layer to zero. Given that the retina is always receiving visual input in an awake state, the lowest layer of the network was always Downloaded from https://academic. oup. com/nc/article/2023/1/niad018/7248966 by guest on 25 August 2024 