A neural model of sequential movement planning and control of eye movements: Item-Order-Rank working memory and saccade selection by the supplementary eye fields

Abstract

How does working memory store multiple spatial positions to control sequences of eye movements, particularly when the same items repeat at multiple list positions, or ranks, during the sequence? An Item-Order-Rank model of working memory shows how rank-selective representations enable storage and recall of items that repeat at arbitrary list positions. Rank-related activity has been observed in many areas including the posterior parietal cortices (PPC), prefrontal cortices (PFC) and supplementary eye fields (SEF). The model shows how rank information, originating in PPC, may support rank-sensitive PFC working memory representations and how SEF may select saccades stored in working memory. It also proposes how SEF may interact with downstream regions such as the frontal eye fields (FEF) during memory-guided sequential saccade tasks, and how the basal ganglia (BG) may control the flow of information. Model simulations reproduce behavioral, anatomical and electrophysiological data under multiple experimental paradigms, including visually- and memory-guided single and sequential saccade tasks. Simulations reproduce behavioral data during two SEF microstimulation paradigms, showing that their seemingly inconsistent findings about saccade latency can be reconciled.

Memory for serial order across domains: An overview of the literature and directions for future research.

“…(Bullock, 2004;Bullock & Rhodes, 2003;Rhodes, Bullock, Verwey, Averbeck, & Page, 2004). For example, computational models that use the CQ mechanism have been developed and successfully applied to data from a range of serial performance domains, including typing (Rumelhart & Norman, 1982), speech production (Bohland, Bullock, Guenther, 2010;Dell, 1986;Dell, Burger, & Svec, 1997;Hartley & Houghton, 1996;Houghton, 1990), sequence learning (Rhodes & Bullock, 2002;Rhodes et al, 2004), spelling (Glasspool & Houghton, 2005;Glasspool, Houghton, & Shallice, 1995;Glasspool, Shallice, & Cipolotti, 2006;Houghton, Glasspool, & Shallice, 1994), saccade generation (J. W. Brown, Bullock, & Grossberg, 2004;Silver, Grossberg, Bullock, Histed, & Miller, 2012), action planning (Cooper & Shallice, 2000), music performance (Palmer & Pfordresher, 2003), and of course shortterm memory (Burgess & Hitch, 1992, 2006Henson, 1998b;Page & Norris, 1998). The success of CQ models in these various domains is attributable to their ability to capture error patterns, such as transpositions, that appear to be a characteristic of all serial behaviors.…”

Phoneme restoration and empirical coverage of interactive activation and adaptive resonance models of human speech processing

“…These successful TRACE simulations of phoneme restoration demonstrate that the failures Grossberg and Kazerounian (2011) to simulate those phenomena with TRACE were the result of a flawed approach to simulating noise replacement, not a failure of the model. As I discussed above, their critiques of reduplicated units and nonmodulatory feedback in TRACE are not compelling: reduplicated units provide a plausible means for constructing an echoic memory [possibly quite similar to what IOR memory (Silver et al, 2012) would be like were it scaled up to handle many phonemes and words] or could be eliminated with a programmable blackboard approach (McClelland, 1986), and non-modulatory feedback in TRACE was not the basis for the apparent failure of TRACE to correctly simulate phoneme restoration; rather, that failure was due to poor analogs of noise-and silence-replaced stimuli. I hope that these arguments and simulations will lead to the further elaboration of the cARTWORD model needed before valid comparisons to other models can be made.…”

“…Grossberg and Kazerounian might counter that cARTWORD could be combined with the item-order-rank (IOR) model of working memory proposed in other domains (Silver et al, 2012). The framework is laid out in Fig.…”

John Taylor, learning, attention, and consciousness

“…LIST PARSE models how bottomup, horizontal, and top-down interactions within the laminar circuits of lateral prefrontal cortex may carry out working memory storage of event sequences within layers 6 and 4, how unitization of these event sequences through learning into list chunks may occur within layer 2/3, and how these stored sequences can be recalled at variable rates that are under volitional control by the basal ganglia ( [17]; also see [18]). In particular, the model uses variations of the same circuitry to quantitatively simulate human cognitive data about immediate serial recall and free recall, and monkey neurophysiological data from the prefrontal cortex obtained during sequential sensory-motor imitation and planned performance.…”