Examining Perceptual and Categorical Influences on Visual Working Memory

Visual working memory (VWM) refers to the limited capacity storage of visual information used for behaviors like problem-solving, planning, or reasoning. VWM is a crucial component of cognition, and individual differences in capacity during childhood have been linked to outcomes in academic achievement, fluid intelligence, and socioemotional development. VWM increases in capacity and precision throughout development. Very few studies have investigated what factors influence changes in VWM abilities in preschool-aged children. The first goal of this study was to examine VWM precision development in this age-range. This was accomplished by administering a delayed estimation task. In this task, children touched a color wheel to indicate the color of an item in memory from a two-item array. Mixture modeling was used to measure the likelihood of reporting the target color and precision of the color represented in memory. The second goal of this project was to investigate the underlying neural, perceptual, and categorical mechanisms of VWM development. To measure perceptual mechanisms, children completed a discrimination task where they touched a color wheel to indicate the color of a visually presented color. For categorical mechanisms, children completed production and comprehension tasks for colors. To assess comprehension, children touched a color wheel to indicate the location of ‘blue’ and ‘green’. Lastly, for production, children provided ‘blue’ or ‘green’ labels for stimuli that were randomly sampled between canonical blue and green color values. Forty-four children aged 36-48 months completed these tasks across two sessions, as well as nine adults. Results showed deactivation across the delayed estimation and discrimination tasks in left postcentral gyrus, as well as activation for both in right middle temporal gyrus. In addition, right inferior gyrus was more strongly activated for the discrimination task, and left inferior frontal gyrus was more strongly activated for the delayed estimation task. Activation during both tasks was associated with behavioral measures such as the location of children’s color category boundary during production, suggesting a relationship between VWM precision and perceptual and categorical mechanisms

The Contralateral Delay Activity Tracks the Sequential Loading of Objects into Visual Working Memory, Unlike Lateralized Alpha Oscillations

Visual working memory temporarily represents a continuous stream of task-relevant objects as we move through our environment performing tasks. Previous work has identified candidate neural mechanisms of visual working memory storage; however, we do not know which of these mechanisms enable the storage of objects as we sequentially encounter them in our environment. Here, we measured the contralateral delay activity (CDA) and lateralized alpha oscillations as human subjects were shown a series of objects that they needed to remember. The amplitude of CDA increased following the presentation of each to-be-remembered object, reaching asymptote at about three to four objects. In contrast, the concurrently measured lateralized alpha power remained constant with each additional object. Our results suggest that the CDA indexes the storage of objects in visual working memory, whereas lateralized alpha suppression indexes the focusing of attention on the to-be-remembered objects

Sequential presentation protects working memory from catastrophic interference

Neural network models of memory are notorious for catastrophic interference: old items are forgotten as new items are memorized (e.g., French, 1999; McCloskey & Cohen, 1989). While Working Memory (WM) in human adults shows severe capacity limitations, these capacity limitations do not reflect neural-network style catastrophic interference. However, our ability to quickly apprehend the numerosity of small sets of objects (i.e., subitizing) does show catastrophic capacity limitations, and this subitizing capacity and WM might reflect a common capacity. Accordingly, computational investigations (Knops, Piazza, Sengupta, Eger, & Melcher, 2014; Sengupta, Surampudi, & Melcher, 2014) suggest that mutual inhibition among neurons can explain both kinds of capacity limitations as well as why our ability to estimate the numerosity of larger sets is limited according to a Weber ratio signature. Based on simulations with a saliency map-like network and mathematical proofs, we provide three results. First, mutual inhibition among neurons leads to catastrophic interference when items are presented simultaneously. The network can remember a limited number of items, but when more items are presented, the network forgets all of them. Second, if memory items are presented sequentially rather than simultaneously, the network remembers the most recent items rather than forgetting all of them. Hence, the tendency in WM tasks to sequentially attend even to simultaneously presented items might not only reflect attentional limitations, but an adaptive strategy to avoid catastrophic interference. Third, the mean activation level in the network can be used to estimate the number of items in small sets, but does not accurately reflect the number of items in larger sets. Rather, we suggest that the Weber ratio signature of large number discrimination emerges naturally from the interaction between the limited precision of a numeric estimation system and a multiplicative gain control mechanism

A computational study on altered theta-gamma coupling during learning and phase coding

There is considerable interest in the role of coupling between theta and gamma oscillations in the brain in the context of learning and memory. Here we have used a neural network model which is capable of producing coupling of theta phase to gamma amplitude firstly to explore its ability to reproduce reported learning changes and secondly to memory-span and phase coding effects. The spiking neural network incorporates two kinetically different GABAA receptor-mediated currents to generate both theta and gamma rhythms and we have found that by selective alteration of both NMDA receptors and GABAA,slow receptors it can reproduce learning-related changes in the strength of coupling between theta and gamma either with or without coincident changes in theta amplitude. When the model was used to explore the relationship between theta and gamma oscillations, working memory capacity and phase coding it showed that the potential storage capacity of short term memories, in terms of nested gamma-subcycles, coincides with the maximal theta power. Increasing theta power is also related to the precision of theta phase which functions as a potential timing clock for neuronal firing in the cortex or hippocampus

Brief targeted memory reactivation during the awake state enhances memory stability and benefits the weakest memories.

Reactivation of representations corresponding to recent experience is thought to be a critical mechanism supporting long-term memory stabilization. Targeted memory reactivation, or the re-exposure of recently learned cues, seeks to induce reactivation and has been shown to benefit later memory when it takes place during sleep. However, despite recent evidence for endogenous reactivation during post-encoding awake periods, less work has addressed whether awake targeted memory reactivation modulates memory. Here, we found that brief (50 ms) visual stimulus re-exposure during a repetitive foil task enhanced the stability of cued versus uncued associations in memory. The extent of external or task-oriented attention prior to re-exposure was inversely related to cueing benefits, suggesting that an internally-orientated state may be most permissible to reactivation. Critically, cueing-related memory benefits were greatest in participants without explicit recognition of cued items and remained reliable when only considering associations not recognized as cued, suggesting that explicit cue-triggered retrieval processes did not drive cueing benefits. Cueing benefits were strongest for associations and participants with the poorest initial learning. These findings expand our knowledge of the conditions under which targeted memory reactivation can benefit memory, and in doing so, support the notion that reactivation during awake time periods improves memory stabilization

Evidence accumulation in a Laplace domain decision space

Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log-likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially, across neurons there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform, these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a "decision axis." This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.Comment: Revised for CB