The separable effects of feature precision and item load in visual short-term memory

Visual short-term memory (VSTM) has been described as being limited by the number of discrete visual objects, the aggregate quantity of information across multiple visual objects, or some combination of the two. Many recent studies examining these capacity limitations have shown that increasing the number of items in VSTM increases the frequency and magnitude of errors in a participant's recall of the stimulus. This increase in response dispersion has been interpreted as a loss of precision in an item's representation as the number of items in memory increases, possibly due to a change in the tuning of the underlying representation. However, increased response dispersion can also be caused by a reduction in the total memory strength available for decision making as a consequence of a reduction in the total amount of a fixed resource representing a stimulus. We investigated the effects of load on the precision of memory representations in a fine orientation discrimination task. Accuracy was well captured by extending a simple sample-size model of VSTM, using a tuning function to account for the effect of orientation precision on performance. The best model of the data was one in which the item strength decreased progressively with memory load at all stimulus exposure durations but in which tuning bandwidth was invariant. Our results imply that memory strength and feature precision are experimentally dissociable attributes of VSTM

Erratum: Causal Knowledge Promotes Behavioral Self-Regulation: An Example using Climate Change Dynamics (PLoS ONE (2017) 12:9 (E0184480) DOI: 10.1371/Journal.pone.0184480)

In the Task overview: Managing a dynamic human-climate system subsection of the Introduction, there is an error in equation 4. There is a factor of τ that is missing from the denominator of the first term that appears on the right-hand side of the equation. Please view the complete, correct equation here [Formula Presented]

Predicting perceptual decision biases from early brain activity

Perceptual decision making is believed to be driven by the accumulation of sensory evidence following stimulus encoding. More controversially, some studies report that neural activity preceding the stimulus also affects the decision process. We used a multivariate pattern classification approach for the analysis of the human electroencephalogram (EEG) to decode choice outcomes in a perceptual decision task from spatially and temporally distributed patterns of brain signals. When stimuli provided discriminative information, choice outcomes were predicted by neural activity following stimulus encoding; when stimuli provided no discriminative information, choice outcomes were predicted by neural activity preceding the stimulus. Moreover, in the absence of discriminative information, the recent choice history primed the choices on subsequent trials. A diffusion model fitted to the choice probabilities and response time distributions showed that the starting point of the evidence accumulation process was shifted toward the previous choice, consistent with the hypothesis that choice priming biases the accumulation process toward a decision boundary. This bias is reflected in prestimulus brain activity, which, in turn, becomes predictive of future decisions. Our results provide a model of how non-stimulus-driven decision making in humans could be accomplished on a neural level

Causal knowledge promotes behavioral self-regulation: an example using climate change dynamics

Adopting successful climate change mitigation policies requires the public to choose how to balance the sometimes competing goals of managing CO emissions and achieving economic growth. It follows that collective action on climate change depends on members of the public to be knowledgeable of the causes and economic ramifications of climate change. The existing literature, however, shows that people often struggle to correctly reason about the fundamental accumulation dynamics that drive climate change. Previous research has focused on using analogy to improve people’s reasoning about accumulation, which has been met with some success. However, these existing studies have neglected the role economic factors might play in shaping people’s decisions in relation to climate change. Here, we introduce a novel iterated decision task in which people attempt to achieve a specific economic goal by interacting with a causal dynamic system in which human economic activities, CO emissions, and warming are all causally interrelated. We show that when the causal links between these factors are highlighted, people’s ability to achieve the economic goal of the task is enhanced in a way that approaches optimal responding, and avoids dangerous levels of warming

Flexibility and size heterogeneity of the LH1 light harvesting complex revealed by atomic force microscopy - Functional significance for bacterial photosynthesis

Previous electron microscopic studies of bacterial RC-LH1 complexes demonstrated both circular and elliptical conformations of the LH1 ring, and this implied flexibility has been suggested to allow passage of quinol from the

Information limits within visual short-term memory

© 2016 Dr. Simon David LilburnThis thesis examined separable information constraints upon visual short-term memory through application of signal detection theory and diffusion modelling (R. Ratcliff, 1978) applied to a series of related tasks. Following D. K. Sewell, S. D. Lilburn, and P. L. Smith (2014), I employed simple psychophysical paradigms using the near-threshold presentation of stimulus information and controlling the complexity of decision-making through a post-stimulus probe to obtain both accuracy and response time data. The first part of this thesis examines the relationship between two common visual short-term memory procedures—orientation discrimination and change detection—to characterise the effect of the task type on responding. Modelling of observer sensitivity for Experiment 1 showed that a good account of the data can be obtained with the use of the sample-size relation between performance and memory load, with the addition of an item in change detection trials to account for the effect of probe array on performance. This was supported in Experiment 2 by the higher stimulus contrasts for change detection trials required to offset the decrement in accuracy. Response time modelling using the diffusion model further supported this account, with the addition of a constant time for encoding and comparing the probe array in change detection decisions and the inclusion of an intrusion process to model the entry of non-target information into the decision. The second part of this thesis expanded the modelling of the first part to examine the constraints upon orientation information in two fine orientation discrimination experiments. These experiments required observers to judge the direction of small angular offsets from a known referent. The change in observer sensitivity across different target angular offset conditions was well captured by a Gaussian-shaped tuning function, centred on the orientation of the referent, and weighting squared sensitivity directly. This information constraint was found to be independent of the sample-size relation, leading to the conclusion that the division of memory resources may be separate from the quality of stimulus information. Stimulus exposure duration was modelled as a linear increase in observer sensitivity, also independent of the sample-size or tuning function constraints. Diffusion modelling with these three constraints placed on the drift rate provided a parsimonious description of both the response proportions and response time distributions of a large memory experiment with a small number of parameters. In all, the progression of both sensitivity and response time modelling through this thesis aims to demonstrate three separable constraints on the fundamental capacity of visual short-term memory—the sample-size relation, the tuning channel constraint on orientation information, and the linear constraint on information growth—and the importance of considering the role of decision processes explicitly

From shunting inhibition to dynamic normalization: attentional selection and decision-making in brief visual displays

Normalization models of visual sensitivity assume that the response of a visual mechanism is scaled divisively by the sum of the activity in the excitatory and inhibitory mechanisms in its neighborhood. Normalization models of attention assume that the weighting of excitatory and inhibitory mechanisms is modulated by attention. Such models have provided explanations of the effects of attention in both behavioral and single-cell recording studies. We show how normalization models can be obtained as the asymptotic solutions of shunting differential equations, in which stimulus inputs and the activity in the mechanism control growth rates multiplicatively rather than additively. The value of the shunting equation approach is that it characterizes the entire time course of the response, not just its asymptotic strength. We describe two models of attention based on shunting dynamics, the integrated system model of Smith and Ratcliff (2009) and the competitive interaction theory of Smith and Sewell (2013). These models assume that attention, stimulus salience, and the observer's strategy for the task jointly determine the selection of stimuli into visual short-term memory (VSTM) and the way in which stimulus representations are weighted. The quality of the VSTM representation determines the speed and accuracy of the decision. The models provide a unified account of a variety of attentional phenomena found in psychophysical tasks using single-element and multi-element displays. Our results show the generality and utility of the normalization approach to modeling attention

An information capacity limitation of visual short-term memory

Research suggests that visual short-term memory (VSTM) has both an item capacity, of around 4 items, and an information capacity. We characterize the information capacity limits of VSTM using a task in which observers discriminated the orientation of a single probed item in displays consisting of 1, 2, 3, or 4 orthogonally oriented Gabor patch stimuli that were presented in noise for 50 ms, 100 ms, 150 ms, or 200 ms. The observed capacity limitations are well described by a sample-size model, which predicts invariance of for displays of different sizes and linearity of for displays of different durations. Performance was the same for simultaneous and sequentially presented displays, which implicates VSTM as the locus of the observed invariance and rules out explanations that ascribe it to divided attention or stimulus encoding. The invariance of is predicted by the competitive interaction theory of Smith and Sewell (2013), which attributes it to the normalization of VSTM traces strengths arising from competition among stimuli entering VSTM