Search

The drift-diffusion model (DDM) is a common approach to understanding human decision making. It considers decision making as accumulation of evidence about visual stimuli until sufficient evidence is reached to make a decision (decision boundary). Recently, Smith and colleagues proposed an extension of DDM, the time-varying DDM (TV-DDM). Here, the standard simplification that evidence accumulation operates on a fully formed representation of perceptual information is replaced with a perceptual integration stage modulating evidence accumulation. They suggested that this model particularly captures decision making regarding stimuli with dynamic noise. We tested this new model in two studies by using Bayesian parameter estimation and model comparison with marginal likelihoods. The first study replicated Smith and colleagues' findings by utilizing the classical random-dot kinomatogram (RDK) task, which requires judging the motion direction of randomly moving dots (motion discrimination task). In the second study, we used a novel type of stimulus designed to be like RDKs but with randomized hue of stationary dots (color discrimination task). This study also found TV-DDM to be superior, suggesting that perceptual integration is also relevant for static noise possibly where integration over space is required. We also found support for within-trial changes in decision boundaries ("collapsing boundaries"). Interestingly, and in contrast to most studies, the boundaries increased with increasing task difficulty (amount of noise). Future studies will need to test this finding in a formal model. [ABSTRACT FROM AUTHOR]

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow "vertical" translation between physiological properties of neural systems and emergent "whole-system" performance--enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data. (Contains 31 figures, 2 tables and 6 footnotes.)

Four experiments examined the effects of precues on visual search for targets defined by a color-orientation conjunction. Experiment 1 showed that cueing the identity of targets enhanced the efficiency of search. Cueing effects were stronger with color than with orientation cues, but this advantage was additive across array size. Experiment 2 demonstrated that cueing effects interacted with bottom-up segmentation processes, whereas Experiment 3 showed the stronger effects of color cues remained in a compound task. Experiment 4 confirmed the enhanced effect of color cueing even when verbal rather than visual cues were used. The targets used were balanced for search efficiency within both orientation and color dimensions. We suggest search benefits from the top-down cueing of color compared with orientation because color cueing enhances the segmentation of displays into color groups more efficiently. This enables search to an appropriate color group to be initiated earlier. We discuss how top-down segmentation processes interact with differences in bottom-up segmentation to further improve target detection. (Contains 4 footnotes, 3 tables, and 15 figures.)

Four experiments explored the interrelations between working memory, attention, and eye movements. Observers had to identify a tilted line amongst vertical distractors. Each line was surrounded by a colored shape that could be precued by a matching item held in memory. Relative to a neutral baseline, in which no shapes matched the memory item, search was more efficient when the memory cue matched the shape containing the target, and it was less efficient when the cued stimulus contained a distractor. Cuing affected the shortest reaction times and the first saccade in search. The effect occurred even when the memory cue was always invalid but not when the cue did not have to be held in memory. There was also no evidence for priming effects between consecutive trials. The results suggest that there can be early, involuntary top-down directing of attention to a stimulus matching the contents of working memory.