Gaussian counter models for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks

Highlights: • Visual identification of single stimuli in pure accuracy task is investigated. • Multivariate Wiener and Ornstein–Uhlenbeck counter models are proposed and tested. • Two classes of models, race and first passage time models, are proposed and analyzed. • The models are compared with the Poisson counter model from the literature. • Model selection favors Gaussian race models over Poisson or first passage time models. Abstract: When identifying confusable visual stimuli, accumulation of information over time is an obvious strategy of the observer. However, the nature of the accumulation process is unresolved: for example it may be discrete or continuous in terms of the information encoded. Another unanswered question is whether or not stimulus sampling continues after the stimulus offset. In the present paper we propose various continuous Gaussian counter models of the time course of visual identification of briefly presented, mutually confusable single stimuli in a pure accuracy task. During stimulus analysis, tentative categorizations that stimulus belongs to category are made until a maximum time after the stimulus disappears. Two classes of models are proposed. First, the overt response is based on the categorization that had the highest value at the time the stimulus disappears (race models). Second, the overt response is based on the categorization that made the minimum first passage time through a constant boundary (first passage time models). Within this framework, multivariate Wiener and Ornstein–Uhlenbeck counter models are considered under different parameter regimes, assuming either that the stimulus sampling stops immediately or that it continues for some time after the stimulus offset. Each type of model was evaluated by Monte Carlo tests of goodness of fit against observed probability distributions of responses in two extensive experiments. A comparison of these continuous models with a simple discrete Poisson counter model proposed by Kyllingsbæk, Markussen, and Bundesen (2012) was carried out, together with model selection among the competing candidates. Both the Wiener and the Ornstein–Uhlenbeck race models provide a close fit to individual data on identification of both digits and Landolt rings, outperforming the first passage time model and the Poisson counter race model

Automatic Attraction of Visual Attention by Supraletter Features of Former Target Strings

Observers were trained to search for a particular horizontal string of 3 capital letters presented among similar strings consisting of exactly the same letters in different orders. The training was followed by a test in which the observers searched for a new target that was identical to one of the former distractors. The new distractor set consisted of the remaining former distractors plus the former target. On each trial, three letter-strings were displayed, which included the target string with a probability of .5. In Experiment 1, the strings were centered at different locations on the circumference of an imaginary circle around the fixation point. The training phase of Experiment 2 was similar, but in the test phase of the experiment, the strings were located in a vertical array centered on fixation, and in target-present arrays, the target always appeared at fixation. In both experiments, performance (d’) degraded on trials in which former targets were present, suggesting that the former targets automatically drew processing resources away from the current targets. Apparently, the two experiments showed automatic attraction of visual attention by supraletter features of former target strings

Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field

A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior