The human visual system is developed by viewing natural scenes. In controlled experiments, natural stimuli therefore provide a realistic framework with which to study the underlying information processing steps involved in human vision. Studying the properties of natural images and their effects on the visual processing can help us to understand underlying mechanisms of visual system. In this study, we used a rapid animal vs. non-animal categorization task to assess the relationship between the reaction times of human subjects and the statistical properties of images. We demonstrated that statistical measures, such as the beta and gamma parameters of a Weibull, fitted to the edge histogram of an image, and the image entropy, are effective predictors of subject reaction times. Using these three parameters, we proposed a computational model capable of predicting the reaction times of human subjects.
Most decisions require information gathering from a stimulus presented with different gaps.Indeed, the brain process of this integration is rarely ambiguous. Recently, it has been claimed that humans can optimally integrate the information of two discrete pulses independent of the temporal gap between them. Interestingly, subjects' performance on such a task, with two discrete pulses, is superior to what a perfect accumulator can predict. Although numerous neuronal and descriptive models have been proposed to explain the mechanism of perceptual decision-making, none can explain human behavior on this two-pulse task. In order to investigate the mechanism of decision-making on the noted tasks, a set of modified driftdiffusion models based on different hypotheses were used. Model comparisons clarified that, in a sequence of information arriving at different times, the accumulated information of earlier evidence affects the process of information accumulation of later evidence. It was shown that the rate of information extraction depends on whether the pulse is the first or the second one.The proposed model can also explain the stronger effect of the second pulse as shown by Kiani et al. (2013).
Bias in perceptual decisions can be generally defined as an effect which is controlled by factors other than the decision-relevant information (e.g., perceptual information in a perceptual task, when trials are independent). The literature on decision-making suggests two main hypotheses to account for this kind of bias: internal bias signals are derived from (a) the residual of motor signals generated to report a decision in the past, and (b) the residual of sensory information extracted from the stimulus in the past. Beside these hypotheses, this study suggests that making a decision in the past per se may bias the next decision. We demonstrate the validity of this assumption, first, by performing behavioral experiments based on the two-alternative forced-choice (TAFC) discrimination of motion direction paradigms and, then, we modified the pure drift-diffusion model (DDM) based on the accumulation-to-bound mechanism to account for the sequential effect. In both cases, the trace of the previous trial influences the current decision. Results indicate that the probability of being correct in the current decision increases if it is in line with the previously made decision even in the presence of feedback. Moreover, a modified model that keeps the previous decision information in the starting point of evidence accumulation provides a better fit to the behavioral data. Our findings suggest that the accumulated evidence in the decision-making process after crossing the bound in the previous decision can affect the parameters of information accumulation for the current decision in consecutive trials.
Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation. There is evidence suggesting that corticogeniculate connections may account for this decorrelation in the LGN. In this study, we propose a computational model based on biological evidence reported by Wang et al. (2006), who demonstrated that the influence pattern of V1 feedback is phase-reversed. The output of our model shows how corticogeniculate connections decorrelate LGN responses and make an efficient representation. We evaluated our model using criteria that have previously been tested on LGN neurons through cell recording experiments, including sparseness, entropy, power spectra, and information transfer. We also considered the role of the LGN in higher-order visual object processing, comparing the categorization performance of human subjects with a cortical object recognition model in the presence and absence of our LGN input-stage model. Our results show that the new model that considers the role of the LGN, more closely follows the categorization performance of human subjects.
Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.
When making decisions in real-life, we may receive discrete pieces of evidence during a time period. Although subjects are able to integrate information from separate cues to improve their accuracy, confidence formation is controversial. Due to a strong positive relation between accuracy and confidence, we predicted that confidence followed the same characteristics as accuracy and would improve following the integration of information collected from separate cues. We applied a Random-dot-motion discrimination task in which participants had to indicate the predominant direction of dot motions by saccadic eye movement after receiving one or two brief stimuli (i.e., pulse(s)). The interval of two pulses (up to 1s) was selected randomly. Color-coded targets facilitated indicating confidence simultaneously. Using behavioral data, computational models, pupillometry and EEG methodology we show that in double-pulse trials: (i) participants improve their confidence resolution rather than reporting higher confidence comparing with single-pulse trials, (ii) the observed confidence follow neural and pupillometry markers of confidence, unlike in weak and brief single-pulse trials. Overall, our study showed improvement of associations between confidence and accuracy in decision results from the integration of stimulus separated by different temporal gaps.
Most decisions are based on the accumulation of discrete pieces of evidence. This evidence has usually been separated with the various intervals. Indeed, how the brain gathers and combines distinct pieces of information received at different times is need to be clarified. In order to investigate the kinship between brain function and human behavior, the behavioral experimental studies could be designed. Previous studies demonstrated that subjects gather and effectively combine discrete evidence to improve their accuracy. In addition, it has been shown that the latest information has a larger influence on decisions. However, it remains unclear that why this larger influence of the later pulses occurs and what can affect this influence. Materials and Methods: Dealing with these questions a perceptual decision-making task has been implemented by the psychophysics' toolbox in MATLAB. Subjects, during the task, were instructed to report the direction of motion in a noisy random dot stimulus with certain keys. Stimuli were presented in continuous (one pulse) or discrete (two continuousness pulses separated with different intervals) form. Each of these two forms of stimuli was presented randomly during each session. Each session has been included 300 trials. Each subject has done 3600 trials. Data have been analyzed by regression models. Results: We observed that in double-pulse trials, the strength of the second pulse was more crucial in the accuracy of responses compared to the first pulse. In addition, this accuracy was dependent on the differences between the strength of the first and the last pulses. Conclusion: These findings suggest that a key factor which affects the importance of pulses is the strength of the previous pulse. As the difference between the motion strength increases, the effect of the second pulse on choice accuracy enhanced.
Local contrasts attract human attention to different areas of an image. Studies have shown that orientation, color, and intensity are some basic visual features which their contrasts attract our attention. Since these features are in different modalities, their contribution in the attraction of human attention is not easily comparable. In this study, we investigated the importance of these three features in the attraction of human attention in synthetic and natural images. Choosing 100% percent detectable contrast in each modality, we studied the competition between different features. Psychophysics results showed that, although single features can be detected easily in all trials, when features were presented simultaneously in a stimulus, orientation always attracts subject’s attention. In addition, computational results showed that orientation feature map is more informative about the pattern of human saccades in natural images. Finally, using optimization algorithms we quantified the impact of each feature map in construction of the final saliency map.
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