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]

Everyday perception-action interaction often requires selection of a single goal from multiple possibilities. According to a recent framework of attentional control, object selection is guided not only by the well-established factors of perceptual salience and current goals but also by selection history. Yet, underlying mechanisms linking selection history and visually-guided actions are poorly understood. To examine such interplay and disentangle the impact of target and distractor history on action selection, we employed a priming-of-popout (PoP) paradigm combined with continuous tracking of reaching movements and computational modeling. Participants reached an odd-colored target among homogeneous distractors while we systematically manipulated the sequence of target and distractor colors from one trial to the next. We observed that current reach movements were significantly influenced by the interaction between attraction by the prior target feature and repulsion by the prior distractor feature. With principal component regression, we found that inhibition led by prior distractors influenced reach target selection earlier than facilitation led by the prior target. In parallel, our newly developed computational model validated that current reach target selection can be explained best by the mechanism postulating the preceded impact of previous distractors followed by a previous target. Such converging empirical and computational evidence suggests that the prior selection history triggers a dynamic interplay between target facilitation and distractor inhibition to guide goal-directed action successfully. This, in turn, highlights the necessity of an explicitly integrated approach to determine how visual attentional selection links with adaptive actions in a complex environment. Author summary: Most real-world visual scenes are complex and crowded, where multiple objects compete for attention and goal-directed action. The interactions between mechanisms of attentional selection and action selection are at the root of many complex behaviors. However, their link has been understudied. To examine this interplay and disentangle the impact of target and distractor history on action selection, we employed a priming-of-popout (PoP) paradigm combined with continuous tracking of reaching movements and computational modeling. This integrated approach constitutes a significant departure from existing practice, where attention and action selection mechanisms are typically investigated separately. Our converging evidence supports the notion that prior selection history triggers a dynamic interplay between target facilitation and distractor inhibition to guide goal-directed action successfully. This finding highlights the necessity of an explicitly integrated approach to determine how visual attentional selection links with adaptive actions in complex environments. Taken together, our study provides valuable insights into the mechanisms underlying attentional selection and action selection, and has important implications for future research in this field. [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.

Here we examine the plausibility of deep convolutional neural networks (CNNs) as a theoretical framework for understanding biological vision in the context of image classification. Recent work on object recognition in human vision has shown that both global, and local, shape information is computed, and integrated, early during perceptual processing. Our goal was to compare the similarity in how object shape information is processed by CNNs and human observers. We tested the hypothesis that, unlike the human system, CNNs do not compute representations of global and local object geometry during image classification. To do so, we trained and tested six CNNs (AlexNet, VGG-11, VGG-16, ResNet-18, ResNet-50, GoogLeNet), and human observers, to discriminate geometrically possible and impossible objects. The ability to complete this task requires computation of a representational structure of shape that encodes both global and local object geometry because the detection of impossibility derives from an incongruity between well-formed local feature conjunctions and their integration into a geometrically well-formed 3D global shape. Unlike human observers, none of the tested CNNs could reliably discriminate between possible and impossible objects. Detailed analyses using gradient-weighted class activation mapping (GradCam) of CNN image feature processing showed that network classification performance was not constrained by object geometry. In contrast, if classification could be made based solely on local feature information in line drawings the CNNs were highly accurate. We argue that these findings reflect fundamental differences between CNNs and human vision in terms of underlying image processing structure. Notably, unlike human vision, CNNs do not compute representations of object geometry. The results challenge the plausibility of CNNs as a framework for understanding image classification in biological vision systems. [ABSTRACT FROM AUTHOR]

In the last years, museums around the world began using museum guide robots as an alternative to audio guides. Thus, it is important to identify how these new museum guides can optimally interact with visitors and whether they are more efficient that conventional audio guides. In this paper, we present the results from an experimental study which compared conventional audio guides with robots with different 'personality'. These results demonstrate that people remember significantly more information when they are guided by a cheerful robot than when their guide is a serious one or a conventional audio system. Importantly, we also introduce the idea of two collaborative tour guide robots. This idea was inspired by cognitive studies showing that people remember more when they receive information from two different human speakers. Interestingly, our participants also liked more our collaborative robots, than any version of a single robot and/or audio system. [ABSTRACT FROM AUTHOR]

Potential interactions between objects affect response selection in action-related object pairs. The present paper aimed to examine whether this effect is conditional on the knowledge about object functionality, or whether action-related structures such as handles are sufficient. This issue was investigated by utilizing a paradigm established previously. They presented imperative central targets which overlapped with task-irrelevant object pairs and required speeded left/right responses to the targets. With this paradigm, two stable effects of implied actions between objects on response selection have been identified: an inhibitory effect on responses aligned with the passive object (e.g., a bowl in a bowl–spoon pair) and an advantage associated with responses aligned with the active objects (e.g., a spoon). The present paper utilized these two effects as the indexes of response selection in paired-object scenarios, and found that active–passive object pairs without established functionality (e.g., a saw and a bowl, Experiment 1) generated the same effects, suggesting that response selection does not rely on functionality knowledge of given object pairs. Further, the two effects were also observed in passive–passive object pairs with handles (e.g., a cup–nail pair), but not in those without a handle (e.g., a bowl–nail pair, Experiment 2), and remained when the active objects were replaced by novel objects with handle but no known functionality (Experiment 3), suggesting that the action-related structures of objects are sufficient to affect response selection. The present study empirically illustrated the automaticity and directness of the extraction of potential interaction between objects, probably based on the relative location of action-related structure of objects. [ABSTRACT FROM AUTHOR]

Spontaneous trait inference (STI) and trait transference (STT) refer to the inference of personality traits from behaviors. in STI the inferred trait is attached to the actor, and in STT it is attached to a communicator. Two different explanations are currently discussed in the literature regarding their underlying processes. one claims that a single associative process is responsible for both, and the second postulates an associative process for STT and an attributional process for STI. Here we propose that a dual-processing model is not necessary to account for the empirical data regarding STI and STT. Through a simple connectionist model, based on associative learning, we simulated the four major findings that distinguish STI from STT. Suggestions are made about what kind of evidence would be necessary to consider a dualistic view, and a broader use of this approach applied to dualistic versus single processing disputes is also discussed. [ABSTRACT FROM AUTHOR]

We examined the effects of cue luminance on visual orienting. Experiment 1 established that the commonly-found early facilitation and late inhibition of return (IOR) effects were independent of cue luminance with single cues in terms of their amplitude, although IOR was delayed in the low compared to the high luminance cue condition. In contrast, Experiment 2 revealed that, with dual cues of mixed luminance, both facilitation and IOR effects were found only with bright cues. When cues had equal luminance, however, there were cueing effects for two cued locations but only when the cues were bright. The data were accommodated in a neural network model of biased competition in which cueing effects emerge at more than one location provided input activation is sufficient to overcome competitive damping of the selection system. [ABSTRACT FROM PUBLISHER]

Using a cued conjunction search task, Anderson, Heinke, and Humphreys (2010) demonstrated larger effects from cueing target colour than from cueing target orientation. In this study, we separated the implicit (nonexpectation-dependent) and explicit (expectation-dependent) effects of orientation and colour visual cues. In Experiment 1, we replicated the original findings for short cue durations (100-200 ms), demonstrating that cues matching the physical property of the target on 80% of trials exert a rapid effect on search. These early cueing effects on reaction times were supported by evidence of guidance from cues on early eye movements. Experiment 2 introduced a feature to the cue that randomly matched the colour or orientation of the target. When cue orientation was predictive, there were strong implicit effects based on whether the colour of the cue and target matched. When cue colour was predictive, there were only weak effects from the cue's orientation. Implicit effects from cue colour remained when orientation-predictive cues were used and colour was unlikely to predict the target (Experiment 3). The data suggest that strong effects of colour cueing result from a combination of implicit and explicit processes, whereas effects from orientation cues are largely limited to the explicit guidance of visual search. [ABSTRACT FROM AUTHOR]

The search over time and space (sSoTS) model attempts to simulate both the spatial and the temporal aspects of human visual search using spiking level neurons, which incorporate some biologically plausible aspects of neuronal firing. The model contains pools of units that (a) code basic features of objects, presumed to reside in the ventral visual stream, and (b) respond in a feature-independent way to stimulation at their location, presumed to operate in the posterior parietal cortex. We examined the effects of selective lesioning neurons responding to one side of the location map. Unilateral damage introduced spatial biases into selection that affected conjunction more than single-feature search. In addition, there was an impaired ability to segment stimuli over time as well as space (e.g., in preview search). These results match previously reported data on patients with posterior parietal lesions. In addition we show that spatial biases in selection increase under conditions in which there is decreased activity from excitatory neurotransmitters, mimicking effects of reduced arousal. Further simulations explored the effects of time and of visual grouping on extinction, generating predictions that were then tested empirically. The model provides a framework for linking behavioural data from patients with neural-level determinants of visual attention. [ABSTRACT FROM AUTHOR]

Our aim has been to study visual search in hierarchical patterns through a computational neuroscience perspective. In this way we were able to analyse the relationship between the global precedence effect and attention. The theoretical part of our investigations extended a model built upon the biased-competition hypothesis. An important feature of this extension is that different spatial frequencies are processed with different time constants. Simulations with this model resulted in specific predictions that were confirmed with a novel visual-search experiment utilising hierarchical patterns. Both experiment and simulations suggest that the global-precedence effect prevails under the condition of visual search. [ABSTRACT FROM AUTHOR]

We present a revised version of the Selective Attention for Identification Model (SAIM), using an initial feature detection process to code edge orientations. We show that the revised SAIM can simulate both efficient and inefficient human search, that it shows search asymmetries, and that top-down expectancies for targets play a major role in the model's selection. Predictions of the model for top-down effects are tested with human participants, and important similarities and dissimilarities are discussed. [ABSTRACT FROM AUTHOR]

Abstract: In the real world, visual information is selected over time as well as space, when we prioritise new stimuli for attention. Watson and Humphreys [Watson, D., Humphreys, G.W., 1997. Visual marking: prioritizing selection for new objects by top–down attentional inhibition of old objects. Psychological Review 104, 90–122] presented evidence that new information in search tasks is prioritised by (amongst other processes) active ignoring of old items – a process they termed visual marking. In this paper we present, for the first time, an explicit computational model of visual marking using biologically plausible activation functions. The “spiking search over time and space” model (sSoTS) incorporates different synaptic components (NMDA, AMPA, GABA) and a frequency adaptation mechanism based on [Ca2+] sensitive K+ current. This frequency adaptation current can act as a mechanism that suppresses the previously attended items. We show that, when coupled with a process of active inhibition applied to old items, frequency adaptation leads to old items being de-prioritised (and new items prioritised) across time in search. Furthermore, the time course of these processes mimics the time course of the preview effect in human search. The results indicate that the sSoTS model can provide a biologically plausible account of human search over time as well as space. [Copyright &y& Elsevier]

On the basis of a computational and neurodynamical model, we investigate a cognitive impairment in stroke patients termed visual neglect. The model is based on the “biased competition hypothesis” and is structured in several network modules which are related to the dorsal and ventral pathway in the visual cortex. By damaging the model, visual neglect can be simulated and explained as an unbalanced neurodynamical competition. We predict that acquiring knowledge of objects can increase the frequency of saccades to previously ignored object parts. This prediction is confirmed in a single case study by monitoring eye movements of a neglect patient. [Copyright &y& Elsevier]

We have presented a computational model called selective attention for identification model (SAIM), that can account for a broad range of psychological and neuropsychological phenomena on attention [1]. In this paper we report on work that extends SAIM to model data from visual search tasks. The results show that SAIM can capture important aspects of findings in visual search experiments, including variations of search slopes with the similarity between targets and distractors. SAIM is also capable of simulating experimental findings for redundant targets, including the violation of the Miller inequality. SAIM explains these experimental findings as the consequence of competitive processes involved in object identification. [Copyright &y& Elsevier]