Continuous action with a neurobiologically inspired computational approach reveals the dynamics of selection history.

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.

Deep neural networks and image classification in biological vision.

In this paper we consider recent advances in the use of deep convolutional neural networks to understanding biological vision. We focus on claims about the plausibility of feedforward deep convolutional neural networks (fDCNNs) as models of image classification in the biological system. Despite the putative similarity of these networks to some properties of the biological vision system, and the remarkable levels of performance accuracy of some fDCNNs, we argue that their plausibility as a framework for understanding image classification remains unclear. We highlight two key issues that we suggest are relevant to the evaluation of any form of DNN used to examine biological vision: (1) Network transparency under analysis - that is, the challenge of understanding what networks do, and how they do it. (2) Identifying appropriate benchmarks for comparing network performance and the biological system using both quantitative and qualitative performance measures. We show that there are important divergences between fDCNNs and biological vision that reflect fundamental differences in computational architectures, and representational structures, supporting image classification in these networks and the biological system.

A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision.

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.

Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness.

General anesthetics are routinely used to induce unconsciousness, and much is known about their effects on receptor function and single neuron activity. Much less is known about how these local effects are manifest at the whole-brain level nor how they influence network dynamics, especially past the point of induced unconsciousness. Using resting-state functional magnetic resonance imaging (fMRI) with nonhuman primates, we investigated the dose-dependent effects of anesthesia on whole-brain temporal modular structure, following loss of consciousness. We found that higher isoflurane dose was associated with an increase in both the number and isolation of whole-brain modules, as well as an increase in the uncoordinated movement of brain regions between those modules. Conversely, we found that higher dose was associated with a decrease in the cohesive movement of brain regions between modules, as well as a decrease in the proportion of modules in which brain regions participated. Moreover, higher dose was associated with a decrease in the overall integrity of networks derived from the temporal modules, with the exception of a single, sensory-motor network. Together, these findings suggest that anesthesia-induced unconsciousness results from the hierarchical fragmentation of dynamic whole-brain network structure, leading to the discoordination of temporal interactions between cortical modules.

A computational implementation of a Hebbian learning network and its application to configural forms of acquired equivalence.

We describe and report the results of computer simulations of the three-layer Hebbian network informally described by Honey, Close, and Lin (2010): A general account of discrimination that has been shaped by data from configural acquired equivalence experiments that are beyond the scope of alternative models. Simulations implemented a conditional principle-components analysis Hebbian learning algorithm and were of four published experimental demonstrations of configural acquired equivalence. Experiments involved training rats on appetitive biconditional discriminations in which discrete cues (w and x) signaled food delivery (+) or its absence (-) in 4 different contexts (A, B, C, and D): Aw+ Bw- Cw+ Dw- Ax- Bx+ Cx- Dx+. Contexts A and C acquired equivalence. In 3 of the experiments acquired equivalence was evident from subsequent revaluation, from compound testing or from whole-/part-reversal training. The fourth experiment added concurrent biconditional discriminations with the same contexts but a pair of additional discrete cues (y and z). The congruent form of the discrimination, in which A and C provided the same information about y and z, was solved relatively readily. Parametric variation allowed the network to successfully simulate the results of each of the 4 experiments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum.

Background: A considerable amount of research has discussed whether autism and psychiatric/neurodevelopmental conditions in general are best described categorically or dimensionally. In recent years, finite mixture models have been increasingly applied to mixed populations of autistic and non-autistic individuals to answer this question. However, the use of such methods with mixed populations may not be appropriate for two reasons: First, subgroups within mixed populations are often skewed and thus violate mixture models assumptions, which are based on weighted sum of Gaussian distributions. Second, these analyses have, to our knowledge, been solely applied to enriched samples, where the prevalence of the clinical condition within the study sample far exceeds epidemiological estimates. Method: We employed a dual Weibull mixture model to examine the distribution of the Autism Spectrum Quotient scores of a mixed sample of autistic and non-autistic adults (N = 4717; autism = 811), as well as of a derived sample (from the enriched sample; N = 3973; autism = 67) that reflects the current prevalence of autism within the general population. Results: In a mixed autistic and non-autistic population, our model provided a better description of the underlying structure of autistic traits than traditional finite Gaussian mixture models and performed well when applied to a sample that reflected the prevalence of autism in the general population. The model yielded results, which are consistent with predictions of current theories advocating for the co-existence of a mixed categorical and dimensional architecture within the autism spectrum. Conclusion: The results provide insight into the continuum nature of the distribution of autistic traits, support the complementary role of both categorical and dimensional approaches to autism spectrum condition, and underscore the importance of analyzing samples that reflect the epidemiological prevalence of the condition. Owing to its flexibility to represent a wide variety of distributions, the Weibull distribution might be better suited for latent structure studies, within enriched and prevalence-true samples.

Excitatory versus inhibitory feedback in Bayesian formulations of scene construction.

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects-as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

Looking for intoolligence: A unified framework for the cognitive study of human tool use and technology.

Humans have considerably modified their environment by making and building a number of tools, technologies, and constructions. This unique ability compared to other animals is the focus of researchers in different fields of psychology. However, there is confusion about the definitions proposed, generating difficulties in making connections between those different fields. This article presents the first unified framework (i.e., intoolligence) aiming to overcome these issues by focusing on the cognitive processes involved in the different forms taken by human tool use and technology, rather than on the overt behavior. To lay the foundation for intoolligence, we first address a series of epistemological misconceptions, which are the root cause for the current confusion. Particularly, we discuss the limitations of the widespread idea that tool use relies on specific cognitive skills, centered on the manipulative aspect of tool use. We develop, based on this analysis, details concerning our framework, which is based on the key principle that making and using are 2 independent cognitive steps. This distinction allows us to redefine tool use by breaking it down into 3 modes: assistive tool use, arbitrary tool use, and free tool use. This article opens a new chapter on the topic of human tool use and technology. (PsycINFO Database Record

The involvement of the dorsal stream in processing implied actions between paired objects: A TMS study.

Perceiving and selecting the action possibilities (affordances) provided by objects is an important challenge to human vision, and is not limited to single-object scenarios. Xu et al. (2015) identified two effects of implied actions between paired objects on response selection: an inhibitory effect on responses aligned with the passive object in the pair (e.g. a bowl) and an advantage associated with responses aligned with the active objects (e.g. a spoon). The present study investigated the neurocognitive mechanisms behind these effects by examining the involvement of the ventral (vision for perception) and the dorsal (vision for action) visual streams, as defined in Goodale and Milner's (1992) two visual stream theory. Online repetitive transcranial magnetic stimulation (rTMS) applied to the left anterior intraparietal sulcus (aIPS) reduced both the inhibitory effect of implied actions on responses aligned with the passive objects and the advantage of those aligned with the active objects, but only when the active objects were contralateral to the stimulation. rTMS to the left lateral occipital areas (LO) did not significantly alter the influence of implied actions. The results reveal that the dorsal visual stream is crucial not only in single-object affordance processing, but also in responding to implied actions between objects.

Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information.

We present an extension of a neurobiologically inspired robotics model, termed CoRLEGO (Choice reaching with a LEGO arm robot). CoRLEGO models experimental evidence from choice reaching tasks (CRT). In a CRT participants are asked to rapidly reach and touch an item presented on the screen. These experiments show that non-target items can divert the reaching movement away from the ideal trajectory to the target item. This is seen as evidence attentional selection of reaching targets can leak into the motor system. Using competitive target selection and topological representations of motor parameters (dynamic neural fields) CoRLEGO is able to mimic this leakage effect. Furthermore if the reaching target is determined by its colour oddity (i.e. a green square among red squares or vice versa), the reaching trajectories become straighter with repetitions of the target colour (colour streaks). This colour priming effect can also be modelled with CoRLEGO. The paper also presents an extension of CoRLEGO. This extension mimics findings that transcranial direct current stimulation (tDCS) over the motor cortex modulates the colour priming effect (Woodgate et al., 2015). The results with the new CoRLEGO suggest that feedback connections from the motor system to the brain's attentional system (parietal cortex) guide visual attention to extract movement-relevant information (i.e. colour) from visual stimuli. This paper adds to growing evidence that there is a close interaction between the motor system and the attention system. This evidence contradicts the traditional conceptualization of the motor system as the endpoint of a serial chain of processing stages. At the end of the paper we discuss CoRLEGO's predictions and also lessons for neurobiologically inspired robotics emerging from this work.

Modeling human target reaching with an adaptive observer implemented with dynamic neural fields.

Humans can point fairly accurately to memorized states when closing their eyes despite slow or even missing sensory feedback. It is also common that the arm dynamics changes during development or from injuries. We propose a biologically motivated implementation of an arm controller that includes an adaptive observer. Our implementation is based on the neural field framework, and we show how a path integration mechanism can be trained from few examples. Our results illustrate successful generalization of path integration with a dynamic neural field by which the robotic arm can move in arbitrary directions and velocities. Also, by adapting the strength of the motor effect the observer implicitly learns to compensate an image acquisition delay in the sensory system. Our dynamic implementation of an observer successfully guides the arm toward the target in the dark, and the model produces movements with a bell-shaped velocity profile, consistent with human behavior data.

Metacognitive impairments in schizophrenia are arrested at extreme levels of psychopathy: The cut-off effect.

Psychopathy and metacognitive difficulties have been associated with the occurrence of violence in schizophrenia. However, evidence suggests that nonschizophrenic psychopaths match or even outperform healthy controls on tests of metacognition. We hypothesize that the metacognitive difficulties associated with schizophrenia may be ameliorated by comorbid psychopathy. To this end, metacognition (using the Metacognition Assessment Scale-Abbreviated [MAS-A]) and psychopathy (using the Hare Psychopathy Checklist-Revised [PCL-R]) are assessed in 79 patients with schizophrenia with a history of criminal offending. Piecewise regression reveals that the association between metacognition and psychopathy changes from a negative to a positive association at a breakpoint corresponding to a score of 24 on the PCL-R. This score is within the range of the cut-off point used for the diagnosis of psychopathy in Europe, which corresponds to a score of 26 on the PCL-R. The patients scoring above 24 on the PCL-R demonstrated better overall metacognitive abilities, suggesting that these patients constitute a specific group in which schizophrenia has an attenuated impairing effect on metacognition. However, this effect was absent for the Mastery subscale of the MAS-A, which refers to the ability to use one's own mental states to solve social and psychological dilemmas. Our results suggest that the relative preservation of metacognitive abilities in schizophrenic patients with extreme levels of psychopathy may contribute to their offending behavior as it may enable them to understand how to manipulate and extort their victims. However, enhancing the Mastery domain of metacognition in these patients may attenuate this offending behavior.

Implied actions between paired objects lead to affordance selection by inhibition.

Evidence from experiments with single objects indicates that perceiving objects leads to automatic extraction of affordances. Here we examined the influence of implied between-object actions on affordance processing. Images of task-irrelevant object pairs (e.g., a spoon and a bowl) were followed by imperative central targets. Participants made speeded left/right responses to targets, and the responses randomly aligned with the affordance of one of the objects. The orientation of one object was manipulated across trials, leaving the colocation between objects correct or incorrect for potential interaction. Four experiments demonstrated that positioning the objects correctly for between-object actions led to a prioritization of the object active in the action (e.g., the spoon) over the passive (e.g., the bowl) object. Moreover, there was an inhibitory effect on responses to the passive object: responses congruent with the passive object were slower when pairs of objects were shown as if in interaction, compared with when they were not. The effects did not change in the single-hand response task but disappeared when the passive objects were absent-though an affordance should still have been presented by the active object. These results present evidence for affordance selection in action-related object pairs, and suggest inhibition of the action afforded by the passive objects under conditions of affordance competition.

Motor cortex guides selection of predictable movement targets.

The present paper asks whether the motor cortex contributes to prediction-based guidance of target selection. This question was inspired by recent evidence that suggests (i) recurrent connections from the motor system into the attentional system may extract movement-relevant perceptual information and (ii) that the motor cortex cannot only generate predictions of the sensory consequences of movements but may also operate as predictor of perceptual events in general. To test this idea we employed a choice reaching task requiring participants to rapidly reach and touch a predictable or unpredictable colour target. Motor cortex activity was modulated via transcranial direct current stimulation (tDCS). In Experiment 1 target colour repetitions were predictable. Under such conditions anodal tDCS facilitated selection versus sham and cathodal tDCS. This improvement was apparent for trajectory curvature but not movement initiation. Conversely, where no predictability of colour was embedded reach performance was unaffected by tDCS. Finally, the results of a key-press experiment suggested that motor cortex involvement is restricted to tasks where the predictable target colour is movement-relevant. The outcomes are interpreted as evidence that the motor system contributes to the top-down guidance of selective attention to movement targets.

Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework.

In this study, we applied Bayesian-based distributional analyses to examine the shapes of response time (RT) distributions in three visual search paradigms, which varied in task difficulty. In further analyses we investigated two common observations in visual search-the effects of display size and of variations in search efficiency across different task conditions-following a design that had been used in previous studies (Palmer, Horowitz, Torralba, & Wolfe, Journal of Experimental Psychology: Human Perception and Performance, 37, 58-71, 2011; Wolfe, Palmer, & Horowitz, Vision Research, 50, 1304-1311, 2010) in which parameters of the response distributions were measured. Our study showed that the distributional parameters in an experimental condition can be reliably estimated by moderate sample sizes when Monte Carlo simulation techniques are applied. More importantly, by analyzing trial RTs, we were able to extract paradigm-dependent shape changes in the RT distributions that could be accounted for by using the EZ2 diffusion model. The study showed that Bayesian-based RT distribution analyses can provide an important means to investigate the underlying cognitive processes in search, including stimulus grouping and the bottom-up guidance of attention.

What causes IOR? Attention or perception? - manipulating cue and target luminance in either blocked or mixed condition.

Inhibition of return (IOR) refers to the performance disadvantage when detecting a target presented at a previously cued location. The current paper contributes to the long-standing debate whether IOR is caused by attentional processing or perceptual processing. We present a series of four experiments which varied the cue luminance in mixed and blocked conditions. We hypothesised that if inhibition was initialized by an attentional process the size of IOR should not vary in the blocked condition as participants should be able to adapt to the level of cue luminance. However, if a perceptual process triggers inhibition both experimental manipulations should lead to varying levels of IOR. Indeed, we found evidence for the latter hypothesis. In addition, we also varied the target luminance in blocked and mixed condition. Both manipulations, cue luminance and target luminance, affected IOR in an additive fashion suggesting that the two stimuli affect human behaviour on different processing stages.

Top-down guidance of eye movements in conjunction search.

We provide novel evidence for a two-stage model of visual search applied to the selection based on top-down cues to stimulus colour and orientation. The model supposes that visual displays are first parsed into colour based groups, a process that is modulated by the presence of an initial cue to the target's colour. Subsequently search is directed to other featural properties represented within the colour-based groups. Cues to the orientation of the target are only effective at this second stage. The results provide strong evidence that colour and orientation cues operate in different ways to guide search.

A robotics-based approach to modeling of choice reaching experiments on visual attention.

The paper presents a robotics-based model for choice reaching experiments on visual attention. In these experiments participants were asked to make rapid reach movements toward a target in an odd-color search task, i.e., reaching for a green square among red squares and vice versa (e.g., Song and Nakayama, 2008). Interestingly these studies found that in a high number of trials movements were initially directed toward a distractor and only later were adjusted toward the target. These "curved" trajectories occurred particularly frequently when the target in the directly preceding trial had a different color (priming effect). Our model is embedded in a closed-loop control of a LEGO robot arm aiming to mimic these reach movements. The model is based on our earlier work which suggests that target selection in visual search is implemented through parallel interactions between competitive and cooperative processes in the brain (Heinke and Humphreys, 2003; Heinke and Backhaus, 2011). To link this model with the control of the robot arm we implemented a topological representation of movement parameters following the dynamic field theory (Erlhagen and Schoener, 2002). The robot arm is able to mimic the results of the odd-color search task including the priming effect and also generates human-like trajectories with a bell-shaped velocity profile. Theoretical implications and predictions are discussed in the paper.

Bottom-up guidance to grouped items in conjunction search: evidence for color grouping.

Previous studies have demonstrated that observers can search through a subset of items carrying a minority feature to find a conjunction target (Sobel & Cave, 2002). We examined whether subset search takes place when participants have less specific foreknowledge of the target (when the target is one of two possible items), measuring eye movements as well as reaction times. When there were unequal ratios of distractors, fixations were initially directed to the small subset. These initial eye movements were often directed between items with the same feature, suggesting guidance from pooled feature values. There was stronger guidance within color- than orientation-defined groups, although the features were balanced for salience. The results suggest that grouping of items by color operates more globally than grouping in orientation.