Methods and measurements to compare men against machines

Wichmann, Felix A.; Janssen, Dirk-Jan; Geirhos, Robert; Aguilar, Guillermo; Schütt, Heiko H.; Maertens, Marianne; Bethge, Matthias

doi:10.2352/issn.2470-1173.2017.14.hvei-113

Cited by 15 publications

(13 citation statements)

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“…Prior work showed that FCNNs are a good computational model of the ventral visual pathway (Khaligh-Razavi & Kriegeskorte, 2014; Güçlü & van Gerven, 2015; Yamins, Hong, Cadieu, & DiCarlo, 2013; Yamins & DiCarlo, 2016; Kriegeskorte, 2015). FCNNs performed well the perceptual identification task with clear images, also in keeping with prior studies (Geirhos et al, 2017; Wichmann et al, 2017; Geirhos et al, 2018; Ghodrati, Farzmahdi, Rajaei, Ebrahimpour, & Khaligh-Razavi, 2014). FCNNs are sensitive to image perturbations (Kubilius et al, 2019) and accordingly, FCNNs classification performance dropped as the noise level increased and eventually fell to chance levels.…”

Section: Discussionsupporting

confidence: 87%

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks

Mei

Santana²,

Soto

2021

Preprint

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Despite advances in the neuroscience of visual consciousness over the last decades, we still lack a framework for understanding the scope of unconscious processing and how it relates to conscious experience. Previous research observed brain signatures of unconscious contents in visual cortex, but these have not been identified in a reliable manner, with low trial numbers and signal detection theoretic constraints not allowing to decisively discard conscious perception. Critically, the extent to which unconscious content is represented in high-level processing stages along the ventral visual stream and linked prefrontal areas remains unknown. Using a within-subject, high-precision, highly-sampled fMRI approach, we show that unconscious contents, even those associated with null sensitivity, can be reliably decoded from multivoxel patterns that are highly distributed along the ventral visual pathway and also involving prefrontal substrates. Notably, the neural representation in these areas generalised across conscious and unconscious visual processing states, placing constraints on prior findings that fronto-parietal substrates support the representation of conscious contents and suggesting revisions to models of consciousness such as the neuronal global workspace. We then provide a computational model simulation of visual information processing/representation in the absence of perceptual sensitivity by using feedforward convolutional neural networks trained to perform a similar visual task to the human observers. The work provides a novel framework for pinpointing the neural representation of unconscious knowledge across different task domains.

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Section: Discussionsupporting

confidence: 87%

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks

Mei

Santana²,

Soto

2021

Preprint

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“…Recently, a more generic approach, called maximum likelihood difference scaling (MLDS), has become popular in vision science ( Maloney & Yang, 2003 ; Knoblauch & Maloney, 2010 ). There have been reports that both naive as well as seasoned observers find the method of triads with supra-threshold stimuli intuitive and fast, requiring less training ( Aguilar et al., 2017 ; Wichmann et al., 2017 ) than for the more traditional methods in psychophysics such as direct magnitude estimation or, in particular, methods based on JNDs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of perceptual scales using ordinal embedding

2020

Self Cite

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In this article, we address the problem of measuring and analyzing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: The sensation of the stimulus is evaluated via relative judgments of the following form: "Is stimulus S i more similar to stimulus S j or to stimulus S k ?" We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments. We review two relevant and well-known methods in psychophysics that are partially applicable in our setting: nonmetric multidimensional scaling (NMDS) and the method of maximum likelihood difference scaling (MLDS). Considering various scaling functions, we perform an extensive set of simulations to demonstrate the performance of the ordinal embedding methods. We show that in contrast to existing approaches, our ordinal embedding approach allows, first, to obtain reasonable scaling functions from comparatively few relative judgments and, second, to estimate multidimensional perceptual scales. In addition to the simulations, we analyze data from two real psychophysics experiments using ordinal embedding methods. Our results show that in the one-dimensional perceptual scale, our ordinal embedding approach works as well as MLDS, while in higher dimensions, only our ordinal embedding methods can produce a desirable scaling function. To make our methods widely accessible, we provide an R-implementation and general rules of thumb on how to use ordinal embedding in the context of psychophysics.

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“…In [26], the authors aimed to obtain a better understanding of the similarities and differences in overt classification behavior-and thus, very likely, computation-between Deep Neural Networks (DNN) and human vision.…”

Section: Related Workmentioning

confidence: 99%

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

et al. 2020

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Unmanned Aircraft Systems (UASs) have been recognized as an important resource in search-and-rescue (SAR) missions and, as such, have been used by the Croatian Mountain Search and Rescue (CMRS) service for over seven years. The UAS scans and photographs the terrain. The high-resolution images are afterwards analyzed by SAR members to detect missing persons or to find some usable trace. It is a drawn out, tiresome process prone to human error. To facilitate and speed up mission image processing and increase detection accuracy, we have developed several image-processing algorithms. The latest are convolutional neural network (CNN)-based. CNNs were trained on a specially developed image database, named HERIDAL. Although these algorithms achieve excellent recall, the efficiency of the algorithm in actual SAR missions and its comparison with expert detection must be investigated. A series of mission simulations are planned and recorded for this purpose. They are processed and labelled by a developed algorithm. A web application was developed by which experts analyzed raw and processed mission images. The algorithm achieved better recall compared to an expert, but the experts achieved better accuracy when they analyzed images that were already processed and labelled.

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Methods and measurements to compare men against machines

Cited by 15 publications

References 0 publications

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks

Estimation of perceptual scales using ordinal embedding

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

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