Non-holistic coding of objects in lateral occipital complex with and without attention

Abstract

A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based (holistic) format. By examining the viewpoint invariance of object recognition, previous behavioral and neuroimaging studies have yielded ambiguous results, supporting both types of representational formats. A critical factor distinguishing the two formats could be the availability of attentional resources, as a number of studies have found greater viewpoint invariance for attended compared to unattended objects. It has therefore been suggested that attention is necessary to enable part-based representations, whereas holistic representations are automatically activated irrespective of attention. In this functional magnetic resonance imaging study we used a multivariate approach to probe the format of object representations in human lateral occipital complex (LOC) and its dependence on attention. We presented human participants with intact and half-split versions of objects that were either attended or unattended. Cross-classifying between intact and split objects, we found that the objectrelated information coded in activation patterns of intact objects is fully preserved in the patterns of split objects and vice versa. Importantly, the generalization between intact and split objects did not depend on attention. Our findings demonstrate that LOC codes objects in a non-holistic format, both in the presence and absence of attention.

A haplotype of the dopamine transporter gene modulates regional homogeneity, gray matter volume, and visual memory in children with attention-deficit/hyperactivity disorder

“…Combining our findings of gene–brain–behavior linkage within one study and several previous studies describing dopamine-posterior brain regions (Nakama et al 2011; Albrecht et al 2016), posterior brain regions-visual memory (Simons et al 2001; Guggenmos et al 2015), and dopamine-visual memory (Rhodes et al 2004; 2006) relationships, three major conclusions are made. First, dexamphetamine, a dopamine-releasing agent, altered the resting-state electroencephalogram of healthy participants in the posterior brain regions, including parietal, occipital–temporal, and occipital regions (Albrecht et al 2016).…”

“…In addition, methamphetamine users showed altered GMV in the occipital regions (Nakama et al 2011). Second, visual memory involves a network of the posterior brain regions, including parietal and occipital lobes (Simons et al 2001; Guggenmos et al 2015). Third, the deficits in visual memory performance were restored by treatment with methylphenidate in children with ADHD (Rhodes et al 2004, 2006), which inhibited the action of DAT to achieve the therapeutic effects.…”

Multisensory Part-based Representations of Objects in Human Lateral Occipital Cortex

“…Additionally, we believe our part-based decoding of novel objects presents a significant step towards understanding the nature of object representations in LOC. The only previous study that used a similar decoding analysis (Guggenmos et al, 2015) employed simpler stimuli (two-part objects) and presented objects only visually. Our study used a richer set of stimuli and showed that decoding of a novel object is possible from both visual and haptic activation in LOC.…”

“…This suggests that spatial relations are encoded explicitly by this region. More recently, Guggenmos and colleagues (2015) tested whether LOC encodes objects in a part-based or holistic manner by measuring decoding accuracy for split and intact objects. They showed that a classifier trained on neural activations for intact objects can successfully discriminate between activations for split objects (e.g., a camera with its lens and body separate) and vice versa.…”

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

“…Although in general it is never possible to guarantee that any software is free from bugs, we regularly run a number of basic analyses as benchmarks to check if these analyses still run and create the same output as in previous analyses. TDT has been extensively tested by many beta users and, importantly, several published results used prior versions of the toolbox (Hesselmann et al, 2011 ; Christophel et al, 2012 , in press ; Hebart et al, 2012 , in press ; Reverberi et al, 2012 ; Christophel and Haynes, 2014 ; Ritter et al, 2014 ; Van Kemenade et al, 2014a , b ; Ludwig et al, 2015 ; Bilalić et al, in press ; Guggenmos et al, in press ).…”