Visual Cognition: In sight, in mind

A region of the brain called the perirhinal cortex represents both what things look like and what they mean.
  1. Mariam Aly  Is a corresponding author
  1. Columbia University, United States

When we look around at the world, we can appreciate what things look like and also what they are used for. For example, when we look at a couch, we see its long flat surface, its cushions, and its back. We also know that a couch is a good place to sit or nap. How does the brain represent, and integrate, these different kinds of information? This is a tricky question because these details are often related. A futon and a couch have similar functions and they look similar too. Because of this, it can be difficult to tell whether a given brain region codes for an object’s appearance (known as a percept) or its function (a concept).

Now, in eLife, Chris Martin, Morgan Barense and colleagues – who are based at the University of Toronto, Mount Allison University, the Rotman Research Institute, and Queen's University in Kingston – report how they have been able to tease out percepts and concepts in the brain (Martin et al., 2018). Their ingenious approach involved using the names of pairs of objects that look similar but have different functions, and other pairs with similar functions but different looks. For example, a tennis ball and a lemon are both roundish and yellow, but serve different purposes; a tennis ball and a tennis racket, on the other hand, do not look alike but are both involved in playing tennis.

Martin et al. asked over a thousand people to rate how much each pair of named objects looked alike, and another equally large group to describe conceptual features of those objects, for example, their function, or where they are typically found. For each pair of objects, these experiments gave one number that indicated the perceptual similarity of the objects, and a second number that indicated their conceptual similarity. Equipped with this information, Martin et al. could test different hypotheses of how percepts and concepts are represented in the brain.

One possibility was that some brain regions represent visual form (Martin and Chao, 2001) and others represent the function or meaning of objects (Patterson et al., 2007). An additional possibility, not exclusive of the first, was that some brain regions could simultaneously represent both (Barense et al., 2012a2012a; Clarke and Tyler, 2014; Murray and Bussey, 1999).

Functional magnetic resonance imaging (fMRI) examines brain activity on a moment-by-moment basis. Martin et al. used fMRI to observe how activity in different brain regions changed when individuals were shown the names of the objects, and did one of two tasks. In one task, individuals had to make judgments about what the object looked like; in the other task they had to make judgments about its conceptual features (e.g., what it is used for). Martin et al. could then look at the patterns of activity in different brain regions while people performed these two tasks, and relate those activity patterns to the ratings of perceptual and conceptual similarity they had obtained earlier (Kriegeskorte et al., 2008).

Martin et al. hypothesized that a region of the brain called the perirhinal cortex would represent what things looked like and what they meant. Prior studies have separately linked this brain region to both of these functions (e.g., Barense et al., 2012b; Wright et al., 2015), but could not disentangle perceptual and conceptual similarity. Having overcome that challenge with their experimental design, Martin et al. found that activity patterns in the perirhinal cortex did indeed reflect both perceptual and conceptual similarity. This result was obtained whether individuals were judging what objects looked like or what they meant, suggesting that this region of the brain may integrate percepts and concepts relatively automatically. Other regions of the brain represented either what things looked like or what they meant, but it was only the perirhinal cortex where both of these representations were integrated (Figure 1).

How visual and conceptual similarity are represented in different regions of the brain.

Objects that are represented similarly in a given brain region are shown close together, with thick solid lines connecting them. Objects that are somewhat similar are shown at intermediate distance, with thin solid lines connecting them. Objects that are represented distinctly are shown further apart, with thin dashed lines between them. (A) A region of the brain called the lateral occipital cortex, shown in blue, represents objects that look alike – like a lemon and a tennis ball – in similar ways. (B) The temporal pole and parahippocampal cortex, shown in green, represent objects that are conceptually related – like a tennis ball and tennis racket – in similar ways. (C) The perirhinal cortex, shown in red, integrates these different kinds of information such that objects that are conceptually related or that look alike are represented in similar ways.

IMAGE CREDIT: Object images courtesy of Bainbridge and Oliva (2015).

Martin et al. have furthered our understanding of how we can perceive and understand objects, and their findings open some exciting avenues for future research. It remains unclear whether the exact same neurons in the perirhinal cortex represent both percepts and concepts at the same time, or if they are represented by distinct, but intermingled, populations of neurons. fMRI allows researchers to see at a general level which brain regions are active, but it cannot identify exactly which neurons are responding or how. Future studies that record from individual neurons will provide a complementary picture to this latest work.


Article and author information

Author details

  1. Mariam Aly

    Mariam Aly is in the Department of Psychology, Columbia University, New York, United States

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4033-6134

Publication history

  1. Version of Record published: March 1, 2018 (version 1)


© 2018, Aly

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


  • 2,181
  • 154
  • 0

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Mariam Aly
Visual Cognition: In sight, in mind
eLife 7:e35663.

Further reading

    1. Neuroscience
    Alina Tetereva, Narun Pat
    Research Article

    One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36–100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

    1. Developmental Biology
    2. Neuroscience
    Jonathan AC Menzies, André Maia Chagas ... Claudio R Alonso
    Research Article

    Movement is a key feature of animal systems, yet its embryonic origins are not fully understood. Here, we investigate the genetic basis underlying the embryonic onset of movement in Drosophila focusing on the role played by small non-coding RNAs (microRNAs, miRNAs). To this end, we first develop a quantitative behavioural pipeline capable of tracking embryonic movement in large populations of fly embryos, and using this system, discover that the Drosophila miRNA miR-2b-1 plays a role in the emergence of movement. Through the combination of spectral analysis of embryonic motor patterns, cell sorting and RNA in situs, genetic reconstitution tests, and neural optical imaging we define that miR-2b-1 influences the emergence of embryonic movement by exerting actions in the developing nervous system. Furthermore, through the combination of bioinformatics coupled to genetic manipulation of miRNA expression and phenocopy tests we identify a previously uncharacterised (but evolutionarily conserved) chloride channel encoding gene – which we term Movement Modulator (Motor) – as a genetic target that mechanistically links miR-2b-1 to the onset of movement. Cell-specific genetic reconstitution of miR-2b-1 expression in a null miRNA mutant background, followed by behavioural assays and target gene analyses, suggest that miR-2b-1 affects the emergence of movement through effects in sensory elements of the embryonic circuitry, rather than in the motor domain. Our work thus reports the first miRNA system capable of regulating embryonic movement, suggesting that other miRNAs are likely to play a role in this key developmental process in Drosophila as well as in other species.