Reaction Time for Object Categorization Is Predicted by Representational Distance

2014 ◽  
Vol 26 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Thomas A. Carlson ◽  
J. Brendan Ritchie ◽  
Nikolaus Kriegeskorte ◽  
Samir Durvasula ◽  
Junsheng Ma
Keyword(s):  
Decision Making ◽  
Temporal Cortex ◽  
Internal Representation ◽  
Inferior Temporal Cortex ◽  
Object Categorization ◽  
Object Representations ◽  
Multivariate Pattern Analysis ◽  
Individual Object ◽  
Multivariate Pattern ◽  
The Brain

How does the brain translate an internal representation of an object into a decision about the object's category? Recent studies have uncovered the structure of object representations in inferior temporal cortex (IT) using multivariate pattern analysis methods. These studies have shown that representations of individual object exemplars in IT occupy distinct locations in a high-dimensional activation space, with object exemplar representations clustering into distinguishable regions based on category (e.g., animate vs. inanimate objects). In this study, we hypothesized that a representational boundary between category representations in this activation space also constitutes a decision boundary for categorization. We show that behavioral RTs for categorizing objects are well described by our activation space hypothesis. Interpreted in terms of classical and contemporary models of decision-making, our results suggest that the process of settling on an internal representation of a stimulus is itself partially constitutive of decision-making for object categorization.

A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction

2010 ◽  
Vol 22 (12) ◽  
pp. 2979-3035 ◽  
Author(s):  
Stefan Klampfl ◽  
Wolfgang Maass
Keyword(s):  
Feature Extraction ◽  
Theoretical Basis ◽  
Learning Algorithm ◽  
Temporal Cortex ◽  
Pattern Discrimination ◽  
Stimulus Onset ◽  
Inferior Temporal Cortex ◽  
Linear Discriminant ◽  
Discrimination Capability ◽  
The Brain

Neurons in the brain are able to detect and discriminate salient spatiotemporal patterns in the firing activity of presynaptic neurons. It is open how they can learn to achieve this, especially without the help of a supervisor. We show that a well-known unsupervised learning algorithm for linear neurons, slow feature analysis (SFA), is able to acquire the discrimination capability of one of the best algorithms for supervised linear discrimination learning, the Fisher linear discriminant (FLD), given suitable input statistics. We demonstrate the power of this principle by showing that it enables readout neurons from simulated cortical microcircuits to learn without any supervision to discriminate between spoken digits and to detect repeated firing patterns that are embedded into a stream of noise spike trains with the same firing statistics. Both these computer simulations and our theoretical analysis show that slow feature extraction enables neurons to extract and collect information that is spread out over a trajectory of firing states that lasts several hundred ms. In addition, it enables neurons to learn without supervision to keep track of time (relative to a stimulus onset, or the initiation of a motor response). Hence, these results elucidate how the brain could compute with trajectories of firing states rather than only with fixed point attractors. It also provides a theoretical basis for understanding recent experimental results on the emergence of view- and position-invariant classification of visual objects in inferior temporal cortex.

scholarly journals The Neural Representational Space of Social Memory

Open Mind ◽  
2019 ◽  
Vol 3 ◽  
pp. 1-12 ◽  
Author(s):  
Sarah L. Dziura ◽  
James C. Thompson
Keyword(s):  
Social Networks ◽  
Incidental Learning ◽  
Multivariate Pattern Analysis ◽  
Temporoparietal Junction ◽  
Representational Space ◽  
Analysis Technique ◽  
The Social ◽  
Multivariate Pattern ◽  
The Relationship ◽  
The Brain

Social functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. This study utilized an incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain’s response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.

scholarly journals Outcome contingency selectively affects the neural coding of outcomes but not of tasks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass
Keyword(s):  
Decision Making ◽  
Parietal Cortex ◽  
Neural Coding ◽  
Free Choice ◽  
Pattern Analysis ◽  
Maintenance Phase ◽  
Large Network ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.

scholarly journals Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

Neuron ◽  
2008 ◽  
Vol 60 (6) ◽  
pp. 1126-1141 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Marieke Mur ◽  
Douglas A. Ruff ◽  
Roozbeh Kiani ◽  
Jerzy Bodurka ◽  
...  
Keyword(s):  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Object Representations

scholarly journals Short-Term and Procedural Memory for Colours and Inferior Temporal Cortex Activity

1997 ◽  
Vol 10 (2-3) ◽  
pp. 83-92
Author(s):  
E. Castro-Sierra ◽  
E. Paredes-Díaz ◽  
J. A. Lazareff
Keyword(s):  
Short Term Memory ◽  
Temporal Cortex ◽  
Normal Subjects ◽  
Procedural Memory ◽  
Size Perception ◽  
Inferior Temporal Cortex ◽  
Short Term ◽  
Procedural Task ◽  
The Brain ◽  
Normal Controls

Two children (male, 10 years, and female, 13 years one month) with tumours of the inferior temporal (IT) cortex of the brain were studied post-surgically for their abilities to carry out a short-term memory test. This involved: differences in colour, number and shape of small plastic objects; differences in receptacles where these objects should be placed and in ways in which this placement should be done; a procedural task involving differences either in colour or in size of wooden rings employed in the task. Their performances in these tests, and those of patients with tumours of other encephalic areas, were compared with the performances of normal controls. The subjects with IT tumours spent a significantly greater amount of time than normal subjects of their age in carrying out the procedural task involving differences in colour. One of the IT subjects also spent a significantly greater amount of time in the procedural task involving size differences. Other differences in the performances of patients with encephalic tumours and the performances of normal controls were not significant. Results are discussed in relation to findings of colour and size perception and memory localized to the inferior temporal and middle temporal cortices.

scholarly journals Representational confusion: the plausible consequence of demeaning your data

2017 ◽  
Author(s):  
Fernando M. Ramírez
Keyword(s):  
Neural Coding ◽  
Pattern Analysis ◽  
Brain Area ◽  
Multivariate Pattern Analysis ◽  
Class Membership ◽  
Multivariate Pattern ◽  
Potential Interactions ◽  
The Impact ◽  
The Brain ◽  
Extract Information

AbstractThe use of multivariate pattern analysis (MVPA) methods has enjoyed this past decade a rapid increase in popularity among neuroscientists. More recently, similarity-based multivariate methods aiming not only to extract information regarding the class membership of stimuli from their associated brain patterns, say, decode a face from a potato, but to understand the form of the underlying representational structure associated with stimulus dimensions of interest, say, 2D grating or 3D face orientation, have flourished under the name of Representational Similarity Analysis (RSA). However, data-preprocessing steps implemented prior to RSA can significantly change the covariance (and correlation) structure of the data, hence possibly leading to representational confusion—i.e., a researcher inferring that brain area A encodes information according to representational scheme X, and not Y, when the opposite is true. Here, I demonstrate with simulations that time-series demeaning (including z-scoring) can plausibly lead to representational confusion. Further, I expose potential interactions between the effects of data demeaning and how the brain happens to encode information. Finally, I emphasize the importance in the context of similarity analyses of at least occasionally explicitly considering the direction of pattern vectors in multivariate space, rather than focusing exclusively on the relative location of their endpoints. Overall, I expect this article will promote awareness of the impact of data demeaning on inferences regarding representational structure and neural coding.

scholarly journals Ensemble Statistics Can Be Available before Individual Item Properties: Electroencephalogram Evidence Using the Oddball Paradigm

2021 ◽  
pp. 1-13
Author(s):  
Michael L. Epstein ◽  
Tatiana A. Emmanoui
Keyword(s):  
Oddball Paradigm ◽  
Multivariate Pattern Analysis ◽  
Object Processing ◽  
Individual Object ◽  
Object Properties ◽  
Stimulus Evaluation ◽  
Ensemble Perception ◽  
Behavioral Studies ◽  
Item Properties ◽  
Multivariate Pattern

Abstract Behavioral studies have shown that statistical properties of object groups are perceived accurately with brief exposure durations. This finding motivated the hypothesis that ensemble perception occurs rapidly in vision. However, the precise timing of ensemble perception remains unclear. Here, we used the superior temporal resolution of electroencephalography to directly compare the timing of ensemble processing to that of individual object processing. The P3b was chosen as a particular component of interest, as it is thought to measure the latency of stimulus evaluation. Participants performed a simple “oddball” task in which sets of 51 lines with varied orientations sequentially flashed briefly on the display. In these sequences, there was a 20% chance of an individual oddball, wherein one marked object tilted clockwise, and a 20% chance of an ensemble oddball, wherein the average orientation of the set tilted 20% clockwise. In counterbalanced blocks, participants were instructed to respond to either individual or ensemble oddballs. ERP analysis was performed to test the timing of this processing. At parietal electrodes, P3b components were found for both individual and ensemble oddballs. Ensemble P3b components were found to occur significantly earlier than individual P3b components, as measured with both 50% area latency and 50% onset latency. Using multivariate pattern analysis, ensemble oddball trials were classifiable from standard trials significantly earlier in their timecourse than individual oddball trials. Altogether, these results provide compelling evidence that ensemble perception occurs rapidly and that ensemble properties can be available earlier than individual object properties.

scholarly journals An Application of Univariate and Multivariate Approaches in fMRI to Quantifying the Hemispheric Lateralization of Acoustic and Linguistic Processes

2012 ◽  
Vol 24 (3) ◽  
pp. 636-652 ◽  
Author(s):  
Carolyn McGettigan ◽  
Samuel Evans ◽  
Stuart Rosen ◽  
Zarinah K. Agnew ◽  
Poonam Shah ◽  
...  
Keyword(s):  
Speech Intelligibility ◽  
Pattern Analysis ◽  
Temporal Cortex ◽  
Hemispheric Lateralization ◽  
Multivariate Pattern Analysis ◽  
Hemispheric Asymmetries ◽  
Intelligible Speech ◽  
Hemisphere Dominance ◽  
The Right ◽  
Multivariate Pattern

The question of hemispheric lateralization of neural processes is one that is pertinent to a range of subdisciplines of cognitive neuroscience. Language is often assumed to be left-lateralized in the human brain, but there has been a long running debate about the underlying reasons for this. We addressed this problem with fMRI by identifying the neural responses to amplitude and spectral modulations in speech and how these interact with speech intelligibility to test previous claims for hemispheric asymmetries in acoustic and linguistic processes in speech perception. We used both univariate and multivariate analyses of the data, which enabled us to both identify the networks involved in processing these acoustic and linguistic factors and to test the significance of any apparent hemispheric asymmetries. We demonstrate bilateral activation of superior temporal cortex in response to speech-derived acoustic modulations in the absence of intelligibility. However, in a contrast of amplitude-modulated and spectrally modulated conditions that differed only in their intelligibility (where one was partially intelligible and the other unintelligible), we show a left dominant pattern of activation in STS, inferior frontal cortex, and insula. Crucially, multivariate pattern analysis showed that there were significant differences between the left and the right hemispheres only in the processing of intelligible speech. This result shows that the left hemisphere dominance in linguistic processing does not arise because of low-level, speech-derived acoustic factors and that multivariate pattern analysis provides a method for unbiased testing of hemispheric asymmetries in processing.

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