Trends in Cognitive Sciences
Repetition and the brain: neural models of stimulus-specific effects
Introduction
When stimuli are repeated, neural activity is usually reduced. This neural repetition effect has been reported at multiple spatial scales, from the level of individual cortical neurons in monkeys 1, 2, 3 to the level of hemodynamic changes (measuring the pooled activation of millions of neurons) in humans using functional magnetic resonance imaging (e.g. fMRI 4, 5, 6, 7, 8, 9, 10). Repetition-related reductions also occur at multiple temporal scales, both in their longevity – from milliseconds [3] to minutes [9] and days [11] – and in the latency of their expression 12, 13. The phenomenon also occurs in multiple brain regions, and across an impressively large number of experimental conditions.
This stimulus-specific reduction in neural activity has been referred to as adaptation 14, 15, 16, mnemonic filtering [17], repetition suppression [18], decremental responses [19] and neural priming [20]. We will use ‘repetition suppression’ (RS) to refer to decreased neural responses following stimulus repetition. As will be apparent below, it is important to distinguish the scale at which RS arises, because the correspondence across scales (e.g. neural firing and hemodynamic responses) might not be simple.
Interest in repetition effects has recently intensified, for two main reasons. First, repetition effects have proved useful for inferring the nature of representations across different stages of a processing stream. This approach has been used behaviorally (e.g. using visual aftereffects to infer the nature of orientation tuning [21] or face representation 22, 23), with single-cell recording [24], and more recently has become popular with fMRI, particularly given claims that it enables improved spatial resolution [7] (Box 1). The second reason for heightened interest is the possibility that RS might be the neural correlate of priming 25, 26, 27. Priming refers to improved processing of a repeated stimulus according to some behavioral measure (e.g. greater accuracy in identifying the stimulus, or faster response times to make a decision about it), and often occurs under the same experimental conditions as RS. Nevertheless, it is important to note that, under certain conditions, priming can be associated with increased activity, rather than reduction (for discussion of repetition enhancement effects and changes in frontal cortices that might contribute to priming, see 27, 28).
The purpose of this review is to consider several kinds of neural models that have been proposed to account for repetition suppression (RS). We focus primarily on studies using visually presented objects and their effects on the ventral object processing stream, to maximize overlap between monkey and human studies. We evaluate the neural models in terms of their ability to account for the main properties of RS as measured with single-cell recordings, fMRI and electroencephalogram/magnetoencephalogram (EEG/MEG), discuss implications of these models for interpreting experimental results, and propose directions for distinguishing between the models.
Section snippets
Repetition suppression as measured with single-cell recording
Stimulus-specific repetition-related reductions in firing rates have been found in physiological recordings of neurons in macaque inferior temporal (IT) cortex 14, 15, 17, 18, 19 (Figure 1). These repetition effects have been reported for awake behaving animals performing various visual tasks (e.g. match to sample [17], recognition memory 19, 29), as well as in anesthetized animals [30], and occur for both behaviorally relevant and irrelevant stimuli [2]. RS effects are stimulus specific in the
Repetition suppression as measured with fMRI
The basic phenomenon of RS measured with fMRI, also referred to as fMRI-adaptation, has been replicated many times in ventral temporal cortex (see review [32]) as well other areas such as medial temporal [6] and frontal cortex 33, 34, 35. Many adaptation paradigms have been used to measure RS, including multiple repetitions of the same stimulus without intervening items [7] (Figure 2a: block design), or after a single presentation with either no [36] or many [37] intervening items (Figure 2a:
Repetition effects as measured with EEG/MEG
Repetition effects have also been studied by measuring changes in the electrical (EEG) or magnetic (MEG) field, usually recorded above the scalp. These effects reflect changes in the amplitude and/or synchrony of local field potentials (LFPs) caused by transmembrane currents in large numbers of neurons. Most EEG studies examine event-related potentials (ERPs), which reflect changes in electrical potential during the few hundred milliseconds following stimulus onset, averaged across trials. The
Relating different types of data
Relating the data recorded from single cells by electrodes (firing rates, multi-unit activity: MUA, or LFP) to the data recorded by fMRI (BOLD) and by EEG/MEG (synchronous changes in LFPs over many neurons) is non-trivial. In general terms, increases in MUA correlate with increases in LFP, and increases in LFP correlate with increases in BOLD [58] and presumably EEG/MEG power. However, the special constraints of each technique give rise to situations where their measures might dissociate. For
Models for repetition suppression
There are multiple potential neural causes of the RS measured with single-cell recording, fMRI or EEG/MEG. We now describe three models that have been sketched previously in the literature (Figure 4): (i) the Fatigue model, whereby the amplitude of firing of stimulus-responsive neurons decreases 2, 16; (ii) the Sharpening model, whereby fewer neurons respond 1, 18, 26; and (iii) the Facilitation model, whereby the latency [60] and/or duration of neural activity is shortened 3, 27. An important
Distinguishing the neural models
There are three main directions in which these models can be distinguished: (i) examining the relationship between RS and stimulus selectivity; (ii) examining the effect of repetition on the tuning of cortical responses along a stimulus dimension; and (iii) examining the temporal window in which processing occurs for new and repeated stimuli.
Conclusions
Adaptation paradigms have become increasingly popular for examining the processing characteristics of different cortical regions of the brain. Clearly, progress has been made using RS to infer the nature of representations in different cortical regions, or as a marker for increased processing efficiency, without a complete understanding of its neural basis. Nonetheless, it is our belief that the specific neural mechanisms matter, because interpretation and design of experiments depend on the
Acknowledgements
The authors have contributed equally to this manuscript. We would like to thank Steve Gotts for many helpful conversations, Rory Sayres and Sven Heinrich for contributing to Figure 2, and Thomas Gruber for contributing Figure 3. We would also like to thank Dave Andresen, Sharon Gilai-Dotan, Sven Heinrich, David Leopold, David McMahon, Rafael Malach, Mortimer Mishkin, Carl Olson, Leslie Ungerleider, Anthony Wagner, and Nathaniel Witthoff for their insightful comments on the manuscript. Finally,
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