Special issue: ViewpointSelecting appropriate designs and comparison conditions in repetition paradigms
Introduction
Repetition of a stimulus leads to prior exposure-dependent changes in response properties of cortical neurons, widely known as repetition suppression (Desimone, 1996, Ringo, 1996) or adaptation (Kohn, 2007). Repetition suppression is defined as a stimulus-specific reduction in neuronal activity (e.g., neuronal firing rate, functional magnetic resonance imaging (fMRI) blood-oxygen-level dependent (BOLD) signal or scalp-recorded potential) to repeated compared to unrepeated stimuli (Grill-Spector, Henson, & Martin, 2006; depicted in Fig. 1a). In a small number of cases stimulus repetition can also lead to an increase in a measure of neuronal activity, termed repetition enhancement (Henson et al., 2000, James et al., 2000, Segaert et al., 2013). Adaptation occurs with stimulus repetition but indexes a more diverse array of effects than repetition suppression, including changes in response magnitude and the shape and width of tuning curves to stimulus features (Kohn, 2007, Wissig and Kohn, 2012). These adaptation effects can vary over the time course of a neuron's response, and the time window of analysis can be crucial to delineate adaptation effects in the initial part of the response (e.g., Kaliukhovich and Vogels, 2012, Liu et al., 2009) from later effects of adaptation on intracortical interactions (Patterson, Wissig, & Kohn, 2013) or prediction-related suppression of neuronal activity (Grotheer and Kovács, 2015, Summerfield et al., 2011, Todorovic and de Lange, 2012).
The studies summarized in Vogels (2016) have greatly influenced our understanding of the complex array of repetition effects in the visual system. Several key experiments (De Baene and Vogels, 2010, Sawamura et al., 2006) have found not only increases or decreases in neuronal activity with repetition, but also inherited effects from earlier visual processing and changes in the stimulus-selectivity of neurons. These results have provided valuable information about the inferences that can be drawn from repetition effects observed using fMRI BOLD or electroencephalography (EEG)/magnetoencephalography (MEG). They also demonstrate that careful integration of evidence across recording methods is necessary to understand repetition effects, given the differential sensitivity of each recording method to different aspects of neuronal activity (Logothetis, 2008). In this sense, each recording method provides a “piece of the puzzle”, in which suppression in one measure may even co-occur with enhancement in another (De Baene and Vogels, 2010, Stopfer and Laurent, 1999).
These studies also remind us that repetition-induced suppression or enhancement is a change measure and therefore inherently relies on a comparison condition: the response to an equivalent unrepeated stimulus. The unrepeated stimulus condition dictates the detection of repetition-specific suppression/enhancement separate from other phenomena such as changes in attention and expectation (Kovács & Vogels, 2014). A significant challenge in harmonizing evidence across studies and measures of neuronal activity stems from the different paradigms, featuring different unrepeated stimulus conditions, which are used to define repetition effects. The choice of unrepeated stimulus condition guides the interpretability, strength, specificity and possibly even direction of the effect.
This viewpoint focuses on factors relevant to repeated-unrepeated stimulus comparisons in adaptation and repetition paradigms. Firstly, general issues relating to repeated-unrepeated stimulus comparisons in repetition paradigms are identified. Following this, the basic versions of major paradigms in the field are evaluated according to advantages and disadvantages of the unrepeated stimulus condition choice. This viewpoint concludes with a discussion of how confounding factors differ across paradigms, and ways to investigate the influences of confounding factors on existing reports of repetition-specific effects.
Section snippets
Inherited adaptation from earlier stages
Repetition effects occur in multiple levels of the visual system, and there is strong evidence of changes in high-level visual areas as a result of altered input from upstream regions such as early visual cortex (Kohn and Movshon, 2003, Larsson and Harrison, 2015, Solomon et al., 2004, Tolias et al., 2005). Isolating high-level repetition effects is especially difficult because repeated stimuli typically share low-level visual features such as local orientation or curvature information, in
Within-trial repetition designs
Within-trial repetition designs present trials of two consecutive stimuli (adapter and test) separated by an interstimulus interval, displayed in Fig. 1. These designs allow event-related fMRI analyses to estimate the time-course of the BOLD response, at the cost of decreased detection power compared to blocked designs (Liu, Frank, Wong, & Buxton, 2001). In these cases it is important to balance the trial order across conditions, for example using M-sequences (Buračas & Boynton, 2002).
Integrating evidence across paradigms
From the evaluation of the above paradigms it appears that there is no optimal repeated-unrepeated stimulus comparison for all purposes. Within-trial, blocked and oddball designs can be affected by inherited adaptation effects and imbalances in stimulus-specific expectations between the repeated and unrepeated stimuli, both of which may have contributed to observed repetition effects in many previous experiments. Across-trial and delayed match-to-sample designs provide some control over these
Conclusions
The issues related to repeated-unrepeated stimulus comparisons described in this viewpoint can affect the magnitude, detectability, stimulus-selectivity and even direction of observed repetition effects. In order to develop quantitative models of repetition effects across recording methods, these unrepeated stimulus-related confounds should be further investigated to estimate the magnitude of their effects and specificity to different measures of neuronal activity. Existing designs (Grotheer
Acknowledgements
D.F. thanks Hannah A.D. Keage for her insightful commentary on earlier drafts, and Alina Peter, whose discussions with the author formed the basis of this paper.
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2021, Neuroscience and Biobehavioral ReviewsCitation Excerpt :Any co-occurring differences with respect to other factors may potentially inflate or mimic effects of ES. In fact, this is also a central concern in relation to how other response modulations, such as RS, are measured (reviewed in Feuerriegel, 2016). Recently, the designs of many past experiments that tested for ES have been called into question.
Visual mismatch responses index surprise signalling but not expectation suppression
2021, CortexCitation Excerpt :For example, Javitt et al. (1998) presented auditory deviants at probabilities ranging between .56% and 15%, and found progressively more negative-going mismatch response amplitudes in smaller deviant probability blocks (see also Pincze et al., 2002). Experiments presenting stimuli at very low probabilities should include a familiarisation period to avoid potential confounding effects of stimulus novelty that may differ between expected and surprising stimuli (reviewed in Schomaker & Meeter, 2015; Feuerriegel, 2016). We also note that other phenomena than those identified here are likely to influence VMR magnitudes, and may operate over a broad range of timescales (see Maheu et al., 2019; Sawamura et al., 2006; Ulanovsky et al., 2004).
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2020, CortexCitation Excerpt :When interpreting these findings, it is important to differentiate the neural mechanisms of RS from how RS is measured within an experiment (typically as a difference between a comparable repeated and unrepeated stimulus condition). In such experiments, any effect that will influence repeated and unrepeated stimulus-evoked responses in different ways will also contribute to the measured magnitude of RS, even if that effect is unrelated to the underlying processes responsible for RS (reviewed in Feuerriegel, 2016). In Summerfield et al. (2008) and similar experiments, participants could learn to expect stimulus repetitions in the 75% repetition blocks, whereby in the same block unrepeated stimulus trials were relatively rare and surprising.