Qualitative Versus Quantitative Individual Differences in Cognitive Neuroscience

Individual differences in cognitive performance can be quantitative or qualitative in nature. Accounting for qualitative as well as quantitative individual differences is of importance for cognitive neuroscience, where a central goal is not only to relate brain function to behavior generally, but also to understand and predict individual behavior from neural data. In turn, cognitive neuroscience can help determine the nature of individual differences by revealing the underlying neural mechanisms and uncover qualitative individual differences that are not immediately apparent from behavioral data, enhancing our understanding of why and how people behave the way they do

Temporal attending and prediction influence the perception of metrical rhythm:Evidence from reaction times and ERPs

The processing of rhythmic events in music is influenced by the induced metrical structure. Two mechanisms underlying this may be temporal attending and temporal prediction. Temporal fluctuations in attentional resources may influence the processing of rhythmic events by heightening sensitivity at metrically strong positions. Temporal predictions may attenuate responses to events that are highly expected within a metrical structure. In the current study we aimed to disentangle these two mechanisms by examining responses to unexpected sounds, using intensity increments and decrements as deviants. Temporal attending was hypothesized to lead to better detection of deviants in metrically strong (on the beat) than weak (offbeat) positions due to heightened sensitivity on the beat. Temporal prediction was hypothesized to lead to best detection of increments in offbeat positions and decrements on the beat, as they would be most unexpected in these positions. We used a speeded detection task to measure detectability of the deviants under attended conditions (Experiment 1). Under unattended conditions (Experiment 2), we used EEG to measure the mismatch negativity (MMN), an ERP component known to index the detectability of unexpected auditory events. Furthermore, we examined the amplitude of the auditory evoked P1 and N1 responses, which are known to be sensitive to both attention and prediction. We found better detection of small increments in offbeat positions than on the beat, consistent with the influence of temporal prediction (Experiment 1). In addition, we found faster detection of large increments on the beat as opposed to offbeat (Experiment 1), and larger amplitude P1 responses on the beat as compared to offbeat, both in support of temporal attending (Experiment 2). As such, we showed that both temporal attending and temporal prediction shape our processing of metrical rhythm

Disentangling beat perception from sequential learning and examining the influence of attention and musical abilities on ERP responses to rhythm

AbstractBeat perception is the ability to perceive temporal regularity in musical rhythm. When a beat is perceived, predictions about upcoming events can be generated. These predictions can influence processing of subsequent rhythmic events. However, statistical learning of the order of sounds in a sequence can also affect processing of rhythmic events and must be differentiated from beat perception. In the current study, using EEG, we examined the effects of attention and musical abilities on beat perception. To ensure we measured beat perception and not absolute perception of temporal intervals, we used alternating loud and soft tones to create a rhythm with two hierarchical metrical levels. To control for sequential learning of the order of the different sounds, we used temporally regular (isochronous) and jittered rhythmic sequences. The order of sounds was identical in both conditions, but only the regular condition allowed for the perception of a beat. Unexpected intensity decrements were introduced on the beat and offbeat. In the regular condition, both beat perception and sequential learning were expected to enhance detection of these deviants on the beat. In the jittered condition, only sequential learning was expected to affect processing of the deviants. ERP responses to deviants were larger on the beat than offbeat in both conditions. Importantly, this difference was larger in the regular condition than in the jittered condition, suggesting that beat perception influenced responses to rhythmic events in addition to sequential learning. The influence of beat perception was present both with and without attention directed at the rhythm. Moreover, beat perception as measured with ERPs correlated with musical abilities, but only when attention was directed at the stimuli. Our study shows that beat perception is possible when attention is not directed at a rhythm. In addition, our results suggest that attention may mediate the influence of musical abilities on beat perception

Rhythmic abilities in humans and non-human animals: a review and recommendations from a methodological perspective

Rhythmic behaviour is ubiquitous in both human and non-human animals, but it is unclear whether the cognitive mechanisms underlying the specific rhythmic behaviours observed in different species are related. Laboratory experiments combined with highly controlled stimuli and tasks can be very effective in probing the cognitive architecture underlying rhythmic abilities. Rhythmic abilities have been examined in the laboratory with explicit and implicit perception tasks, and with production tasks, such as sensorimotor synchronization, with stimuli ranging from isochronous sequences of artificial sounds to human music. Here, we provide an overview of experimental findings on rhythmic abilities in human and non-human animals, while critically considering the wide variety of paradigms used. We identify several gaps in what is known about rhythmic abilities. Many bird species have been tested on rhythm perception, but research on rhythm production abilities in the same birds is lacking. By contrast, research in mammals has primarily focused on rhythm production rather than perception. Many experiments also do not differentiate between possible components of rhythmic abilities, such as processing of single temporal intervals, rhythmic patterns, a regular beat or hierarchical metrical structures. For future research, we suggest a careful choice of paradigm to aid cross-species comparisons, and a critical consideration of the multifaceted abilities that underlie rhythmic behaviour. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.Animal science

What do we need to hear a beat? The influence of attention, musical abilities, and accents on the perception of metrical rhythm

In this dissertation, I examine beat perception, the process that allows us to make music together. I explore the effects of attention, musical abilities, and accents on beat perception. Additionally, I address several methodological issues that arise when probing beat perception with event-related potentials (ERPs). Using a web-based setup, we show that musical training increases the sensitivity of listeners to the structure of accents in rhythm that indicates the beat. Furthermore, listeners responded qualitatively differently to different types of accents. Using a speeded detection task, we show that the influence of a perceived beat on processing of rhythmic events can be characterized both by temporal fluctuations in attention and temporal predictions. Using EEG, we provide evidence for the presence of beat perception when attention is directed away from rhythm. In highly trained musicians we found that the P1 response, an obligatory ERP response to sound, was larger for events on the beat than off the beat. In both musically trained and untrained participants, unexpected silences and intensity decreases elicited larger mismatch negativity (MMN) and P3a responses on the beat than off the beat, even when attention was not directed at a rhythm. Throughout this dissertation, I show the importance of differentiating between beat perception and other factors that may influence ERP responses, such as acoustic variation and statistical learning of a sound sequence. Finally, I propose that beat perception can be regarded as an ability that consists of several different mechanisms that together allow us to synchronize to music