"Core Perception": Re-imagining Precocious Reasoning as Sophisticated Perceiving.

A critical question in speech perception is the relative independence of perceptual and semantic processing. Answering this requires addressing two issues. First, does perceptual processing complete before semantic processing (discrete stages vs. continuous cascades)? Second, do semantic expectations affect perceptual processing (feedback)? These questions are difficult to address as there are few measures of early perceptual processing for speech. We extend a recent electroencephalography (EEG) paradigm which has shown sensitivity to pre-categorical encoding of Voice-Onset Time (VOT; Toscano et al., 2010). By measuring the timecourse over which perceptual and semantic factors affect the neural signal, we quantify how these processes interact. Participants (N = 31) heard sentences (Good dogs also sometimes—) which biased them to expect a target word (bark rather than park). We manipulated VOT of the target word and coarticulation leading to it. A component-independent analysis determined when each cue affects the continuous EEG signal every 2 msec. This revealed an early window (125–225 msec) sensitive exclusively to bottom-up information, a late window (400–575 msec) sensitive to semantic information, and a critical intermediate window (225–350 msec) during which VOT and coarticulation are processed simultaneously with semantic expectations. This suggests continuous cascades and early interactions between perceptual and semantic processes.

Long-term music training can positively impact speech processing. A recent framework developed to explain such cross-domain plasticity posits that music training-related advantages in speech processing are due to shared cognitive and perceptual processes between music and speech. Although perceptual and cognitive processing advantages due to music training have been independently demonstrated, to date no study has examined perceptual and cognitive processing within the context of a single task. The present study examines the impact of long-term music training on speech learning from a rigorous, computational perspective derived from signal detection theory. Our computational models provide independent estimates of cognitive and perceptual processing in native English-speaking musicians (n = 15, mean age = 25 years) and non-musicians (n = 15, mean age = 23 years) learning to categorize non-native lexical pitch patterns (Mandarin tones). Musicians outperformed non-musicians in this task. Model-based analyses suggested that musicians shifted from simple unidimensional decision strategies to more optimal multidimensional (MD) decision strategies sooner than non-musicians. In addition, musicians used optimal decisional strategies more often than non-musicians. However, musicians and non-musicians who used MD strategies showed no difference in performance. We estimated parameters that quantify the magnitude of perceptual variability along two dimensions that are critical for tone categorization: pitch height and pitch direction. Both musicians and non-musicians showed a decrease in perceptual variability along the pitch height dimension, but only musicians showed a significant reduction in perceptual variability along the pitch direction dimension. Notably, these advantages persisted during a generalization phase, when no feedback was provided. These results provide an insight into the mechanisms underlying the musician advantage observed in non-native speech learning.

Infant cognition

Representation of the body is a nuclear aspect of self-image. Body representation includes both subjective and intersubjective experiences. The importance of visual body representation in our social life is demonstrated by the time we spend to take care of our physical appearance, including use of plastic surgery, as well as by the severe mental disorders associated to its alteration, including eating disorders (EDs) and body dysmorphic disorders. In spite of these issues, (neuro)psychological research on body representation has so far mainly focused on the body as a motor device, devoted to the perception of and interaction with objects. The complexity of factors involved in the development, maintenance, and plasticity of body image representations requires an integrated approach that facilitates the evaluation and treatment of EDs with novel, evidence-based, and more efficacious protocols. This special issue of European Psychologist aims to advance such an integrative approach, with a collection of papers that gather different perspectives on the phenomenological, cognitive, (neuro)psychological, and cultural aspects of body representation disorders.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Altruism is critical for cooperation and productivity in human societies but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we elucidate the neural origins of individual and contextual differences in altruism by examining altruistic choices in different inequality contexts with computational modeling and electroencephalography (EEG). Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism that relate to differences in the initial processing of choice-relevant information. Editor's evaluation In this important paper, the authors use a sophisticated combination of computational modeling and EEG to show that variation in generosity produced by changes in context (i.e., disadvantageous vs. advantageous inequality) and variation due to individual differences in concern for others both seem to occur early, during the perceptual or valuation stage of a choice, rather than later on during choice comparison. However, these two sources of variation also appear to operate through distinct mechanisms during this stage of processing, which spurs further questions about the drivers of human prosocial behavior. This paper will be of considerable interest to researchers studying the psychological and neural basis of variation in prosocial behavior. https://doi.org/10.7554/eLife.80667.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Altruism – incurring own costs to benefit others – is fundamental for cooperation and productivity in human societies (de Waal, 2008; Piliavin and Charng, 1990). It not only plays crucial roles in shaping socio-political ideology and welfare (e.g. via tax policies and charity; Bechtel et al., 2018; Offer and Pinker, 2017) but is also essential for collective management of challenging situations, such as political, financial, and public health crises. While altruism is thought to be a stable behavioral tendency shaped by the evolutionary advantages of the ability to cooperate, it is unclear why this tendency varies so strongly across individuals, contexts, and cultures (Bester and Güth, 1998; Hamilton, 1964a; Hamilton, 1964b; Lebow, 2018; Piliavin and Charng, 1990). Is altruism governed by a set of unitary neuro-cognitive mechanisms that are engaged to varying degrees in different situations or different people (Tricomi et al., 2010)? Or are there fundamentally different types of altruistic actions that are guided by different neuro-cognitive processes triggered by different contexts (Hein et al., 2016)? From a neurobiological perspective, both these possibilities appear plausible. On the one hand, all altruistic actions necessitate the ability to override self-interest, a parsimonious brain mechanism (Bester and Güth, 1998) that is thought to be facilitated more or less by different contexts and that could be expressed to different degrees in different people (Morishima et al., 2012; Trivers, 1971). On the other hand, empirical observations suggest that altruism varies with a range of factors such as others' previous actions (e.g. empathy-based vs. reciprocity-based altruism) or their perceived similarity (e.g. social distance; Hein et al., 2016; Vekaria et al., 2017). It is thus often argued that in different contexts or different individuals, superficially similar altruistic actions can be guided by distinct motives (such as personal moral norms, responsibility, or empathy), which may be controlled by fundamentally different types of neurocognitive mechanisms (Hein et al., 2016; Piliavin and Charng, 1990; Zaki and Mitchell, 2011). One specific context factor that is often discussed in this context is the inequality in resources held by the actor and the recipient of a possible distribution: People are more willing to share if they possess more than the recipient (advantageous inequality, ADV) than if they possess less (disadvantageous inequality; DIS) (Charness and Rabin, 2002; Fehr and Schmidt, 1999; Gao et al., 2018; Güroğlu et al., 2014; Morishima et al., 2012; Tricomi et al., 2010). Although this consistent effect has been formalized with the same utility model across contexts, this model needs to comprise two distinct latent parameters quantifying altruism in the two contexts (i.e. decision weights on others' payoffs that are specific for ADV and DIS), and these are often uncorrelated and differ strongly from each other (Gao et al., 2018; Morishima et al., 2012). These observations, together with distinct psychological accounts for the distribution behaviors in different contexts (i.e. 'guilt' in the advantageous and 'envy' in the disadvantageous inequality context), imply that altruistic choices in the two contexts may be driven by fundamentally different psychological processes (Fehr and Schmidt, 1999; Gao et al., 2018). Moreover, modeling studies often reveal that these altruism parameters vary strongly between different people for the same choice set (Fehr and Schmidt, 1999), and neuroimaging studies have shown that while distributional behavior in both contexts correlates with activity in brain regions commonly associated with motivation (e.g. the putamen and orbitofrontal cortex), either context also leads to activity in a set of distinct areas (the dorsolateral and dorsomedial prefrontal cortex in advantageous and the amygdala and anterior cingulate cortex in disadvantageous inequality; Gao et al., 2018; Yu et al., 2014). Finally, neuroanatomical research shows that only for advantageous inequality, individual variations in altruistic preferences relate to gray matter volume in the temporoparietal junction (TPJ; Morishima et al., 2012). While these behavioral modeling and neural findings suggest clear contextual and individual differences in altruism, it is still unclear what specific neurocognitive mechanisms these differences could arise from. Previous research on individual and contextual differences in altruism has largely used unitary computational models focusing exclusively on valuation (rather than attempting to separate distinct aspects of the choice process), and has used functional magnetic resonance imaging (fMRI) to identify spatial patterns of neural activity that correlate with valuation processes during wealth distribution behaviors in different contexts (Charness and Rabin, 2002; Fehr and Schmidt, 1999; Gao et al., 2018; Güroğlu et al., 2014; Morishima et al., 2012; Tricomi et al., 2010). For example, recent studies combined computational modeling with fMRI techniques to show that the value of altruistic choice can be modeled as the weighted sum of self- and other-interest, and that different attributes are integrated into an overall value signal correlating with BOLD activity in the ventromedial prefrontal cortex (vmPFC) (Crockett et al., 2017; Crockett et al., 2013; Hutcherson et al., 2015; Hare et al., 2010). However, since these studies neither formally examined the difference in altruistic choices between advantageous and disadvantageous inequality contexts, nor focused on separating different aspects of the decision mechanisms of altruistic choice, they can hardly address the question of whether and how different mechanisms are involved in different types of altruistic actions in different contexts (Crockett et al., 2013; Crockett et al., 2008; Gao et al., 2018). To systematically investigate this issue, it would be beneficial to harness the fact that altruistic decisions – like all choices – are guided by processes unfolding at different temporal stages (Seo and Lee, 2012; Shin et al., 2021; Tump et al., 2020). These processes include (1) initial perception of the objective information related to wealth distribution (e.g. payoff numbers) (Nieder, 2016; Pinel et al., 2004), (2) biased representations of the subjectively decision-relevant information attributes, such as attention-guided weighing of self- vs other-payoffs (Chen and Krajbich, 2018; Teoh et al., 2020), (3) integration of all these attributes and subjective preferences into decision values (Collins and Frank, 2018; Harris et al., 2018; Hutcherson et al., 2015), and (4) final decision processes that transform the decision values into motor responses (O'Connell et al., 2012; Polanía et al., 2014). Taking into account this temporal unfolding of the neurocognitive processes further refines the questions about the origins of differences in altruistic behavior: Do altruistic choices involve different sets of computations throughout all the temporally different processing stages (i.e., initial perceptual processing, valuation, final integrative choice mechanisms) in these different contexts and by different individuals (as suggested by Gao et al., 2018; Tricomi et al., 2010)? Or do individuals mainly perceive and attend to the choice-relevant information differently, before passing on this information to valuation and integrative decision mechanisms devoted to all types of altruistic choices (as suggested by Yu et al., 2014)? Answering these questions by means of modelling and neural recording techniques that allow a detailed focus on different temporal stages of altruistic choice processes could help us understand the biological origins of altruism, reveal why people differ strongly in altruistic behavior, and develop more efficient strategies to facilitate altruism. In the current study, we take such an approach. We combined a modified dictator game that independently varies payoffs to a player versus another person, and thereby also the inequality between both players, with electroencephalography (EEG) and sequential sampling modeling (SSM). This allowed us to identify electrophysiological markers of the initial perceptual processing and biased representation of the decision-relevant information (i.e. stimulus-locked event-related potentials [ERPs] related to the payoffs and the inequality context) as well as of the processes integrating this information into a decision variable used to guide choice (i.e. response-locked evidence accumulation [EA] signals; Balsdon et al., 2021; Hutcherson et al., 2015; Krajbich et al., 2015; Nassar et al., 2019). Thus, our approach differs from that of fMRI studies identifying brain areas involved in the valuation of own and others' payoffs (Fehr and Schmidt, 1999; Morishima et al., 2012; Sáez et al., 2015), since the temporal resolution of fMRI measures makes it difficult to separate response-locked decision-making processes from stimulus-locked perceptual processes and to examine the independent dynamics of these processes during distribution decisions. Our approach is also motivated by studies of nonsocial decisions showing that SSMs may provide a useful framework for investigating the temporal dynamics of the processes that integrate different choice attributes into the decision outcome (Harris et al., 2018; Maier et al., 2020). Many studies have shown that SSMs can identify these processes not just computationally, but also at the neural level, for both the perceptual (Brunton et al., 2013; Kelly and O'Connell, 2013; Ossmy et al., 2013) and value-based decision making (Glaze et al., 2015; Hutcherson et al., 2015; Pisauro et al., 2017; Polanía et al., 2014). The SSM framework provides a formal way to predict the temporal dynamics of processes that integrate evidence for one choice option over another for the temporal period leading up until choice, and to separate these from initial perceptual processes time-locked to stimulus presentation. Neural signals corresponding to these predicted evidence-accumulation signals have been identified with EEG for perceptual decision making across different sensory modalities or stimulus features (Kelly and O'Connell, 2013; O'Connell et al., 2012; Wyart et al., 2012) as well as for value-based decision making (Pisauro et al., 2017; Polanía et al., 2014). These studies have identified evidence accumulation processes either as the model-free build-up rate of the centroparietal positivity (CPP) (Kelly and O'Connell, 2013; Loughnane et al., 2018; Loughnane et al., 2016; O'Connell et al., 2012) or in SSM-prediction-based neural signals measured over parietal and/or frontal regions (Pisauro et al., 2017; Polanía et al., 2014). Both types of neural signals are commonly interpreted as reflecting integration of the choice-relevant evidence to reach a decision, rather than basic motor planning which is usually identified by a fundamentally different neural signal, the contralateral action readiness potential (Kornhuber and Deecke, 2016; Schurger et al., 2021). The cortical origins of these signals may in principle correspond to locations identified by fMRI studies of corresponding SSM-predicted evidence accumulation traces, but note that these studies were not able to study the temporal dynamics of such signals and to unambiguously separate them into stimulus-locked perceptual versus response-locked decision processes (Gluth et al., 2012; Hare et al., 2011; Hutcherson et al., 2015; Rodriguez et al., 2015). Studies using this approach to investigate different types of decisions have identified different cortical areas that implement evidence-accumulation signals in different choice contexts (e.g. parietal regions specifically for perceptual decision making vs. both frontal and parietal regions for value-based decision making Polanía et al., 2014). This shows that different types of decisions may, even if they are reported via the same manual actions, draw on evidence accumulation computations that are instatiated in distinct brain regions. Moreover, altruistic decisions driven by different motives, or made by individuals with different social preferences, have also been found to involve activity in different neural networks (Hein et al., 2016). Therefore, it is necessary to differentiate whether the contextual and individual differences in altruistic decisions reflect recruitment of different brain areas/signals and/or of different computations that are performed within these brain areas. If different final decision mechanisms (i.e. computational and/or neural mechanisms) were to be involved in the two types of altruistic choices, or in different individuals, we should observe response-locked evidence-accumulation signals in different brain areas (e.g. frontal vs. parietal regions), or even different types of computations, in the two types of inequality contexts and/or different individuals. Conversely, if the same final decision mechanism is employed for both types of choice contexts, we should observe similar evidence-accumulation neural signals in similar brain areas, but systematic variations across contexts and/or individuals in those signals (e.g. responses in different brain areas and/or with different temporal characteristics) related to early perceptual/attentional processing of choice-relevant information, such as the available payoff magnitudes (Harris et al., 2018). Here, we apply this approach and use SSMs fitted to individuals' wealth distribution behaviors to predict the underlying neural evidence accumulation dynamics. We then employ these predicted EA signals in our EEG analyses to examine whether a similar neural choice system accumulates the choice-relevant evidence in both inequality contexts, or whether distinct neural systems implement this decision process for the different contexts. Then, we examine whether the different features of each choice problem that ultimately need to be integrated into the choice-relevant evidence – that is, the specific payoffs available to oneself and the other person – are initially processed in a different manner for different contexts and in different individuals. This allows us to directly approach the question of whether contextual and individual differences in altruism arise from differences in the decision mechanisms that integrate and compare choice-relevant information at the final stage of the choice process, or rather from differences in the initial processing and biased representation of the choice-relevant information that is ultimately integrated into the final decision mechanism. Results We recorded 128-channel EEG data from healthy participants playing a modified Dictator Game (DG). On each trial of this task, participants played as proposers and chose between two possible allocations of monetary tokens between themselves and an unknown partner. We systematically varied the allocation options from trial to trial so that in half of the trials, participants received less than their partners for both choice options (disadvantageous context [DIS]) and in the other half they got more than their partners for both options (advantageous context [ADV]). These two types of trials were randomly intermixed and were only defined by the size of the payoffs presented on the screen. On each trial, we presented the two options sequentially, to allow clear identification of time points at which the information associated with each option was processed (Figure 1A, see Materials and methods for details). This sequential presentation allowed us to establish the inequality context with the presentation of the first option, without having to explicitly instruct participants about thetwo contexts. We then studied individuals' sensitivity to self-payoff and other-payoff by focusing on how the choice of the second option depended on the change in these variables from the first to the second option. Importantly, as shown in the payoff schedule of all trials (Figure 1—figure supplement 1), we matched self-/other-payoff differences and the resulting absolute levels of inequality across both contexts and also across the second and the first options (Figure 1—figure supplement 1 middle and right panels). This allowed us to compare choices and response times, model-defined neural choice processes time-locked to the response, and neural processing of different stimulus information (self- and other-payoff) between the two contexts. Figure 1 with 2 supplements see all Download asset Open asset Experimental design and behavioral results. We employed a modified dictator game to measure individuals' wealth distribution behaviors. (A) Example of display in a single trial. In the task, participants played as proposers to allocate a certain amount of monetary tokens between themselves and anonymous partners. At the beginning of each trial, participants were presented with one reference option in blue and were asked to keep their eyes on the central cross for at least 1 s to start the trial, as indicated by the change in font color from blue to green. When the second option was presented, participants had to choose between the two options within 3 s. The selected option was highlighted in blue before the inter-trial interval. Font color assignment to phases (i.e. blue and green to response) was counterbalanced across participants. (B) Payoff information and context affect choice systematically. The generalized linear mixed-effects model shows the effects of multiple predictors on the probability to choose the second option; (C) Payoff information and context affect response times systematically. The linear mixed-effects model shows the effects of multiple predictors on response times (RTs). ΔS, Self-payoff Change; ΔO, Other-payoff Change; CON, Context; C, Constant; •••, p < 0.001; ••, p < 0.01; •, p < 0.05. Error bars indicate 95% confidence interval (CI) of the estimates, N=38. Based on the model fits and their predicted response-locked evidence accumulation EEG traces, we first tested whether similar or different neural processes (i.e. brain regions or physiological markers) underlie the ultimate choice process in the two inequality contexts, in similarity to how this has been studied for other types of decisions (Polanía et al., 2014). Then, we clarified whether neural processing of the stimulus information – which subsequently feeds into the decision processes – differs across contexts and individuals. For this analysis, we examined stimulus-locked event-related potentials (ERPs), in a way that has also been used to differentiate neural processing of decision-relevant features in non-social value-based decision making (e.g. perceptions of health and taste of food items) (Harris et al., 2018). Finally, we explored how individual differences in altruism are related to large-scale information communications between regions associated with these two sets of processes (i.e. response-locked decision processes and stimulus-locked perceptual processes), by examining inter-regional synchronization in the gamma-band frequency (30–90 Hz). This last analysis was motivated by the consideration that evidence accumulation processes need to integrate evidence input from different neural sources (e.g. perceptual processes) (Polanía et al., 2014), and by the proposal that coherent phase-coupling in the gamma band between different groups of neurons may serve as a fundamental process of neural communication for information transmission (Bosman et al., 2014; Fries, 2009; Fries, 2005; Vinck et al., 2013), as already shown for non-social value-based decisions (Polanía et al., 2014; Siegel et al., 2008). Behavior: Altruism depends differentially on self- and other-payoffs across contexts Before performing model-based analyses, we ran model-free linear mixed-effects regressions to establish that the choice-relevant information (i.e. self-payoff, other-payoff, and inequality context [ADV and DIS]) indeed systematically affects individual wealth distribution choices. These analyses confirmed that both self-payoff and other-payoff were important factors underlying individuals' choices. Specifically, participants chose the second option more often when either they or the receiver profited more from this choice (main effect Self-payoff Change (ΔS): beta = 3.77, 95% CI [3.65–3.89], p < 0.001; main effect Other-payoff Change (ΔO): beta = 0.56, 95% CI [0.51–0.61], p < 0.001, ΔS(ΔO): participants' own (partners') payoff change between the second and the first option) (Supplementary file 1, Figure 1B). However, participants were less influenced by changes in their own payoff when they had more money than the other (ADV, interaction Self-payoff Change and beta = 95% CI to p < or when the receiver got payoffs from this choice other-payoff, interaction Self-payoff Change and Other-payoff Change (ΔO): beta = 95% CI p = This effect was when the participants had more money than the receiver interaction Self-payoff Change Other-payoff Change and beta = 95% CI p < 0.001; file 1, Figure 1B). For of these see Appendix 1 and Figure 1—figure supplement for by model-based analyses see Appendix that we also models without interaction effects and/or main but model analyses the model (Supplementary file linear mixed-effects model suggested that the presentation (i.e. first or of options would not affect individuals' choices Appendix 1 and file other-payoff, and context also how participants their decisions. were for absolute values of self-payoff change (main effect Self-payoff Change beta = 95% CI to p < and other-payoff change (main effect of Other-payoff Change beta = 95% CI to p = (Figure both these effects were different for the two inequality contexts, with response times more strongly in the disadvantageous inequality context between Self-payoff Change and beta = 95% CI p < 0.001; interaction between Other-payoff Change and beta = 95% CI p = file Figure These effects are consistent with the central of the SSM framework that evidence will up evidence accumulation and resulting choice, thereby already that an decision process may integrate self- and other-payoff to guide individual decisions of these see Figure 1—figure supplement EEG similar parietal evidence accumulation across contexts To address the question of whether distribution choices are by similar or different neural decision processes across both inequality contexts, we fitted a sequential sampling model to participants' behavioral data and used it to predict neural evidence accumulation signals for the two contexts. Our analyses EA signals over similar parietal regions for both contexts and EA signals that would indicate the use of fundamentally different final choice mechanisms in the different contexts. Specifically, we first fitted the SSM by trials as or choices, on whether the selected the option with more or less distribution of monetary tokens between both For each trial, the model used the subjective value difference between the more option and the more option using the utility see Materials and as input to predict evidence accumulation signals until the when the decision was For we used the choice model which a process et al., (1) (2) with for = for disadvantageous inequality = ADV for advantageous inequality context), s for participants = and for trials = participants' payoff of the option in for s and trial the payoff in the option in for s and trial This model allowed us to parameters that correspond to different aspects of and the choice the decision on others decision and rate to as well as parameters which are less to be to the or neural mechanisms underlying valuation or decision and time Materials and methods for a detailed model these we could examine the effects of context on both basic altruistic (i.e. and the final decision process that the subjective values on from perception and valuation processes (i.e. and Although the payoff of each option was for each trial, participants still had to evidence by and the difference in payoffs between so the decision time may have the evidence accumulation when the decision process the The thus how the decision process the or of evidence see Materials and methods for the of models and the of the model we used for our To the evidence accumulation process, we EA by the fitted model for the context and the payoffs on each trial. that these were of the EA processes underlying choice, since the fitted model could both choices and across the two contexts. For both types of choices and contexts and DIS), the of the data by the model was than (Figure and the was than (Figure right Materials and The model also response that choices are during advantageous inequality overall in ADV vs. 95% CI = = p < and for

Perceptual processing demands influence voluntary task choice

The N170 event-related potential (ERP) component reflects visual perceptual processes and is known to have a source in the lateral occipital cortex (LOC) and temporal lobe regions. Convergent evidence from neuropsychological and neuroimaging studies suggests that the LOC is recruited for action tasks in which visibility of a target is unavailable and a perceptual memory of the target's characteristics must be used instead. We tested the hypothesis that the N170 reflects the contribution of additional ventral stream processes required for performing actions in which vision of a target is occluded. We predicted that the amplitude of the ERP in the latency range of the N170 would be larger when perceptual mechanisms are engaged to a greater extent. Participants were auditorily cued to touch target dots appearing on a touchscreen. Two viewing conditions varied with respect to the contribution of the ventral visuomotor stream during response initiation. In condition 1, the target disappeared with movement initiation whereas in condition 2, it disappeared with the cue to respond. The N170 during the response-initiation phase of trials was larger in amplitude for condition 2. The effect was observed over temporal electrode sites bilaterally, likely reflecting an overlap between auditory cue-related processes and additional perceptual processes within regions in the inferior-temporal cortex. Thus, the N170 may be a marker of neural activity within the ventral stream, further supporting the notion that actions initiated in the absence of a visual target rely more on perceptual representations than those directed towards visually available targets.

Grounded cognition suggests that conceptual processing shares cognitive resources with perceptual processing. Hence, conceptual processing should be affected by perceptual processing, and vice versa. The current study explored the relationship between conceptual and perceptual processing of size. Within a pair of words, we manipulated the font size of each word, which was either congruent or incongruent with the actual size of the referred object. In Experiment 1a, participants compared object sizes that were referred to by word pairs. Higher accuracy was observed in the congruent condition (e.g., word pairs referring to larger objects in larger font sizes) than in the incongruent condition. This is known as the size-congruency effect. In Experiments 1b and 2, participants compared the font sizes of these word pairs. The size-congruency effect was not observed. In Experiments 3a and 3b, participants compared object and font sizes of word pairs depending on a task cue. Results showed that perceptual processing affected conceptual processing, and vice versa. This suggested that the association between conceptual and perceptual processes may be bidirectional but further modulated by semantic processing. Specifically, conceptual processing might only affect perceptual processing when semantic information is activated. The current study

An intriguing finding of research on emotional processing is a discrepancy between perception and behavior. Perceptually, a robust finding is that negative stimuli are processed faster and more efficiently than positive stimuli. Behaviorally, a similarly robust finding is that response times are slower for negative than for positive stimuli. We proposed and tested a novel account to explain this still unexplained discrepancy, on the basis of the assumption that negative valence narrows perceptual processes to the benefit of speeded perception, but broadens motor processes at the cost of slowed responding. Participants performed a valence judgment task in which they responded with their left or right hand to negative and positive stimuli that were presented on the left or right, and we measured the activation of relevant/deactivation of irrelevant perceptual and motor processes, as revealed by the lateralization of electroencephalographic brain oscillations. Stimulus-related lateralization of alpha activity (8-12Hz) over perceptual areas was increased for negative stimuli, indicating more efficient perceptual processing. By contrast, response-related lateralization of beta activity (20-25Hz) over motor areas was decreased for negative stimuli, indicating less efficient response activation. Consistent with our predictions, more detailed analyses showed that both lateralization effects were caused by dynamics at the level of inhibiting irrelevant processes. For negative as compared to positive stimuli, the inhibition of irrelevant perceptual processes was increased, but the inhibition of irrelevant motor processes was decreased. These findings indicate that the discrepancy between perception and behavior in emotional processing may stem from asymmetrical effects of emotional valence on the breadth of cortical activations in perceptual and motor networks.

Research on multitasking harkens back to the beginnings of cognitive psychology. The central question has always been how we manage to perform multiple actions at the same time. Here, we highlight the role of specific inputand output-modalities involved in coordinating multiple action demands (i.e., crossmodal action). For a long time, modalityand content-blind models of multitasking have dominated theory, but a variety of recent findings indicate that modalities and content substantially determine performance. Typically, the term ‘‘input modality’’ refers to sensory channels (e.g., visual input is treated differently from auditory input), and the term ‘‘output modality’’ is closely associated with effector systems (e.g., hand vs. foot movements). However, this definition may be too narrow. The term ‘‘input modality’’ sometimes refers to a dimension within a sensory channel (e.g., shape/color in vision). Furthermore, the linkage between output-modalities and effector systems may not be specific enough to illuminate some notorious twilight zones (e.g., to distinguish between hand and wrist movements). As a consequence, we will use ‘‘modality’’ as an umbrella term here to capture various sources of stimulus variability used to differentiate the task-relevant information and sources of motor variability used to differentiate responses. Many of the pioneering studies involved the observation of dual-task performance in two continuous tasks that typically consisted of complex action sequences (e.g., reading and writing, see Solomons & Stein, 1896; Spelke, Hirst, & Neisser, 1976). However, it soon became apparent that tighter experimental control was necessary to pinpoint the specific cognitive mechanisms supporting multitasking. The PRP paradigm: an experimental breakthrough. The development of the psychological refractory period (PRP) paradigm (Telford, 1931; Welford, 1952) provided a methodological breakthrough that allowed researchers to exactly control the flow of information in both tasks. The PRP paradigm involves two elementary tasks with a limited set of clearly defined stimuli and responses. The mechanisms underlying multitasking are studied by systematically manipulating the temporal overlap of the two tasks, which is achieved by varying the delay between the presentations of the stimuli for the two tasks (stimulus onset asynchrony, SOA). The PRP effect refers to the typical finding that reaction times (RTs) for the second task increase with decreasing SOA, an effect that has been replicated in numerous studies with a variety of stimulus and response modalities (see Bertelson, 1966; Pashler, 1994; Smith, 1967). The RSB model: a powerful explanatory concept? The most influential and elegant account of the PRP effect has been the response selection bottleneck (RSB) model (Telford, 1931; Welford, 1952). A starting assumption of the RSB model is that tasks at hand can be divided into three successive cognitive processing steps, namely perceptual processing (i.e., stimulus encoding/categorization), response selection (i.e., deciding which response corresponds to the stimulus according to the task rules), and response execution processes. In a number of experiments, the duration of each of these processing stages was systematically manipulated for each of the two tasks (see Pashler, 1994). As a result, the most convincing hypothesis to accommodate the corresponding findings was the assumption that perceptual processing and response L. Huestegge (&) RWTH Aachen University, Aachen, Germany e-mail: lynn.huestegge@psych.rwth-aachen.de

Crossmodal perceptual illusions such as ventriloquism, the McGurk effect, the rubber hand, and the sound-induced flash demonstrate that one sense can causally impact perceptual processing and experience that is associated with another sense. This chapter argues that such causal interactions between senses are not merely accidental. Interactions between senses are part of typical perceptual functioning. Unlike synesthesia, they reveal principled perceptual strategies for dealing with noisy, fallible sensory stimulation from multiple sources. Recalibrations resolve conflicts between senses and weight in deference to the more reliable modality. Coordination between senses thus improves the coherence and the reliability of human perceptual capacities. Therefore, some perceptual processes of the sort relevant to empirical psychology are multisensory.

Explicit memory failure is the defining cognitive feature of Alzheimer's disease and relates to the hallmark neuropathological features (plaques and tangles) of this illness. However, a pattern of preserved and impaired implicit memory has been found in Alzheimer's disease patients that may be explained by the association between the processing demands of certain implicit tests and the level of regional Alzheimer's disease neuropathology. In this study, we tested the hypothesis that these neuropathological features are related to implicit memory--measured by repetition priming--in a test that emphasized conceptual (or meaning-based) cognitive processing, and that the pathological changes are not related to implicit memory in a repetition priming test that emphasized perceptual (or sensory-based) cognitive processing. Subjects were older nuns, priests and brothers participating in the Religious Orders Study who agreed to annual neurological and neuropsychological evaluation for Alzheimer's disease and common neurological conditions of ageing, and brain autopsy at time of death. Explicit memory was measured by seven tests of episodic recall and recognition and converted to a previously established summary measure. Implicit memory was measured by four repetition priming tests. One test, category exemplar priming, emphasized conceptual, or meaning-based cognitive processing. A second test, word-identification priming, emphasized perceptual, or sensory-based cognitive processing. Two additional priming tests, picture-naming and word-stem completion, invoke both conceptual and perceptual processes. Neuritic and diffuse plaques, and neurofibrillary tangles identified by Bielschowsky silver stain, were quantified from five regions separately (frontal, parietal, temporal, entorhinal cortex and the hippocampus) and converted to a previously established summary measure. In linear regression analyses--controlling for age, sex and education--higher levels of Alzheimer's disease neuropathology were related to lower levels of explicit memory proximate to death. Higher levels of neuropathology were also related to lower levels of priming on the category-exemplar test, but were not related to levels of priming on the word-identification, picture-naming, or word-stem completion tests. The results suggest that hallmark indices of Alzheimer's disease neuropathology are associated with performance on priming tests to the extent that conceptual, but not perceptual, processing resources are required.

In classic Psychological-Refractory-Period (PRP) dual-task paradigms, decreasing stimulus onset asynchronies (SOA) between the two tasks typically lead to increasing reaction times (RT) to the second task and, when task order is non-predictable, to prolonged RTs to the first task. Traditionally, both RT effects have been advocated to originate exclusively from the dynamics of a central bottleneck. By focusing on two specific electroencephalographic brain responses directly linkable to perceptual or motor processing stages, respectively, the present study aimed to provide a more detailed picture as to the origin(s) of these behavioral PRP effects. In particular, we employed 2-alternative forced-choice (2AFC) tasks requiring participants to identify the pitch of a tone (high versus low) in the auditory, and the orientation of a target object (vertical versus horizontal) in the visual, task, with task order being either predictable or non-predictable. Our findings show that task order predictability (TOP) and inter-task SOA interactively determine the speed of (visual) perceptual processes (as indexed by the PCN timing) for both the first and the second task. By contrast, motor response execution times (as indexed by the LRP timing) are influenced independently by TOP for the first, and SOA for the second, task. Overall, this set of findings complements classical as well as advanced versions of the central bottleneck model by providing electrophysiological evidence for modulations of both perceptual and motor processing dynamics that, in summation with central capacity limitations, give rise to the behavioral PRP outcome.

Social vision research, which examines, in part, how humans visually perceive social stimuli, is well-positioned to improve understandings of social inequality. However, social vision research has rarely prioritized the perspectives of marginalized group members. We offer a theoretical argument for diversifying understandings of social perceptual processes by centering marginalized perspectives. We examine (a) how social vision researchers frame their research questions and who these framings prioritize and (b) how perceptual processes (person perception; people perception; perception of social objects) are linked to group membership and thus comprehensively understanding these processes necessitates attention to marginalized perceivers. We discuss how social vision research translates into theoretical advances and to action for reducing negative intergroup consequences (e.g., prejudice). The purpose of this article is to delineate how prioritizing marginalized perspectives in social vision research could develop novel questions, bridge theoretical gaps, and elevate social vision’s translational impact to improve outcomes for marginalized groups. Public Social vision research is a subfield of psychology and vision science which examines how people visually perceive social stimuli and what the downstream consequences of these perceptions are. Social vision work includes, for example, examination of how White people visually perceive racial minorities and how these perceptions lead to social categorizations of racial minorities as outgroups, and therefore contribute to behaviors such as stereotyping and prejudice. Social vision research has rarely prioritized the perspectives of marginalized group members. It therefore cannot fully explain the contributions of perception to intergroup relations, which are necessarily bidirectional. We offer a theoretical argument for diversifying understandings of social perceptual processes by centering marginalized perspectives to understand how people with marginalized identities see their social worlds. We believe that prioritizing these marginalized perspectives has the potential to contribute to the development of a psychological science with heightened capacity to improve the well-being of people with marginalized identities.

Bernard, Gervais, Allen, Campomizzi, and Klein (2012) report an inversion effect only when participants viewed sexualized male body images and not when they viewed sexualized female body images. On the basis of a belief that face and person recognition is subject to an inversion effect (Rossion, 2008; Yin, 1969) but that object recognition is not, the authors concluded that “at a basic cognitive level, sexualized men were perceived as persons, whereas sexualized women were perceived as objects” (p. 470). The inference is that different visual-recognition processes are applied to images of males and images of females. This conclusion is unwarranted on empirical, methodological, and logical grounds.