Commentary : the poverty of embodied cognition

(Fodor, 1975), and are carried by brain regions other than sensorimotor areas (Bechtel et al., 1998). Over the last few decades, this view has been questioned. Numerous researchers argue that cognitive processes are fundamentally rooted in sensorimotor activity and that the body both constrains and enables cognition (Wilson, 2002; Clark, 2009). Such a view is called "the embodied cognition" (EC) and it is widely applied in various fields of cognitive science, from linguistics to robotics. Recently, Goldinger et al. (2016), however, questioned its applicability. They emphasized that some assumptions of EC are unacceptable, and others proffer nothing new. Primarily, they claim that EC offers no useful insight into the classic problems of experimental psychology. Although, we agree with some theses presented in the article (e.g., radical embodiment that rejects the existence of mental representation is a blind alley), it appears the authors' methodological view on EC is inadequat

Does spatial navigation have a blind-spot? Visiocentrism is not enough to explain the navigational behavior comprehensively

Preparation of the manuscript was supported by the research grant 2015/19/B/HS1/03310 “Mechanisms of geometric cognition” funded by National Science Centre, Poland.In this paper, we argue that the issues described arise not because of the lack of theoretical inspiration, but rather due to an insufficient understanding of the subtleties of insect behavior. In our view, implementation of the insects’ models of navigation in the explanation of the vertebrates’ spatial behavior omits some important aspects, i.e., multimodal integration. Thus, we want to ask again the initial question posed by Wystrach and Graham (2012b) pointing out that significant progress in insects’ research, which suggests that we might have had underestimated insects’ cognitive abilities (Loukola et al., 2017; Peng and Chittka, 2017). Those results demonstrated insects’ capacity to obtain abstract information from multimodal input during complex tasks. Movement through a real environment provides a variety of cues, not only visual ones, thus in the following article we argue that multimodal integration is crucial to navigation.National Science Centre, Polan

No fingers, no SNARC? : neither the finger counting starting hand, nor its stability robustly affect the SNARC effect

The Spatial-Numerical Association of Response Codes (SNARC) effect (i.e., faster left/right sided responses to small/large magnitude numbers, respectively) is considered to be strong evidence for the link between numbers and space. Studies have shown considerable variation in this effect. Among the factors determining individual differences in the SNARC effect is the hand an individual uses to start the finger counting sequence. Left-starters show a stronger and less variable SNARC effect than right-starters. This observation has been used as an argument for the embodied nature of the SNARC effect. For this to be the case, one must assume that the finger counting sequence (especially the starting hand) is stable over time. Subsequent studies challenged the view that the SNARC differs depending on the finger counting starting hand. At the same time, it has been pointed out that the temporal stability of the finger counting starting hand should not be taken for granted. Thus, in this preregistered study, we aimed to replicate the difference in the SNARC between left- and right-starters and explore the relationship between the self-reported temporal stability of the finger counting starting hand and the SNARC effect. In line with the embodied cognition account, left-starters who declare more temporarily stable finger counting habits should reveal a stronger SNARC effect. Results of the preregistered analysis did not show the difference between left- and right-starters. However, further exploratory analysis provided weak evidence that this might be the case. Lastly, we found no evidence for the relationship between finger counting starting hand stability and the SNARC effect. Overall, these results challenge the view on the embodied nature of the SNARC effect

Waiting for a digital therapist : three challenges on the path to psychotherapy delivered by artificial intelligence

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called “general” or “human-like” AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy

Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code

Restricting movements of lower face leaves recognition of emotional vocalizations intact but introduces a valence positivity bias

Blocking facial mimicry can disrupt recognition of emotion stimuli. Many previous studies have focused on facial expressions, and it remains unclear whether this generalises to other types of emotional expressions. Furthermore, by emphasizing categorical recognition judgments, previous studies neglected the role of mimicry in other processing stages, including dimensional (valence and arousal) evaluations. In the study presented herein, we addressed both issues by asking participants to listen to brief non-verbal vocalizations of four emotion categories (anger, disgust, fear, happiness) and neutral sounds under two conditions. One of the conditions included blocking facial mimicry by creating constant tension on the lower face muscles, in the other condition facial muscles remained relaxed. After each stimulus presentation, participants evaluated sounds’ category, valence, and arousal. Although the blocking manipulation did not influence emotion recognition, it led to higher valence ratings in a non-category-specific manner, including neutral sounds. Our findings suggest that somatosensory and motor feedback play a role in the evaluation of affect vocalizations, perhaps introducing a directional bias. This distinction between stimulus recognition, stimulus categorization, and stimulus evaluation is important for understanding what cognitive and emotional processing stages involve somatosensory and motor processes

Replicability or reproducibility? : on the replication crisis in computational neuroscience and sharing only relevant detail

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code